Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network

2017

Preprint

233

335

a b s t r a c t a r t i c l e i n f oUrban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon.

Section: Street Network Analysismentioning

confidence: 99%

OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks

2017

Preprint

233

335

“…Finally, PageRank -the algorithm Google uses to rank web pages -is a variant of network centrality, namely eigenvector centrality (Brin and Page 1998). PageRank ranks nodes based on the structure of incoming links and the rank of the source node, and may also be applied to street networks (Agryzkov et al 2012;Chin and Wen 2015).…”

Section: Street Network Analysismentioning

confidence: 99%

OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks

Computers, Environment and Urban Systems

2017

1,089

341

Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon. transportation (e.g., Marshall and Garrick 2010;Parthasarathi et al. 2013), and others on the topology, complexity, and resilience of street networks (e.g., Jiang and Claramunt 2004;). This article argues that current limitations of data availability, consistency, and technology have made researchers' work gratuitously difficult. In turn, this empirical literature often suffers from four shortcomings which this article examines: small sample sizes, excessive network simplification, difficult reproducibility, and the lack of consistent, easy-to-use research tools. These shortcomings are by no means fatal, but their presence limits the scalability, generalizability, and interpretability of empirical street network research.To address these challenges, this article presents OSMnx, a new tool that easily downloads and analyzes street networks for anywhere in the world. OSMnx contributes five primary capabilities for researchers and practitioners. First, it enables automated and on-demand downloading of political boundary geometries, building footprints, and elevations. Second, it can automate and customize the downloading of street networks from OpenStreetMap and construct them into multidigraphs. Third, it can correct and simplify network topology. Fourth, it can save/load street networks to/from disk in various file formats. Fifth and finally, OSMnx has built-in functions to analyze street networks, calculate routes, project and visualize networks, and quickly and consistently calculate various metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network.This article is organized as follows. First, it intr...

“…These measures are described in Table 1 and extended technical definitions and mathematical derivations can be found in [3,16,32,45,48,51,73,[91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106]. Note that we distinguish between "edges" and physical "streets" in certain instances to not double-count bidirectional streets with reciprocal edges pointing in both directions.…”

Section: Graph Analysismentioning

confidence: 99%

Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood

2019

Urban Science

OpenStreetMap provides a valuable crowd-sourced database of raw geospatial data for constructing models of urban street networks for scientific analysis. This paper reports results from a research 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. It constructed nonplanar directed multigraphs for each and analyzed their structural and morphological characteristics. The resulting 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 GIS shapefiles that can be immediately loaded and analyzed in standard tools such as ArcGIS, QGIS, NetworkX, graph-tool, igraph, or Gephi. The repository also contains measures of each network’s metric and topological characteristics common in urban design, transportation planning, civil engineering, and network science. No other such dataset exists. These data offer researchers and practitioners a new ability to quickly and easily conduct graph-theoretic circulation network analysis anywhere in the U.S. using standard, free, open-source tools.