Urban Street Network Centrality

Check out the journal article about OSMnx.

We can measure and visualize how “important” a node or an edge is in a network by calculating its centrality. Lots of flavors of centrality exist in network science, including closeness, betweenness, degree, eigenvector, and PageRank. Closeness centrality measures the average shortest path between each node in the network and every other node: more central nodes are closer to all other nodes. We can calculate this easily with OSMnx, as seen in this GitHub demo. For example, here is the node closeness centrality for Piedmont, California:

Urban street network graph node closeness betweenness centrality

Continue reading Urban Street Network Centrality

Estimating Daytime Population Density

I was recently asked: “how might someone figure out the local daytime population density across the Bay Area from public data?” My answer, in short, was that you really couldn’t accurately. But you could at least produce a coarse, biased estimate. Here’s how.

I examined the Bay Area’s tract-level daytime population density using three input data products: the 2010 TIGER/Line census tracts shapefile with DP1 attributes, the 2010 California LEHD LODES data, and the census bureau’s 2010 US states shapefile. I preferred the 2010 census demographic data to (more recent) ACS data because the ACS tract-level variables are five-year rolling averages. Given this, I preferred not to compare 2014 LODES data to 2010-2014 ACS data as the Bay Area experienced substantial housing, economic, and demographic upheaval over this interval – patterns obscured in the ACS rolling average. To avoid inconsistent comparison, I opted for more stale – but more accurate and comparable – data.

Map of the estimated daytime population density in the San Francisco Bay Area

Continue reading Estimating Daytime Population Density

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

Isochrone Maps with OSMnx + Python

Check out the journal article about OSMnx.

How far can you travel on foot in 15 minutes? Urban planners use isochrone maps to show spatial horizons (i.e., isolines) that are equal in time. Isochrones depict areas according to how long it takes to arrive there from some point. These visualizations are particularly useful in transportation planning as they reveal what places are accessible within a set of time horizons.

We can create isochrone maps for anywhere in the world automatically with Python and its OSMnx package:

OSMnx map of isochrone isolines in Berkeley California's street network

Continue reading Isochrone Maps with OSMnx + Python

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

Urban Form Figure-Ground Diagrams

Check out the journal article about OSMnx.

I previously demonstrated how to create figure-ground square-mile visualizations of urban street networks with OSMnx to consistently compare city patterns, design paradigms, and connectivity. OSMnx downloads, analyzes, and visualizes street networks from OpenStreetMap but it can also get building footprints. If we mash-up these building footprints with the street networks, we get a fascinating comparative window into urban form:

Figure-ground map of building footprints and street network in New York, San Francisco, Monrovia, and Port au Prince from OpenStreetMap data, created in Python with OSMnx

Continue reading Urban Form Figure-Ground Diagrams

Getting Started with Python

Piedmont, California street network created in Python with OSMnx, networkx, matplotlibThis is a guide for absolute beginners to get started using Python. Since releasing OSMnx a few weeks ago, I’ve received a lot of comments from people who would love to try it out, but don’t know where to begin with Python. I’ll demonstrate how to get Python up and running on your system, how to install packages, and how to run code.

Continue reading Getting Started with Python

Square-Mile Street Network Visualization

Check out the journal article about OSMnx.

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

Continue reading Square-Mile Street Network Visualization