![]() If anyone knows of R, Python, or Java libraries for these sorts of computations, that would be great to know! I haven't kept up on QGIS in years, but since they're open source, they might have some tools to assist in this since it integrates with Python. If you have a budget, you can also just pay for such GIS services. I'm sure the API has changed since then, however. Still, I made an ArcGIS (or started to) tool for doing just that. It's designed to service web pages with limited traffic and API requests. Two functions added with SAS 9.2, GEODIST and ZIPCITYDISTANCE, compute distances between two sets of longitude and. , haven, Import and Export SPSS, Stata and SAS Files. As popular as these kinds of mapping tools have become, there is no automatic way to import their data into SAS.Reading in spatial data: Google Earth (.kml) files Google Earth can be a source of spatial data e.g., the Northern Gateway (Canada) and Keystone XL (US) pipelines (publicized by NGOs) Ex.: reading in the Statistics Canada 2011. They have limitations, as it's not a service designed for analysis. Functions for Handfully Manipulating and Analyzing Data with ame Format. GEODIST function to find the straight line distance between locations. Manhattan distance would of course work well on such a network as a basic approach, but if you don't have too much data, you can try to use Google API. Using macro and data step SAS code, the shortest driving distance between two. Otherwise, you'll need a true road network layer to do drive times (to deal with one-way traffic and such elevation might matter, too, if you need great accuracy). You can either take distances as the crow flies with a general average that one can drive such and such distance over this much time. ZIPCITYDISTANCE even gives you the distance between two zip codes. Also the various ZIP functions (ZIPCITY, ZIPCITYDISTANCE, ZIPFIPS, ZIPNAME, ZIPNAMEL, ZIPSTATE) can be very useful. Does that make sense? Of course, if you have an actual density layer, then just computing the weighted center of mass should work to get a better centroid of the polygon.Īs for driving distances, that's tricky. For anyone who doesnt know, it returns the geodetic distance in kilometers or miles between two latitude and longitude coordinates. Then you use that remaining space (unit weight unless you have true grid density values) to compute the center of mass of the polygon. If you have another layer of data for population residence (parcels of the right type?), or negatively, a layer reflecting non-population centers like parks or industry, then you can take your areal boundary and crop out or keep only the part of it that is relevant. I never did it before, but we talked about this in my GIS training. ![]()
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