Dear Amit, Good morning. All excellent suggestions and glad you summarized them here. These are in default use -- built-up area, LULC fields, road network, population density, commercial activity density, and road activity points density (like stops). https://urbanemissions.info/india-air-quality/india-ncap-cities/
We have a template tool for playing with these combinations. https://urbanemissions.info/tools -- look for the gridding tool. This has been the default setup for a long time and the process works (not just in India, but anywhere). Question is not if we can grid the emissions. We want a new layer -- vehicle density from the images at the grid level -- a new proxy layer. Given the buzz around AI and Satellites, this is an open question to the community -- is this something we can make for Indian cities? If yes, how. With best wishes, Sarath -- *Dr. Sarath Guttikunda* *https://urbanemissions.info <https://urbanemissions.info/about-ueinfo/>* On Mon, Jul 7, 2025 at 11:01 PM Amit Shirsath <[email protected]> wrote: > Hi Dr. Sarath, > > There is another approach, though not very accurate and too complex. > > - There is Vahan data available at RTO level. These RTO offices > usually serve the whole district or municipal corporation or a few taluka, > etc. GIS boundaries for these entities are generally available online. So, > RTO can be mapped to a geographical boundary. > - Also, there are .tiff images available for all India on Bhuvan. > Using Zonal Histogram with these images, one can derive Land Use Land Cover > (LULC) for all India. I had made LULC at 100m x 100m grids for all India a > year back. So, 1 Sqkm is completely feasible. > - Once LULC is available, we have to identify areas where high density > of "Builtup Cover" is available. In theory, it is the area where some kind > of construction is there like buildings, bungalows, informal settlements, > etc. There can be different approaches to identify this, as classes > available with Bhuvan files may not directly correspond to what we want. > - I am making the assumption that the "Builtup Cover" areas will have > vehicle ownership or usage. This assumption has to be refined further based > on your requirement. > - Once RTO boundary and "Builtup Cover" is available, the total > vehicle sales can be distributed to "Builtup Cover" area proportionally. > - So, finally you get the number of vehicles per grid. > - One advantage of using Vahan data, is that the vehicles are > categorised by Vehicle Class, category, Fuel, Norm (BS IV, BS III, etc.) > and Maker. So, you can assign weight > > > *Notes:* > > - The vehicle classifications on Vahan portal may not be available for > all the years. > - Here, we are assuming that the vehicle purchased within an RTO > boundary is operating within the same RTO, which is not true in many cases. > For example, Nowadays, I am observing many high end consumer vehicles with > "DD" registration numbers operating in Mumbai. Also, some large commercial > vehicles like tourist buses with "NL" registration numbers operate on > Mumbai, Pune, Bangalore routes. > - However, If this type of migration percentage is lower compared to > the total number of vehicles sold in the last 15 years, then this method > can be useful. > > > > > > > On Fri, Jul 4, 2025 at 5:00 PM Sarath Guttikunda <[email protected]> > wrote: > >> Dear Anupam, >> >> Thanks for the response. The main bottleneck in this process is having >> access to high-resolution imagery. The codes are all open and many groups >> seem to be using them in places other than India. If you come across any >> useful applications, we would be open to exploring those opportunities. >> >> On the second point, yes, GPS-based data would be much more useful, but >> again, that's not a free dataset. The Google Distance API actually uses >> cell signals to gather congestion-related information, but it's not a free >> dataset, and over the last three or four years, they've really increased >> the price tag for using that API. >> >> In our emissions distribution step, having granular information is much >> more useful -- granular in space and time. Using an image provides a static >> dataset on where vehicles are on the road and also where parking lots are >> (space dimension). This, again, is a proxy that could align with just road >> density, which is the theory we've been using for the longest time. If we >> can add another layer of vehicle density to it, the spatial analysis of >> emissions at higher resolutions, especially in big cities, will improve, >> along with some of our other analyses. >> >> Hoping others on the list can add to this discussion. >> >> With best wishes, >> Sarath >> >> -- >> *Dr. Sarath Guttikunda* >> >> *https://urbanemissions.info <https://urbanemissions.info/about-ueinfo/>* >> >> >> On Tue, Jul 1, 2025 at 9:35 PM Anupam Sobti <[email protected]> >> wrote: >> >>> The constraint here might be availability of usable high resolution >>> imagery. I believe Google prohibits use of its imagery and the imagery that >>> these tutorials use are not available in India. Some people use night time >>> lights overlayed on street networks as a proxy for vehicle density but I >>> think GPS based google data would be more reliable. >>> >>> Regards >>> Anupam >>> >>> On Wednesday, June 18, 2025 at 12:18:08 PM UTC+5:30 Sarath Guttikunda >>> wrote: >>> >>>> In our group, we frequently perform extensive numerical analysis, >>>> particularly to understand emission intensities, both spatially and >>>> temporally. The spatial understanding of emissions is a significant >>>> component of our work, as it is crucial for accurately placing emissions >>>> before they are modeled and concentration maps are generated. >>>> >>>> This process is technically known as the "gridding of emissions." For >>>> example, if we know there are one thousand trucks operating in a city, each >>>> traveling one hundred kilometers a day, we can multiply these figures by an >>>> emission factor for a specific pollutant to determine the total emission >>>> intensity of trucks moving within the city's airshed. The question then >>>> becomes: how do we distribute these emissions into various grids for a >>>> city? We typically work with one-square-kilometer grids on average, and you >>>> can see some examples below. >>>> https://urbanemissions.info/india-air-quality/india-ncap-city-airsheds/ >>>> >>>> One of the proxies we use for trucks is highways. The assumption is >>>> that most trucks will travel on highways and spend the majority of their >>>> time there. Therefore, we assign a higher weight to the grids that >>>> intersect with highways. We also incorporate other layers of information >>>> with additional weights. For instance, industrial hubs, commercial hubs, >>>> malls, and markets are places where these vehicles are likely to go and >>>> spend some time. This methodical approach generates various weighting >>>> functions, and once we have the emission intensities, it produces a gridded >>>> emission file. So far this method of madness works and we have a good >>>> understanding of how the layers are behaving with some plus minus. We have >>>> an example tool to play with this method -- >>>> https://urbanemissions.info/tools/ >>>> >>>> We aim to improve this process. One of the layers we introduced in the >>>> past was speed information from the Google Maps API. We can download speed >>>> data, which also indicates congestion times. We utilized this as another >>>> proxy to understand where and for how long vehicles spend time, and >>>> accordingly, assign weights. See example image for Mumbai here - >>>> https://urbanemissions.info/india-apna/mumbai-india/ >>>> >>>> A new approach we want to explore, given some recently available >>>> information (and algorithms), is vehicle density. This would again be >>>> a static input. For example, if you take a satellite image and apply an >>>> algorithm, you could determine how many vehicles are visible within each >>>> grid. Because this is a static image for a specific time, we cannot use it >>>> as a layer for all-purpose gridding. However, it would serve as an >>>> additional layer of information that accurately reflects what is happening >>>> on the ground. It could also be used to extract information about official >>>> and unofficial parking lots where vehicles spend a significant amount of >>>> time on a given day. This would allow us to extract valuable insights. >>>> >>>> There are many online examples of this being done using geostationary >>>> images in Europe and the United States and most of them require an image >>>> and rest seem to work (take it with a pinch of salt -- non-it-person >>>> speaking). >>>> >>>> https://www.linkedin.com/posts/giswqs_geoai-geospatial-ai-activity-7309216621997281280-46yC/ >>>> >>>> https://up42.com/marketplace/analytics/detection-cars-oi >>>> A commercial portal -- but seems to do exactly what we want at a price >>>> >>>> So, the question to the group today is this: If there is a grid file, >>>> let us say for Bangalore -- has anyone done anything similar to create >>>> a vehicle density map, regardless of the vehicle type? or have any >>>> ideas on how to approach this for Indian cities? >>>> >>>> Please keep in mind that the ultimate goal is not to identify >>>> individual vehicles or count vehicles from traffic cameras. The focus >>>> is on a static image: if we have one, can we, or has anyone, worked on >>>> creating a vehicle density map from it? >>>> >>>> Any sights into making an example and hopefully scaling it up is >>>> appreciated. >>>> >>>> Looking forward to the follow ups. >>>> >>>> With best wishes, >>>> Sarath >>>> >>>> -- >>>> *Dr. Sarath Guttikunda* >>>> >>>> *https://urbanemissions.info >>>> <https://urbanemissions.info/about-ueinfo/>* >>>> >>> -- >>> Datameet is a community of Data Science enthusiasts in India. Know more >>> about us by visiting http://datameet.org >>> --- >>> You received this message because you are subscribed to the Google >>> Groups "datameet" group. >>> To unsubscribe from this group and stop receiving emails from it, send >>> an email to [email protected]. >>> To view this discussion visit >>> https://groups.google.com/d/msgid/datameet/c0afb013-c9b2-4f13-8b92-570b3d79b916n%40googlegroups.com >>> <https://groups.google.com/d/msgid/datameet/c0afb013-c9b2-4f13-8b92-570b3d79b916n%40googlegroups.com?utm_medium=email&utm_source=footer> >>> . >>> >> -- >> Datameet is a community of Data Science enthusiasts in India. Know more >> about us by visiting http://datameet.org >> --- >> You received this message because you are subscribed to the Google Groups >> "datameet" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected]. >> To view this discussion visit >> https://groups.google.com/d/msgid/datameet/CAAj%2BwWo%3DT5siPy4red1eLVexAMoeKPE3V3d69WrLAt8tN9%2BQow%40mail.gmail.com >> <https://groups.google.com/d/msgid/datameet/CAAj%2BwWo%3DT5siPy4red1eLVexAMoeKPE3V3d69WrLAt8tN9%2BQow%40mail.gmail.com?utm_medium=email&utm_source=footer> >> . >> > > > -- > Regards, > Amit Shirsath > +91 9970944537 > www.amitshirsath.com > > It's easier to invent the future than predict it. -- Alan Kay > > -- > Datameet is a community of Data Science enthusiasts in India. Know more > about us by visiting http://datameet.org > --- > You received this message because you are subscribed to the Google Groups > "datameet" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To view this discussion visit > https://groups.google.com/d/msgid/datameet/CALVzC30LGr84vHm4ZU4Cz%3D3HQRWcnM_TX9z0STu%3DvWbTzzL6tg%40mail.gmail.com > <https://groups.google.com/d/msgid/datameet/CALVzC30LGr84vHm4ZU4Cz%3D3HQRWcnM_TX9z0STu%3DvWbTzzL6tg%40mail.gmail.com?utm_medium=email&utm_source=footer> > . > -- Datameet is a community of Data Science enthusiasts in India. Know more about us by visiting http://datameet.org --- You received this message because you are subscribed to the Google Groups "datameet" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. 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