Tag: city

Urban

Urban Street Network Orientation

Post author By gboeing
Post date 2019-09-09
26 Comments on Urban Street Network Orientation

My new article, Urban Spatial Order: Street Network Orientation, Configuration, and Entropy, has just been published in one of my favorite journals: Applied Network Science (download free PDF). This study explores the spatial signatures of urban evolution and central planning. It examines street network orientation, connectivity, granularity, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx for modeling and visualization:

So, who’s got a grid and who doesn’t? Each of the cities above is represented by a polar histogram (aka rose diagram) depicting how its streets orient. Each bar’s direction represents the compass bearings of the streets (in that histogram bin) and its length represents the relative frequency of streets with those bearings. The cities above are in alphabetical order. Here they are again, re-sorted from most-ordered/gridded city (Chicago) to most-disordered (Charlotte):

Tags academia, amsterdam, athens, atlanta, baghdad, baltimore, bangkok, barcelona, beijing, beirut, berlin, bogota, boston, budapest, buenos aires, cairo, cape town, caracas, casablanca, charlotte, chicago, city, cleveland, clustering, complex systems, complexity, connectivity, copenhagen, dallas, damascus, data, data science, dc, denver, design, detroit, dubai, dublin, entropy, geopandas, geospatial, gis, glasgow, hanoi, havana, helsinki, history, hong kong, honolulu, houston, istanbul, jakarta, jerusalem, johannesburg, journal article, kabul, karachi, kathmandu, kiev, kyoto, lagos, land use, las vegas, lima, lisbon, london, los angeles, madrid, manhattan, manila, maps, math, matplotlib, melbourne, mexico city, miami, minneapolis, modeling, mogadishu, montreal, moscow, mumbai, munich, nairobi, networks, new delhi, new orleans, new york, orientation, orlando, osaka, oslo, osmnx, paris, philadelphia, phnom penh, phoenix, pittsburgh, planning, polar histogram, port au prince, portland, prague, pyongyang, python, reykjavik, rio de janeiro, rome, san francisco, sao paulo, sarajevo, science, scikit-learn, seattle, seoul, shanghai, singapore, spatial analysis, spatial networks, st louis, stockholm, street-network, sydney, taipei, tehran, tokyo, toronto, ulaanbaatar, urban, urban design, urban planning, vancouver, venice, vienna, visualization, warsaw, washington

Planning

Online Rental Housing Market Representation

Post author By gboeing
Post date 2019-08-29
No Comments on Online Rental Housing Market Representation

My article, Online Rental Housing Market Representation and the Digital Reproduction of Urban Inequality, has just been published in Environment and Planning A (download free PDF). It explores the representation of different communities in online rental listings from two perspectives: 1) how might biases in representativeness impact housing planners’ knowledge of rental markets, and 2) how might information inequality impact residential mobility, community legibility, gentrification, and housing voucher utilization?

Data

New Article in Frontiers in Neurology

Post author By gboeing
Post date 2019-07-31
No Comments on New Article in Frontiers in Neurology

I recently teamed up with an international group of public health researchers and spatial analysts to co-author an article, An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services, that has been accepted for publication at Frontiers in Neurology (download free PDF).

Tags city, clustering, data, data science, geospatial, gis, land use, maps, modeling, neighborhood, networks, osmnx, python, science, spatial analysis, street-network, tutorial, urban, urban planning, visualization

Academia

AAG Transactions in GIS Plenary

Post author By gboeing
Post date 2019-04-05
1 Comment on AAG Transactions in GIS Plenary

I am giving the Transactions in GIS plenary address at the AAG conference this afternoon. I’ll be reflecting on urban science, spatial networks, and tool-building in academia, focusing on OSMnx. A paper will be forthcoming soon, but in the meantime, for any interested plenary session attendees or other folks, here are a few links to more info and related resources:

Getting started

What is OSMnx? What does it do? Here’s a succinct overview.

The easiest way to get started with street network modeling and analysis in OSMnx is with this docker image and these example/tutorial Jupyter notebooks. The OSMnx software documentation is available here and this journal article introduces it more formally.

Data

US Street Network Models and Measures

Post author By gboeing
Post date 2019-03-12
2 Comments on US Street Network Models and Measures

My new article, “Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood” has been published in Urban Science. This paper reports results from a broader project that collected raw street network data from OpenStreetMap using the Python-based OSMnx software for every U.S. city and town, county, urbanized area, census tract, and Zillow-defined neighborhood boundary. It constructed nonplanar directed multigraphs for each and analyzed their structural and morphological characteristics.

The resulting public data repository contains over 110,000 processed, cleaned street network graphs (which in turn comprise over 55 million nodes and over 137 million edges) at various scales—comprehensively covering the entire U.S.—archived as reusable open-source GraphML files, node/edge lists, and ESRI shapefiles that can be immediately loaded and analyzed in standard tools such as ArcGIS, QGIS, NetworkX, graph-tool, igraph, or Gephi.

Data

Street Network Analysis in a Docker Container

Post author By gboeing
Post date 2019-02-18
2 Comments on Street Network Analysis in a Docker Container

Containerization is the way of the ~~future~~ present. I’ve heard feedback from some folks over the past few months who would like to play around with OSMnx for street network analysis, transport modeling, and urban design—but can’t because they can’t install Python and its data science stack on their computers. Furthermore, it would be nice to have a consistent reference environment to deploy on AWS or elsewhere in the cloud.

So, I’ve created a docker image containing OSMnx, Jupyter, and the rest of the Python geospatial data science stack, available on docker hub alongside additional usage instructions. If you’re starting from scratch, you can get started in four simple steps:

Planning

New Chapter: Street Network Morphology

Post author By gboeing
Post date 2019-01-31
1 Comment on New Chapter: Street Network Morphology

My chapter The Morphology and Circuity of Walkable and Drivable Street Networks is now in-press for publication in the forthcoming book The Mathematics of Urban Morphology (download free PDF). The book integrates recent theoretical and empirical work from urban planning, geography, sociology, architecture, economics, and mathematics around the theme of how we model and understand the urban form’s physical patterns and shaping processes. Fellow authors in this volume include Michael Batty, Diane Davis, Keith Clarke, Bin Jiang, Kay Axhausen, Carlo Ratti, and Stephen Marshall. The book itself can be purchased here.

Planning

New Article: Planar Models of Street Networks

Post author By gboeing
Post date 2018-11-16
2 Comments on New Article: Planar Models of Street Networks

My article, “Planarity and Street Network Representation in Urban Form Analysis,” was recently published in Environment and Planning B: Urban Analytics and City Science. Models of street networks underlie research in urban travel behavior, accessibility, design patterns, and morphology. These models are commonly defined as planar, meaning they can be represented in two dimensions without any underpasses or overpasses. However, real-world urban street networks exist in three-dimensional space and frequently feature grade separation such as bridges and tunnels: planar simplifications can be useful but they also impact the results of real-world street network analysis. This study measures the nonplanarity of drivable and walkable street networks in the centers of 50 cities worldwide, then examines the variation of nonplanarity across a single city. While some street networks are approximately planar, I empirically quantify how planar models can inconsistently but drastically misrepresent intersection density, street lengths, routing, and connectivity.

Urban

New Article: Complexity in Urban Form and Design

Post author By gboeing
Post date 2018-09-28
1 Comment on New Article: Complexity in Urban Form and Design

My article, Measuring the Complexity of Urban Form and Design, is now in-press for publication at Urban Design International (download free PDF). Cities are complex systems composed of many human agents interacting in physical urban space. This paper develops a typology of measures and indicators for assessing the physical complexity of the built environment at the scale of urban design. It extends quantitative measures from city planning, network science, ecosystems studies, fractal geometry, statistical physics, and information theory to the analysis of urban form and qualitative human experience.

$The Mandelbrot set, a mathematical fractal. Venice's fractal urban form and fabric. The Eiffel Tower's fractal architecture in Paris.$

Planning

New Article: Urban Street Networks in EP-B

Post author By gboeing
Post date 2018-08-28
1 Comment on New Article: Urban Street Networks in EP-B

My article, “A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood,” was recently published in Environment and Planning B: Urban Analytics and City Science. This study uses OSMnx to download and analyze 27,000 street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales – namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs.

These 27,000 street networks as well as their measures have been shared in a free public repository at the Harvard Dataverse for anyone to re-purpose. This study’s empirical findings emphasize measures relevant to graph theory, transportation, urban design, and morphology, such as structure, connectedness, density, centrality, and resilience. It uses graph Maximum Betweenness Centrality and Average Node Connectivity to examine how “resilient” a street network is, in terms of how reliant it is on important nodes and how easy it is to disconnect it.