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/CAAj%2BwWqk_QNnyhqUgO-gXjLSksrGo3zFWJR1ZjZp_mkyPYLLVA%40mail.gmail.com.
