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:
This 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.
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 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'))
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.
A few months ago, I wrote about the large investments that U.S. universities are making in their football stadiums. This also included a visual analysis of stadium capacity around the country. 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.
I received a few comments interested in further analysis of the actual attendance of games held in these stadiums. While capacity is interesting because it represents an expectation and sustained investment by the school, attendance represents the utilization of that investment. My stadium capacity data covered every NCAA division I football stadium in the U.S. as of the 2015 college football season. So, I downloaded the NCAA’s 2015 home game attendance data to compare. My data, code, and analysis are in this GitHub repo. First, I visualized the FBS attendance figures themselves:
This is a summary of our JPER journal article (available here) about Craigslist rental listings’ insights into U.S. housing markets.
Rentals 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:
- 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.
- 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.”
- 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.
- 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.
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:
The fall semester begins next week at UC Berkeley. For the third year in a row, Paul Waddell and I will be teaching CP255: Urban Informatics and Visualization.
This masters-level course trains students to analyze urban data, develop indicators, conduct spatial analyses, create data visualizations, and build interactive web maps. To do this, we use the Python programming language, open source analysis and visualization tools, and public data.
This course is designed to provide future city planners with a toolkit of technical skills for quantitative problem solving. We don’t require any prior programming experience – we teach this from the ground up – but we do expect prior knowledge of basic statistics and GIS.
Our teaching materials, including IPython Notebooks, tutorials, and guides are available in this GitHub repo, updated as the semester progresses.
Western Europe gets all the attention, but that means it also gets all the tourists. Here are some of my favorite old cities that I’ve visited on the other side of the continent, along with a few photos I took while there. Granted, a few of these places are now squarely on the backpacker circuit, but many remain underexplored. What they all share is an incredible, exhilarating sense of urbanism — old and new.
Eastern Europe itself is hard to define. Competing designations might include only the former Soviet states, or all the formerly communist European nations. Others might separate a limited Eastern Europe out from Central and Southeastern Europe. Here I will play fast and loose with the geographic boundaries: these are just cities somewhere vaguely toward the eastern side of the continent. Apologies to any readers whose country is usually considered a part of Central or Southern Europe.
First up: Mostar. A small city in the south of Bosnia and Herzegovina, Mostar is most famous for its medieval Ottoman center and its Old Bridge, or Stari Most: