Tag: maps

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Planning

Square-Mile Street Network Visualization

Check out the journal article about OSMnx. All figures in this article come from this journal article, which you can read/cite for more.

The heart of Allan Jacobs’ classic book on street-level urban form and design, Great Streets, features dozens of hand-drawn figure-ground diagrams in the style of Nolli maps. Each depicts one square mile of a city’s street network. Drawing these cities at the same scale provides a revealing spatial objectivity in visually comparing their street networks and urban forms.

We can recreate these visualizations automatically with Python and the OSMnx package, which I developed as part of my dissertation. With OSMnx we can download a street network from OpenStreetMap for anywhere in the world in just one line of code. Here are the square-mile diagrams of Portland, San Francisco, Irvine, and Rome created and plotted automatically by OSMnx:

OSMnx: Figure-ground diagrams of one square mile of Portland, San Francisco, Irvine, and Rome shows the street network, urban form, and urban design in these cities

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Planning

OSMnx: Python for Street Networks

OSMnx: New York City urban street network visualized and analyzed with Python and OpenStreetMap dataIf you use OSMnx in your work, please cite the journal article.

OSMnx is a Python package to retrieve, model, analyze, and visualize street networks from OpenStreetMap. Users can download and model walkable, drivable, or bikeable urban networks with a single line of Python code, and then easily analyze and visualize them. You can just as easily download and work with amenities/points of interest, building footprints, elevation data, street bearings/orientations, and network routing. If you use OSMnx in your work, please download/cite the paper here.

In a single line of code, OSMnx lets you download, model, and visualize the street network for, say, Modena Italy:

import osmnx as ox
ox.plot_graph(ox.graph_from_place('Modena, Italy'))

OSMnx: Modena Italy networkx street network in Python from OpenStreetMap

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Categories
Data

R-tree Spatial Indexing with Python

r-tree spatial index with python geopandas: Thumbnail of Walnut Creek, California city boundary and street intersections inside and outside city limits Check out the journal article about OSMnx, which implements this technique.

A spatial index such as R-tree can drastically speed up GIS operations like intersections and joins. Spatial indices are key features of spatial databases like PostGIS, but they’re also available for DIY coding in Python. I’ll introduce how R-trees work and how to use them in Python and its geopandas library. All of my code is in this notebook in this urban data science GitHub repo.

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Planning

Craigslist and U.S. Rental Housing Markets

This is a summary of our JPER journal article (available here) about Craigslist rental listings’ insights into U.S. housing markets.

Small map of 1.5 million Craigslist rental listings in the contiguous U.S., divided into quintiles by each listing's rent per square footRentals make up a significant portion of the U.S. housing market, but much of this market activity is poorly understood due to its informal characteristics and historically minimal data trail. The UC Berkeley Urban Analytics Lab collected, validated, and analyzed 11 million Craigslist rental listings to discover fine-grained patterns across metropolitan housing markets in the United States. I’ll summarize our findings below and explain the methodology at the bottom.

But first, 4 key takeaways:

  1. There are incredibly few rental units below fair market rent in the hottest housing markets. Some metro areas like New York and Boston have only single-digit percentages of Craigslist rental listings below fair market rent. That’s really low.
  2. This problem doesn’t exclusively affect the poor: the share of its income that the typical household would spend on the typical rent in cities like New York and San Francisco exceeds the threshold for “rent burden.”
  3. Rents are more “compressed” in soft markets. For example, in Detroit, most of the listed units are concentrated within a very narrow band of rent/ft² values, but in San Francisco rents are much more dispersed. Housing vouchers may end up working very differently in high-cost vs low-cost areas.
  4. Craigslist listings correspond reasonably well with Dept of Housing and Urban Development (HUD) estimates, but provide up-to-date data including unit characteristics, from neighborhood to national scales. For example, we can see how rents are changing, neighborhood by neighborhood, in San Francisco in a given month.
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Planning

How to Visualize Urban Accessibility and Walkability

Tools like WalkScore visualize how “walkable” a neighborhood is in terms of access to different amenities like parks, schools, or restaurants. It’s easy to create accessibility visualizations like these ad hoc with Python and its pandana library. Pandana (pandas for network analysis – developed by Fletcher Foti during his dissertation research here at UC Berkeley) performs fast accessibility queries over a network. I’ll demonstrate how to use it to visualize urban walkability. My code is in these IPython notebooks in this urban data science course GitHub repo.

First I give pandana a bounding box around Berkeley/Oakland in the East Bay of the San Francisco Bay Area. Then I load the street network and amenities from OpenStreetMap. In this example I’ll look at accessibility to restaurants, bars, and schools. But, you can create any basket of amenities that you are interested in – basically visualizing a personalized “AnythingScore” instead of a generic WalkScore for everyone. Finally I calculate and plot the distance from each node in the network to the nearest amenity:

Berkeley Oakland California street network walking accessibility and walkability

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Data

Mapping Everywhere I’ve Ever Been in My Life

I recently wrote about visualizing my Foursquare check-in history and mapping my Google location history, and it inspired me to mount a more substantial project: mapping everywhere I’ve ever been in my life (!!). I’ve got 4 years of Foursquare check-ins and Google location history data. For everything pre-smart phone, I typed up a simple spreadsheet of places I’d visited in the past and then geocoded it with the Google Maps API. All my Python and Leaflet code is available in this GitHub repo and is easy to re-purpose to visualize your own location history.

I’ll show the maps first, then run through the process I followed, below. First off, I used Python and matplotlib basemap to create this map of everywhere I’ve ever been:

Location History World Map, data from Foursquare and Google, made with Python matplotlib basemap

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Categories
Data

Mapping Your Google Location History with Python

Small map of my Google location history data in the San Francisco Bay Area, 2012-2016I recently wrote about visualizing my Foursquare check-in history and it inspired me to map my entire Google location history data – about 1.2 million GPS coordinates from my Android phone between 2012 and 2016. I used Python and its pandas, matplotlib, and basemap libraries. The Python code is available in this notebook in this GitHub repo, and it’s simple to re-use to visualize your own location history.

Just download your JSON file from Google then run the code. First I load the JSON file and parse the latitude, longitude, and timestamp with pandas. Then I map my worldwide data set:

Map of my Google location history data worldwide, 2012-2016

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Categories
Data

Analyzing Last.fm Listening History

Last.fm is a web site that tracks your music listening history across devices (computer, phone, iPod, etc) and services (Spotify, iTunes, Google Play, etc). I’ve been using Last.fm for nearly 10 years now, and my tracked listening history goes back even further when you consider all my pre-existing iTunes play counts that I scrobbled (ie, submitted to my Last.fm database) when I joined Last.fm.

Using Python, pandas, matplotlib, and leaflet, I downloaded my listening history from Last.fm’s API, analyzed and visualized the data, downloaded full artist details from the Musicbrainz API, then geocoded and mapped all the artists I’ve played. All of my code used to do this is available in this GitHub repo, and is easy to re-purpose for exploring your own Last.fm history. All you need is an API key.

Last.fm artists played the most

First I visualized my most-played artists, above. Across the dataset, I have 279,769 scrobbles (aka, song plays). I’ve listened to 26,761 different artists and 66,377 different songs across 38,026 different albums from when I first started using iTunes circa 2005 through the present day. This includes pretty close to every song I’ve played on anything other than vinyl during that time.

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Data

Visualize Foursquare Location History

I started using Foursquare at the end of 2012 and kept with it even after it became the pointless muck that is Swarm. Since I’ve now got 4 years of location history (ie, check-ins) data, I decided to visualize and map it with Python, matplotlib, and basemap. The code is available in this GitHub repo. It’s easy to re-purpose to visualize your own check-in history: you just need to plug in your Foursquare OAuth token then run the notebook.

First the notebook downloads all my check-ins from the Foursquare API. Then I mapped all of them, using matplotlib basemap.

Map of Foursquare Swarm check-in location history

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Data

America’s College Football Stadiums

stadiums-fbs-conf-barAlso check out this follow-up analysis of stadium attendance.

The 2016 college football championship game between Clemson and Alabama was held at University of Phoenix Stadium, where the NFL’s Arizona Cardinals play. Interestingly, this NFL (ironic, given its name) stadium is considerably smaller than the home stadiums of either Clemson or Alabama. In fact every NFL stadium is considerably smaller than the largest college stadiums. Outside of North Korea, the 8 largest stadiums in the world are college football stadiums, and the 15 largest college football stadiums are larger than any NFL stadium.

Americans are obsessed with college football, but how much is too much? Today most athletic departments are subsidized by their schools. Public universities increased their annual football spending by $1.8 billion between 2009-2013 while racking up huge debts to finance stadiums with little chance of profit. This interactive map shows each NCAA Division I college football team’s home stadium: collectively they seat 8.5 million people. Click any point for details about stadium capacity and year built:

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