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6

Do a spatial join! First, set up your data frame in a projected coordinate system of your choice (whatever units you want your distances to show up in). So, say you're working in State Plane Feet, make sure all your layers are in State Plane Feet, so if they're not project them into it. From there, Right click on the points layer and click Joins & ...


3

your coordinates are probably in lat/long with degrees as a unit. therefore, along meridians or near the equator, one degree is approximately 111km (circumference/360). note that this will change depending on the distance to the equator. A good practice is to use a local projected coordinate system that is appropriate for your location in order to have a ...


1

If you work with effective distances (along roads), you need some network analyst and iteratively select each school and the students from those schools (with Python or model builder) If you work with bird flight distance, as an alternative to the method proposed by Vince, you could compute the XY coordinates of your schools and of your students, then you ...


1

Euclidean Distance and Zonal Statistics (As Table) will tell you the average distance to the closest point for each polygon. It's worth considering that Euclidean distance is not walking distance. Walking routes are restricted: a person can't (reasonably) walk over a building, an interstate highway, a river/stream, etc. A more accurate method would be to ...


1

for the average walking distance to the closest point, you can indeed use the Euclidian distance to create a raster of the distance to the closest point. Then you can compute the average distance using zonal statistics (or zonal statistics as a table if you prefer a vector output)



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