Dear José and Roger, Thank you very much for your answers! Your detailed explanations are really helpful and I will take your recommendations to continue my research work. Kind regards,Anaïs
On Mon, 2020-04-27 at 11:04 +0200, Roger Bivand wrote: > On Sat, 25 Apr 2020, Jose Ramon Martinez Batlle wrote: > Dear Anaïs. > I am sure more experienced members will give you a better answer, but > untilthat I will try to help. > 1) If I understood correctly, the spatial objects have 15 000 and 30 > 000points in each case study, respectively. If this is the case, I am > afraidthat nb objects of such large datasets surely would have an > impact on thesystem performance when used in subsequent tasks. The > best I can suggest isto try some sort of spatial binning if possible > (e.g. hexbins), but at thesame time accounting for the modifiable > areal unit problem. > 2) The spdep:localG help page states that "For inference, a > Bonferroni-typetest is suggested in the references, where tables of > critical values may befound". The source mentioned is free access, > and can be found here: > Ord, J. K. and Getis, A. 1995 Local spatial autocorrelation > statistics:distributional issues and an application. Geographical > Analysis, 27, 286–306 > https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1995.tb00912.x > Standard measures (critical values) for selected percentiles and > number ofentities, are included in Table 3 of the cited reference. > Since the valuesreturned from localG are Z-values, you can use them > to determine whetherthe critical value chosen is exceeded and thus > infer significant localspatial association for each entity. > Thanks, José, you are quite correct that false discovery rate > problems are among the main reasons why so-called "hot-spot" analyses > may be very misleading, in appearing to give an inferential basis for > apparent map pattern. > In our survey paper with David Wong referenced on ?localG, > https://doi.org/10.1007/s11749-018-0599-x, we show that the > analytical and bootstrap-based inferences are similar - the normality > is related not to the underlying variable seen globally, but the the > local behaviour of the statistic. For this reason, bootstrap > permutation implementations are not included in spdep, though the > code is available if need be. Please indicate whether users would > like this code included for comparative purposes here or in a github > issue on https://github.com/r-spatial/spdep/issues/. > Further, the LOSH statistic, which is a measure of local spatial > heteroscedasticity building on local G, provides a little insight > into the problems raised for so-called "hot-spot" analyses by > variability across the study area in the behaviour of the variable of > interest. If, for example, the variable of interest is influenced by > a background variable with a spatial pattern, we will probably find > "hot-spots" which look like the omitted background variable on a map. > While local G cannot take residuals of a linear model, local Moran's > I can do so. For local G, we do not have exact case-by-case standard > deviates; we do have these for local Moran's I as discussed in the > article with David Wong, and they very typically reduce strongly the > counts of apparently significant local statistcs even before > adjusting p-values for FDR. Finally, only some local measures can > adjust for global autocorrelation - unadjusted local measures also > respond to the presence of global autocorrelation. > On balance, judicious choice of class intervals in mapping a variable > of interest may prove more helpful than trying to present wobbly > inferences from ESDA. > Hope this isn't too discouraging, > Roger > > > Kind regards.José > El vie., 24 abr. 2020 a las 14:00, Anaïs Ladoy (<anais.la...@epfl.ch> > )escribió: > Dear list members, > I'm currently working on a point dataset, from which I want to > conducta Hot Spot Analysis with local Gi* statistics (Getis-Ord). > I'm trying to find a way of computing its significance. I see two > waysof computing significance in this case: > 1) Compare the obtained local Gi from spdep::localG to a > normaldistribution. But here I have several questions :a) In my first > case study (BMI value of 15 000 participants in a cohortstudy), the > distribution of local Gi is far from normal (it is bimodalwith a mode > around very negative values and a mode around 0). However,I do need a > normal distribution of Gi in order to compare it with anormal > distribution, right? Or am I missing something here? What shouldI do > in this case?b) In my second case study (Years of life lost for 30 > 000 individuals),the distribution of Gi returned by spdep::localG is > approximatelynormal but the standard deviation is far from 1. In > fact, inspdep::localG, the Gi values are supposedly standardized > (from what Iunderstood using an analytical mean and variance). Should > I use theseto compare to a normal distribution, or should I use raw G > values(using return_internals=TRUE) and standardize them with the > observedmean and variance of Gi? Does it cause a problem that my > observedvariance does not match the analytical variance? > 2) Compute permutationsHowever this is not implemented in R for > localG. I tried using PySALbut the initial file is big and the weight > file is huge, and mycomputer crashes. Any thoughts to solve this > issue? > Thank you for any feedback.Kind regards,Anaïs > --Anaïs LadoyPhD student, Laboratory of Geographic Information > Systems, SwissFederal Institute of Technology in Lausanne (EPFL), > Switzerland. > _______________________________________________R-sig-Geo mailing > listr-sig-...@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > > > > > [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo