Tag: visualization

Off the Grid at TRB

I am presenting my ongoing research into the recent evolution of American street network planning and design at the annual meeting of the Transportation Research Board in Washington DC on January 13. This presentation asks the question: how has street network design changed over time, especially in recent years? I analyze the street networks of every US census tract and estimate each’s vintage.

Street network designs grew more disconnected, coarse-grained, and circuitous over the 20th century… but the 21st century has witnessed a promising rebound back toward more traditional, dense, and interconnected grids. Higher griddedness is associated with less car ownership, even when controlling for related socioeconomic, topographical, and other urban factors.

Update: the paper has been published in JAPA.

Urban

Big Data in Urban Morphology

Post author By gboeing
Post date 2019-11-04
1 Comment on Big Data in Urban Morphology

My new article “Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology” has been published in the International Journal of Information Management (download free PDF). It builds on recent work by Crooks et al, presenting workflows to integrate data-driven and narrative approaches to urban morphology in today’s era of ubiquitous urban big data. It situates this theoretically in the visual culture of planning to present a visualization-mediated interpretative process of data-driven urban morphology, focusing on transportation infrastructure via OSMnx.

Urban

Urban Street Network Orientation

Post author By gboeing
Post date 2019-09-09
27 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

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

Data

Street Network Analysis in a Docker Container

Post author By gboeing
Post date 2019-02-18
3 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:

Academia

Spring Teaching

Happy new year! In the spring semester I’ll be teaching two new courses: Big Data for Cities and Advanced Spatial Analysis of Urban Systems. The former serves as a sort of gateway course to Northeastern’s urban informatics master’s program, introducing students to urban theories and scientific methods of analyzing urban data. The latter introduces advanced students to a computational workflow of spatial analysis and statistics with Python, PostGIS, and other open-source tools. I’ll be creating my lectures as Jupyter notebooks and will share a GitHub link soon when they’re all together.

Tags academia, data, data science, geospatial, gis, modeling, science, spatial analysis, teaching, urban informatics, urban planning, visualization

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.

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.

Planning

City Street Orientations around the World

Post author By gboeing
Post date 2018-07-11
92 Comments on City Street Orientations around the World

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.

By popular request, this is a quick follow-up to this post comparing the orientation of streets in 25 US cities using Python and OSMnx. Here are 25 more cities around the world:

Tags bangkok, barcelona, beijing, berlin, budapest, cairo, city, complexity, data, data science, delhi, design, dubai, geocoding, geopandas, geospatial, gis, glasgow, hong kong, lagos, land use, livability, london, madrid, maps, matplotlib, melbourne, mexico city, modeling, moscow, mumbai, munich, neighborhood, networks, osmnx, paris, planning, python, rio de janeiro, rome, science, seoul, street-network, streets, sydney, tehran, tokyo, toronto, urban, urban design, urban planning, venice, visualization, warsaw

Planning

Comparing US City Street Orientations

Post author By gboeing
Post date 2018-07-10
174 Comments on Comparing US City Street Orientations

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):