Our paper on collecting and analyzing U.S. housing rental markets through Craigslist rental listings has been accepted for publication by the Journal of Planning Education and Research. Check out the article here. This map of rental listings in the contiguous U.S. is divided into quintiles by rent per square foot:
I’ve previously discussed visualizing the GPS location data from my summer travels with CartoDB, Leaflet, and Mapbox + Tilemill. I also visualized different aspects of this data set in Python, using the matplotlib plotting library. However, these spatial scatter plots used unprojected lat-long data which looked pretty distorted at European latitudes.
Today I will show how to convert this data into a projected coordinate reference system and plot it again using matplotlib. These projected maps will provide a much more accurate spatial representation of my spatial data and the geographic region. All of my code is available in this GitHub repo, particularly this notebook.
This guide was updated in June 2016 to reflect changes to the dependencies and the ability to install with Python wheels.
I recently went through the exercise of installing geopandas on Windows and getting it to run. Having learned several valuable lessons, I thought I’d share them with the world in case anyone else is trying to get this toolkit working in a Windows environment (also see this GitHub gist I put together).
It seems that pip installing geopandas works fine on Linux and Mac. However, several of its dependencies have C extensions that cause compilation failures with pip on Windows. This guide gets around that issue. For preliminaries, I have this working on Windows 7, 8, and 10. My Python environments are Anaconda, 64-bit, with both Python 2.7 and 3.5. I’m running geopandas version 0.2 with GDAL 2.0.2, Fiona 1.7.0, pyproj 18.104.22.168, and shapely 1.5.16.
I’ve previously discussed visualizing the GPS location data from my summer travels with CartoDB, Leaflet, and Mapbox + Tilemill. Today I will explore visualizing this data set in Python, using the matplotlib plotting library. All of my code is available in this GitHub repo, particularly this notebook.
Lastly, I reduced the size of this spatial data set so Leaflet can render it more quickly on low-power mobile devices. I discussed why this is important and how to do it with the DBSCAN clustering algorithm and also with the Douglas-Peucker algorithm. The final data set I’ll be working with is available here.
In this tutorial, I demonstrate how to reduce the size of a spatial data set of GPS latitude-longitude coordinates using Python and its scikit-learn implementation of the DBSCAN clustering algorithm. All my code is in this IPython notebook in this GitHub repo, where you can also find the data.
This tutorial demonstrates how to reverse geocode a set of latitude-longitude coordinates to city and country using Python and the Google Maps API.
I have previously written about my GPS location data from this summer’s travels. The data set, gathered with the OpenPaths app, contains lat-long coordinates and timestamps. Without city or country data, any visualizations would be very simplistic because all I have is coordinates and timestamps. It would be nice to reverse geocode these coordinates to add city and country data to each point. Then, I could create richer, more informative marker popups that include this new geographical information.
I recently discussed OpenPaths and my goals in visualizing location data from my summer travels. In this post, I’ll explore visualizing this dataset with CartoDB. The OpenPaths data from my summer travels, which I’ll be working with in these examples, is available here and I discuss how I reverse-geocoded it here. CartoDB is a simple cloud-based tool for building web maps. You can import data through their web-based dashboard and quickly turn it into a dynamic map or visualization.