New Article: Craigslist Housing Markets in JPER

Our article “New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings” is finally appearing in print in the Journal of Planning Education and Research‘s forthcoming winter issue. We collected, validated, and analyzed 11 million Craigslist rental listings to discover fine-grained patterns across metropolitan housing markets in the United States.

Map of 1.5 million Craigslist rental listings in the contiguous U.S., divided into quintiles by each listing's rent per square foot. Published in JPER: the Journal of Planning Education and Research.

Continue reading New Article: Craigslist Housing Markets in JPER

New Article: OSMnx in CEUS

My article “OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks” was published in the journal Computers, Environment and Urban Systems earlier this month. OSMnx is a Python package that lets you download a street network anywhere in the world at any scale with a single line of code, then analyze or visualize it with one more line of code.

OSMnx: Figure-ground diagrams of one square mile of each street network, from OpenStreetMap, made in Python with matplotlib, geopandas, and NetworkX

Continue reading New Article: OSMnx in CEUS

Describing Cities with Computer Vision

What does artificial intelligence see when it looks at your city? I recently created a Twitter bot in Python called CityDescriber that takes popular photos of cities from Reddit and describes them using Microsoft’s computer vision AI. The bot typically does pretty well with straightforward images of city skylines and street scenes:

Some are even kind of wryly poetic, such as this description of Los Angeles: Continue reading Describing Cities with Computer Vision

Craft Beer, Urban Planning, and Gentrification

I co-authored a chapter in the new book Untapped: Exploring the Cultural Dimensions of Craft Beer with the estimable Jesus Barajas and Julie Wartell. Our chapter is titled “Neighborhood Change, One Pint at a Time” and it explores the relationship between craft breweries, urban planning and policy, and gentrification.

We found that many cities have changed their zoning codes recently (and even offer subsidies) to make it easier to establish craft breweries and brewpubs, with the goal of economic development. There’s a strong narrative (and anecdotal evidence) of craft breweries “revitalizing” neglected neighborhoods. New breweries often seek inexpensive industrial spaces in close proximity to urban centers and residential districts. In turn, they serve as anchor institutions that appeal to whiter, wealthier, and more educated demographic groups. Many craft brewers explicitly see themselves as agents of neighborhood revitalization and “committed urbanists.” The brewers we interviewed uniformly stated that neighborhood character was an important or even primary reason for their location choice, and many referred to themselves as pioneers and catalysts in neglected historic neighborhoods.

Locations of craft breweries and brew pubs across the United States. California, Colorado, Oregon, Washington, Michigan have the most craft beer locations.

Continue reading Craft Beer, Urban Planning, and Gentrification

OSMnx and Street Network Elevation Data

Check out the journal article about OSMnx.

OSMnx can now download street network elevation data for anywhere in the world. In one line of code it downloads the elevation in meters of each network node, and in one more line of code it can calculate every street (i.e., edge) grade. Here is the complete street network of San Francisco, California, with nodes colored according to their elevation:

OSMnx street network elevation data for San Francisco, California to calculate street grade and steepness

Continue reading OSMnx and Street Network Elevation Data

Urban Form Analysis with OpenStreetMap Data

Figure-ground diagrams of urban form and building footprints in London, Paris, Venice, and Brasilia depict modernism's inversion of traditional spatial orderCheck out the journal article about OSMnx. This is a summary of some of my recent research on making OpenStreetMap data analysis easy for urban planners. It was also published on the ACSP blog.

OpenStreetMap – a collaborative worldwide mapping project inspired by Wikipedia – has emerged in recent years as a major player both for mapping and acquiring urban spatial data. Though coverage varies somewhat worldwide, its data are of high quality and compare favorably to CIA World Factbook estimates and US Census TIGER/Line data. OpenStreetMap imported the TIGER/Line roads in 2007 and since then its community has made numerous corrections and improvements. In fact, many of these additions go beyond TIGER/Line’s scope, including for example passageways between buildings, footpaths through parks, bike routes, and detailed feature attributes such as finer-grained street classifiers, speed limits, etc.

This presents a fantastic data source to help answer urban planning questions, but OpenStreetMap’s data has been somewhat difficult to work with due to its Byzantine query language and coarse-grained bulk extracts provided by third parties. As part of my dissertation, I developed a tool called OSMnx that allows researchers to download street networks and building footprints for any city name, address, or polygon in the world, then analyze and visualize them. OSMnx democratizes these data and methods to help technical and non-technical planners and researchers use OpenStreetMap data to study urban form, circulation networks, accessibility, and resilience.

Continue reading Urban Form Analysis with OpenStreetMap Data

Animating the Lorenz Attractor with Python

Edward Lorenz, the father of chaos theory, once described chaos as “when the present determines the future, but the approximate present does not approximately determine the future.”

Lorenz first discovered chaos by accident while developing a simple mathematical model of atmospheric convection, using three ordinary differential equations. He found that nearly indistinguishable initial conditions could produce completely divergent outcomes, rendering weather prediction impossible beyond a time horizon of about a fortnight.

Lorenz system attractor animated GIF created with Python matplotlib scipy numpy PIL

Continue reading Animating the Lorenz Attractor with Python

OSMnx: Python for Street Networks

OSMnx: New York City urban street network visualized and analyzed with Python and OpenStreetMap dataCheck out the journal article about OSMnx.

OSMnx is a Python package for downloading administrative boundary shapes and street networks from OpenStreetMap. It allows you to easily construct, project, visualize, and analyze complex street networks in Python with NetworkX. You can get a city’s or neighborhood’s walking, driving, or biking network with a single line of Python code. Then you can simply visualize cul-de-sacs or one-way streets, plot shortest-path routes, or calculate stats like intersection density, average node connectivity, or betweenness centrality. You can download/cite the paper here.

In a single line of code, OSMnx lets you download, construct, 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 Continue reading OSMnx: Python for Street Networks

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.

Continue reading R-tree Spatial Indexing with Python

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.

Continue reading Craigslist and U.S. Rental Housing Markets