Tag: pandas

Data

Using geopandas on Windows

Post author By gboeing
Post date 2014-09-02
65 Comments on Using geopandas on Windows

This guide was written in 2014 and updated slightly in November 2020.

I recently went through the exercise of installing geopandas on Windows. 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. It seems that pip installing geopandas usually works fine on Linux and Mac. However, several of its dependencies have C extensions that can cause compilation failures with pip on Windows. This guide gets around that issue.

Tags data, geopandas, geopy, geospatial, gis, maps, modeling, pandas, projection, python, shapely, tutorial, visualization, windows

Data

Visualizing Summer Travels Part 5: Python + Matplotlib

Post author By gboeing
Post date 2014-08-29
1 Comment on Visualizing Summer Travels Part 5: Python + Matplotlib

This post is part of a series on visualizing data from my summer travels.

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.

Tags clustering, data, dbscan, geopandas, geopy, geospatial, gis, maps, matplotlib, numpy, pandas, python, shapefiles, shapely, travel, tutorial, visualization

Data

Clustering to Reduce Spatial Data Set Size

Post author By gboeing
Post date 2014-08-20
49 Comments on Clustering to Reduce Spatial Data Set Size

Read/cite the paper 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.

Traditionally it’s been a problem that researchers did not have enough spatial data to answer useful questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Too many scattered points on a map can overwhelm a viewer looking for a simple narrative. Furthermore, rendering a JavaScript web map (like Leaflet) with millions of data points on a mobile device can swamp the processor and be unresponsive.

Tags clustering, data, dbscan, geopy, geospatial, gis, k-means, maps, numpy, pandas, python, scikit-learn, scipy, tutorial, visualization