Grouping latitude and longitude coordinates by city neighborhood

Question

I'm working on a model to predict whether craigslist apartment listings are over or under market value. I'm a complete novice with GIS.

I have the lat and long as well as what neighborhood the author says the apartment is in. But seeing how some neighborhoods are nicer or better known than others, the apartment's actual address is often in other neighborhoods.

The picture is an example of apartments listed under the neighborhood SOMA but many are outside the neighborhood borders.

Is there any way in Python to group lat and longs by neighborhood?

Answer 1

This tutorial from Boundless has some excellent examples of how to work through similar problems in the borough/neighborhood hierarchy in NYC. It doesn't take long to work through the tutorials and you'll feel a lot more at ease with PostGIS in no time.

Answer 2

Please NOTE

I know this is not a complete answer (yet!), but since I would like to post some screenshots, it is much easier to insert them here than link to upload pages that might get rid of the images at some point.

I am hoping to edit this post as I can figure out the solution with David. Anyone else, please feel free to suggest a wokflow or an alternate solution. I would be happy to add the screenshots in my posting!

You might also want to read the many comments below the question to get a clearer idea of what needs to be solved.

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First of all, load your neighborhoods shapefile in QGIS. To do that click the following menu:

This will let you navigate to your file. Make sure you filter for shapefiles, so it is easier to find.

You will now see the file under Layers (top left corner). It is important that you save it anew and give it the correct coordinate system. As you want it to work with your lat/lon values, you would be using WGS84.

Right-click your dataset and go to Save As, and make sure to choose WGS84:

That saves your file with the right coordinate system. Now, you will add the file to QGIS. You can get rid of the original one (right-click: Remove).

Now you would want to add your CSV:

If you downloaded the file from your Google table, like I did, then you do not have to do much here. Just browse to the file and leave everything as is. Click OK, and make sure to enter WGS84 again.

Now your points will be shown ontop of your neighborhoods. To see the tabular data you can right-click either of the files and open their attribute tables:

You will quickly notice that the names of the neighborhoods do not always coincide, which might make the analysis a little more complicated, as we might have to edit the table(s).

Again, I will do my best to edit this post as we go along. I will think of a workflow and try to post it this weekend! If anyone else has suggestions, please enter them as comments, or add a new answer, and briefly note that it is adding to what has been discussed here.

Answer 3

First yo'll have to get hold of your neighborhoods, as a non-usaian I have no idea if these are publicly available in any form, but what you want is polygons describing your neighborhoods. Preferably as geographic coordinates (lat/lon) in GeoJSON format, but if you get them in some other projection or format: don't worry, use gdal/ogr to massage them into shape (not shapefile).

Saying "In Python", i guess you want an open source solution, go install shapely (available through pip)

Then look up the Shapely docs to figure out how to find what is inside a polygon.