Tag: livability

Categories
Planning

Comparing US City Street Orientations

City street network grid orientations, order, disorder, entropy, rose plot, polar histogram made with Python, OSMnx, OpenStreetMap, matplotlib.This post is adapted from this research paper that you can read/cite for more info. It analyzes and visualizes 100 cities around the world.

“We say the cows laid out Boston. Well, there are worse surveyors.” –Ralph Waldo Emerson. In 1960, one hundred years after Emerson’s quote, Kevin Lynch published The Image of the City, his treatise on the legibility of urban patterns. How coherent is a city’s spatial organization? How do these patterns help or hinder urban navigation? I recently wrote about visualizing street orientations with Python and OSMnx. That is, how is a city’s street network oriented in terms of the streets’ compass bearings? How well does it adhere to a straightforward north-south-east-west layout? I wanted to revisit this by comparing 25 major US cities’ orientations (EDIT: by popular request, see also this follow-up comparing world cities):

City street network grid orientations, rose plot, polar histogram made with Python, OSMnx, OpenStreetMap, matplotlib. Atlanta, Boston, Buffalo, Charlotte, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Las Vegas, Los Angeles, Manhattan, New York, Miami, Minneapolis, Orlando, Philadelphia, Phoenix, Portland, Sacramento, San Francisco, Seattle, St Louis, Tampa, Washington DC.

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Categories
Data

OSMnx Features Round-Up

OSMnx is a Python package for quickly and easily downloading, modeling, analyzing, and visualizing street networks and other spatial data from OpenStreetMap. Here’s a quick round-up of recent updates to OSMnx. I’ll try to keep this up to date as a single reference source. A lot of new features have appeared in the past few months, and people have been asking about what’s new and what’s possible. You can:

  • Download and model street networks or other networked infrastructure anywhere in the world with a single line of code
  • Download any other spatial geometries, place boundaries, building footprints, or points of interest as a GeoDataFrame
  • Download by city name, polygon, bounding box, or point/address + network distance
  • Download drivable, walkable, bikeable, or all street networks
  • Download node elevations and calculate edge grades (inclines)
  • Impute missing speeds and calculate graph edge travel times
  • Simplify and correct the network’s topology to clean-up nodes and consolidate intersections
  • Fast map-matching of points, routes, or trajectories to nearest graph edges or nodes
  • Save networks to disk as shapefiles, geopackages, and GraphML
  • Save/load street network to/from a local .osm xml file
  • Conduct topological and spatial analyses to automatically calculate dozens of indicators
  • Calculate and visualize street bearings and orientations
  • Calculate and visualize shortest-path routes that minimize distance, travel time, elevation, etc
  • Visualize street networks as a static map or interactive leaflet web map
  • Visualize travel distance and travel time with isoline and isochrone maps
  • Plot figure-ground diagrams of street networks and building footprints
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Categories
Planning

Estimating Daytime Population Density

Check out the journal article about this project.

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

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Categories
Academia

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.

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Categories
Planning

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.

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Categories
Data

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

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Categories
Planning

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.

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Categories
Planning

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

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Categories
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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Categories
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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