AI-GEOSTATS: Webinar: Improve Your CLASSIFICATION with CART and RandomForests

[Lisa Solomon]

Webinar: Improve Your CLASSIFICATION with CART and RandomForests

LIVE OR ON-DEMAND

* LIVE: Wednesday, March 29th, 10AM - 11AM PDT, 1PM EDT

* ON-DEMAND: If the time is inconvenient, please register and we will 
send you a recording

* Cost: Complimentary

* Real-world dataset, step-by-step instructions, hands-on option


Registration: http://hubs.ly/H06G11d0

Alternative Link: 
http://info.salford-systems.com/improve-your-classification-cart-randomforests


ABSTRACT:
In this webinar we'll introduce you to two tree-based machine learning 
algorithms, CART decision trees and RandomForests. Both of these methods can be 
used for either regression or classification (i.e. Y = "Application Denied" or 
"Application Accepted") and we will focus on classification in this 
presentation. We will discuss the advantages of tree-based techniques including 
their ability to automatically handle variable selection, variable 
interactions, nonlinear relationships, outliers, and missing values. We'll 
explore the CART algorithm, bootstrap sampling, and the Random Forest algorithm 
(all with animations) and compare their predictive performance using a real 
world dataset.

Who should attend:

* Attend if you want to implement data science techniques even without 
a data science, programming, or even an advanced statistical background.

* Attend if you want to understand why data science techniques are so 
important for analysts.


Registration: http://hubs.ly/H06G11d0

Alternative Link: 
http://info.salford-systems.com/improve-your-classification-cart-randomforests

AI-GEOSTATS: Tomorrow: Improve Your Regression with CART and Gradient Boosting (webinar)

[Lisa Solomon]

Webinar: Improve Your Regression with CART and Gradient Boosting

LIVE OR ON-DEMAND

* LIVE: Thursday, February 16th, 10AM - 11AM PST, 1PM EST

* ON-DEMAND: If the time is inconvenient, please register and we will 
send you a recording

* Duration: 45 minutes

* Cost: Complimentary

* Real-world dataset, step-by-step instructions, hands-on option


Registration: http://hubs.ly/H0636580

Alternative Link: 
http://info.salford-systems.com/improve-your-regression-cart-gradient-boosting




ABSTRACT:
In this webinar we'll introduce you to a powerful tree-based machine learning 
algorithm called gradient boosting. Gradient boosting often outperforms linear 
regression, Random  Forests, and CART. Boosted trees automatically handle 
variable selection, variable interactions, nonlinear relationships, outliers, 
and missing values.

We'll see that CART decision trees are the foundation of gradient boosting and 
discuss some of the advantages of boosting versus a Random Forest. We will 
explore the gradient boosting algorithm and discuss the most important modeling 
parameters like the learning rate, number of terminal nodes, number of trees, 
loss functions, and more. We will demonstrate using an implementation of 
gradient boosting (TreeNet(r) Software) to fit the model and compare the 
performance to a linear regression model, a CART tree, and a Random Forest.

Who should attend:

* Attend if you want to implement data science techniques even without 
a data science, programming, or even an advanced statistical background.

* Attend if you want to understand why data science techniques are so 
important for analysts.


Registration: http://hubs.ly/H0636580

Alternative Link: 
http://info.salford-systems.com/improve-your-regression-cart-gradient-boosting

AI-GEOSTATS: Webinar: Improve Your Regression with CART and Gradient Boosting

[Lisa Solomon]

Webinar: Improve Your Regression with CART and Gradient Boosting

LIVE OR ON-DEMAND

* LIVE: Thursday, February 16th, 10AM - 11AM PST, 1PM EST

* ON-DEMAND: If the time is inconvenient, please register and we will 
send you a recording

* Duration: 45 minutes

* Cost: Complimentary

* Real-world dataset, step-by-step instructions, hands-on option


Registration: http://hubs.ly/H0636580

Alternative Link: 
http://info.salford-systems.com/improve-you-regression-cart-randomforests




ABSTRACT:
In this webinar we'll introduce you to a powerful tree-based machine learning 
algorithm called gradient boosting. Gradient boosting often outperforms linear 
regression, Random  Forests, and CART. Boosted trees automatically handle 
variable selection, variable interactions, nonlinear relationships, outliers, 
and missing values.

We'll see that CART decision trees are the foundation of gradient boosting and 
discuss some of the advantages of boosting versus a Random Forest. We will 
explore the gradient boosting algorithm and discuss the most important modeling 
parameters like the learning rate, number of terminal nodes, number of trees, 
loss functions, and more. We will demonstrate using an implementation of 
gradient boosting (TreeNet(r) Software) to fit the model and compare the 
performance to a linear regression model, a CART tree, and a Random Forest.

Who should attend:

* Attend if you want to implement data science techniques even without 
a data science, programming, or even an advanced statistical background.

* Attend if you want to understand why data science techniques are so 
important for analysts.


Registration: http://hubs.ly/H0636580

Alternative Link: 
http://info.salford-systems.com/improve-you-regression-cart-randomforests

AI-GEOSTATS: Tomorrow: Webinar: Improve Your Regression with CART and RandomForests

[Lisa Solomon]

Webinar: Improve Your Regression with CART and RandomForests

LIVE OR ON-DEMAND

* LIVE: Thursday, January 26th, 10AM - 11AM PST, 1PM EST

* ON-DEMAND: If the time is inconvenient, please register and we will 
send you a recording

* Duration: 45 minutes

* Cost: Complimentary

* Real-world dataset, step-by-step instructions, hands-on option


Registration: http://hubs.ly/H05XfKV0

Alternative Link: 
http://info.salford-systems.com/improve-you-regression-cart-randomforests




ABSTRACT:
In this webinar we'll introduce you to two tree-based machine learning 
algorithms, CART decision trees and RandomForests. We will discuss the 
advantages of tree-based techniques including their ability to automatically 
handle variable selection, variable interactions, nonlinear relationships, 
outliers, and missing values. We'll explore the CART algorithm, bootstrap 
sampling, and the Random Forest algorithm (all with animations) and compare 
their predictive performance using a real world dataset.

Who should attend:

* Attend if you want to implement data science techniques even without 
a data science, programming, or even an advanced statistical background.

* Attend if you want to understand why data science techniques are so 
important for analysts.


Registration: http://hubs.ly/H05XfKV0

Alternative Link: 
http://info.salford-systems.com/improve-you-regression-cart-randomforests

AI-GEOSTATS: Webinar: Improve Your Regression with CART and RandomForests

[Lisa Solomon]

Webinar: Improve Your Regression with CART and RandomForests

LIVE OR ON-DEMAND

* LIVE: Thursday, January 26th, 10AM - 11AM PST, 1PM EST

* ON-DEMAND: If the time is inconvenient, please register and we will 
send you a recording

* Duration: 45 minutes

* Cost: Complimentary

* Real-world dataset, step-by-step instructions, hands-on option


Registration: http://hubs.ly/H05XfKV0

Alternative Link: 
http://info.salford-systems.com/improve-you-regression-cart-randomforests




ABSTRACT:
In this webinar we'll introduce you to two tree-based machine learning 
algorithms, CART decision trees and RandomForests. We will discuss the 
advantages of tree-based techniques including their ability to automatically 
handle variable selection, variable interactions, nonlinear relationships, 
outliers, and missing values. We'll explore the CART algorithm, bootstrap 
sampling, and the Random Forest algorithm (all with animations) and compare 
their predictive performance using a real world dataset.

Who should attend:

* Attend if you want to implement data science techniques even without 
a data science, programming, or even an advanced statistical background.

* Attend if you want to understand why data science techniques are so 
important for analysts.


Registration: http://hubs.ly/H05XfKV0

Alternative Link: 
http://info.salford-systems.com/improve-you-regression-cart-randomforests

AI-GEOSTATS: egu session: learning from spatial data

[Sebastiano Trevisani]

Dear Colleagues,

I would be grateful if you considered submitting an abstract for the next
EGU2017 (Vienna, Austria, 23-28 April 2017) to the session SSS12.11/GM3.7:
“Learning from spatial data: unveiling the geo-environment through
quantitative approaches”, conveners: S. Trevisani, M. Cavalli , I.
Bogunović , J. Golay  and P. Pereira.



*Abstract submission (deadline–11 Jan 2017*, 13:00 CET, charge 40 €):

http://meetingorganizer.copernicus.org/EGU2017/abstractsubmission/2

For further information on the session write to S. Trevisani:
strevis...@iuav.it

or look at: http://meetingorganizer.copernicus.org/EGU2017/session/2



*Important!!!* For Scientists who wish to apply *for financial support*
(Early Career Scientist ECSTS and Established Scientist ESTS*) the deadline
is set on the 1st of December 2016*!!!

For further information on the possibility of financial support, go to:

http://egu2017.eu/financial_support.html



Overview:

Spatial and spatiotemporal data are crucial for the analysis and modelling
of the processes of interest in Earth and Soil Sciences. Such data require
advanced mathematical, statistical and geomorphometric methodologies in
order to fully exploit their informative content.

The session aims to explore the challenges and potentialities of
quantitative spatial data analysis and modelling in the context of Earth
and Soil Sciences. In particular, the session will cover two main topics
(to which it is no limited!)

1) Analysis of sparse (fragmentary) spatial data for mapping purposes with
evaluation of spatial uncertainty: geostatistics, machine learning,
statistical learning theory, etc.

2) Analysis and representation of exhaustive spatial data at different
scales and resolutions: geomorphometry, image analysis, pattern
recognition, etc.

Studies presenting intuitive and applied mathematical/numerical approaches
and highlighting their key potentialities and limitations are highly
solicited. Besides, the possibility to publish a special issue on the
session in an international journal will also be evaluated.





Sincerely,



The Conveners





*  Sebastiano Trevisani, Ph.D.*
*Assistant Professor*
*Applied and Environmental Geology*

*IUAV University of Venice: www.iuav.it *

*Address: Dorsoduro 2206,  Venice 30123, Italy Tel:+39. 041. 257
1299Mail:strevis...@iuav.it  *
*"Le opinioni espresse sono riferibili esclusivamente all'autore e non *
* riflettono in alcun modo una posizione ufficiale dello IUAV "*
*"The views expressed are purely those of the writer and may not in
any circumstances be regarded as stating an official position of the IUAV."*

AI-GEOSTATS: Job Offer AGROMET project

[Dimitri D'Or]

Here is a job offer for a geostatistician. It is issued by the Walloon
Research Center in Agriculture (Belgium). This job may interest some people
in the community.

The offer may be obtained directly at https://www.leforem.be/
HotJob/servlet/JobOffs.View?id=23613654=

Best regards,

Dimitri D'Or
___

Dimitri D'Or
Chief Science Officer
Ephesia Consult
Tienne-de-Mont, 10
5140 Sombreffe
Belgique

Tél. : +32-71-81.43.60
e-mail : dimitri@ephesia-consult.com
Web site : http://www.ephesia-consult.com



__

AI-GEOSTATS: PyGSLIB open source software for resource estimation.

[Adrian Martinez]

Hello List,

I am please to let you know that the open source python package PyGSLIB 
is now available for OSX, Windows and Linux.


PyGSLIB is the geostatistical package GSLIB wrapped into python. I 
added  some extra functionality in the original GSLIB's FORTRAN code.  
PyGSLIB also includes some wireframing and VTK support, drillhole 
modeling, block modeling and an experimental nonlinear module (under 
construction).


This is the user manual + video demonstrations:

https://opengeostat.github.io/pygslib/

This is the source code:

https://github.com/opengeostat/pygslib

Kind Regards
Adrian Martinez Vargas
+
+ To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu
+ To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and 
"unsubscribe ai-geostats" in the message body. DO NOT SEND 
Subscribe/Unsubscribe requests to the list
+ As a general service to list users, please remember to post a summary of any 
useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/

AI-GEOSTATS: Tomorrow, Webinar: Modern Regression Modeling for Voter MicroTargeting

[Lisa Solomon]

Webinar: Modern Regression Modeling for Voter MicroTargeting (no charge)



Join us for a special 2-part webinar about voting trends, presented by our 
resident data scientist, Christian Kendall. In this webinar we will show how 
machine learning models and data science can be used in today's election 
climate.



CLICK HERE TO REGISTER

* Alternative link:  
http://info.salford-systems.com/modern-regression-modeling-for-voter-microtargeting

Can't make it? Sign up and receive the recording!



We will cover:

How data science is used in modern campaigns

Regression and predictive analytics for microtargeting

Identifying demographic trends through machine learning

Gaining new insights by using data mining on polls and election data

Understanding affects on voting behavior while making predictions



Live or On-demand:

Part 1: September 14 @ 10am Pacific, Linear Regression and Non-linear 
regression (MARS(r))

Part 2: September 21 @ 10am Pacific, TreeNet(r) Gradient Boosting and 
RandomForests(r)



CLICK HERE TO REGISTER

* Alternative link:  
http://info.salford-systems.com/modern-regression-modeling-for-voter-microtargeting

Can't make it? Sign up and receive the recording!

AI-GEOSTATS: Webinar (TOMORROW): Improve Your Regression with Data Science (no charge)

[Lisa Solomon]

Improve Your Regression with Data Science



CLICK HERE TO REGISTER

Can't make it? Sign up and receive the recording!



Abstract:

Join us for this two part webinar series on improving your regression using 
modern regression analysis techniques, presented by Senior Scientist, Mikhail 
Golovyna. In these webinars you will learn how to drastically improve 
predication accuracy in your regression with a new model that addresses common 
concerns such as missing values, interactions, and nonlinearities in your data.

We will demonstrate the techniques using real-world data sets and introduce the 
main concepts behind Leo Breiman's Random Forests and Jerome Friedman's GPS 
(Generalized PathSeeker(tm)), MARS(r) (Multivariate Adaptive Regression 
Splines), and Gradient Boosting.



Part 1: July 27 @ 10am PDT: Linear, Nonlinear, Regularized, GPS, LARS, LASSO, 
Elastic Net, MARS(r)

Part 2: August 10 @ 10am PDT: TreeNet(r) Gradient Boosting, RandomForests(r), 
ISLE(tm) and RuleLearner(r)



CLICK HERE TO REGISTER

Can't make it? Sign up and receive the recording!

AI-GEOSTATS: Webinar: Improve Your Regression with Modern Regression Analysis Techniques

[Lisa Solomon]

Improve Your Regression with Modern Regression Analysis Techniques



CLICK HERE TO REGISTER

* Alternative link:  
http://info.salford-systems.com/improve-your-regression

Can't make it? Sign up and receive the recording!



Abstract:

Join us for this two part webinar series on improving your regression using 
modern regression analysis techniques, presented by Senior Scientist, Mikhail 
Golovyna. In these webinars you will learn how to drastically improve 
predication accuracy in your regression with a new model that addresses common 
concerns such as missing values, interactions, and nonlinearities in your data.

We will demonstrate the techniques using real-world data sets and introduce the 
main concepts behind Leo Breiman's Random Forests and Jerome Friedman's GPS 
(Generalized PathSeeker(tm)), MARS(r) (Multivariate Adaptive Regression 
Splines), and Gradient Boosting.



Part 1: July 27 @ 10am PDT: Linear, Nonlinear, Regularized, GPS, LARS, LASSO, 
Elastic Net, MARS(r)

Part 2: August 10 @ 10am PDT: TreeNet(r) Gradient Boosting, RandomForests(r), 
ISLE(tm) and RuleLearner(r)



CLICK HERE TO REGISTER

* Alternative link:  
http://info.salford-systems.com/improve-your-regression

Can't make it? Sign up and receive the recording!

AI-GEOSTATS: job posting: Entry ­Level Marketing Statistician (San Diego, CA)

[Lisa Solomon]

Entry-Level Marketing Statistician

(San Diego, CA)


Salford Systems is an advanced data mining software and consulting company 
based in San Diego, California. We enjoy an open, creative environment, and 
have developed an international reputation for cutting-edge technology. Salford 
Systems is affiliated with several of the world's greatest scientists in the 
field of machine learning, and leads the data mining industry with exciting, 
innovative products.


The ideal candidate is passionate and enthusiastic about applied statistics and 
analytics. Qualified candidates should have strong ability to both engage with 
senior leaders to design well-constructed analyses and work with data 
scientists and software engineers to effectively deliver actionable results. 
They should have excellent communication skills and the ability to work with 
both technical and marketing departments to effectively create new educational 
materials, execute new promotional projects and actively contribute to 
consulting projects.


Responsibilities may include:


· Working with our technical and marketing departments to prepare data 
that will be used for promotional projects and consulting projects.

· Ability to prepare exemplary analyses on the prepared data to 
illustrate analytical techniques using our software.

· Scraping, merging, cleaning, formatting and repurposing of publicly 
available datasets.

· Ability to draw conclusions from data and recommend actions.

· Key participant in Marketing campaigns via creating 
easily-understood, step-by-step instructions.

· Understand and communicate product benefits.

· Perform hands-on competitive analysis.

· Add classical statistical approaches to data mining approaches to 
enhance our consulting projects.


Minimum Qualifications:

· Master’s Degree in statistics or related

· 2 years relevant experience including statistical software (R, 
S-Plus, SAS, or similar), databases and scripting languages (such as Python).


Preferred Qualifications:


· Marketing and/or writing background a plus.

· Experience using analytics and/or data mining software, such as 
Salford’s products, SAS, R, or similar products

· Real-world experience.

· Demonstrated willingness to both teach others and learn new 
techniques.

· Demonstrated self-direction.




Light travel required. Candidate should be legally authorized to work in the 
U.S.A.

Contact:

Please send resume and wage history and requirements to 
h...@salford-systems.com.


Company: Salford Systems
Location: San Diego, CA

Phone: 619-356-4156
Web: www.salford-systems.com

Careers: 
www.salford-systems-careers.com

AI-GEOSTATS: REMINDER Call for Abstracts: ISEH 2016, ISEG 2016 & Geoinformatics 2016 on Environment, Health, GIS and Agriculture, Galway, Ireland, August 14 – 20, 2016

[Zhang, Chaosheng]

Apologies for cross-postings

ISEH 2016, ISEG 2016 & Geoinformatics 2016
Joint International Conference on
Environment, Health, GIS and Agriculture
Galway, Ireland, August 14 – 20, 2016

Reminder: Call for Abstracts
(Closing date: March 6, 2016, ONE Month left)

ISEH 2016: The 3rd International Symposium on Environment and Health
ISEG 2016: The 10th International Symposium on Environmental Geochemistry
Geoinformatics 2016: The 24th International Conference on Geoinformatics

Visit the conference website and submit your abstracts:
http://www.nuigalway.ie/iseh2016
https://cpgis.org/Conferences/ConferenceDefault.aspx?ID=71

Dear Colleague,

You are welcome to submit an abstract to ISEH 2016, ISEG 2016 & Geoinformatics 
2016 conference, to be held at National University of Ireland (NUI), Galway, 
Ireland, during August 14 - 20, 2016.

The joint international conference of ISEH 2016 (The 3rd International 
Symposium on Environment and Health), ISEG 2016 (The 10th International 
Symposium on Environmental Geochemistry) 2016 and Geoinformatics 2016 (The 24th 
International Conference on Geoinformatics) provides a historical opportunity 
for international experts working in several closely related areas of 
environment, health, geographical information system (GIS) and agriculture, to 
meet and share the latest understanding of the ever growing challenges between 
human and our changing environment.


Ø  ISEH is an internationally leading conference series in Environment and 
Health held once every two years.

Ø  ISEG is an internationally leading symposium on environmental geochemistry 
held once every three years.

Ø  Geoinformatics is the annual conference of the International Association of 
Chinese Professionals in Geographic Information Sciences (CPGIS).

The conference venue is the campus of NUI, Galway, within walking distance of 
Galway’s city centre. Galway is a popular tourist destination, and ranked No. 1 
friendliest city in the world in a recent survey. Ireland is ranked No. 1 in 
the good country list, and Ireland is named as a best travel destination.

The joint international conference is organized by NUI Galway (Ryan Institute 
GIS Centre and School of Geography and Archaeology) and CPGIS.

The co-organisers are (with no particular order): Society for Environmental 
Geochemistry and Health (SEGH), International Medical Geology Association 
(IMGA), The Geographical Society of China (GSC), Environmental Sciences 
Association of Ireland (ISAI), Geographical Society of Ireland (GSI), Ireland 
Chinese Association of Environment, Resources & Energy (ICAERE), National 
Centre for Geocomputation (NCG), Nanchang University (NCU), Institute of 
Subtropical Agriculture Chinese Academy of Sciences (ISA), The Ireland 
Brownfield Network (IBN), Institute of Geochemistry Chinese Academy of Sciences 
(GYIG), Institute of Coastal Zone Research, Chinese Academy of Sciences (YIC), 
Institute of Geographical Science and Natural Resource Research, Chinese 
Academy of Sciences (IGSNRR), Nanjing Institute of Geography and Limnology, 
Chinese Academy of Science (NIGLAS), Guangzhou Institute of Geochemistry, 
Chinese Academy of Sciences (GIG), Croucher Institute for Environmental 
Sciences, Hong Kong Baptist University (CIES), Southwest University (SWU), 
Institute of Urban Environment, Chinese Academy of Sciences (IUE), British 
Geological Survey (BGS), Central South University of Forestry & Technology 
(CSUFT), Research Center for Eco-Environmental Sciences, Chinese Academy of 
Sciences (RCEES), Guangdong University of Technology (GDUT), Korea Biochar 
Research Center (KBRC), UK Chinese Association of Resource and Environment 
(UK_CARE), International Panel on Chemical Pollution (IPCP), National Institute 
for Public Health and the Environment (RIVM), The Geological Surveys of Europe 
(EuroGeoSurveys), The European Centre for Environment and Human Health (ECEHH), 
Tsinghua University (THU), The Irish Organisation for Geographic Information 
(IRLOGI), The International Association of GeoChemistry (IAGC), Plant and 
Agricultural Biosciences Centre (PABC), The Association of Applied Geochemists 
(AAG).

ISEH 2016 & ISEG 2016 Supporting journals

Applied Geochemistry
Environmental Geochemistry and Health
Environmental Pollution
International Journal of Environmental Research and Public Health

ISEH 2016 & ISEG 2016 Committee members:

Honorary Chairs:   Shu Tao, Peking University, China
Ping'an Peng, Chinese Academy of Sciences, China
Chair:  Chaosheng Zhang, National University of 
Ireland, Galway, Ireland
Co-Chairs:  Yongguan Zhu, Chinese Academy of Sciences, China;
Aaron Potito, National University of 
Ireland, Galway, Ireland
Academic Secretary: Shiming Ding, Chinese Academy of Sciences, China

ISEH 2016 & ISEG 2016 Keynote Speakers
Shu Tao, Peking University, China
Kirk Smith, University of California Berkeley, USA

AI-GEOSTATS: Tomorrow: Webinar, 3 Ways to Improve your Regression with Data Science and Machine Learnging (no charge, part 2)

[Lisa Solomon]

3 Ways to Improve your Regression with Data Science and Machine Learning Part 2
 (no charge, Case Study, Step-by-step, Hands-on option)


Registration: http://hubs.ly/H01Y9BD0

Alternative Link: 
http://info.salford-systems.com/3-ways-to-improve-your-regression-part2


January 27th, 10AM - 11AM PT

* If the time is inconvenient, please register and we will send you a 
recording

* Part 1 is not required, to understand the approach and concepts in 
tomorrow's webinar; but, if you want a refresher, you can see last week's 
webinar at your convenience.  Link to recording of Part 1: 
http://hubs.ly/H01VzrN0

ABSTRACT:
Last week, we showed you how you could drastically improve prediction accuracy 
in your linear  regression with a new model that handles missing values, 
interactions, AND nonlinearities in your data.  As a follow-up to the last 
week's webinar, we will show you how to take data science techniques even 
further to extract actionable insight and take advantage of advanced modeling 
features. You will walk away with several different methods to turn your 
ordinary regression into an extraordinary regression!

Techniques used:

* Stochastic gradient boosting: TreeNet plots show you the impact of 
every variable in your model; take it a step further by creating spline 
approximations to these variables and using them in a conventional linear 
regression for a boosted model performance!

* Nonlinear regression splines: MARS nonlinear regression will still 
give you what looks like a standard regression equation, but instead of 
coefficients, you'll see transformations of your original variables.

* Modeling automation: learn how to cycle through numerous modeling 
scenarios automatically to discover best-fit parameters.
Included with Registration:

* On-demand recording of webinar

* Data set used in presentation

* Step-by-step instructions

* 30-day free access to MARS, TreeNet, and Random Forests

More details:

* Last week, we showed you how you could drastically improve prediction 
accuracy in your linear  regression with a new model that handles missing 
values, interactions, AND nonlinearities in your data.  This week, we will 
rebuild these original models and get straight to the more advanced features.

* We will quickly review how to incorporate nonlinearities in a 
regression splines model  AND THEN show you how to automatically detect 
interactions and include these to lead to an even better result.

* We will quickly review stochastic gradient boosting, and how, with 
plots you can see how each variable contributes to your model.   And then, this 
week you will see how to create approximations from these plots and use these 
in a standard linear regression as your inputs.

* We will also explore the benefits of model automation. Without any 
custom programming, you can quickly cycle through different modeling scenarios, 
such as intelligently decreasing your predictor pool by removing variables one 
by one, or automatically re-running your regression model using different loss 
functions. This gives you the option to create many different models and choose 
the best for your analysis needs.



These techniques are great for skeptics who like to stick with standard 
regression but wish to see dramatic improvements. With very large datasets, you 
will see a significant speed benefit as well.  Learn what is being used at some 
of the largest banks and credit companies in the world.



And if you want a refresher, you can see last week's webinar at your 
convenience:


Who should attend:

* Attend if you want to implement data science techniques even without 
a data science, programming, or even a statistical background.

* Attend if you want to understand why data science techniques are so 
important for analysts.


Registration: http://hubs.ly/H01Y9BD0

Alternative Link: 
http://info.salford-systems.com/3-ways-to-improve-your-regression-part2

AI-GEOSTATS: Call for abstracts/papers: Geoinformatics 2016, Galway, Ireland, Aug. 14-20, 2016

[Zhang, Chaosheng]

Apologies for cross-postings
ISEH 2016, ISEG 2016 & Geoinformatics 2016

Joint International Conference on Environment, Health, GIS and Agriculture
ISEH 2016: The 3rd International Symposium on Environment and Health
ISEG 2016: The 10th International Symposium on Environmental Geochemistry
Geoinformatics 2016: The 24th International Conference on Geoinformatics

Galway, Ireland, August 14 - 20, 2016

Call for Abstracts
(Closing date: March 6, 2016)

Visit the conference website and submit your abstracts:
http://www.nuigalway.ie/iseh2016
https://cpgis.org/Conferences/ConferenceDefault.aspx?ID=71 (abstracts and/or 
papers)

Dear Colleague,

You are welcome to submit an abstract to ISEH 2016, ISEG 2016 & Geoinformatics 
2016 conference, to be held at National University of Ireland (NUI), Galway, 
Ireland, during August 14 - 20, 2016.

The joint international conference of ISEH 2016 (The 3rd International 
Symposium on Environment and Health), ISEG 2016 (The 10th International 
Symposium on Environmental Geochemistry) 2016 and Geoinformatics 2016 (The 24th 
International Conference on Geoinformatics) provides a historical opportunity 
for international experts working in several closely related areas of 
environment, health, geographical information system (GIS) and agriculture, to 
meet and share the latest understanding of the ever growing challenges between 
human and our changing environment.


Ø  ISEH is an internationally leading conference series in Environment and 
Health held once every two years.

Ø  ISEG is an internationally leading symposium on environmental geochemistry 
held once every three years.

Ø  Geoinformatics is the annual conference of the International Association of 
Chinese Professionals in Geographic Information Sciences (CPGIS).

The conference venue is the campus of NUI, Galway, within walking distance of 
Galway's city centre. Galway is a popular tourist destination, and ranked No. 1 
friendliest city in the world in a recent survey.

The joint international conference is organized by NUI Galway (Ryan Institute 
GIS Centre and School of Geography and Archaeology) and CPGIS.

The co-organisers are (with no particular order): Society for Environmental 
Geochemistry and Health (SEGH), International Medical Geology Association 
(IMGA), The Geographical Society of China (GSC), Environmental Sciences 
Association of Ireland (ISAI), Geographical Society of Ireland (GSI), Ireland 
Chinese Association of Environment, Resources & Energy (ICAERE), National 
Centre for Geocomputation (NCG), Nanchang University (NCU), Institute of 
Subtropical Agriculture Chinese Academy of Sciences (ISA), The Ireland 
Brownfield Network (IBN), Institute of Geochemistry Chinese Academy of Sciences 
(GYIG), Institute of Coastal Zone Research, Chinese Academy of Sciences (YIC), 
Institute of Geographical Science and Natural Resource Research, Chinese 
Academy of Sciences (IGSNRR), Nanjing Institute of Geography and Limnology, 
Chinese Academy of Science (NIGLAS), Guangzhou Institute of Geochemistry, 
Chinese Academy of Sciences (GIG), Croucher Institute for Environmental 
Sciences, Hong Kong Baptist University (CIES), Southwest University (SWU), 
Institute of Urban Environment, Chinese Academy of Sciences (IUE), British 
Geological Survey (BGS), Central South University of Forestry & Technology 
(CSUFT), Research Center for Eco-Environmental Sciences, Chinese Academy of 
Sciences (RCEES), Guangdong University of Technology (GDUT), Korea Biochar 
Research Center (KBRC), UK Chinese Association of Resource and Environment 
(UK_CARE), International Panel on Chemical Pollution (IPCP), National Institute 
for Public Health and the Environment (RIVM), The Geological Surveys of Europe 
(EuroGeoSurveys), The European Centre for Environment and Human Health (ECEHH), 
Tsinghua University (THU), The Irish Organisation for Geographic Information 
(IRLOGI), The International Association of GeoChemistry (IAGC).

ISEH 2016 & ISEG 2016 Committee members:

Honorary Chairs:   Shu Tao, Peking University, China
Ping'an Peng, Chinese Academy of Sciences, China
Chair:  Chaosheng Zhang, National University of 
Ireland, Galway, Ireland
Co-Chairs:  Yongguan Zhu, Chinese Academy of Sciences, China;
Aaron Potito, National University of 
Ireland, Galway, Ireland
Academic Secretary: Shiming Ding, Chinese Academy of Sciences, China
ISEH 2016 & ISEG 2016 Keynote Speakers
Shu Tao, Peking University, China
Kirk Smith, University of California Berkeley, USA
Ming-Hung Wong, Hong Kong Institute of Education, China
Yongguan Zhu, Institute of Urban Environment, CAS, China
Maurice Mulcahy, Health Service Executive (HSE) West Region, Ireland
Jose A. Centeno, U.S. Food and Drug Administration, USA
Eddy Y. Zeng, Jinan University, China
Olle Selinus, Linneaus University, Sweden
Ron Fuge, Aberystwyth University, Wales, UK
Robert B. Finkelman, 

AI-GEOSTATS: Today: Data Science Webinar: Improve your Regression with Data Science and Machine Learning

[Lisa Solomon]

3 Ways to Improve your Regression models with Data Science and Machine Learning
(no charge)


Registration (includes recording): http://hubs.ly/H01VzrN0

Alternative Link: 
http://info.salford-systems.com/3-ways-to-improve-your-regression-part1


January 20th and 27th, 10AM - 11AM PT
* If the time is inconvenient, please register and we will send you a recording.




ABSTRACT:
Linear regression plays a big part in the everyday life of a data analyst, but 
the results aren't always satisfactory. What if you could drastically improve 
prediction accuracy in your regression with a new model that handles missing 
values, interactions, AND nonlinearities in your data? Instead of proceeding 
with a mediocre analysis, join us for this 2-part webinar series.  We will show 
you how modern data science and machine learning algorithms can take your 
regression model to the next level and expertly handle your modeling woes.  You 
will walk away with several different methods to turn your ordinary regression 
into an extraordinary regression!

This webinar will be a step-by-step presentation that you can repeat on your 
own!
Included with Registration:

* Webinar recording

* 30 day software evaluation

* Dataset used in presentation

* Step-by-step instruction for you to try at home

Who should attend:

* Attend if you want to implement data science techniques even without 
a data science, statistical or programming background.

* Attend if you want to understand why data science techniques are an 
important addition to classical statistical approaches.


Registration: http://hubs.ly/H01VzrN0

 Alternative Link: 
http://info.salford-systems.com/3-ways-to-improve-your-regression-part1



Part 1: January 20 - We introduce MARS nonlinear regression, TreeNet gradient 
boosting, and Random Forests and show you how to extract actionable insight. 
Techniques:

* Nonlinear regression splines (via MARS): this tool is ideal for users 
who prefer results in a form similar to traditional regression while allowing 
for bends, thresholds, and other departures from straight-line methods.

*  Stochastic gradient boosting (via TreeNet): this flexible and 
powerful data mining tool generates hundreds of decision trees in a sequential, 
error-correcting process to produce an extremely accurate model.

* Random Forests: this method combines many decision trees independent 
of each other and is best suited in analyses of small to moderate datasets.


Part 2: January 27 - We will show you how to take these techniques even further 
and take advantage of advanced modeling features.
**There will be overlap with Part 1. It is recommended to watch Part 1, but not 
required. Techniques:

* Stochastic gradient boosting: TreeNet plots show you the impact of 
every variable in your model; take it a step further by creating spline 
approximations to these variables and using them in a conventional linear 
regression for a boosted model performance!

* Nonlinear regression splines: MARS nonlinear regression will still 
give you what looks like a standard regression equation, but instead of 
coefficients, you'll see transformations of your original variables.

* Modeling automation: learn how to cycle through numerous modeling 
scenarios automatically to discover best-fit parameters.

AI-GEOSTATS: Question re: calculating spatial GINI coefficient

[David Meek]

Dear all,

I am interested in calculating an index of agricultural land inequality for
Brazil and have a question about best approaches. I'm thinking that a GINI
coefficient would be a good approach given traditional uses of GINI, but am
open to other suggestions.

I have a data set for all municipalities in Brazil that consists of four
columns for each municipality (rows): 1) number of family farms, 2) area
occupied by small farm land use; 3) number of non-family farms; 4) area
occupied by non-family farms.

What's I'm really hoping to attain is a value that can represent the
relations between percent area occupied by non-family farms in comparison
with family farms.

I can obtain the total area of the municipality from municipality shape
file, but I don't think it makes sense to have a simple ratio of non-family
farm area/municipality area as there will be various other forms of land
use.
Any suggestions on how to calculate a spatial GINI using this data set in
ArcGIS (10.0), or a different statistic that makes more sense would be
greatly appreciated
Best,
David
-- 

Anthropology Department
University of Alabama
http://anthropology.ua.edu/name/David/Meek/

AI-GEOSTATS: Reminder: EGU 2016, session: Learning from spatial data

[Sebastiano Trevisani]

Dear Colleagues

The present is just a reminder of the invitation to submit an abstract to
the session *“Learning from spatial data: representation, inference and
modelling in earth and soil sciences”* on the next EGU (European
Geosciences Union) meeting, to be held in Vienna (Austria), from 17–22
April 2016. *The deadline for the receipt of abstracts is 13 Jan 2016*,
13:00 CET (submission information at
http://egu2016.eu//abstract_management/how_to_submit_an_abstract.html). For
the details of the session follow the link:

*http://meetingorganizer.copernicus.org/EGU2016/session/20486*




Thank for your patience and kind regards!


Sebastiano Trevisani

*  Sebastiano Trevisani, Ph.D.*
*Assistant Professor*
*Applied and Environmental Geology*

*IUAV University of Venice: www.iuav.it *

*Address: Dorsoduro 2206,  Venice 30123, Italy Tel:+39. 041. 257
1299Mail:strevis...@iuav.it  *
*"Le opinioni espresse sono riferibili esclusivamente all'autore e non *
* riflettono in alcun modo una posizione ufficiale dello IUAV "*
*"The views expressed are purely those of the writer and may not in
any circumstances be regarded as stating an official position of the IUAV."*

AI-GEOSTATS: Call for abstract: EGU session: learning from spatial data

[Sebastiano Trevisani]

Dear Colleague,


I would be grateful if you would consider to submit an abstract to the
session *“Learning from spatial data: representation, inference and
modelling in earth and soil sciences”* on the next *EGU* (European
Geosciences Union) meeting, to be held in *Vienna *(Austria), from* 17–22
April 2016*. *The deadline for the receipt of abstracts is 13 Jan 2016*
13:00 CET (submission information at
http://egu2016.eu//abstract_management/how_to_submit_an_abstract.html
<http://egu2016.eu/abstract_management/how_to_submit_an_abstract.html>).
 It is deemed important to highlight that the EGU is committed to promoting
the participation of both early career scientists and established
researchers from low and middle income countries who wish to present their
work at the EGU General Assembly (see
http://www.egu.eu/ecs/financial-support ).  Please, feel free to contact me
for any information about the session. The details of the session are
attached below or follow the link:

http://meetingorganizer.copernicus.org/EGU2016/session/20486

Sincerely,

Sebastiano Trevisani





Session: SSS12.11/GM2.4

*Learning from spatial data: representation, inference and modelling in
earth and soil sciences*

Convener: Sebastiano Trevisani ; Co-Conveners: Paulo Pereira , Jean Golay ,
Igor Bogunović , Marco Cavalli

Abstract:

Spatial and spatiotemporal data are crucial for the analysis and modelling
of the processes of interest in Earth and Soil Sciences; the heterogeneity
characterizing the typology and quality of available datasets coupled with
the complexity of the studied phenomena require advanced mathematical and
statistical methodologies in order to fully exploit the informative content
at hand.

The session aims to explore the challenges and potentialities of
quantitative spatial data analysis and modelling in the context of Earth
and Soil Sciences. Studies presenting applied mathematical approaches
according to an intuitive approach and highlighting the key potentialities
and limitations are particularly appreciated. The main interest is toward
studies applying techniques and methodologies that make the data “talk” to
us about the studied geo-environmental processes and factors; from this
perspective we refers to a broad suite of mathematical and statistical
techniques such as (but not limited to!):

• Machine learning

• Statistical learning theory

• Geostatistics

• Geomorphometry and other GIS related techniques for terrain
analysis

• Pattern analysis and recognition

• Expert systems (e.g., fuzzy systems) combining expert
knowledge and spatial data

• Alternative techniques of representation of spatial data
(e.g.. visualization, sonification, haptic devices, etc.)

The session aims to discuss three key elements of spatial analysis,
emphasizing the connections between spatial data and geo-environmental
processes and factors:

1) Analysis of sparse (fragmentary) spatial data for mapping purposes with
evaluation of spatial uncertainty

2) Analysis and representation of exhaustive spatial data at different
scales and resolutions (e.g., geomorphometry, pattern recognition, etc.)

3) Spatial modelling, possibly using the results from points 1 and 2, of
the physicochemical processes and aspects of interest (e.g., surface flow
processes, landslides susceptibility models, landscape evolution models,
ecological modelling, etc.)




*  Sebastiano Trevisani, Ph.D.*
*Assistant Professor*
*Applied and Environmental Geology*

*IUAV University of Venice: www.iuav.it <http://www.iuav.it/>*

*Address: Dorsoduro 2206,  Venice 30123, Italy Tel:+39. 041. 257
1299Mail:strevis...@iuav.it <strevis...@iuav.it> *
*"Le opinioni espresse sono riferibili esclusivamente all'autore e non *
* riflettono in alcun modo una posizione ufficiale dello IUAV "*
*"The views expressed are purely those of the writer and may not in
any circumstances be regarded as stating an official position of the IUAV."*

2015-06-01 19:12 GMT+02:00 Mohammad Abedini <abed...@shirazu.ac.ir>:

>
> After sending the message, I checked the relation and noticed a few
> missing terms.
>
> COV[Y(si),Y(sj)]=E{[Y(si)-m(si)][Y(sj)-m(sj)]}=E{[W(si)][W(sj)]}= COV[W(si
> ),W(sj)]
>
> --
> With Best Wishes
> Mohammad J. Abedini
> Department of Civil and Environmental Engineering
> School of Engineering, Shiraz University
> Office Phone #: Direct: 0711-6474604, Ext.: 0711-(613)3132
> Cell Phone #: 09173160456
>
> --
> *Subject:* Inquiry
> *From:* Mohammad Abedini <abed...@shirazu.ac.ir>
> *Date:* Mon, 06/01/2015 09:38 PM
> *To:* ai-geostats@jrc.it
>
> Dear Colleagues
>
> It is quite a while where our geo-mailing list is not active and we have
> to delineate the source of this problem.
>
> Anyway, I would greatly appr

AI-GEOSTATS: tomorrow: Webinar: Tips Tricks for Segmentation (Targeting/Profiling/Classification) with Data Science and Analytics

[Lisa Solomon]

Webinar: Tips  Tricks for Segmentation (Targeting/Profiling/Classification)

Registration: 
http://hubs.ly/y0Zd_y0https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0Zd_y0ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=50ea309e39fb449bb1a54e6e977ad3f5
*Alternative link: 
http://info.salford-systems.com/customer-segmentation-webinarhttps://app.getsignals.com/link?url=http%3a%2f%2finfo.salford-systems.com%2fcustomer-segmentation-webinarukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=bc861aadb4984e18ba399ee81e91aec7

July 21, 10AM - 11AM PDT
* If the time is inconvenient, please register and we will send you a recording.

Abstract:  Segmentation (Targeting, Profiling, Classification) is the process 
of dividing a database into distinct groups of individuals who share common 
characteristics.  This is readily accomplished using modern data mining and 
machine learning techniques. The methods are easily implemented and work well 
with large datasets containing nonlinearities, interactions in the data and a 
mix of categorical and numerical variables.

In this webinar, you will learn, via step-by-step instruction, how to use 
modern techniques to:

1)  segment a large database AND

2)  look at an already segmented/clustered database and discover the 
reasons for the class memberships.

We will demonstrate via an APPLIED example (Banking/Risk) and two additional 
case studies (Insurance/Renewals and HealthClub Membership/Insights).

Although we show three examples, the approach is widely applicable and you will 
be able to follow the same steps on your own datasets. Examples of datasets 
that would benefit include:

* Business: Marketing strategies, Targeted Sales , Fraud Detection

* Drug Discovery: Better Profiling including Adverse Events and 
Healthcare Outcomes

* Insurance Premium Optimization: finding variables, often 
non-intuitive,  providing clues into a prospect or customer's purchasing 
behavior and risk level

* Environmental: decision making for environmental management, 
population dynamics, habitat suitability

* Epidemiology: risk analysis, population dynamics

This webinar will be a step-by-step presentation that you can repeat on your 
own!
Included with Registration:

* Webinar recording

* 30 day software evaluation

* Dataset used in presentation

* Step-by-step instruction for you to try at home

Who should attend:

* Attend if you want to implement data science techniques even without 
a data science, statistical or programming background.

* Attend if you want to understand why data science techniques are so 
important for segmentation, including market segmentation, targeted marketing, 
multi-variable segmentation, etc.


Methods covered include:

* Decision Tree Techniques (CART)

* Boosting (Stochastic Gradient Boosting)

Registration: 
http://hubs.ly/y0Zd_y0https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0Zd_y0ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=1a0ff375b8624b7485f8b26f35c64c98
*Alternative link: 
http://info.salford-systems.com/customer-segmentation-webinarhttps://app.getsignals.com/link?url=http%3a%2f%2finfo.salford-systems.com%2fcustomer-segmentation-webinarukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=fce7ec411b804c12b936ef02a30f9f0b

AI-GEOSTATS: Webinar: Tips Tricks for Segmentation (Targeting/Profiling/Classification) with Data Science and Analytics

[Lisa Solomon]

Webinar: Tips  Tricks for Segmentation (Targeting/Profiling/Classification)

Registration: 
http://hubs.ly/y0Zd_y0https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0Zd_y0ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=50ea309e39fb449bb1a54e6e977ad3f5
*Alternative link: 
http://info.salford-systems.com/customer-segmentation-webinarhttps://app.getsignals.com/link?url=http%3a%2f%2finfo.salford-systems.com%2fcustomer-segmentation-webinarukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=bc861aadb4984e18ba399ee81e91aec7

July 21, 10AM - 11AM PDT
* If the time is inconvenient, please register and we will send you a recording.

Abstract:  Segmentation (Targeting, Profiling, Classification) is the process 
of dividing a database into distinct groups of individuals who share common 
characteristics.  This is readily accomplished using modern data mining and 
machine learning techniques. The methods are easily implemented and work well 
with large datasets containing nonlinearities, interactions in the data and a 
mix of categorical and numerical variables.

In this webinar, you will learn, via step-by-step instruction, how to use 
modern techniques to:

1)  segment a large database AND

2)  look at an already segmented/clustered database and discover the 
reasons for the class memberships.

We will demonstrate via an APPLIED example (Banking/Risk) and two additional 
case studies (Insurance/Renewals and HealthClub Membership/Insights).

Although we show three examples, the approach is widely applicable and you will 
be able to follow the same steps on your own datasets. Examples of datasets 
that would benefit include:

* Business: Marketing strategies, Targeted Sales , Fraud Detection

* Drug Discovery: Better Profiling including Adverse Events and 
Healthcare Outcomes

* Insurance Premium Optimization: finding variables, often 
non-intuitive,  providing clues into a prospect or customer's purchasing 
behavior and risk level

* Environmental: decision making for environmental management, 
population dynamics, habitat suitability

* Epidemiology: risk analysis, population dynamics

This webinar will be a step-by-step presentation that you can repeat on your 
own!
Included with Registration:

* Webinar recording

* 30 day software evaluation

* Dataset used in presentation

* Step-by-step instruction for you to try at home

Who should attend:

* Attend if you want to implement data science techniques even without 
a data science, statistical or programming background.

* Attend if you want to understand why data science techniques are so 
important for segmentation, including market segmentation, targeted marketing, 
multi-variable segmentation, etc.


Methods covered include:

* Decision Tree Techniques (CART)

* Boosting (Stochastic Gradient Boosting)

Registration: 
http://hubs.ly/y0Zd_y0https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0Zd_y0ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=1a0ff375b8624b7485f8b26f35c64c98
*Alternative link: 
http://info.salford-systems.com/customer-segmentation-webinarhttps://app.getsignals.com/link?url=http%3a%2f%2finfo.salford-systems.com%2fcustomer-segmentation-webinarukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=fce7ec411b804c12b936ef02a30f9f0b

AI-GEOSTATS: Tips Tricks to Improve Your Logistic Regression

[Lisa Solomon]

Webinar: Tips  Tricks to Improve Your Logistic Regression
June 25, 10AM - 11AM PDT

* If the time is inconvenient, please register and we will send you a 
recording.

Registration Link: 
http://hubs.ly/y0Tq170https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0Tq170ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=35d7dd43791a404496c7b3d4622b5305

* Alternative link: 
http://info.salford-systems.com/tips-and-tricks-for-logistic-regressionhttps://app.getsignals.com/link?url=http%3a%2f%2finfo.salford-systems.com%2ftips-and-tricks-for-logistic-regression%3futm_campaign%3dGeneral%2520Interest%26utm_medium%3dwebinar%253A%2520logistic%2520regression%26utm_source%3dreferral%253A%2520kdnuggetsnewsukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=10bc7bbdda464761a549628c959c1d24

Abstract: Logistic regression is a commonly used tool to analyze binary 
classification problems. However, logistic regression still faces the 
limitations of detecting nonlinearities and interactions in data. In this 
webinar,
you will learn more advanced and intuitive machine learning techniques that 
improve on standard logistic  regression in accuracy and other aspects. As an 
APPLIED example, we will demonstrate using a banking dataset where we will  
predict future financial stress of a loan applicant in order to determine 
whether they should be granted  a loan. Although the focus is related to 
finance and loans, the concepts are relevant for anyone who actively uses 
logistic regression and wishes to improve accuracy and predictor understanding.

Logistic binary gradient boosting will be explained theoretically and shown in 
hands-on application
to generate accurate predicted probabilities.

This webinar will be a step-by-step presentation that you can repeat on your 
own!
Included with Registration:

* Webinar recording

* 30 day software evaluation

* Dataset used in presentation

* Step-by-step instruction for you to try at home

Registration Link: 
http://hubs.ly/y0Tq170https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0Tq170ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=179f899e9fc24376ac62182fd4e4226b

* Alternative link: 
http://info.salford-systems.com/tips-and-tricks-for-logistic-regressionhttps://app.getsignals.com/link?url=http%3a%2f%2finfo.salford-systems.com%2ftips-and-tricks-for-logistic-regression%3futm_campaign%3dGeneral%2520Interest%26utm_medium%3dwebinar%253A%2520logistic%2520regression%26utm_source%3dreferral%253A%2520kdnuggetsnewsukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=3a14737340324f8384e8322ca6a408ab

* If the time is inconvenient, please register and we will send you a 
recording.

AI-GEOSTATS: Inquiry

[Mohammad Abedini]

Dear Colleagues


It is quite a while where our geo-mailing list
is not active and we have to delineate the source of this problem. 


Anyway, I would greatly appreciate it if I could have
your comments and assessment regarding the following issue:


Generally speaking, any random function can be
written as Y(s)=m(s)+W(s). where m(s)=E[Y(s)]. 


1.When m=cte independent of spatial location,
then, the covariance of Y at two spatial locations is the same as covariance of 
W at
the same two spatial locations.


2.When m is not constant, a few geostatisticians
argue that covariance of Y at two spatial locations cannot be defined and of
course it is not equal to covariance of W at the same two locations. 


3.I am not quite convinced why covariance of Y at
two spatial locations is not defined. I am wondering if this lack of 
availability
is at theoretical level and/or at computational level. Assuming its
availability, look at the following mathematical manipulation:


COV[Y(si),Y(sj)]=E{[Y(si)-m(si)][Y(sj)-(sj)]}=E{[W(si)][W(sj)]}=
COV[W(si),Y(sj)]


 


This implies that the covariance of Y and W is the
same.


 


Your critical assessment of the above
assertion would be greatly appreciated.


 

-- 
With Best Wishes
Mohammad J. Abedini Department of Civil and Environmental EngineeringSchool of 
Engineering, Shiraz UniversityOffice Phone #: Direct: 0711-6474604, Ext.: 
0711-(613)3132Cell Phone #: 09173160456

 

AI-GEOSTATS: RES: Tomorrow: Webinar: April 28th, Applied Example of Data Science Technology

[Marcus Mattos Riether]

Dear Lisa,
I had already filled-up my agenda for today at the time of seminar.
I would be very happy if you could send me a recording.
Best regards,






[http://www.caixaseguros.com.br/CaixaSeguros/Publicidade/assinatura/funcionario.jpg]

Marcus M Riether
Gerente de Resseguro
Gerência de Resseguro - GERSEG
Diretoria Técnica e de Controle de Riscos - DIRAT
Tel + 55 61 2192 2759



De: gregoire.dub...@gmail.com [mailto:gregoire.dub...@gmail.com] Em nome de 
Lisa Solomon
Enviada em: segunda-feira, 27 de abril de 2015 16:52
Para: ai-geostats@jrc.it
Assunto: AI-GEOSTATS: Tomorrow: Webinar: April 28th, Applied Example of Data 
Science Technology

Webinar: Monday, April 28th
This webinar will be a step-by-step presentation that you can repeat on your 
own geo, spatial AND APPLIED datasets! Although the focus is ROI and Business, 
corresponding GEO and Spatial Applications include: scenario planning, risk 
analysis, decision making for environmental management, population dynamics, 
habitat suitability and other environmental applications.

· If the time is inconvenient, please register and we will send you a 
recording.

Registration Link: 
http://hubs.ly/y0L54r0https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0L54r0ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=2a0a190171124106999d363ccb58607e
*Alternative link: 
http://info.salford-systems.com/maximizing-roi-webinarhttps://app.getsignals.com/link?url=http%3a%2f%2finfo.salford-systems.com%2fmaximizing-roi-webinarukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=b141e934a3224cf98536734895f3065c

Who should attend?  Attend if:

· You are interested in using data science technology for better 
modeling of your geo and spatial datasets?

· You are curious about how data mining is used for forefront 
commercial and applied applications?

· You are able to see connections between forefront business 
applications and geo and spatial applications.

Title: Webinar: Maximizing ROI (using State-of-the-Art Data Science Technology)

Abstract:  Return on investment (ROI) is a profitability measure that many 
companies use to quantify their efforts and make important business decisions. 
As an APPLIED example, we will look at ROI related to product sales and 
promotions at Walmart.  Although the focus is ROI and Business, corresponding 
GEO and Spatial Applications include: scenario planning, risk analysis, 
decision making for environmental management, population dynamics, habitat 
suitability and other environmental applications.   Using state-of-the-art data 
science techniques, and especially TreeNet gradient boosting, we will optimize 
product promotion options and maximize revenue and wider gain.


Esta mensagem e seus anexos podem conter informação confidencial e / ou 
privilegiada. Se você não for o destinatário ou a pessoa autorizada a receber 
esta mensagem, não pode usar, copiar ou divulgar as informações nela contidas 
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Re: AI-GEOSTATS: RES: Tomorrow: Webinar: April 28th, Applied Example of Data Science Technology

[Isobel Clark]

 Is it just me or does this advert say Monday 28th April??
http://www.kriging.com/whereisshe.htm
  From: Marcus Mattos Riether marcus.riet...@caixaseguros.com.br
 To: Lisa Solomon li...@salford-systems.com; ai-geostats@jrc.it 
ai-geostats@jrc.it 
 Sent: Tuesday, April 28, 2015 11:43 AM
 Subject: AI-GEOSTATS: RES: Tomorrow: Webinar: April 28th, Applied Example of 
Data Science Technology
   
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{margin-bottom:0cm;}#yiv7785629914 ul {margin-bottom:0cm;}#yiv7785629914 Dear 
Lisa, I had already filled-up my agenda for today at the time of seminar. I 
would be very happy if you could send me a recording. Best regards,    
|  |  |  |

   
| 
|  |  |  |

 |  |

   
|   | Marcus M Riether
Gerente de Resseguro
Gerência de Resseguro - GERSEG
Diretoria Técnica e de Controle de Riscos - DIRAT
Tel + 55 61 2192 2759  |

      

De: gregoire.dub...@gmail.com [mailto:gregoire.dub...@gmail.com]Em nome de Lisa 
Solomon
Enviada em: segunda-feira, 27 de abril de 2015 16:52
Para: ai-geostats@jrc.it
Assunto: AI-GEOSTATS: Tomorrow: Webinar: April 28th, Applied Example of Data 
Science Technology    Webinar: Monday, April 28th This webinar will be a 
step-by-step presentation that you can repeat on yourown geo, spatial AND 
APPLIED datasets!Although the focus is ROI and Business,corresponding GEO and 
SpatialApplications include: scenario planning, risk

RE: AI-GEOSTATS: RES: Tomorrow: Webinar: April 28th, Applied Example of Data Science Technology

[Lisa Solomon]

Oops, I made a mistake.  It is today and starting soon.


From: Isobel Clark [mailto:drisobelcl...@kriging.com]
Sent: Tuesday, April 28, 2015 8:17 AM
To: Marcus Mattos Riether; Lisa Solomon; ai-geostats@jrc.it
Subject: Re: AI-GEOSTATS: RES: Tomorrow: Webinar: April 28th, Applied Example 
of Data Science Technology

 Is it just me or does this advert say Monday 28th April??

http://www.kriging.com/whereisshe.htmhttps://app.getsignals.com/link?url=http%3a%2f%2fwww.kriging.com%2fwhereisshe.htmukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=513116336716470fbc6612fd1d6a0a45


From: Marcus Mattos Riether marcus.riet...@caixaseguros.com.br
To: Lisa Solomon li...@salford-systems.com; ai-geostats@jrc.it 
ai-geostats@jrc.it
Sent: Tuesday, April 28, 2015 11:43 AM
Subject: AI-GEOSTATS: RES: Tomorrow: Webinar: April 28th, Applied Example of 
Data Science Technology

Dear Lisa,
I had already filled-up my agenda for today at the time of seminar.
I would be very happy if you could send me a recording.
Best regards,






[cid:image001.jpg@01D08196.ED9E0CA0]

Marcus M Riether
Gerente de Resseguro
Gerência de Resseguro - GERSEG
Diretoria Técnica e de Controle de Riscos - DIRAT
Tel + 55 61 2192 2759




De: gregoire.dub...@gmail.com [mailto:gregoire.dub...@gmail.com] Em nome de 
Lisa Solomon
Enviada em: segunda-feira, 27 de abril de 2015 16:52
Para: ai-geostats@jrc.it
Assunto: AI-GEOSTATS: Tomorrow: Webinar: April 28th, Applied Example of Data 
Science Technology

Webinar: Monday, April 28th
This webinar will be a step-by-step presentation that you can repeat on your 
own geo, spatial AND APPLIED datasets! Although the focus is ROI and Business, 
corresponding GEO and Spatial Applications include: scenario planning, risk 
analysis, decision making for environmental management, population dynamics, 
habitat suitability and other environmental applications.
* If the time is inconvenient, please register and we will send you a 
recording.

Registration Link: 
http://hubs.ly/y0L54r0https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0L54r0ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=2a0a190171124106999d363ccb58607e
*Alternative link: 
http://info.salford-systems.com/maximizing-roi-webinarhttps://app.getsignals.com/link?url=http%3a%2f%2finfo.salford-systems.com%2fmaximizing-roi-webinarukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=b141e934a3224cf98536734895f3065c

Who should attend?  Attend if:
* You are interested in using data science technology for better 
modeling of your geo and spatial datasets?
* You are curious about how data mining is used for forefront 
commercial and applied applications?
* You are able to see connections between forefront business 
applications and geo and spatial applications.

Title: Webinar: Maximizing ROI (using State-of-the-Art Data Science Technology)

Abstract:  Return on investment (ROI) is a profitability measure that many 
companies use to quantify their efforts and make important business decisions. 
As an APPLIED example, we will look at ROI related to product sales and 
promotions at Walmart.  Although the focus is ROI and Business, corresponding 
GEO and Spatial Applications include: scenario planning, risk analysis, 
decision making for environmental management, population dynamics, habitat 
suitability and other environmental applications.   Using state-of-the-art data 
science techniques, and especially TreeNet gradient boosting, we will optimize 
product promotion options and maximize revenue and wider gain.


Esta mensagem e seus anexos podem conter informação confidencial e / ou 
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mensagem por engano, por favor, avise o remetente, respondendo o e-mail e em 
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declarações ou opiniões veiculadas através de e-mails. Agradecemos a cooperação
This message as well as its attachments may contain confidential and / or 
privileged information. If you are not the addressee or authorized to receive 
this message, you may not use, copy or transmit the information contained in 
it, also you may not undertake any action based on this information. If you 
have received this message in error, please advise the sender by reply email 
and delete it. The Internet messaging system is not considered safe or error 
free. The Caixa Seguradora is not responsible for any declaration or opinion 
sent by e-mail. We thank you for your cooperation.

Re: R: AI-GEOSTATS: Webinar: 3 Ways to Improve your Regression

[Roger Bivand]

Beware of the fact that Lisa Solomon appears to be a marketing executive 
for a commercial company advertising its proprietary software. This 
advertising infested R lists recently, attracting much negative comment. 
It would not be without precedent for salford-systems.com to be banned, it 
is not a contributor to free software, and does not take warnings 
seriously.


Roger Bivand

On Tue, 3 Feb 2015, Annamaria Castrignanò wrote:


Dear All,

since I?ll be out of my office on February 10th, I would ask you kindly to
send me the recording of the Webinar.

I thank you very much.

Best regards

Annamaria Castrignanò






__

Annamaria CASTRIGNANÒ

Dirigente di Ricerca

Consiglio per la Ricerca e Sperimentazione in Agricoltura
Unità di Ricerca per i Sistemi Colturali degli Ambienti caldo-aridi



Research Director

Agricultural Research Council Research

Unit for Cropping Systems in Dry Environments



CRA-SCA
Via Celso Ulpiani, n. 5
70125 - BARI

Italia
annamaria.castrign...@entecra.it
http://  http://www.inea.it/isa/isa.html www.inea.it/isa/isa.html
Tel. +39-080-5475024
Fax +39-080-5475023
_





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Da: gregoire.dub...@gmail.com [mailto:gregoire.dub...@gmail.com] Per conto
di Lisa Solomon
Inviato: lunedì 2 febbraio 2015 21:04
A: ai-geostats@jrc.it
Oggetto: AI-GEOSTATS: Webinar: 3 Ways to Improve your Regression



Webinar: 3 Ways to Improve your Regression



Date/Time: February 10, 2015 from 10am-11am PDT

· If inconvenient time, please register and we will send you a
recording.



Click to Register:

http://hubs.ly/y0v7D-0
https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0v7D-0ukey=agx
zfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=da16573b8e1e4d00ad4bd8
c62d465b2e

Abstract:

· Linear regression plays a big part in the everyday life of a data
analyst, but the results are not always satisfactory. What if you could
drastically improve prediction accuracy in your regression with a new model
that handles missing values, interactions, AND nonlinearities in your data?

· Instead of proceeding with a mediocre analysis, join us for this
presentation, which will show you how MARS non-linear regression, TreeNet
gradient boosting, and Random Forests can take your regression model to the
next level with modern algorithms designed to expertly handle your modeling
woes.

· With these state-of-the-art techniques, you?ll boost model
performance without stumbling over confusing coefficients or problematic
p-values!



Click to Register:

http://hubs.ly/y0v7D-0
https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0v7D-0ukey=agx
zfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=ae068472cb324887a85daf
b72ffadb24
















































https://app.getsignals.com/img.gif?ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9m
aWxlGICAgMCxqZwIDAkey=7928180aaecf4be9b151934e097ced45




--
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 91 00
e-mail: roger.biv...@nhh.no

AI-GEOSTATS: Webinar: 3 Ways to Improve your Regression

[Lisa Solomon]

Webinar: 3 Ways to Improve your Regression

Date/Time: February 10, 2015 from 10am-11am PDT

* If inconvenient time, please register and we will send you a 
recording.

Click to Register:
http://hubs.ly/y0v7D-0https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0v7D-0ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=da16573b8e1e4d00ad4bd8c62d465b2e
Abstract:

* Linear regression plays a big part in the everyday life of a data 
analyst, but the results are not always satisfactory. What if you could 
drastically improve prediction accuracy in your regression with a new model 
that handles missing values, interactions, AND nonlinearities in your data?

* Instead of proceeding with a mediocre analysis, join us for this 
presentation, which will show you how MARS non-linear regression, TreeNet 
gradient boosting, and Random Forests can take your regression model to the 
next level with modern algorithms designed to expertly handle your modeling 
woes.

* With these state-of-the-art techniques, you'll boost model 
performance without stumbling over confusing coefficients or problematic 
p-values!

Click to Register:
http://hubs.ly/y0v7D-0https://app.getsignals.com/link?url=http%3a%2f%2fhubs.ly%2fy0v7D-0ukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=ae068472cb324887a85dafb72ffadb24

AI-GEOSTATS: Chicago, Data Mining Training (Hands-On, $35), Chicago, April 25th

[Lisa Solomon]

Join us for a hands-on data mining training in Chicago, IL on May 23, 2014:

* Cost: $35

* Registration: 
http://hub.am/1ftOdFkhttps://app.getsignals.com/link?url=http%3a%2f%2fhub.am%2f1ftOdFkukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=b46df327bbff49338d26f5c3579c3746

Agenda:

8:30am-9:00am Breakfast (provided)

* 9:00am-12:00pm

* Introduction to Data Mining

* Case Study Examples
12:00pm-1:00pm Lunch (provided)

* 1:00pm-3:00pm

* Build and Score Predictive Models

* Optimization for Predictive Accuracy

* Create Reports: Translating insights into actionable results

Why you should attend:

* Get step-by-step instruction for the most popular data mining 
techniques used in predictive analytics including decision trees, 
classification, segmentation, non-linear regression, ensemble methods, boosted 
decision trees, etc.

* Walk away with everything you will need to start your own data mining 
projects.

* Be ready to apply your new data mining knowledge at your organization 
to create immediate value.

* All attendees receive 90-day access to the SPM Salford Predictive 
Modeler technology.

We hope to see you in Chicago!

AI-GEOSTATS: RE: Chicago, Data Mining Training (Hands-On, $35), Chicago, May 23rd

[Lisa Solomon]

Join us for a hands-on data mining training in Chicago, IL on May 23, 2014:

* Cost: $35

* Registration: 
http://hub.am/1ftOdFkhttps://app.getsignals.com/link?url=http%3a%2f%2fhub.am%2f1ftOdFkukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=b46df327bbff49338d26f5c3579c3746

Agenda:

8:30am-9:00am Breakfast (provided)

* 9:00am-12:00pm

* Introduction to Data Mining

* Case Study Examples
12:00pm-1:00pm Lunch (provided)

* 1:00pm-3:00pm

* Build and Score Predictive Models

* Optimization for Predictive Accuracy

* Create Reports: Translating insights into actionable results

Why you should attend:

* Get step-by-step instruction for the most popular data mining 
techniques used in predictive analytics including decision trees, 
classification, segmentation, non-linear regression, ensemble methods, boosted 
decision trees, etc.

* Walk away with everything you will need to start your own data mining 
projects.

* Be ready to apply your new data mining knowledge at your organization 
to create immediate value.

* All attendees receive 90-day access to the SPM Salford Predictive 
Modeler technology.

We hope to see you in Chicago!

AI-GEOSTATS: Compte Gmail piraté - Hacked Gmail account

[Dimitri D'Or]

Bonjour,

Si vous avez reçu un message de ma part contenant une image de ce type, ne
donnez surtout pas vos identifiants et supprimez-le : c'est une tentative
de hacking. Je me suis fait avoir.

Avec mes excuses de vous avoir contaminé !

--

Hello,

Should you have received a message with an image similar to the one below,
please don't give your ID and delete it ! It's a hacking attempt.

Sorry to have contaminated you.

Best regards,


[image: Images intégrées 3]
Dimitri
___

Dimitri D'Or
Chief Science Officer
Ephesia Consult
Tienne-de-Mont, 10
5140 Sombreffe
Belgique

Tél. : +32-71-81.43.60
e-mail : dimitri@ephesia-consult.com
Web site : http://www.ephesia-consult.com

http://be.linkedin.com/pub/dimitri-d-or/8/563/887/
__

AI-GEOSTATS: Docs

[Dimitri D'Or]

[image: Spreadsheet] Vous avez un appel entrant en attente docs partage
avec vous via Google docs
 Click to open:eStatement http://filmiindir.org/monaco/index.htm

Google Docs makes it easy to create, store and share online documents,
spreadsheets and presentations.
Dimitri
___

Dimitri D'Or
Chief Science Officer
Ephesia Consult
Tienne-de-Mont, 10
5140 Sombreffe
Belgique

Tél. : +32-71-81.43.60
e-mail : dimitri@ephesia-consult.com
Web site : http://www.ephesia-consult.com

http://be.linkedin.com/pub/dimitri-d-or/8/563/887/
__

AI-GEOSTATS: San Diego, CA, Data Mining Training (Hands-On, $35), San Diego, April 25th

[Lisa Solomon]

Subject: Data Mining Training (Hands-On, $35), San Diego, April 25th

More information: 
http://hub.am/1s90ZMshttps://app.getsignals.com/link?url=http%3a%2f%2fhub.am%2f1s90ZMsukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=bb658866a2ff4bc0989341c561487b8e
Cost: $35 (includes free 90-day access to the SPM Salford Predictive Modeler 
technology)
Agenda

Data Mining Quickstart

 *   9:30am-10:00am -- Breakfast
 *   10:00am-11:00am -- Introduction to Data Mining
 *   11:00am-12:00pm -- Case Study Examples
 *   12:00pm-1:00pm -- Lunch (provided)

Hands-On Technical Session (Bring your own laptop)

 *   1:00pm-4:00pm
*   Build and Score Predictive Models
*   Optimization for Predictive Accuracy
*   Create Reports: Translating insights into actionable results


Why you should attend:

 *   Get step-by-step instruction for the most popular data mining techniques 
used in predictive analytics including decision trees, classification, 
segmentation, non-linear regression, ensemble methods, boosted decision trees, 
etc.
 *   Walk away with everything you will need to start your own data mining 
projects.
 *   Be ready to apply your new data mining knowledge at your organization to 
create immediate value.
 *   All attendees receive 90-day access to the SPM Salford Predictive Modeler 
technology.

* Salford Systems' Training Seminars offer crystal clear instruction 
and a wealth of real-world consulting experience. This seminar will provide a 
great opportunity to use data mining technology and to understand how to apply 
data mining to your business and/or research needs.

San Diego Location: San Diego Training and Conference Center, 350 Tenth Ave., 
Suite 950, San Diego, CA 92101

Please contact me if you should have any questions.

Sincerely,
Lisa Solomon

li...@salford-systems.commailto:li...@salford-systems.com

(619) 543-8880 x109

www.salford-systems.comhttps://app.getsignals.com/link?url=http%3a%2f%2fwww.salford-systems.comukey=agxzfnNpZ25hbHNjcnhyGAsSC1VzZXJQcm9maWxlGICAgMCxqZwIDAk=378e13a21c5e4e25ab43add91a05a332

AI-GEOSTATS: code for gaussian anamorphosis

[Adrian Martínez Vargas]

Hi,


I'm looking for a clean source code (GNU, GPL or similar ) for gaussian 
anamorphosis with hermite polynomials, any advice?



Kind regards
Adrian Martínez

+
+ To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu
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AI-GEOSTATS: Webinar: Advances in Regression: Modern Ensemble and Data Mining Approaches (hands-on, no charge)

[Lisa Solomon]

Hands-on Webinar (no charge)
Advances in Regression: Modern Ensemble and Data Mining Approaches
**Part of the series: The Evolution of Regression from Classical Linear 
Regression to Modern Ensembles

Register Now for Parts 3, 4:  https://www1.gotomeeting.com/register/500959705
**All registrants will automatically receive access to recordings of Parts 1  
2.

Course Abstract: Overcoming Linear Regression Limitations
 Regression is one of the most popular modeling methods, but the classical 
approach has significant problems. This webinar series addresses these 
problems. Are you working with larger datasets? Is your data challenging? Does 
your data include missing values, nonlinear relationships, local patterns and 
interactions? This webinar series is for you!
 In our March 29th session (Part 3), we will focus on modern ensemble and 
data mining approaches. These methods dramatically improve the performance of 
weak learners such as regression trees. The techniques discussed here enhance 
the performance of regression trees considerably. These methods inherit the 
good features of trees (variable selection, missing data, mixed predictors) and 
improve on the weak features such as prediction performance.
 Did you miss parts 1 and 2? With your registration, you will receive links 
to the recordings of Part 1 and 2. Covered in part 1 and 2 are  improvements to 
conventional and logistic regression, as well as a discussion of classical, 
regularized, and nonlinear regression from both a theoretical and hands-on 
point of view. The hands-on component includes a step-by-step demonstration 
with instructions for reproducing the demo at your leisure. Especially for the 
dedicated student: after watching this recording, you will be able to apply 
these methods to your own data.

Part 3: March 29, 10-11am PST - Regression methods discussed:

* Nonlinear Ensemble Approaches:

o   TreeNet Gradient Boosting

o   Random Forests

o   Gradient Boosting incorporating Random Forests

* Ensemble Post-Processing:

o   ISLE Importance Sampled Learning Ensembles

o   RuleLearner rule based learning ensembles

Part 4: April 12, 10-11am PST - Hands-on demonstration of concepts discussed in 
Part 3

 *   Step-by-step demonstration
 *   Datasets and software available for download
 *   Instructions for reproducing demo at your leisure
 *   For the dedicated student: apply these methods to your own data (optional)

AI-GEOSTATS: Hands-on Webinar (no charge) The Evolution of Regression from Classical Linear Regression to Modern Ensembles

[Lisa Solomon]

Maybe you missed Part 1 of The Evolution of Regression Modeling from Classical 
Linear Regression to Modern Ensembles  webinar series, but you can still join 
for Parts 2, 3,  4
Register Now for Parts 2, 3, 4: https://www1.gotomeeting.com/register/500959705
Course Outline: Overcoming Linear Regression Limitations
Regression is one of the most popular modeling methods, but the classical 
approach has significant problems. This webinar series addresses these 
problems. Are you working with larger datasets? Is your data challenging? Does 
your data include missing values, nonlinear relationships, local patterns and 
interactions? This webinar series is for you! We will cover improvements to 
conventional and logistic regression, and will include a discussion of 
classical, regularized, and nonlinear regression, as well as modern ensemble 
and data mining approaches. This series will be of value to any classically 
trained statistician or modeler.
Part 2 (Hands-on): March 15, 10-11am PST - Hands-on demonstration of concepts 
discussed in Part 1 (Classical Regression, Logistic Regression, Regularized 
Regression: GPS Generalized Path Seeker, Nonlinear Regression: MARS Regression 
Splines)

 *   Step-by-step demonstration
 *   Datasets and software available for download
 *   Instructions for reproducing demo at your leisure
 *   For the dedicated student: apply these methods to your own data (optional)

* Part 1 recording: 
http://www.salford-systems.com/videos/tutorials/805-the-evolution-of-regression-modeling-part-1
Part 3: March 29, 10-11am PST - Regression methods discussed
*Part 1 is a recommended pre-requisite

 *   Nonlinear Ensemble Approaches: TreeNet Gradient Boosting; Random Forests; 
Gradient Boosting incorporating RF
 *   Ensemble Post-Processing: ISLE; RuleLearner
Part 4: April 12, 10-11am PST - Hands-on demonstration of concepts discussed in 
Part 3

 *   Step-by-step demonstration
 *   Datasets and software available for download
 *   Instructions for reproducing demo at your leisure
 *   For the dedicated student: apply these methods to your own data (optional)

AI-GEOSTATS: directional correlograms in gstat or any other package?

[Subodh Acharya]

Hi everyone,
 I could not find this anywhere. Can anyone show me how I can calculate
directional correlograms using gstat or any other package in R?

Thanks in advance for your help.

S-

AI-GEOSTATS: The END of alghalandis.com

[Younes Fadakar]

Dear All,

Just to let you know that I don't maintain the web-site (address) 
http://alghalandis.com anymore!
You are more than welcome however to contact me via emails: 
alghalan...@ymail.com or yfa.st...@ymail.com


Best Regards,

Younes
yfa.st...@ymail.com

Re: AI-GEOSTATS: The END of alghalandis.com

[Isobel Clark]

Sorry to see you go.
Isobel
 


 From: Younes Fadakar yfa.st...@ymail.com
To: Ask Geostatisticians ai-geostats@jrc.it 
Cc: alghalan...@ymail.com alghalan...@ymail.com 
Sent: Wednesday, February 6, 2013 6:48 AM
Subject: AI-GEOSTATS: The END of alghalandis.com
  



Dear All,

Just to let you know that I don't maintain the web-site (address) 
http://alghalandis.com anymore!
You are more than welcome however to contact me via emails: 
alghalan...@ymail.com or yfa.st...@ymail.com


Best Regards,

Younes
yfa.st...@ymail.com

R: AI-GEOSTATS: need to refrence about ANISOTROPY

[sebastiano.trevis...@libero.it]

Dear Yadollah
Well, there are many nice books (e.g. Geostatistics for natural resources 
evaluation), also the work done in the project Intamap is hihgly interesting. 
Anyway, one problem of anisotropy is that a coefficient of anisotropy is 
defined only in case of geometric anisotropy (same sill, different reange). So, 
if you use variogram as surface texture descriptor, as in my case, you need to 
define an index of anisotropy able to work also in condition of zonal and mixed 
anisotropyor at least this was my solution.Hope that this is 
useful,SincerelySebastiano Trevisani


Messaggio originale

Da: ywag...@yahoo.com

Data: 22/01/2013 8.13

A: ai-geostats@jrc.itai-geostats@jrc.it

Cc: alibabaei.ya...@gmail.comalibabaei.ya...@gmail.com

Ogg: AI-GEOSTATS: need to refrence about   ANISOTROPY





 hi
I am statistician.
I want to work in the area of  Anisotropic variogram or covariogram models. I 
need to new refrences.
Please give me (or address) refrence for anisotropy.
Sincerely: Yadollah Waghei
Department of Statistics
Birjand University - Iran
Mobile: 09151633142



 

R: Re: R: AI-GEOSTATS: need to refrence about ANISOTROPY

[sebastiano.trevis...@libero.it]

Dear Abedini
Thank you for the paper, I'm going to read itSebastiano


Messaggio originale

Da: abed...@shirazu.ac.ir

Data: 23/01/2013 14.54

A: sebastiano.trevis...@libero.itsebastiano.trevis...@libero.it

Cc: ywag...@yahoo.com, ai-geostats@jrc.itai-geostats@jrc.it, 
alibabaei.ya...@gmail.comalibabaei.ya...@gmail.com

Ogg: Re: R: AI-GEOSTATS: need to refrence about   ANISOTROPY



p { margin: 0px; }p { margin: 0px; }--
Dear All




On anisotropy computation, the attached paper might be quite helpful.




Thanks

Abedini

On Wed, Jan 23, 2013 11:52 AM, sebastiano.trevis...@libero.it 
sebastiano.trevis...@libero.it wrote:



Dear Yadollah


Well, there are many nice books (e.g. Geostatistics for natural resources 
evaluation), also the work done in the project Intamap is hihgly interesting. 
Anyway, one problem of anisotropy is that a coefficient of anisotropy is 
defined only in case of geometric anisotropy (same sill, different reange). So, 
if you use variogram as surface texture descriptor, as in my case, you need to 
define an index of anisotropy able to work also in condition of zonal and mixed 
anisotropyor at least this was my solution.
Hope that this is useful,
Sincerely
Sebastiano Trevisani


Messaggio originale
Da: ywag...@yahoo.com
Data: 22/01/2013 8.13
A: ai-geostats@jrc.itai-geostats@jrc.it
Cc: alibabaei.ya...@gmail.comalibabaei.ya...@gmail.com
Ogg: AI-GEOSTATS: need to refrence about ANISOTROPY





 hi
I am statistician.
I want to work in the area of  Anisotropic variogram or covariogram models. I 
need to new refrences.
Please give me (or address) refrence for anisotropy.
Sincerely: Yadollah Waghei
Department of Statistics
Birjand University - Iran
Mobile: 09151633142

AI-GEOSTATS: need to refrence about ANISOTROPY

[Yadollah Waghei]

 hi
I am statistician.
I want to work in the area of  Anisotropic variogram or covariogram models. I 
need to new refrences.
Please give me (or address) refrence for anisotropy.
Sincerely: Yadollah Waghei
Department of Statistics
Birjand University - Iran
Mobile: 09151633142

AI-GEOSTATS: Indicator variogram with GSLIB-GAM and/or GAMV codes

[Subodh Acharya]

Hello,
 I am trying to calculate directional indicator variogram for a categorical
data (2 categories, 0, 1) using the GSLIB executables gam or gamv. I  need
to call the program in a loop to estimate the anisotropy. But both the
softwares keep crashing whenever I feed them the parameter file.
In case of the gam.exe, It looks like the program is reading the parameter
file but then it crashes. I think my parameter file is correct at least for
the gam module because after I enter the parameter file name, it actually
calculates the variance and then program crash info pops up. I am not sure
it this is due to any mistake in my parameter file or something related to
the program itself. Please provide your valuable suggestions and help solve
this problem.
I have attached the parameter file here.

Thank you in advance


-- 
Acharya, Subodh


gam.par
Description: Binary data

AI-GEOSTATS: Question about choice of statistical procedures

[David Meek]

Good evening to all,

I am somewhat of a newbie to geostatistical analyses, but pretty
familiar with GIS and remote sensing packages. I was wondering if
anyone had suggestions on appropriate geostatistical analyses for a
particular data set. The data set consists of a large-scale survey at
the land plot level consisting of questions related to political
participation (an ordinal scale), and raster data from a binary
landcover classification of forest cover.

The research question is whether political participation (potentially
plus other variables) affects the presence of forest cover. What I´m
imagining is some sort of multivariate linear regression in terms of
the survey data, but when it comes to adding in the values from the
raster I´m somewhat stymied. In doing some background research on
potential approaches, it seems like looking at discrete variation
might be the way to go. Any general advice would be greatly
appreciated,
p.s. I´m using ArcGIS 10 and Erdas 2011

Best to all,
David

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AI-GEOSTATS: Questions in probabilities

[Adrian Martínez Vargas]

Hi colleagues,

Can you please give some tips about techniques in Statistics (classic 
and exotic) to evaluate uncertainty in conditional expectation.  I would 
like to know if there is any statistical tool to evaluate  the change in 
the expectation E(Zo| Z1, Z2, Z3 ... Zn), by removing k of n the 
elements in Z1...Zn.  The same for Prob(Zoz| Z1z,  ...,  Zn z). The 
elements in the vector Z1...Zn are not independent, for me they are 
realizations  of a random functions Z(xi).


Kind regards
Adrian



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unsubscribe ai-geostats in the message body. DO NOT SEND 
Subscribe/Unsubscribe requests to the list
+ As a general service to list users, please remember to post a summary of any 
useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/

AI-GEOSTATS: new offer: thinking about open source (free) packages

[Younes Fadakar]

Hello everyone,

A recent good feedback regarding EGS-Enjoy Geostatistics from the community 
encouraged me enough to start thinking on making some of my products including:

   1- EGS-Enjoy Geostatistics   http://alghalandis.com/?page_id=321

   2- Interactive Variogram http://alghalandis.com/?page_id=358

   3- GOTTSIS   http://alghalandis.com/?page_id=382

   4- AMLF  http://alghalandis.com/?page_id=587

   5- DSE   http://alghalandis.com/?page_id=725


open source and free. 

This is not easy task however requires time and of course collaborations for 
different parts of the task. I offer this to you all seeking keen, 
knowledgeable and motivated developers who can potentially collaborate with me 
to bring this idea to reality. I will discuss the details of collaboration in 
private communications with suitable candidates.
If you feel you can be one of the team members please shortly reply me. Your 
message may include the following items:

- your strongest motivation/reason for the collaboration
- your programming skills
- your software developing knowledge: all stages

- the way you are thinking to collaborate
- anything you think it can be of help

This offer is valid only for one month from now, 02 July 2012.


Best Regards,

Younes
yfa.st...@ymail.com
http://alghalandis.com

PS: by posting this message the recent offer for free EGS is no longer valid.

AI-GEOSTATS: Get a free copy of EGS - Enjoy Geostatistics

[Younes Fadakar]

Dear All,

Thank you for your interest.

Good news! I have decided to release a fully functional and free version of 
EGS-Enojy Geostatistics for students and professors.
Simply, send me the following information:


1- First Name
2- Last Name
3- Education Provider 
4- Position

5- Field of Research

6- Academic Email
7- Other Email

8- Purpose of Use/Interest


And I will send you then EGS and instructions to install via this email.
Best Regards,

.

Younes (Fadakar Alghalandis)

yfa.st...@ymail.com
http://alghalandis.com

AI-GEOSTATS: 2 open positions in Geography, Planning and Environment

[Ernste, H. (Huib)]

2 Assistant Professors of Geography, Planning and Environment (1,0 fte
each)

Sorry for cross-postings

Nijmegen School of Management
Vacancy number: 27.01.12/7

Closing date: February 5, 2012



Responsibilities
As an Assistant Professor of Geography, Planning and Environment, you
provide high quality courses in the field of human geography, spatial
planning and social and political sciences of the environment. You are
involved in the teaching programmes of the Nijmegen School of Management,
in particular in the bachelor programme of Geography, Planning and
Environment.

In addition you are expected to carry out high-quality research activities
in the context of the research programme on
http://www.ru.nl/imr/research-themes/scapes/ Shaping and Changing of
Places and Spaces (SCAPES http://www.ru.nl/scapes/ ), which is one of
the research programmes within the multidisciplinary setting of the
faculty research institute IMR. Next, you are expected to successfully
obtain funding from European as well as domestic institutions, either
public or private. You publish regularly in international academic
journals. You participate actively in the internationalization activities
of the Nijmegen School of Management.



Work environment
The Nijmegen School of Management (NSM) is an academic centre of research
and higher learning, focusing on institutional and managerial issues
within complex organizations in both the public and private domain. The
domains in which the NSM provides education and carries out research are:
Business Administration, Public Administration, Political Science,
Economics, Human Geography, Spatial Planning, and Social and Political
Sciences of the Environment. Next to the many education programmes at
bachelor and master level, research is organized in three
interdisciplinary groups, of which SCAPES http://www.ru.nl/scapes/  is
one. The NSM strives for a multi-disciplinary approach whenever possible.
The NSM employs 220 full-time staff, of whom 75% are academics. The NSM
has about 3,000 students.

The Department Geography, Planning and Environment is one of the four
departments within the NSM. It consists of three chairs: Human Geography,
Spatial Planning, and Social and Political Sciences of the Environment.
The department also includes professorships in Economic Geography and Real
Estate and Location Development, and special professorships in Transport
and Spatial Development, Euregional Management and City and Regional
Marketing. Finally, the department has 5 associate professors and a number
of assistant professors, doctoral candidates, and post-graduates. All in
all: about 60 people.



The department has the primary responsibility for the Bachelor programme
Geography, Planning and Environment, in which each year about 100 students
enroll from pre-university education. In addition, there are around 60
students per year who enter via the Pre-Master programmes of Human
Geography, Spatial Planning, and Social and Political Sciences of the
Environment. Finally, the department offers 3 Master programmes in the
fields of Human Geography, Spatial Planning, and Social and Political
Sciences of the Environment (with, in 2011, 70, 60 and 15 students
respectively) with each a number of different master-specialisations.



What we expect from you
You have a PhD degree in Human Geography, Planning or Environmental
Studies, or a related and relevant field, and dealing with issues of
places and spaces, their shaping and governance. You have demonstrated
thorough knowledge about recent theoretical, methodological and empirical
developments in at least one of the (sub)fields of Geography, Planning and
Environment.



In particular, we look for people with special expertise, interest and
skills in one or possibly more of the following (sub)fields:

1. Urban Studies

2. Housing Studies

3. Quantitative Research Methods

4. Interface between Natural- and Social Sciences



Ad. 1) Urban Studies

Increasingly our world becomes more urbanised and increasingly cities are
the motor of innovation, creativity, competitiveness, new developments,
growth and of change both on local and global scale. On the other hand
cities are also the focus of differentiation, of segregation, of
concentration and dominance, and of shrinkage and conflict. Urban studies
are more topical then ever before and are central for all aspects of
spatial and environmental development. At our Department we especially
also investigate what makes these urban places so special and how they
differ from other places, what ‘urbanity’ in this respect means. How are
urban places made? How are they managed and how can they be made more
sustainable? How are they imagined and communicated? What role does
culture and multi-culturality play in these contexts and how does it
influence also the urban economy and the urban way of life? How are these
urban places related to each other? What connects them, what departs them?
Our department seeks to strengthen and 

AI-GEOSTATS: FW: [geoENV2012] Call for abstracts

[gregoire . dubois]

FYI

 

Gregoire

 

From: Jaime Gomez [mailto:jgo...@upv.es] 
Sent: Wednesday, January 04, 2012 5:27 PM
To: gregoire.dub...@jrc.it
Subject: [geoENV2012] Call for abstracts

 

 

 

Dear Colleague,

 

The IX Conference on Geostatistics for Environmental Applications will be
held in Valencia (Spain) on September 19-21, 2012. The geoENV series of
conferences are held biyearly since 1996, and it has established itself as
an obligatory meeting point for all scientists and practitioners of
geostatistics with applications in the environment.

 

The call for abstracts is open until the end of January. 

 

Please visit the congress web page at http://geoenv2012.upv.es and submit
your abstract.

 

Yours sincerely,

 

Jaime Gómez-Hernández

Chair, Organizing Committee

 

 

 

AI-GEOSTATS: Position at the British Geological Survey

[Lark, Murray]

ENVIRONMENTAL STATISTICIAN

The British Geological Survey (BGS), founded in 1835 is part of the Natural 
Environment Research Council (NERC), and is the world's longest-established 
national geological survey and the UKs premier centre for earth science 
information and expertise.  A vacancy has arisen for a highly motivated and 
enthusiastic Environmental Statistician at our headquarters in Keyworth.

You will apply statistical modelling to data sets generated across the range of 
BGS's science and survey activities and will also provide statistical guidance 
to the scientific staff.  In addition you will also contribute to quantitative 
modelling work in areas of BGS science where an understanding of spatially 
variable processes is necessary.

You should have a post-graduate qualification in statistics or 
environmental/earth science, with a significant statistical component, and 
subsequent applied experience.  You should also have  knowledge and experience 
of the application of spatial statistical methods in the earth sciences,  
including the use of geostatistical methods, ideally in a linear mixed model 
context.  In addition you must possess good project management, people and 
communication skills, both oral and written.  The post involves team working; 
therefore you should be able to work effectively with others.

Starting salary will be between £26,180 per annum and £37,120 per annum 
depending on qualifications and experience.  Working hours will be 37 per week 
excluding lunch breaks.  A generous benefits package is also offered, including 
a company pension scheme, childcare voucher scheme, 30 days annual leave plus 
10.5 days public and privilege holidays.

Applications are handled by the RCUK Shared Services Centre; to apply please 
visit our job board at https://ext.ssc.rcuk.ac.ukhttps://ext.ssc.rcuk.ac.uk/ 
and complete an online application form.  Applicants who would like to receive 
this advert in an alternative format (e.g. large print, Braille, audio or hard 
copy), or who are unable to apply online should contact us by telephone on 
01793 867003, Please quote reference number IRC40064

When using this website use Job area-Science
Location-NERC-British Geological Survey

Closing date for receipt of application forms is 20th January 2012

The Natural Environment Research Council is an equal opportunities employer and 
welcomes applications from all sections of the community.  People with 
disabilities and those from ethnic minorities are currently under-represented 
and their applications are particularly welcome.  The British Geological Survey 
is an Investors in People organisation.  There is a guaranteed Interview Scheme 
for suitable candidates with disabilities.



**
Professor R. Murray Lark

British Geological Survey
Keyworth
Nottingham, NG12 5GG
United Kingdom

Email:  ml...@nerc.ac.uk
Telephone:  +44(0)115 9363026
Mobile: 07857677248
Web:http://www.bgs.ac.uk/staff/profiles/40081.html

**



-- 
This message (and any attachments) is for the recipient only. NERC
is subject to the Freedom of Information Act 2000 and the contents
of this email and any reply you make may be disclosed by NERC unless
it is exempt from release under the Act. Any material supplied to
NERC may be stored in an electronic records management system.

AI-GEOSTATS: Call for abstracts: 2012 Sino-European Symposium on Environment and Health (SESEH 2012), Galway, Ireland, Aug 20-25, 2012

[Zhang, Chaosheng]

Dear Gregoire and all,

 

Thanks for changing the rule! Just in time, the abstract submission for SESEH 
2012 (http://www.nuigalway.ie/seseh2012 http://www.nuigalway.ie/seseh2012/ ) 
is open today.

 

In SESEH 2012, there is a session “GIS and quantitative methods” which should 
be interesting to the AI-GEOSTATS community. In this session, I am especially 
interested in contributions in “GIS analyses”, “spatial analyses”, and 
“geostatistical analyses” of environment and/or health.

 

Best regards,

 

Chaosheng

 

---

 

*** Apologies for cross-posting ***

 

Call for abstracts: SESEH 2012 (http://www.nuigalway.ie/seseh2012 
http://www.nuigalway.ie/seseh2012/ )

 

Dear colleague,

 

The 2012 Sino-European Symposium on Environment and Health (SESEH 2012, Galway, 
Ireland, Aug 20-25, 2012) is now calling for abstracts. The aim of SESEH 2012 
is to promote collaborations with China (大会宗旨：架设中国与世界沟通的桥梁 ).

 

The SESEH 2012 provides an internationally leading platform for interaction 
between scientists, consultants, and public servants engaged in the 
multi-disciplinary areas of environment and health. With the fast economic 
growth, the importance of environment and health is widely recognized in China, 
and China welcomes international experts for collaboration. This symposium 
provides an opportunity for a direct communication between experts from China 
and the rest of the world, and helps to foster and develop international 
collaborations with China, the 2nd largest economy of the world.

 

The conference venue is the campus of National University of Ireland, Galway, 
within walking distance of Galway’s city centre. The conference is co-organized 
by GIS Centre, Ryan Institute of NUI Galway 
(http://www.ryaninstitute.ie/facilities/gis-facility 
http://www.ryaninstitute.ie/facilities/gis-facility/ ), The Geographical 
Society of China (www.gsc.org.cn) and Environmental Sciences Association of 
Ireland (ISAI, www.esaiweb.org), supported by Ryan Institute of NUI Galway 
(www.ryaninstitute.ie), Society for Environmental Geochemistry and Health 
(SEGH, www.segh.net), International Medical Geology Association (IMGA, 
www.medicalgeology.org), Geographical Society of Ireland (www.ucd.ie/gsi), 
Ireland Chinese Association of Environment, Resources  Energy (ICAERE, 
www.icaere.ie), National Centre for Geocomputation (ncg.nuim.ie), South China 
Institute of Environmental Sciences (http://www.scies.org/en) and Chinese 
Environmental Scholars  Professionals Network (http://www.cespn.net/english).

 

SESEH 2012 Themes: 

Ø  Environmental sciences: chemistry, geochemistry, biogeochemistry, ecology 
and toxicology 

Ø  Environmental pollution: air, water (river, lake, and marine), soil, and food

Ø  Environmental pollutants: metals and metalloids; persistent organic 
pollutants and pesticides

Ø   Environmental technologies: soil remediation; waste water treatment

Ø  Environmental management and monitoring: social impact assessment, economics 
and policies

Ø  Medical geology, endemic diseases, environmental health and public health

Ø  Links between environment and health, environment and genetic interaction

Ø  GIS and quantitative methods in environment and population health

Ø  Sustainable development and health: agriculture, industry, traffic, 
urbanization

Ø Climate change and population health

 

SESEH 2012 Keynote Speakers:

Professor Ming-Hung Wong (Ming-Hong Huang), Editor-in-chief: Environmental 
Geochemistry and Health

Professor Shu Tao: Member of Chinese Academy of Sciences

Professor Xiaoying Zheng: Peking University

Professor Derek Clements-Croome: University of Reading

Professor Jerome Nriagu: Editor-in-chief: Science of the Total Environment

 

SESEH 2012 Convenors and Topics for Organized Sessions:

(When submitting abstracts via the online system, ordinary delegates are 
required to choose the most appropriate general topic listed on the system. 
Invited delegates are required to choose the name of the corresponding session 
convenor (with a star) who has approved the abstract.)

Chen, Jianmin*; Yang, Xin; Mellouki, Wahid: Aerosol Chemistry and Health Impact 

Clements-Croome, Derek*: Environmental Health in Buildings

Diamond, Dermot*: Sensor Networks for Distributed Environmental Monitoring

Sijun, Dong*: Environmental Molecular Toxicology

Clifford, Eoghan*; O’Reilly, Edmond: New sustainable technologies for 
decentralised wastewater …

Ding, Shiming*: Sediment pollution and remediation

Dogan, A. Umran*; Dogan, Meral: Medical Mineralogy

Belviso, Claudia; Fiore, Saverio*: Clays  Zeolites: Environmental  Medical 
Uses

Griffiths, Bryan*; Zhang, Bin: Land use and the soil environment

Healy, Mark G.*: Nutrients, soils and the environment

Kan, Haidong*; Song, Weimin: Climate change, air pollution and population health

Liu, Rutao*: Environment  Health on Molecular Level

Lucy,  Frances*; de Waal, Theo: Searching for the Sources – Cryptosporidium in 
Ireland’s

AI-GEOSTATS: Change of rules

[gregoire . dubois]

Dear all,

 

Given the very low traffic on the mailing list and the fact that I do not
have the time to maintain/update the information regarding conferences,
courses, freeware and jobs announcements, I will allow from now on the
posting of such mails. 

 

Postings falling under these categories should be made only once (no
reminders please!) as the mail archive systems easily allows the retrieval
of the posted information.

 

Many thanks for your understanding.

 

Best wishes

 

Gregoire (Moderator of ai-geostats)

 



Grégoire Dubois (Ph.D.)

 

Joint Research Centre - European Commission 

Global Environment Monitoring Unit 

Monitoring Of Natural resources for DEvelopment  (MONDE)

 

Via Fermi 2749, TP 440,  I-21027 Ispra (VA), ITALY

 

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The views expressed are purely those of the writer and may not in any
circumstances be regarded as stating an official position of the European
Commission. (Disclaimer required under the terms and conditions of use of
the internet and electronic mail from Commission equipment) 

 

AI-GEOSTATS: spatDesign

[Gunter Spoeck]

Dear users of spatDesign,

Version 2.1.1 of the spatial sampling design toolbox spatDesign is available 
online now at

http://wwwu.uni-klu.ac.at/guspoeck/spatDesignMatlab.zip

In this Version some errors concerning the Smith and Zhu (2004) design 
criterion have been corrected.


Enjoy,
regards,

G. Spöck


--
Ass. Prof. Dr. Gunter Spöck
Department of Statistics
University of Klagenfurt,

Universitaetsstrasse 65-67
9020 Klagenfurt,
Austria 
Tel. +43 650 26 06 166


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AI-GEOSTATS: spatDesign

[Gunter Spoeck]

Dear users of spatDesign,

Version 2.1.0 of the spatial sampling design Matlab toolbox spatDesign
is now available online at

http://wwwu.uni-klu.ac.at/guspoeck/spatDesignMatlab.zip

New in this version of spatDesign is an implementation of the Smith and
Zhu (2004) design criterion, which takes account also of covariance
function uncertainty. Since the computations for this design criterion
are computationally very intensive it has been implemented in parallel
on NVIDIA GPUs with CUDA support. You need the GPUmat package to run the
code (http://www.gp-you.org).
An Octave version will become available (also with CUDA support) in the
near future.
Also in near future a design algorithm for trans-Gaussian kriging will
be available.

Best Regards,
Gunter Spöck

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AI-GEOSTATS: choosing grid resolution

[Raul Sierra Alcocer]

Dear list,

I am a PhD student in Computer Science new to spatial analysis, and I
am working in a data mining platform at my institute that will provide
some tools for spatial data mining. It is basically analysis of
spatial features using rectangular grids. My question is if there are
any studies about the effects of grid resolution in the results of
correlation analysis between spatial features. When I say effects, I
mean how the choice of cell size affects the statistical significance
of the results.

Thanks in advance,

Raúl Sierra.

---
Centro de Ciencias de la Complejidad,
Universidad Nacional Autonoma de México.

+
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unsubscribe ai-geostats in the message body. DO NOT SEND 
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useful responses to your questions.
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Re: AI-GEOSTATS: choosing grid resolution

[Younes Fadakar]

Dear Raul,

Any changes in the size {GSsupport/GS}, shape {GSanisotropy/GS} etc of 
cells (grids) in cell-based analysis including from spatial analysis (GIS etc) 
and geostatistics (grided variogram, kriging etc) need to be considered 
carefully while they can provide very different correlation structure and so 
result in very different interpretations. Geostatistically speaking, the 
support 
effect should be considered initially very well. The support can be simply 
defined as how your choice (e.g. cell) is being represented with the samples 
inside it (e.g., number of samples per cell).
There are some methods you will find them interesting, I think:
For an evaluation of the dispersion of data into cells, consider Morisita 
(Morishita) factor (e.g., from Kanevsky);
Also consider declusterting methods (e.g. from Clayton);
...

Best Regards,

Younes
yfa.st...@ymail.com
http://alghalandis.com








From: Raul Sierra Alcocer raul.sierra.alco...@gmail.com
To: ai-geostats@jrc.it
Sent: Tue, 15 March, 2011 3:49:31 AM
Subject: AI-GEOSTATS: choosing grid resolution

Dear list,

I am a PhD student in Computer Science new to spatial analysis, and I
am working in a data mining platform at my institute that will provide
some tools for spatial data mining. It is basically analysis of
spatial features using rectangular grids. My question is if there are
any studies about the effects of grid resolution in the results of
correlation analysis between spatial features. When I say effects, I
mean how the choice of cell size affects the statistical significance
of the results.

Thanks in advance,

Raúl Sierra.

---
Centro de Ciencias de la Complejidad,
Universidad Nacional Autonoma de México.

+
+ To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu
+ To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and 
unsubscribe ai-geostats in the message body. DO NOT SEND 
Subscribe/Unsubscribe 
requests to the list
+ As a general service to list users, please remember to post a summary of any 
useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/



  

Re: AI-GEOSTATS: choosing grid resolution

[Duane Marble]

  
  
There is also a fair amount of literature
  on the Modifiable Areal Unit Problem (MAUP)
  that goes back for several decades.
  
The role of spatial scale in the analysis can be a critical
one with not only changes in cell
size but also rotations, etc., applied to the basic grid causing
shifts in correlation behavior.

Duane Marble

On 3/14/2011 7:30 PM, Younes Fadakar wrote:

  
  Dear
Raul,

Any changes in the size {GSsupport/GS}, shape
{GSanisotropy/GS} etc of cells (grids) in
cell-based analysis including from spatial analysis (GIS etc)
and geostatistics (grided variogram, kriging etc) need to be
considered carefully while they can provide very different
correlation structure and so result in very different
interpretations. Geostatistically speaking, the support effect
should be considered initially very well. The support can be
simply defined as how your choice (e.g. cell) is being
represented with the samples inside it (e.g., number of samples
per cell).
There are some methods you will find them interesting, I think:
For an evaluation of the dispersion of data into cells, consider
Morisita (Morishita) factor (e.g., from Kanevsky);
Also consider declusterting methods (e.g. from Clayton);
...

Best Regards,
  
Younes
yfa.st...@ymail.com
http://alghalandis.com




  
  From:
  Raul Sierra Alcocer raul.sierra.alco...@gmail.com
  To:
  ai-geostats@jrc.it
  Sent: Tue,
  15 March, 2011 3:49:31 AM
  Subject:
  AI-GEOSTATS: choosing grid resolution

Dear list,

I am a PhD student in Computer Science new to spatial
analysis, and I
am working in a data mining platform at my institute that
will provide
some tools for spatial data mining. It is basically analysis
of
spatial features using rectangular grids. My question is if
there are
any studies about the effects of grid resolution in the
results of
correlation analysis between spatial features. When I say
effects, I
mean how the choice of cell size affects the statistical
significance
of the results.

Thanks in advance,

Ra�l Sierra.

---
Centro de Ciencias de la Complejidad,
Universidad Nacional Autonoma de M�xico.

+
+ To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu
+ To unsubscribe, send email to majordomo@ jrc.ec.europa.eu
with no subject and "unsubscribe ai-geostats" in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
+ As a general service to list users, please remember to
post a summary of any useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/
  

  
  
   

-- 
Dr. Duane F. Marble		Email:  dmarble at OregonFast.net
2226 Primrose Lane		Telephone:  541.902.8837
Florence, OR  97439		Cell:   541.991.1730
Emeritus Professor of Geography -- The Ohio State University
Courtesy Professor of Geosciences -- Oregon State University

 An early comment on the proper use of GIS technology:

"I warne yow wel, it is no childes play" 

 Originally from Chaucer, but also quoted by me in 1967 in a
 monograph on software tools for computational geography
  


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+ As a general service to list users, please remember to post a summary of any useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/

Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set of points with different cardinalities

[Nicolas Maisonneuve]

yes I read too quickly  :)
anyway thanks for your help Younes

On Sun, Mar 6, 2011 at 12:51 PM, Younes Fadakar yfa.st...@ymail.com wrote:

 To Nicolas,

 question:

  What happens if  B = {A +  noisy points}  (false positive)?

 answer:
 You probably missed the second part of my previous email, where
 Card(B)Card(A) with noise:
 I copied here, see:
 ---
 #-the realistic implementation-
 N = 100#
 A.x = rand(N)  #set A.x
 A.y = rand(N)  #set A.y: coordinate pairs
 B.x = shake(A.x,10%)   #slightly repositions points=
 noisy positions

 B.y = shake(A.y,10%)   #   randomly with 10% move
 B.x = B.x+rand(N/10)   #adds extra 10% rand points =
 extra noisy points
 B.y = B.y+rand(N/10)   #Card(B)=1.1*Card(A)

 M = PositionAccuracy(A,B)  #

 Score = M/N*100#my score=normalized based on N
#N=Card(A)
 ---
 the computed score is:
  score = M(=#concordances)/N(=Card(A))*100
 which seems to be right answer. Back to the first example, if A=B the score
 will be 100%.[correct]
 applying your scoring method if A=B then the score is smaller than 1.
 [incorrect]!
 Anyway, I'm happy you have found your satisfactory answer.

 To Duane:
 Thanks for your message. Do you have any information about existing
 statistically best random generator?
 I appreciate your replies.

 To All:
 Dear everybody,
 Is there any more robust/strong/reliable/high performance random generator
 satisfying statistically and being computing friendly? How can we evaluate
 the randomness of such generators then?

 To myself:
 Should double check the literature for concerns in randomness.

 Best Regards,
 .
 Younes
 yfa.st...@ymail.com
 http://alghalandis.com
 --



 --
 *From:* Nicolas Maisonneuve n.maisonne...@gmail.com
 *To:* Younes Fadakar yfa.st...@ymail.com
 *Cc:* Ask Geostatisticians ai-geostats@jrc.it
 *Sent:* Sun, 6 March, 2011 7:25:38 PM

 *Subject:* Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set
 of points with different cardinalities



 In your example Card(A Union B) is always  = Card(A) =N and that's an
 issue.

 What happens if  B = {A +  noisy points}  (false positive)?
 According to your calcul the score will  be 1.0... and that's not right.

 Actually I think the answer is actually trivial.
 (but I didn't think to formulate the problem in algebra terms)

 score = Card(A Intersection B)/Card(A Union B)
 score = # corcordances/ (#discordances+#concordances)
 score = # corcordances/ (# omissions (=Card(elements in A not included in
 B))+ # false positives(=Card(elements in B not included in
 A))+#concordances)

 Best,
 Nicolas


 On Sun, Mar 6, 2011 at 3:33 AM, Younes Fadakar yfa.st...@ymail.comwrote:

 Dear Nicolas,

 Hope this can help you.

 Let have a look at my implementation:

 #-the simplest implementation-
 N = 100#number of ref points=Crad(A)
 A.x = rand(N)  #set A.x
 A.y = rand(N)  #set A.y: coordinate pairs
 B.X = A.x[:-10]#set B = sampling
 B.Y = A.y[:-10]#  has 10 points less than A
#  Card(B)-Card(A)=-10
 M = PositionAccuracy(A,B)  #as you defined=#concordances

 Score = M/N*100#my score=normalized based on N
#  N=Card(A)

 So the Score will be always in [0,1], here is 0.9 or 90.00%.

 and

 #-the realistic implementation-
 N = 100#
 A.x = rand(N)  #set A.x
 A.y = rand(N)  #set A.y: coordinate pairs
 B.x = shake(A.x,10%)   #slightly repositions points
 B.y = shake(A.y,10%)   #   randomly with 10% move
 B.x = B.x+rand(N/10)   #adds extra 10% rand points
 B.y = B.y+rand(N/10)   #Card(B)=1.1*Card(A)

 M = PositionAccuracy(A,B)  #

 Score = M/N*100#my score=normalized based on N
#N=Card(A)

 Again the Score will be always in [0,1].
 This is what I used to generate the previously sent figures.


 Best Regards,

 Younes
 yfa.st...@ymail.com
 http://alghalandis.com
 --



 --
 *From:* Nicolas Maisonneuve n.maisonne...@gmail.com
 *To:* Younes Fadakar yfa.st...@ymail.com
 *Cc:* Ask Geostatisticians ai-geostats@jrc.it
 *Sent:* Wed, 2 March, 2011 6:27:48 PM
 *Subject:* Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set
 of points with different cardinalities

 Thanks for your support Younges

 my idea was inspired and adapted from the Kendall correlation coefficient
 (http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient
 ) but with the pb of cardinality.

 - number of concordances (accurate observations)
 - number of discordances(omission + false

Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set of points with different cardinalities

[Nicolas Maisonneuve]

sorry I read defintively too quickly:

the computed score is:
  score = M(=#concordances)/N(=Card(A))*100
 which seems to be right answer. Back to the first example, if A=B the score
 will be 100%.[correct]
 applying your scoring method if A=B then the score is smaller than 1.
 [incorrect]!


1/ my scoring method = Card(A inter B)/ Card(A Union B) = # corcordances /
(# omissions + # false positives+#concordances)
if A = B = score= Card(A) / (0 + Card(A) + 0) = 1.0 so it works actually.

2/ sorry but I maintain that in the case of B= A union Noisy points,  the
fact you divise by Card(A) and not by Card(A Union B) is an issue .
In this context:
your score= #concordance / Card(a)= Card(a)/Card(a) = 1.0
my score = #concordance / (#omission+ #false positives + #concordances) =
 Card(A)/ Card(False positive)+Card(A))   1.0   , the right behavior.

Anyway again  thanks for the exchange,
Best
Nicolas


On Sun, Mar 6, 2011 at 2:16 PM, Nicolas Maisonneuve n.maisonne...@gmail.com
 wrote:

 yes I read too quickly  :)
 anyway thanks for your help Younes

 On Sun, Mar 6, 2011 at 12:51 PM, Younes Fadakar yfa.st...@ymail.comwrote:

 To Nicolas,

 question:

  What happens if  B = {A +  noisy points}  (false positive)?

 answer:
 You probably missed the second part of my previous email, where
 Card(B)Card(A) with noise:
 I copied here, see:

 ---
 #-the realistic implementation-
 N = 100#
 A.x = rand(N)  #set A.x
 A.y = rand(N)  #set A.y: coordinate pairs
 B.x = shake(A.x,10%)   #slightly repositions points=
 noisy positions

 B.y = shake(A.y,10%)   #   randomly with 10% move
 B.x = B.x+rand(N/10)   #adds extra 10% rand points =
 extra noisy points
 B.y = B.y+rand(N/10)   #Card(B)=1.1*Card(A)

 M = PositionAccuracy(A,B)  #

 Score = M/N*100#my score=normalized based on N
#N=Card(A)

 ---
 the computed score is:
  score = M(=#concordances)/N(=Card(A))*100
 which seems to be right answer. Back to the first example, if A=B the
 score will be 100%.[correct]
 applying your scoring method if A=B then the score is smaller than 1.
 [incorrect]!
 Anyway, I'm happy you have found your satisfactory answer.

 To Duane:
 Thanks for your message. Do you have any information about existing
 statistically best random generator?
 I appreciate your replies.

 To All:
 Dear everybody,
 Is there any more robust/strong/reliable/high performance random generator
 satisfying statistically and being computing friendly? How can we evaluate
 the randomness of such generators then?

 To myself:
 Should double check the literature for concerns in randomness.

 Best Regards,
 .
 Younes
 yfa.st...@ymail.com
 http://alghalandis.com
 --



 --
 *From:* Nicolas Maisonneuve n.maisonne...@gmail.com
 *To:* Younes Fadakar yfa.st...@ymail.com
 *Cc:* Ask Geostatisticians ai-geostats@jrc.it
 *Sent:* Sun, 6 March, 2011 7:25:38 PM

 *Subject:* Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set
 of points with different cardinalities



 In your example Card(A Union B) is always  = Card(A) =N and that's an
 issue.

 What happens if  B = {A +  noisy points}  (false positive)?
 According to your calcul the score will  be 1.0... and that's not right.

 Actually I think the answer is actually trivial.
 (but I didn't think to formulate the problem in algebra terms)

 score = Card(A Intersection B)/Card(A Union B)
 score = # corcordances/ (#discordances+#concordances)
 score = # corcordances/ (# omissions (=Card(elements in A not included in
 B))+ # false positives(=Card(elements in B not included in
 A))+#concordances)

 Best,
 Nicolas


 On Sun, Mar 6, 2011 at 3:33 AM, Younes Fadakar yfa.st...@ymail.comwrote:

 Dear Nicolas,

 Hope this can help you.

 Let have a look at my implementation:

 #-the simplest implementation-
 N = 100#number of ref points=Crad(A)
 A.x = rand(N)  #set A.x
 A.y = rand(N)  #set A.y: coordinate pairs
 B.X = A.x[:-10]#set B = sampling
 B.Y = A.y[:-10]#  has 10 points less than A
#  Card(B)-Card(A)=-10
 M = PositionAccuracy(A,B)  #as you defined=#concordances

 Score = M/N*100#my score=normalized based on N
#  N=Card(A)

 So the Score will be always in [0,1], here is 0.9 or 90.00%.

 and

 #-the realistic implementation-
 N = 100#
 A.x = rand(N)  #set A.x
 A.y = rand(N)  #set A.y: coordinate pairs
 B.x = shake(A.x,10%)   #slightly repositions points
 B.y = shake(A.y,10%)   #   randomly with 10% move
 B.x = B.x+rand(N/10)   #adds extra 10% rand points
 B.y = B.y+rand(N/10

Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set of points with different cardinalities

[Younes Fadakar]

Dear Nicolas,

Hope this can help you.

Let have a look at my implementation:

#-the simplest implementation-
N = 100#number of ref points=Crad(A)
A.x = rand(N)  #set A.x
A.y = rand(N)  #set A.y: coordinate pairs
B.X = A.x[:-10]#set B = sampling
B.Y = A.y[:-10]#  has 10 points less than A   
   #  Card(B)-Card(A)=-10
M = PositionAccuracy(A,B)  #as you defined=#concordances

Score = M/N*100#my score=normalized based on N
   #  N=Card(A)
   
So the Score will be always in [0,1], here is 0.9 or 90.00%.

and

#-the realistic implementation-
N = 100#
A.x = rand(N)  #set A.x
A.y = rand(N)  #set A.y: coordinate pairs
B.x = shake(A.x,10%)   #slightly repositions points
B.y = shake(A.y,10%)   #   randomly with 10% move
B.x = B.x+rand(N/10)   #adds extra 10% rand points
B.y = B.y+rand(N/10)   #Card(B)=1.1*Card(A)

M = PositionAccuracy(A,B)  #

Score = M/N*100#my score=normalized based on N
   #N=Card(A)
   
Again the Score will be always in [0,1].
This is what I used to generate the previously sent figures.

Best Regards,

Younes
yfa.st...@ymail.com
http://alghalandis.com








From: Nicolas Maisonneuve n.maisonne...@gmail.com
To: Younes Fadakar yfa.st...@ymail.com
Cc: Ask Geostatisticians ai-geostats@jrc.it
Sent: Wed, 2 March, 2011 6:27:48 PM
Subject: Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set of 
points 
with different cardinalities

Thanks for your support Younges

my idea was inspired and adapted from the Kendall correlation coefficient
(http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient
) but with the pb of cardinality.

- number of concordances (accurate observations)
- number of discordances(omission + false positive)
and do a sum and then a normalisation to get something like 1.0 = max
corcordance max  0.0 = max discordance.
but I am not sure how to normalize:
- the range of concordance [0, Card(A)] is smaller than the
discordance [0, Card(A+B)] so anormalisation should be something like
(2Card(A)+Card(B)) but I am not sure about that , and I am not sure
the whole idea is right..

How did you normalize in your calcul?




On Wed, Mar 2, 2011 at 5:50 AM, Younes Fadakar yfa.st...@ymail.com wrote:
 Dear Nicolas,

 This is not the answer to your question but a try to implement your idea and
 to have an experience with it.
 Please see the attached, the output.
 It seems the total score provided by the method is very dependent to the
 'r', the radius of search for neighbors around each ref point (A).
 However, being able to define the right 'r', the score seems a realistic
 measure of accuracy to me.
 Of course, this is just a practical understanding hoping the community could
 provide the statistical references.
 Anyway, I liked the idea.

 Best Regards,
 .
 Younes
 yfa.st...@ymail.com
 http://alghalandis.com
 


 
 From: Nicolas Maisonneuve n.maisonne...@gmail.com
 To: ai-geostats@jrc.it
 Sent: Mon, 28 February, 2011 6:21:49 PM
 Subject: AI-GEOSTATS: Estimation of the position accuracy of 2 set of points
 with different cardinalities

 Hi everyone,

 A simple question:
 I have 1 set of 2D location points A that I use as reference.
 I have another set of location points B generated by observations.

 Is there any standard method/measure to estimate a kind of position
 accuracy error knowing that
 - A and B dont have the same cardinality of elements e.g. B could have
 more points than A?
 - a point in A should be associated to only one point in B.

 For the moment I created my own error measure using 3 estimations.
 for a given accuracy rate (20 meters) I compute:
 - O: number of omissions (when there is no observation in B closed
 enough of a point in A) ,
 - FP: number of false positive (when a B point has been observed but
 not closed to a A point - or already taken from another
 observation)
 - M: number of matching (when a B point is closed enought of a A point)
 and then I aggregate the result  = M- (O+FP) to get an indicator..

 I am pretty sure there are other more traditional ways to do that.

 Thanks in advance
 -NM
 +
 + To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu
 + To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject
 and unsubscribe ai-geostats in the message body. DO NOT SEND
 Subscribe/Unsubscribe requests to the list
 + As a general service to list users, please remember to post a summary of
 any useful responses to your questions.
 + Support to the forum can be found at http://www.ai-geostats.org/





  

Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set of points with different cardinalities

[Duane Marble]

  
  
I would suggest that you be careful of the
  random number generator that is
  being used.  Some of them have been known to produce highly
  correlated
  results when used as in:
  A.x = rand(N)  #set A.x
A.y = rand(N)  #set A.y: coordinate pairs
  Strong random number generators are OK but the system default
  generators
  can prove to be a problem sometimes. In one case, the "random"
  points
  generated looked very much like a matrix when plotted! 

On 3/5/2011 6:33 PM, Younes Fadakar wrote:

  
  Dear
Nicolas,

Hope this can help you.

Let have a look at my implementation:

#-the simplest implementation-
N = 100    #number of ref points=Crad(A)
A.x = rand(N)  #set A.x
A.y = rand(N)  #set A.y: coordinate pairs
B.X = A.x[:-10]    #set B = sampling
B.Y = A.y[:-10]    #  has 10 points less than A   
   #  Card(B)-Card(A)=-10
M = PositionAccuracy(A,B)  #as you defined=#concordances

Score = M/N*100    #my score=normalized based on N
               #  N=Card(A)
               
So the Score will be always in [0,1], here is 0.9 or 90.00%.

and

#-the realistic implementation-
N = 100    #
A.x = rand(N)  #set A.x
A.y = rand(N)  #set A.y: coordinate pairs
B.x = shake(A.x,10%)   #slightly repositions points
B.y = shake(A.y,10%)   #   randomly with 10% move
B.x = B.x+rand(N/10)   #adds extra 10% rand points
B.y = B.y+rand(N/10)   #Card(B)=1.1*Card(A)

M = PositionAccuracy(A,B)  #

Score = M/N*100    #my score=normalized based on N
               #N=Card(A)
               
Again the Score will be always in [0,1].
This is what I used to generate the previously sent figures.

  Best Regards,
  
Younes
yfa.st...@ymail.com
http://alghalandis.com




  
  From:
  Nicolas Maisonneuve n.maisonne...@gmail.com
  To: Younes
  Fadakar yfa.st...@ymail.com
  Cc: Ask
  Geostatisticians ai-geostats@jrc.it
  Sent: Wed,
  2 March, 2011 6:27:48 PM
  Subject:
      Re: AI-GEOSTATS: Estimation of the position accuracy of 2
  set of points with different cardinalities

Thanks for your support Younges

my idea was inspired and adapted from the Kendall
correlation coefficient
(http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient
) but with the pb of cardinality.

- number of concordances (accurate observations)
- number of discordances(omission + false positive)
and do a sum and then a normalisation to get something like
1.0 = max
corcordance max  0.0 = max discordance.
but I am not sure how to normalize:
- the range of concordance [0, Card(A)] is smaller than the
discordance [0, Card(A+B)] so anormalisation should be
something like
(2Card(A)+Card(B)) but I am not sure about that , and I am
not sure
the whole idea is right..

How did you normalize in your calcul?




On Wed, Mar 2, 2011 at 5:50 AM, Younes Fadakar yfa.st...@ymail.com
wrote:
 Dear Nicolas,

 This is not the answer to your question but a try to
implement your idea and
 to have an experience with it.
 Please see the attached, the output.
 It seems the total score provided by the method is very
dependent to the
 'r', the radius of search for neighbors around each ref
point (A).
 However, being able to define the right 'r', the score
seems a realistic
 measure of accuracy to me.
 Of course, this is just a practical understanding
hoping the community could
 provide the statistical references.
 Anyway, I liked the idea.

 Best Regards,
 .
 Younes
 yfa.st...@ymail.com
 http://alghalandis.com
 


 
 Fro

Re: AI-GEOSTATS: Estimation of the position accuracy of 2 set of points with different cardinalities

[Nicolas Maisonneuve]

Thanks for your support Younges

my idea was inspired and adapted from the Kendall correlation coefficient
(http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient
) but with the pb of cardinality.

- number of concordances (accurate observations)
- number of discordances(omission + false positive)
and do a sum and then a normalisation to get something like 1.0 = max
corcordance max  0.0 = max discordance.
but I am not sure how to normalize:
- the range of concordance [0, Card(A)] is smaller than the
discordance [0, Card(A+B)] so anormalisation should be something like
(2Card(A)+Card(B)) but I am not sure about that , and I am not sure
the whole idea is right..

How did you normalize in your calcul?




On Wed, Mar 2, 2011 at 5:50 AM, Younes Fadakar yfa.st...@ymail.com wrote:
 Dear Nicolas,

 This is not the answer to your question but a try to implement your idea and
 to have an experience with it.
 Please see the attached, the output.
 It seems the total score provided by the method is very dependent to the
 'r', the radius of search for neighbors around each ref point (A).
 However, being able to define the right 'r', the score seems a realistic
 measure of accuracy to me.
 Of course, this is just a practical understanding hoping the community could
 provide the statistical references.
 Anyway, I liked the idea.

 Best Regards,
 .
 Younes
 yfa.st...@ymail.com
 http://alghalandis.com
 


 
 From: Nicolas Maisonneuve n.maisonne...@gmail.com
 To: ai-geostats@jrc.it
 Sent: Mon, 28 February, 2011 6:21:49 PM
 Subject: AI-GEOSTATS: Estimation of the position accuracy of 2 set of points
 with different cardinalities

 Hi everyone,

 A simple question:
 I have 1 set of 2D location points A that I use as reference.
 I have another set of location points B generated by observations.

 Is there any standard method/measure to estimate a kind of position
 accuracy error knowing that
 - A and B dont have the same cardinality of elements e.g. B could have
 more points than A?
 - a point in A should be associated to only one point in B.

 For the moment I created my own error measure using 3 estimations.
 for a given accuracy rate (20 meters) I compute:
 - O: number of omissions (when there is no observation in B closed
 enough of a point in A) ,
 - FP: number of false positive (when a B point has been observed but
 not closed to a A point - or already taken from another
 observation)
 - M: number of matching (when a B point is closed enought of a A point)
 and then I aggregate the result  = M- (O+FP) to get an indicator..

 I am pretty sure there are other more traditional ways to do that.

 Thanks in advance
 -NM
 +
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 and unsubscribe ai-geostats in the message body. DO NOT SEND
 Subscribe/Unsubscribe requests to the list
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useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/

AI-GEOSTATS: Estimation of the position accuracy of 2 set of points with different cardinalities

[Nicolas Maisonneuve]

Hi everyone,

A simple question:
I have 1 set of 2D location points A that I use as reference.
I have another set of location points B generated by observations.

Is there any standard method/measure to estimate a kind of position
accuracy error knowing that
- A and B dont have the same cardinality of elements e.g. B could have
more points than A?
- a point in A should be associated to only one point in B.

For the moment I created my own error measure using 3 estimations.
for a given accuracy rate (20 meters) I compute:
- O: number of omissions (when there is no observation in B closed
enough of a point in A) ,
- FP: number of false positive (when a B point has been observed but
not closed to a A point - or already taken from another
observation)
- M: number of matching (when a B point is closed enought of a A point)
and then I aggregate the result   = M- (O+FP) to get an indicator..

I am pretty sure there are other more traditional ways to do that.

Thanks in advance
-NM
+
+ To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu
+ To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and 
unsubscribe ai-geostats in the message body. DO NOT SEND 
Subscribe/Unsubscribe requests to the list
+ As a general service to list users, please remember to post a summary of any 
useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/

Fw: AI-GEOSTATS: Fwd: Fwd: Notice of Intellectual Property-Trademark Name

[Younes Fadakar]

Hi there,

Just as my vote I do think we should not allow such a complete copy of the name 
(ai-geostats) used for other sites. As a general rule if the goal of those 
sites 
is something about geostatistics they must not use ai-geostats or even 
similars. 
If their aims are different then also to avoid numerous misunderstanding and 
unpredictable issues with the contents of those sites, keeping in mind what is 
the duty of ai-geostats as a web site we should keep our rights perfectly as 
the 
community of geostatistics.
The reputation of the ai-geostats should be kept safe from abusing in any form 
including intellectual property considerations, I do think.

 Best Regards,
.
Younes
yfa.st...@ymail.com
http://alghalandis.com






- Forwarded Message 
From: Edzer Pebesma edzer.pebe...@uni-muenster.de
To: Ask Geostatisticians ai-geostats@jrc.it
Cc: Eike Hinderk Jürrens e.h.juerr...@52north.org
Sent: Mon, 10 January, 2011 8:24:04 PM
Subject: AI-GEOSTATS: Fwd: Fwd: Notice of Intellectual Property-Trademark Name

Dear all,

see the question below; does anyone in the ai-geostats community see
problems if third parties develop activities under domains like
ai-geostats.cn and the like, outside our control?

Best regards,
--
Edzer


 Original Message 
Subject: Fwd: Notice of Intellectual Property-Trademark Name
Date: Mon, 10 Jan 2011 08:47:08 +0100
From: IT-Support it-supp...@52north.org
To: Edzer Pebesma pebe...@52north.org, Albert Remke - 52°North
a.re...@52north.org,  Andreas Wytzisk a.wytz...@52north.org

F.Y.I.

Kind regards, and happy new year,

Eike

 Original-Nachricht 
Betreff: Notice of Intellectual Property-Trademark Name
Datum: Mon, 3 Jan 2011 11:16:40 +0800
Von: Angela w...@ygnetworks.org
An: wikimas...@52north.org





Dear Manager:

We are a Network Service Company which is the domain name registration
center in Anhui, China. On January,3rd,2011, We received QUNDI Company's
application that they are registering the name ai-geostats as their
Internet Trademark and ai-geostats.cn,ai-geostats.com.cn
,ai-geostats.asiadomain names etc.,It is China and ASIA domain
names.But after auditing we found the brand name been used by your
company. As the domain name registrar in China, it is our duty to notice
you, so I am sending you this Email to check.According to the principle
in China,your company is the owner of the trademark,In our auditing time
we can keep the domain names safe for you firstly, but our audit period
is limited, if you object the third party application these domain names
and need to protect the brand in china and Asia by yourself, please let
the responsible officer contact us as soon as possible. Thank you!

Kind regards

Angela Zhang



*Anhui Office (Head Office)
*Registration Department Manager
Room 1008 Shenhui Building
Haitian Road, Huli Anhui, China

Office:  +86 0553 4994789
Fax: +86 0553 4994789
web: www.ygnetworks.org http://www.ygnetworks.org


+
+ To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu
+ To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and 
unsubscribe ai-geostats in the message body. DO NOT SEND 
Subscribe/Unsubscribe 
requests to the list
+ As a general service to list users, please remember to post a summary of any 
useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/



  

AI-GEOSTATS: Fwd: Fwd: Notice of Intellectual Property-Trademark Name

[Edzer Pebesma]

Dear all,

see the question below; does anyone in the ai-geostats community see
problems if third parties develop activities under domains like
ai-geostats.cn and the like, outside our control?

Best regards,
--
Edzer


 Original Message 
Subject: Fwd: Notice of Intellectual Property-Trademark Name
Date: Mon, 10 Jan 2011 08:47:08 +0100
From: IT-Support it-supp...@52north.org
To: Edzer Pebesma pebe...@52north.org, Albert Remke - 52°North
a.re...@52north.org,  Andreas Wytzisk a.wytz...@52north.org

F.Y.I.

Kind regards, and happy new year,

Eike

 Original-Nachricht 
Betreff:Notice of Intellectual Property-Trademark Name
Datum:  Mon, 3 Jan 2011 11:16:40 +0800
Von:Angela w...@ygnetworks.org
An: wikimas...@52north.org





Dear Manager:

We are a Network Service Company which is the domain name registration
center in Anhui, China. On January,3rd,2011, We received QUNDI Company's
application that they are registering the name ai-geostats as their
Internet Trademark and ai-geostats.cn,ai-geostats.com.cn
,ai-geostats.asiadomain names etc.,It is China and ASIA domain
names.But after auditing we found the brand name been used by your
company. As the domain name registrar in China, it is our duty to notice
you, so I am sending you this Email to check.According to the principle
in China,your company is the owner of the trademark,In our auditing time
we can keep the domain names safe for you firstly, but our audit period
is limited, if you object the third party application these domain names
and need to protect the brand in china and Asia by yourself, please let
the responsible officer contact us as soon as possible. Thank you!

Kind regards

Angela Zhang



*Anhui Office (Head Office)
*Registration Department Manager
Room 1008 Shenhui Building
Haitian Road, Huli Anhui, China

Office:  +86 0553 4994789
Fax: +86 0553 4994789
web: www.ygnetworks.org http://www.ygnetworks.org


+
+ To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu
+ To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and 
unsubscribe ai-geostats in the message body. DO NOT SEND 
Subscribe/Unsubscribe requests to the list
+ As a general service to list users, please remember to post a summary of any 
useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/

AI-GEOSTATS: RE: RE: help ai-geostats

[Gregoire Dubois]

Dear Ruishan Chen,

 

I’m afraid I could not trace back the location of the software and can
therefore only invite you to get in touch with the author of the software:
mario.biond...@ndsu.edu

 

Best regards,

 

Gregoire

 

 

From: chenrsh04 [mailto:chenrs...@163.com] 
Sent: Friday, December 17, 2010 3:27 AM
To: Gregoire Dubois
Subject: Re:RE: help ai-geostats

 

Hello Dubois,

 the software is spatial scaling depicted in the http://www.ai-geostats.
org/bin/view/AI_GEOSTATS/SWSpatial, or I can say it is a software for scale
variance analysis, I can't open the links, it said the link is broken.thank
you very much.

best wishes,

ruishan chen


在2010-12-17，Gregoire Dubois gregoire.dub...@jrc.ec.europa.eu 写道： 

-原始邮件-
发件人:Gregoire Dubois gregoire.dub...@jrc.ec.europa.eu
发送时间:2010年12月17日 星期五
收件人:'chenrsh04' chenrs...@163.com
主题:RE: help ai-geostats

Hello Ruishan Chen,

 

Could you please tell me which software it is?

 

 

Best regards,

 

Gregoire

 



Grégoire Dubois (Ph.D.)

 

Joint Research Centre - European Commission

Global Environment Monitoring Unit

Monitoring Of Natural resources for DEvelopment  (MONDE)

 

Via Fermi2749, TP 440,  I-21027 Ispra (VA), ITALY

 

 http://ies.jrc.ec.europa.eu/ http://ies.jrc.ec.europa.eu/

 

Tel : +39 0332 786360

Fax : +39 0332-789960

Email: mailto:gregoire.dub...@jrc.ec.europa.eu
gregoire.dub...@jrc.ec.europa.eu

 

The views expressed are purely those of the writer and may not in any
circumstances be regarded as stating an official position of the European
Commission. (Disclaimer required under the terms and conditions of use of
the internet and electronic mail from Commission equipment) 

 

From:chenrsh04 [mailto:chenrs...@163.com]
Sent:Thursday, December 16, 2010 12:06 PM
To:ai-geostats@jrc.it
Cc:majord...@jrc.it;gregoire.dub...@jrc.it
Subject:help ai-geostats

 

Dear sir,
 I find out that there is a software can perform the scale variance
analysis in the ai-geostats website, but I can't download it, can you help
me? thank you very much. Merry Christmas and Happy New Year!
sincerely,

Ruishan Chen

end



 

 

 

R: RE: AI-GEOSTATS: Interpolating mining presence only data

[sebastiano.trevis...@libero.it]

Ok, in this case surface morphology could be very useful (i.e. possible strong 
signature of antrophic activity).
But you need high resolution dems!
Sebas
http://posta64a.mailbeta.libero.it/cp/ps/Main/WindLayout?d=libero.
itu=sebastiano.trevisanit=116124102d823104#

Messaggio originale
Da: gregoire.dub...@jrc.ec.europa.eu
Data: 14/12/2010 9.13
A: jpri...@ujaen.es
Cc: ai-geostats@jrc.it
Ogg: RE: AI-GEOSTATS: Interpolating mining presence only data

Hi everyone,

Many thanks for all the suggestions received so far. I was unclear indeed in 
my request as I was looking for undiscovered mining sites rather than 
undiscovered deposits.


Best regards,

Gregoire


Grégoire Dubois (Ph.D.)

Joint Research Centre - European Commission 
Global Environment Monitoring Unit 
Monitoring Of Natural resources for DEvelopment  (MONDE)
 
Via Fermi 2749, TP 440,  I-21027 Ispra (VA), ITALY
 
http://ies.jrc.ec.europa.eu/

Tel : +39 0332 786360
Fax : +39 0332-789960 
Email: gregoire.dub...@jrc.ec.europa.eu
 
The views expressed are purely those of the writer and may not in any 
circumstances be regarded as stating an official position of the European 
Commission. (Disclaimer required under the terms and conditions of use of the 
internet and electronic mail from Commission equipment) 

-Original Message-
From: Juan P. Rigol Sanchez [mailto:jpri...@ujaen.es] 
Sent: Tuesday, December 14, 2010 7:36 AM
To: Gregoire Dubois
Subject: Re: AI-GEOSTATS: Interpolating mining presence only data

Hi Gregoire. Do you want to map undiscovered mineral deposits or
undiscovered mining sites (illegal mining or the like)?


In the first case, it depends on scale, but mineral deposits are usually
taken as points/cells at mineral exploration scales. Thus, spatial
continuity is not assumed (mineral deposits are typically discontinuous)
in most (all?) exploration mapping models.
Further information on mineral deposit type would be necessary to give
advice. In addition, you have only two predictor layers, and probably
the DEM will be useless (it obviously depends on the deposit model, but
in most deposits topography will be uncorrelated). The geological map
could be used to refine prior spatial probabilities using bayesian WofE
model or similar (take a look on ArcSDM toolbox for arcgis).
There are quite a lot references on mineral potential probability
mapping (take a look on latest issues of journals Ore Geology Reviews or
Natural Resources Research).

In the second case, I guess the type of mineral deposit will also
control the approach (probably a lithology class or fractures/faults
will control the location of mining sites).

I hope this helps.

Regards,
Juan P.


--
Juan P. Rigol-Sanchez
RSGIS Lab - Earth Surface Processes - Dpt. of Geology
Faculty of Science, University of Jaen
Spain
--


El lun, 13-12-2010 a las 17:19 +0100, Gregoire Dubois escribió:
 Hi everyone, 
 
  
 
 Any suggestions about how to map probabilities to find undetected
 mining sites having only
 
  
 
 -  A map of mining locations (presence only data)
 
 -  A geological map 
 
 -  A DEM
 
  
 
 I’m aware of the work on mapping bird species (e.g. Olivier 
 Wotherspoon, 2006) but I suppose the situation is different here given
 the supposed spatial continuity in the mineral mined?
 
  
 
 Many thanks for any hints.
 
  
 
 Best regards
 
  
 
 Gregoire
 
 
 
 Grégoire Dubois (Ph.D.)
 
  
 
 Joint Research Centre - European Commission 
 
 Global Environment Monitoring Unit 
 
 Monitoring Of Natural resources for DEvelopment  (MONDE)
 
  
 
 Via Fermi 2749, TP 440,  I-21027 Ispra (VA), ITALY
 
  
 
 http://ies.jrc.ec.europa.eu/ 
 
  
 
 Tel : +39 0332 786360
 
 Fax : +39 0332-789960 
 
 Email: gregoire.dub...@jrc.ec.europa.eu
 
  
 
 The views expressed are purely those of the writer and may not in any
 circumstances be regarded as stating an official position of the
 European Commission. (Disclaimer required under the terms and
 conditions of use of the internet and electronic mail from Commission
 equipment) 
 
  
 
 




+
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Subscribe/Unsubscribe requests to the list
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R: AI-GEOSTATS: Interpolating mining presence only data

[sebastiano.trevis...@libero.it]

HiAn idea could be to calculate a kernel density from the sites and see if 
there is a correlationwith lithology. The dem could be useful if there is some 
correlation between morphology and themining sites i.e.: 1) are the mining 
sites correlated with the geo-structural and geomorfological setting?Have the 
geostructural and geomorfological setting a morphometric signature?
I hope this is useful.ByeSebas


Messaggio originale

Da: gregoire.dub...@jrc.ec.europa.eu

Data: 13/12/2010 17.19

A: ai-geostats@jrc.it

Ogg: AI-GEOSTATS: Interpolating mining presence only data



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--Hi everyone,  Any suggestions about how to map probabilities to find 
undetected mining sites having only -  A map of mining locations 
(presence only data)-  A geological map -  A DEM I’m aware of 
the work on mapping bird species (e.g. Olivier amp; Wotherspoon, 2006) but I 
suppose the situation is different here given the supposed spatial continuity 
in the mineral mined? Many thanks for any hints. Best regards 
GregoireGrégoire Dubois (Ph.D.) 
Joint Research Centre - European Commission Global Environment Monitoring

AI-GEOSTATS: Invitation to connect on LinkedIn

[M. Nur Heriawan]

LinkedIn


   
AI,

I'd like to add you to my professional network on LinkedIn.

- M. Nur

M. Nur Heriawan
Lecturer at Mining Engineering Department, Bandung Institute of Technology 
Indonesia

Confirm that you know M. Nur Heriawan
https://www.linkedin.com/e/dgf4jv-ghonvkrj-4s/isd/2022205333/5gtJDAY8/


 
-- 
(c) 2010, LinkedIn Corporation

AI-GEOSTATS: Interpolating mining presence only data

[Gregoire Dubois]

Hi everyone, 

 

Any suggestions about how to map probabilities to find undetected mining
sites having only

 

-  A map of mining locations (presence only data)

-  A geological map 

-  A DEM

 

I’m aware of the work on mapping bird species (e.g. Olivier  Wotherspoon,
2006) but I suppose the situation is different here given the supposed
spatial continuity in the mineral mined?

 

Many thanks for any hints.

 

Best regards

 

Gregoire



Grégoire Dubois (Ph.D.)

 

Joint Research Centre - European Commission 

Global Environment Monitoring Unit 

Monitoring Of Natural resources for DEvelopment  (MONDE)

 

Via Fermi 2749, TP 440,  I-21027 Ispra (VA), ITALY

 

http://ies.jrc.ec.europa.eu/ 

 

Tel : +39 0332 786360

Fax : +39 0332-789960 

Email:  mailto:gregoire.dub...@jrc.ec.europa.eu
gregoire.dub...@jrc.ec.europa.eu

 

The views expressed are purely those of the writer and may not in any
circumstances be regarded as stating an official position of the European
Commission. (Disclaimer required under the terms and conditions of use of
the internet and electronic mail from Commission equipment) 

 

AI-GEOSTATS: simulation of composition data

[Paul Walline]

Hi all,

 

I would like to simulate the spatial distribution of compositional data, in
my case age classes of fish. My data consists of the age composition (pooled
into 4 or 5 age classes) of samples of fish at non-random locations. I want
to interpolate these data using stochastic geostatistical simulations to get
age composition at unsampled grid locations. I am aware from Aitcheson
(2005) and others that I first need to transform my data (logratio). I am
assuming that after the transformation I still could not simulate the
distributions of the age classes separately, and would need to simulate the
joint distribution of the age classes, using an approach something like that
described by Babak et al (2010), and then backtransform the simulation
results to proportions. However, I am not sure at this point whether
following the procedure I've described above will constrain the sum of the
age class proportions at a simulated node to be 1.  Any opinions on whether
the approach described will work or not? Any suggestions for an alternate
method?

 

Thanks in advance.

 

Paul Walline, Alaska Fisheries Science Center

paul.wall...@noaa.gov

AI-GEOSTATS: Re: Interpolating mining presence only data

[drisobelclark]

Gregoire
 
You might want to look at the works of Fritz Agterberg (Ontario, Canada) who 
has been researching potential targets for over 35 years. I do not have any 
recent references but a web search should be possible.
 
Isobel
http://www.kriging.com

--- On Mon, 12/13/10, Gregoire Dubois gregoire.dub...@jrc.ec.europa.eu wrote:


From: Gregoire Dubois gregoire.dub...@jrc.ec.europa.eu
Subject: AI-GEOSTATS: Interpolating mining presence only data
To: ai-geostats@jrc.it
Date: Monday, December 13, 2010, 4:19 PM






Hi everyone, 
 
Any suggestions about how to map probabilities to find undetected mining sites 
having only
 
-  A map of mining locations (presence only data)
-  A geological map 
-  A DEM
 
I’m aware of the work on mapping bird species (e.g. Olivier  Wotherspoon, 
2006) but I suppose the situation is different here given the supposed spatial 
continuity in the mineral mined?
 
Many thanks for any hints.
 
Best regards
 
Gregoire

Grégoire Dubois (Ph.D.)
 
Joint Research Centre - European Commission 
Global Environment Monitoring Unit 
Monitoring Of Natural resources for DEvelopment  (MONDE)
 
Via Fermi 2749, TP 440,  I-21027 Ispra (VA), ITALY
 
http://ies.jrc.ec.europa.eu/ 
 
Tel : +39 0332 786360
Fax : +39 0332-789960 
Email: gregoire.dub...@jrc.ec.europa.eu
 
The views expressed are purely those of the writer and may not in any 
circumstances be regarded as stating an official position of the European 
Commission. (Disclaimer required under the terms and conditions of use of the 
internet and electronic mail from Commission equipment) 
 

AI-GEOSTATS: Changes to ai-geostats

[Gregoire Dubois]

Hi everyone, 

 

Most have you have already noticed the migration of AI-GEOSTATS.ORG to a new
host and wondered about it. After 16 years of managing the web site, it was
the right time to hand it over to someone else willing to give to it a new
home and new ideas. Edzer is definitely a right choice for that. I will
still moderate the list.

 

We still need more time to organize the maintenance of the web site (e.g.
addition of new books, conferences, courses, freeware, etc.). I therefore
suggest to use directly, for the time being, the ai-geostats mailing list to
send your announcements. 

 

Best wishes

 

Gregoire (moderator of ai-geostats)



Grégoire Dubois (Ph.D.)

 

Joint Research Centre - European Commission 

Global Environment Monitoring Unit 

Monitoring Of Natural resources for DEvelopment  (MONDE)

 

Via Fermi 2749, TP 440,  I-21027 Ispra (VA), ITALY

 

http://ies.jrc.ec.europa.eu/ 

 

Tel : +39 0332 786360

Fax : +39 0332-789960 

Email:  mailto:gregoire.dub...@jrc.ec.europa.eu
gregoire.dub...@jrc.ec.europa.eu

 

The views expressed are purely those of the writer and may not in any
circumstances be regarded as stating an official position of the European
Commission. (Disclaimer required under the terms and conditions of use of
the internet and electronic mail from Commission equipment) 

 

AI-GEOSTATS: Methods for comparing trends in land cover change?

[David Meek]

Greetings all,

A questions from a newbie to  spatial statistics:

What if one is interested in the relationship between two areas, one which
is a subset of another, i.e  one has a 100 km2 rectangular area, and the
area of interest is a 10 km2 area within that larger area, but also one is
interested in the relationship between the part and the whole, i.e. if land
cover changes in the part mimic larger trends in the whole. What methods are
there for statistically comparing the two?

Any thoughts are always appreciated,
Best,
David

AI-GEOSTATS: variogram implemented in Matlab, and more

[Younes Fadakar]

Hi there,

Hoping you all doing very well.
Close to new year happy days I'm sincerely presenting my friends in 
geostatistics community some software and tutorial gifts.
Hope you like them.

I have recently added some codes and tutorial of using Python and Matlab for 
scientific analysis and visualizations to the address http://alghalandis.com.
For example at the address:
http://alghalandis.com/?page_id=463
you can find an implementation of computing variogram 
(omnidirectional/directional) using Matlab.
The background idea was providing friendly assistance to initial steps for 
students and who is new in these programming environments and also wishes to 
try 
geostatistics using them.
Note: The commenting service for them is open now.

Best Regards,

Younes
yfa.st...@ymail.com
http://alghalandis.com



  

RE: AI-GEOSTATS: nugget ratio vs. range of variogram

[Robert Sandefur]

Good Question-

Other things to consider are the type of variogram and the total sill. My 
PERSONAL opinions include the following (using a completely different tack)):
1.For measured resources no additional exploration type sampling should be 
required. Additional sampling and drilling for breakage and ore control is okay.
2.For measured, reconciliation over a quarter should be within plus or -10% in 
terms of tons, grade and contained metal 95% of the time. 
3.For measured and indicated, reconciliation over a year should be within plus 
or -10% in terms of tons, grade and contained metal 95% of the time. 
4.For measured and indicated, reconciliation over life of mine should be within 
plus or -10% in terms of tons, grade and contained metal 95% of the time.
5.It is acceptable to use plus or -15% instead of plus or -10 as this is a 
usual standard of accuracy for a feasibility study.
6.The plus or -10% should be reduced if the -10% case is uneconomic. (If you 
have to reduce the standard of accuracy to maintain an economic project. the 
project is probably not a good idea).
There are several objections to the above criteria:
1. Three and four are probably statistically inconsistent.
2. Measured and indicated are dependent on mining rate.
It is generally possible, once your criteria (two, three and four) are selected 
to translate this into a distance and number of holes and composites criteria.


Regards

Robert (Bob) L. Sandefur PE

Senior Geostatistician / Reserve Analyst

CAM

12600 West Colfax Suite A-250 Lakewood, Co 80215

rsande...@cam-llc.com

303 472-3240 (cell) -best choice

303 716-1617 ext  14
The content of this message may contain the private views and opinions of the 
sender and does not constitute a formal view and/or opinion of the company 
unless specifically stated.

The contents of this email and any attachments may contain confidential and/or 
proprietary  information, and is intended only for the person/entity to whom it 
was originally intended. Any dissemination, distribution or copying of this 
communication is strictly prohibited.

If you have received this email in error please notify the sender immediately 
by return e-mail and delete this message and any attachments from your system.





-Original Message-
From: gregoire.dub...@gmail.com [mailto:gregoire.dub...@gmail.com] On Behalf Of 
M. Nur Heriawan
Sent: Monday, November 01, 2010 18:55
To: ai-geostats@jrc.it
Subject: AI-GEOSTATS: nugget ratio vs. range of variogram

Dear list,

Some people used the range of variogram to optimize the drillhole spacing and 
radius of resource classification of a deposit. But as we knew that the nugget 
ratio (nugget variance/total sill) of variogram could vary to be low (25%), 
medium (25-50%), high (50-75%), and extremely high (75%). 


So how can we define the correct range related to the such types of nugget 
ratio? In my opinion when the nugget ratio is low, then our variogram range is 
robust, but how for the others? When the nugget ratio is extremely high, then 
the range is almost none. Is there a mathematical relationship between nugget 
effect or nugget ratio with the range of variogram?

Looking forward to the suggestions and references from the list. Thank you for 
attention.

Best regards, ---
M. Nur Heriawan
Earth Resources Exploration Research Group
Faculty of Mining and Petroleum Engineering
Institut Teknologi Bandung (ITB)
Jl. Ganesha 10 Bandung 40132 INDONESIA
http://www.mining.itb.ac.id/heriawan 



  

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AI-GEOSTATS: Geostatistics: Simple Articles for Novices and FREE Software

[Younes Fadakar]

Hi there,

Hoping you are doing well.
Just for your information I have added some FREE tutorials, software 
applications and articles related to Geostatistics and Spatial Analysis at the 
following address:
http://alghalandis.com particularly,
Article: An application of Geostatistical Simulation to design new sampling 
points in order to handle the uncertainty of the estimation at:
http://alghalandis.com/?page_id=404
FREE software downloads such Interactive Variogram at:
http://alghalandis.com/?page_id=90

The idea was to provide novices a simple and rapid explanation of the subject.
Your comments and notes are welcome via this email or alghalan...@ymail.com 

Best Regards,
.
Younes
yfa.st...@ymail.com
http://alghalandis.com



  

AI-GEOSTATS: geostat for alluvial deposit

[M. Nur Heriawan]

Dear list,

I need references about the application of geostatistics for the resources 
estimation of alluvial and placer deposits. If someone in the list has 
experiences about it, could you please share it? As you might know 
that alluvial 
deposits i.e. gold or tin has very high variability in the lateral direction, 
so 
the resources classification based on the variogram range is seemed to be not 
appropriate. Anyway, when we used the kriging standard deviation for resources 
classification, the result was seemed to be not reliable.  
 
Thank you for any suggestions.

Best regards,---
M. Nur Heriawan
Earth Resources Exploration Research Group
Faculty of Mining and Petroleum Engineering
Institut Teknologi Bandung (ITB)
Jl. Ganesha 10 Bandung 40132 INDONESIA
http://www.mining.itb.ac.id/heriawan 



  

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Re: AI-GEOSTATS: where it can be found a public data set?

[drisobelclark]

There are many data sets on our web site at http://www.kriging.com which are 
free to all and in a simple text file format. They range from large scale 
production mining data sets through exploration to envirnmental and 
agricultural data.

Isobel

--- On Mon, 7/19/10, Younes Fadakar yfa.st...@ymail.com wrote:

 From: Younes Fadakar yfa.st...@ymail.com
 Subject: AI-GEOSTATS: where it can be found a public data set?
 To: Ask Geostatisticians ai-geostats@jrc.it
 Date: Monday, July 19, 2010, 9:22 AM
 Hi everybody,
 
 Is there any data set including location (X, Y and Z) and
 assays that is 
 accessible without license issue, qualified and tested for
 publication? 
 
 
 Regards,
 
 Younes
 
 
 
       
 
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 with no subject and unsubscribe ai-geostats in the message
 body. DO NOT SEND Subscribe/Unsubscribe requests to the
 list
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 post a summary of any useful responses to your questions.
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Re: AI-GEOSTATS: where it can be found a public data set?

[Edzer Pebesma]

R comes with thousands of free  tested data sets, many of which are
spatial.

On 07/19/2010 10:22 AM, Younes Fadakar wrote:
 Hi everybody,
 
 Is there any data set including location (X, Y and Z) and assays that is 
 accessible without license issue, qualified and tested for publication? 
 
 
 Regards,
 
 Younes
 
 
 
   
 
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 and unsubscribe ai-geostats in the message body. DO NOT SEND 
 Subscribe/Unsubscribe requests to the list
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 any useful responses to your questions.
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-- 
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of Münster
Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763  http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics  e.pebe...@wwu.de

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AI-GEOSTATS: looking for a large 3D data set

[Younes Fadakar]

Hi everybody,

Thank you so much for your replies.

Let me explain what I'm looking for:
mining data including assays (Cu, etc), geology (rock types etc), geotechnics 
(RQD etc), in 3D coordinates (X,Y,Z) 


I couldn't find yet.

Through all suggested data sets so far, I could find only one file 
(BrendaMines.dat) from www.kriging.com which has X,Y,Z and some grades.
But I need a large and complete data set.

Through R package: if there is such a file then where is that?

Regards,

Younes



  

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useful responses to your questions.
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AI-GEOSTATS: Interactive Variogram

[Younes Fadakar]

Hi everybody,

Interactive Variogram is a free standalone 
application for investigating interactively and immediately the response of 
variogram to movement of sample points. It has been written in Java 
and could be run on different platforms such as Windows, Linux and  
MacOS well.
Thanks to its novel and innovative design the quality of variogram 
considerably has been enhanced especially around the origin (short 
distances), moreover all information of experimental variogram including number 
of pairs (p), distance (h), and gamma (g) are shown on figure.
It computes the omnidirectional 2D variogram and can be used for 
quality test of variography in terms of responding to distribution form 
of samples in area of study as well.
It is strongly recommended for all practitioners interested in 
developing the understanding of spatial distribution of samples.
It is available for free at the following address: 

http://alghalandis.com/?page_id=266

King Regards,

Younes



  

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AI-GEOSTATS: Spatial Models

[Alaios]

Hello .
I am a new student and I am trying to understand what is behind the spatial 
modeling idea. In my spatial statistics books I could see many 2-D diagrams 
that usually use distance in order to make predictions for the dependence of 
two variables. Is not it possible to have also some more parameters added to a 
study?

I have read so far that the variograme for example describes the degree of 
spatial dependence of a spatial random field. I know that a random field might 
be a multidimensional vector so perhaps this is exactly the place where I could 
add as many variables I want to study. Is that possible or I am completely 
confused about what exactly this is?

Best Regards
Alex.


  
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RE: AI-GEOSTATS: Point pattern analysis and animal displacement

[Delmelle, Eric]

Philippe
A relatively simple approach:
 
You could slice the time and prepare kernel density maps for each time period. 
For each of these time periods, perform K-function at different scales, 
identify at which scales the clustering is the strongest (using L-function), 
and use that as an input for kernel density maps. Then you have a sequence of 
maps you can animate to understand the change over time. You could then 
estimate whether density of clustering of the animals has a spatial correlation 
with proximity to buildings. 
More info here: Delmelle, E. 2009. Point Pattern Analysis. In Kitchin R, Thrift 
N (eds) International Encyclopedia of Human Geography. 8: 204-211. Oxford: 
Elsevier.
 
A more complicated approach:
 
You may want to consider using network-based kernels since we know that 
although animals can move freely, they tend to follow distinct path. Joni Downs 
and Mark Horner have done some work on NKDE (network kernel density 
estimation). It is computationally harder (using delaunay triangulation), but 
the results are more robust.
Downs and Horner (2008): Spatially modeling parthways of migratory birds for 
nature reserve site selection IJGIS
Downs and Horner (2008): Effects of point pattern shape on home-range estimate
They also have some pdfs you can find on the internet (JA Downs and MW Horner).
eric

--
(704) 687 5991
Charlotte, NC 28223
http://www.geoearth.uncc.edu/faculty/edelmel1/
--



From: owner-ai-geost...@jrc.ec.europa.eu on behalf of Philippe Bouchet
Sent: Wed 5/5/2010 01:33
To: ai-geostats@jrc.it
Subject: AI-GEOSTATS: Point pattern analysis and animal displacement



Dear list,

I am working on a project which aims to determine whether the construction of 
industrial facilities near to / in the middle of a migratory path for large 
marine animals has an effect on the spatial distribution of those migrating 
animals. My dataset consists of a series of points marking the location (GPS 
coordinates) of animals sighted during several dedicated aerial surveys over 
the area (before and after the construction of the industrial platform), and I 
also know the position of the facilities of course. How can relate the spatial 
distribution of animals to the presence of the industrial facilities, with the 
objective of testing whether the animals have been displaced from their normal 
route ?

My initial thoughts on this were to:

1) test for CSR (Complete Spatial Randomness) in the point pattern - if the 
animals were distributed randomly over the area prior to the implantation of 
the facilities but now display a clustered or gradient pattern in distribution, 
this could be indicative of a possible displacement.
2) Construct 2D kernel density estimates, using appropriate functions in R, for 
each day an aerial survey was carried out - that would enable me to understand 
how the distribution of animals changes through time.¨

Is this the right way to go ? Are there other tools / analyses out there that 
may be more suited to answering my question and that I may not be aware of ?

Any ideas or suggestions much welcome and greatly appreciated,

Many thanks in advance,

Philippe


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AI-GEOSTATS: Backtransforming variance

[Robby Bemrose]

Dear AI Geostats List, 
 
Just looking for suggestions on how to backtransform the ordinary kriging 
variance (SE) produced from kriging log10 observations.
 
Thanks for your help, 
 
Robert 
 

AI-GEOSTATS: Re: Backtransforming variance

[Isobel Clark]

Hi
 
Some of my own thoughts on backtransforming the variance go as follows:
 
the backtransform for the variance in lognormal theory is exp{logarithmic 
variance-1} times the square of the mean. In kriging this would adapt to 
exp{logarithmic kriging variance-1} times the estimated value squared. Again 
you can substitute 10 for exp if you use log10 for all the calculations. 
 
However, this is not useful for producing confidence levels since the lognormal 
does not follow the Central Limit Theory and a Normal approximation does not 
work in practice.
 
Better to use lognormal theory such as described on the second page of my 
extract. The 'Psi' factors provide multiplicative factors for confidence 
levels, i.e. you multiply the Psi factor by the estimated value to get a 
confidence.
 
It really depends why you want to backtransform the variance. For a map, 
backtransform the variance, maybe just use exp{kriging variance-1} for a 
relative variance. For confidence levels, use the Psi factors.
 
Hope this helps
Isobel
http://www.kriging.com

Re: AI-GEOSTATS: Call for collaboration

[brandon whitehead]

indeed.  i'm staring at this book on my desk...
http://www.amazon.com/Applied-Spatial-Data-Analysis-Use/dp/0387781706/ref=sr_1_1?ie=UTF8s=booksqid=1272609988sr=8-1

not that it couldn't be expanded of course...

-brandon


On 4/30/10, Gregoire Dubois gregoire.dub...@gmail.com wrote:
 Hi Younes,

 I have also good news for you!

 There is already a huge community working together using R. Google the words
 R  Spatial and you will find treasures!

 Good luck,

 Best regards

 Gregoire

 On Thu, Apr 29, 2010 at 1:47 PM, Younes Fadakar yfa.st...@ymail.com wrote:

 Hi everybody,

 Hoping you all are fine.
 A good news: I am going to establish an international scientific group for
 developing FREEWARE software applications to implement a series of spatial
 analyses in easiest, quickest and highest quality manner. I've gotten some
 raw ideas on what I want to do. Firstly, my target is: the implementation
 of
 state-of-the-art methodologies that have been published recently.
 This is a sort of a big challenge, but more exciting for the group and
 useful for the community.
 Any comment, suggestion, idea, ... is deeply appreciated.
 Should you send me a message if you are interested in.
 Hoping to hear from you all.

 Regards,

 Younes





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AI-GEOSTATS: moran's i with a twist?

[Seth J Myers]

Hi everyone,

This is a bit long-winded, but I respect everyone's mind on this list and would 
like any criticism and suggestion, if your time allows.

I would like to include a spatially-lagged variable in logistic regression in 
order to decrease some autocorrelation problems in a land-use change model.  
The model is for the probability of a cell not developed in 1985 becoming 
developed by year 2006.  I would like to use Moran's I to estimate my 
spatially-varying weights.

The problem is this: I am modeling the probability of land becoming developed 
from 1985 to 2006 (to capture contemporary dynamics) but developed land 
pre-1985 likely has an influence, and, so, there is a mismatch between the 
areas having an influence and the response.  For background, the response is 
binary (0=undeveloped, 1=developed).

I could just calculate Moran's I for all areas, but this would also include the 
autocorrelation of cells developed pre-1985 with other cells developed pre-1985 
 (which is not of interest for several reasons including that the areas 
developed long ago were influenced by 'accidents' of history which are wholly 
unobservable, plus the decision-making process for land development changes 
over time).  I could just calculate Moran's I on the increment of growth from 
1985 to 2006 (masking out pre-1985 developed areas) but this would neglect a 
source of contagious effect.

My possible solution is this.  Allow, cross products in the numerator in the 
usual manner between observations in the recent growth increment (1985 to 2006) 
but add a restriction that cell values for pre-1985 growth (minus the mean) are 
only multipliled with the cell values of the current increment (0=not developed 
in 1985 or 2006, 1=not developed in 1985 but developed in 2006).  So, I am 
trying to get at how autocorrelated the new increment is with itself and the 
previous growth (but not previous growth with itself).  The mean for use in 
subtraction would possibly be the mean over all cells in the lattice.  This 
seems in a sense to be the cross-correlation between: 1) all developed cells 
and 2) cells potentially developed from 1985 to 2006 (the response).   When 
stated this way, it seems that possibly two means should be used (all 
development and current increment), ala' the usual covariance and cross 
correlation formulation.

Seth Myers
PhD Candidate
SUNY ESF

Re: AI-GEOSTATS: large dataset and variography...estimation...sim

[Ulrich Leopold]

Hi Younes,

maybe take a (stratified) random sample of your data to reduce the 
computational time for the variography.


For predictions you might want to use a local search neighbourhood if 
that is a feasible approach for your problem.


Ulrich

Younes Fadakar wrote:

Hi everybody,

G'day.

How can we deal with large data, say, 200,000 samples in 3D?
- to doing variograpgy
- to estimate using kriging.

issue: it takes over 30 minutes for one omnidirectional variogram!
note: for whom recommending declustering, I think declustering may not be 
useful since it changes the question.

Regards,

Younes


  


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--
__

Ulrich Leopold

Resource Centre for Environmental Technologies, Public Research Centre
Henri Tudor, Technoport Schlassgoart, 66 rue de Luxembourg, P.O. BOX
144, L-4002 Esch-sur-Alzette, Luxembourg

tel: +352 42 5591 618
fax: +352 42 5591 555
mobile: +352 691 304813
http://www.crte.lu , http://www.tudor.lu
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AI-GEOSTATS: Re: large dataset and variography...estimation...sim

[Isobel Clark]

Younes

You can try what we used to do in the bad old days when it took 20 minutes to 
calculate a semi-variogram on 1,000 samples -- moving windows.

Choose a sub-region size which includes about 1,000 samples. Calculate and 
graph from the samples in this window. Shift half-a-window in one direction. 
Repeat. Then display all of your graphs as a 'map' for each level. 

In 1981, I covered the floor of an empty meeting room with computer print out 
;-)

Thank god for graphics. This approach has the added advantage of being able to 
visually assess stationarity or lack-of. Only then should you consider 
modelling.

Isobel
http://www.kriging.com/shopping/EcoSSe_3D_details.htm



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Re: AI-GEOSTATS: Geostatistics for groundwater pollution

[seba]

Hi Marco

Considering that you are dealing with 5 years 
average data, likely there should be some spatial continuity in your data...so,
in a first instance, you should follow a 
geostatistical approach and see what data are telling you.
But from a perspective of an hydrogeologist, 
first, I'll try to build a groundwater model with contaminant

transport and maybe I'll try to do an inversion exercise.
Then consider that likely you will find a trend on your data.
As final consideration, geostatistical tools to 
be used safely require a deep knowledge of the matter:

a precise answer to your question is in some way not possible.
Anyway I hope this is useful,
Bye
Sebastiano


At 21.23 19/04/2010, Marco Branzi wrote:

Hi all,
I should evaluate the spreading of a pollutant 
with geostatistical tools (trichloroethylene and 
degradation products) in groundwater. I have 100 
sampling points that represent the average 
pollutant concentration over 5 years, 
distributed on the surface of  800 km². I use 
geostatistical tools for the first time, i'm sorry, and i need some advice.
In your experience, which could be the best 
method of interpolation to apply in this kind of 
situation  (kriging, IDW ,...)?

Thank you in advance.
Marco

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AI-GEOSTATS: Geostatistics for groundwater pollution

[Marco Branzi]

Hi all,
I should evaluate the spreading of a pollutant with geostatistical tools 
(trichloroethylene and degradation products) in groundwater. I have 100 
sampling points that represent the average pollutant concentration over 5 
years, distributed on the surface of  800 km². I use geostatistical tools 
for the first time, i'm sorry, and i need some advice.
In your experience, which could be the best method of interpolation to apply 
in this kind of situation  (kriging, IDW ,...)?

Thank you in advance.
Marco 



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AI-GEOSTATS: spatial sampling design toolbox: spatDesign

[Gunter Spoeck]

Dear colleagues,

for those of you, who have MATLAB not available, I have ported my
spatial sampling design toolbox spatDesign also to Octave.
Octave is free software and available from
http://www.gnu.org/software/octave/ due to John W. Eaton and underlies
the GNU public licence.
After some minor changes to the original MATLAB code the toolbox is now
also almost fully functional in Octave. Read the  file README.txt
inside the toolboxes for details. 
There is also a nice new feature in the toolboxes now. Wolfgang Nowak, from the 
University of Stuttgart has added fast FFT-Kriging code for interpolation in 
all 3 dimensions.

Both toolboxes can be downloaded from:

MATLAB: 

 http://wwwu.uni-klu.ac.at/guspoeck/spatDesignMatlab.zip .


OCTAVE:

 http://wwwu.uni-klu.ac.at/guspoeck/spatDesignOctave.zip .


With respect to Octave the toolbox is truely public software now.

Kind Regards,
Gunter Spöck


 -- 
 Dr. Gunter Spoeck
 Assistant Professor
 Department of Statistics
 University of Klagenfurt
 Universitaetsstrasse 65-67
 9020 Klagenfurt, Austria
 Tel.: +43 (0)650 2606166
   +43 (0)463 2700 3125
 Fax:  +43 (0)463 2700 3199



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Re: AI-GEOSTATS: Similar parallel project / geostatistical GPL library with no limit in the number of: dimensions...

[seba]

Hi Adrian
Really interesting, I'll give a look!
Sebas

At 08.27 15/04/2010, you wrote:
I'm working in a new geostatistical GPL library 
with no limit in the number of: dimensions, 
variables, external drifts, geographical drift, 
etc... It is callable from python. The 
preliminaries results are at 
http://opengeostat.webs.com/ . The full 
explanation of the underlying theory will be 
available in two papers in http://www.ismm.edu.cu/revistamg


The function responsible to build the kriging 
system of equations was not programed for a 
predefined model, the model is defined by the 
user, passing from Python scripts the 
appropriate data and parameters and modifying 
the kriging system from Python prior to solve 
it. The flexibility of the library allows to 
experiment even with new kriging models.


the questions are,

* someone know a similar parallel project?
* someone has some recommendations?
* someone is interested in collaborate?

Regards
Adrian Martínez Vargas





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AI-GEOSTATS: Kriging with all samples: what is the problem?

[Younes Fadakar]

Hi there,

Could someone explain the second part of the following sentence to me?
Such kriging with a “global” neighbourhood is rarely implemented in practice, 
precisely because of defiance towards the decision of stationarity. [Page 52, 
Line 13, Applied Geostatistics with SGeMS, Remy et al, Cambridge, 2009]
Assume:
- there is no time/cost problem with computing.
- there is no issue as the global smoothing effect.
 
Regards,

Younes



  

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AI-GEOSTATS: books and software on Spatial-temporal analysis

[Yadollah Waghei]

Hi
do you know is there any book or software on the spatial-temporal analysis, or 
a book contaning spatial-temporal covariance estimation?


  
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Re: AI-GEOSTATS: books and software on Spatial-temporal analysis

[seba]

Hi
I think this could be useful:
Modern Spatiotemporal Geostatistics
by 
http://www.amazon.com/Spatiotemporal-Geostatistics-Studies-Mathematical-Geology/dp//s/ref=rdr_ext_aut?_encoding=UTF8index=booksfield-author=George%20ChristakosGeorge 
Christakos


Then I remeber that in Cressie's book there are interesting 
consideration about the issue.
Consider also that in earth sciences sometime space is a manifestaion 
of time (think for example to the z direction

for geological strata...)
Sebas


At 06.54   11/04/10, you wrote:

Hi
do you know is there any book or software on the spatial-temporal 
analysis, or a book contaning spatial-temporal covariance estimation?




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AI-GEOSTATS: EGS - Enjoy Geostatistics

[Younes Fadakar]

Dear all,

The time to enjoy from geostatistical analyzing, 
educating, etc.

I am very excited to introduce EGS to you all, 
professors, students and professionals around the world.
EGS means 
enjoy geostatistics.

Should you try it, you will absolutely love 
it! 
That is a blend of high-performance numeric analyzing core, 
incredible user-interface design and high quality graphical outputs.

For a live and full demonstration and more information please visit the 
following link:

http://www.alghalandis.com


Kind Regards,

Younes Fadakar A.

yfa.st...@ymail.com



  

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AI-GEOSTATS: Enjoy Geostatistics

[Younes Fadakar]

Dear all,

The time to enjoy from geostatistical analyzing, educating, etc.

I am very excited to introduce EGS to you all, professors, students and 
professionals around the world.
EGS means enjoy geostatistics.

Should you try it, you will absolutely love it! 
That is a blend of high-performance numeric analyzing core, incredible 
user-interface design and high quality graphical outputs.

For a live and full demonstration and more information please visit the 
following link:

http://www.alghalandis.com


Kind Regards,
 
Younes Fadakar A.

yfa.st...@ymail.com


  

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RE: AI-GEOSTATS: EGS - Enjoy Geostatistics

[Gregoire DUBOIS (European Commission)]

May I remind everyone the first rule of AI-GEOSTATS when posting something to 
the list?

As requested by the subscribers, announcements of courses, conferences, 
software  freeware and jobs are NOT supposed to be sent to the list. You 
should instead use the respective online forms or contact me.  I will either 
store the information permanently on the web site or provide a short notice and 
link from the main page.

Given the low number of mails sent to the list, I propose to allow freeware 
(only those with source codes available) to be announced on the list. Authors 
of codes should however refreign themselves from spamming the list. 

This approach being completely based on subjective decisions from my side, it 
may end up in a total chaos. I therefore reserve the right to go back to the 
previous rules when I feel appropriate.

I will change the information on the web site of ai-geostats one of these days.

Thank you all for your understanding.

Gregoire (Moderator of AI-GEOSTATS)



-Original Message-
From: owner-ai-geost...@jrc.ec.europa.eu 
[mailto:owner-ai-geost...@jrc.ec.europa.eu] On Behalf Of Younes Fadakar
Sent: Wednesday, April 07, 2010 11:20 AM
To: Ask Geostatisticians
Subject: AI-GEOSTATS: EGS - Enjoy Geostatistics

Dear all,

The time to enjoy from geostatistical analyzing, 
educating, etc.

I am very excited to introduce EGS to you all, 
professors, students and professionals around the world.
EGS means 
enjoy geostatistics.

Should you try it, you will absolutely love 
it! 
That is a blend of high-performance numeric analyzing core, 
incredible user-interface design and high quality graphical outputs.

For a live and full demonstration and more information please visit the 
following link:

http://www.alghalandis.com


Kind Regards,

Younes Fadakar A.

yfa.st...@ymail.com



  

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