Background¶

As cities become increasingly concerned about traffic, infrstructure maintainence, and the environment; increased use of public transit could be way to address several of these challenges at once. I've become interested in understanding how cities can address these challenges, and began reading about urban planning a few years ago. Through reading what some long-time urban planners have to say, I'm beginning to see that if increasing public transit can help alleviate traffic, maintenance and environmental issues, increasing ridership is a multi-dimensional challenge in its own right. The way many places are organized, daily and individual car use is practically required due to the way that most activites and ammenities are spread over miles and metro areas and frequently aren't connected by transit networks.

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Intro Tour of Python Mapping Libraries¶

There are several libraries for making spatial visualizations in python. This post creates similar maps of baltimore neighborhoods and grocery stores using several libraries:

Static

• geopandas default mapping (matplotlib)

Interactive:

• Altair (Vega)
• Folium (Leaflet)

Each sections starts from a geopandas dataframes, and has subsections walking through:

1. Display Shape
2. Plot a Choropleth

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Bus Mapping and Basic GIS with GeoPandas¶

GeoPandas wraps several of Python GIS tools into a set of convenient functions for storing and operating on shapefiles as DataFrames, and makes working with shapefiles look similar to working with Pandas. In this demo, I'm going to demonstrate some of the basic geopandas functionality for getting started with working with shapefiles, and compute some summary statistics from the data.

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