Category: Data

Data

Urban Analytics: History, Trajectory and Critique

Post author By gboeing
Post date 2022-12-01
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I have a new chapter titled “Urban Analytics: History, Trajectory and Critique,” co-authored with Mike Batty, Shan Jiang, and Lisa Schweitzer, now published in the Handbook of Spatial Analysis in the Social Sciences, edited by Serge Rey and Rachel Franklin.

From our abstract:

Urban analytics combines spatial analysis, statistics, computer science, and urban planning to understand and shape city futures. While it promises better policymaking insights, concerns exist around its epistemological scope and impacts on privacy, ethics, and social control. This chapter reflects on the history and trajectory of urban analytics as a scholarly and professional discipline. In particular, it considers the direction in which this field is going and whether it improves our collective and individual welfare. It first introduces early theories, models, and deductive methods from which the field originated before shifting toward induction. It then explores urban network analytics that enrich traditional representations of spatial interaction and structure. Next it discusses urban applications of spatiotemporal big data and machine learning. Finally, it argues that privacy and ethical concerns are too often ignored as ubiquitous monitoring and analytics can empower social repression. It concludes with a call for a more critical urban analytics that recognizes its epistemological limits, emphasizes human dignity, and learns from and supports marginalized communities.

For more, check out the chapter.

Data

OSMnx v1.2 Released

OSMnx v1.2.0 has been released. It includes several new features, bug fixes, and performance enhancements. I encourage you to upgrade and take advantage of everything new. For more information, check out the OSMnx documentation and usage examples for demonstrations of all you can do.

Data

OSMnx v1.1 Released

OSMnx v1.1.0 has been released. It includes several new features, bug fixes, and performance enhancements. I encourage you to upgrade and take advantage of everything new. I’ll summarize some of the key improvements in this post.

Data

Worldwide Street Network Models and Indicators

Post author By gboeing
Post date 2021-03-09
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My article, “Street Network Models and Indicators for Every Urban Area in the World” has been published by Geographical Analysis. This project was a massive undertaking and I’m excited to share it. As you might guess from the title, I modeled and analyzed the street network of each urban area in the world then deposited all the source code and models and indicators in open repositories for public reuse. The article also includes a high-level analysis of urban street network form across the world.

Cities worldwide exhibit a variety of street patterns and configurations that shape human mobility, equity, health, and livelihoods. Using boundaries derived from the Global Human Settlement Layer, I modeled and analyzed the street networks of every urban area in the world using OSMnx and OpenStreetMap raw data. In total, I modeled over 160 million street network nodes and over 320 million edges across 8,914 urban areas in 178 countries. I attached node elevations and street grades to every node/edge in the final models. All the final models were topologically simplified such that nodes represent intersections and dead-ends, and edges represent the street segments linking them.

Data

GIS and Computational Notebooks

Post author By gboeing
Post date 2021-02-11
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I have a new chapter “GIS and Computational Notebooks,” co-authored with Dani Arribas-Bel, out now in The Geographic Information Science & Technology Body of Knowledge. Want to make your spatial analyses more reproducible, portable, and well-documented? Our chapter is a short, gentle intro to using code and notebooks for modern GIS work.

Science and analytics both struggle with reproducibility, documentation, and portability. But GIS in both research and practice particularly suffers from these problems due to some of its unique characteristics. Our chapter discusses this challenge and its urgency for building better and more actionable knowledge from geospatial data. Then we introduce an emerging solution, the computational notebook, using Jupyter as our central example to illustrate what it does and how it works.

Data

OSMnx 1.0 Is Here

Happy new year! After five years of development and over 2,000 code commits from dozens of contributors, OSMnx v1.0 has officially been released. This has been a long labor of love and I’m thrilled to see it reach this milestone.

Much has changed in recent months with new features added and a few things deprecated. Most of this development occurred in a major overhaul over the summer, which I covered at the time in three previous posts. Among these dozens of enhancements were major speed and efficiency improvements throughout the package, better visualization, a new geometries module for retrieving any geospatial objects from OSM, topological intersection consolidation, and much more. I encourage you to read those posts to familiarize yourself with what’s new.

Data

OSMnx Summer Wrap-Up

OSMnx underwent a major overhaul this summer, with the development of several new features, improvements, and optimizations. This project concluded yesterday with the release of v0.16.0.

This post briefly summarizes what’s changed since the previous mid-summer updates. It covers the new k shortest paths solver, auto-selecting the first polygon when geocoding, better conversion of graph types, and the new geometries module that lets you flexibly download any OSM geospatial objects as a geopandas GeoDataFrame.

Data

What’s New With OSMnx, Part 2

Post author By gboeing
Post date 2020-07-08
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This is a follow-up to last month’s post discussing the many new features, improvements, and optimizations made to OSMnx this summer. As this major improvement project now draws to a close, I will summarize what’s new(er) here. Long story short: there are a bunch of new features and everything in the package has been streamlined and optimized to be easier to use, faster, and more memory efficient.

First off, if you haven’t already, read the previous post about new features including topological intersection consolidation, automatic max speed imputation and travel time calculation, generalized points-of-interest queries, querying OSM by date, and API streamlining. This post covers new changes since then, including improved visualization and plotting, improved graph simplification, the new geocoder module, and other miscellaneous improvements.

Data

What’s New with OSMnx, Part 1

Post author By gboeing
Post date 2020-06-02
11 Comments on What’s New with OSMnx, Part 1

There have been some major changes to OSMnx in the past couple months. I’ll review them briefly here, demonstrate some usage examples, then reflect on a couple upcoming improvements on the horizon. First, what’s new:

new consolidate_intersections function with topological option
new speed module to impute missing street speeds and calculate travel times for all edges
generalized POIs module to query with a flexible tags dict
you can now query OSM by date
you can now save graph as a geopackage file
clean up and streamline the OSMnx API

Tags data, data science, geospatial, gis, maps, modeling, networks, osmnx, planning, python, science, spatial analysis, street-network, transportation, tutorial, urban, urban design, urban planning, visualization

Data

New Article on Computational Notebooks

Post author By gboeing
Post date 2020-05-08
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I have a new article out in Region: Journal of the European Regional Science Association, “Urban Street Network Analysis in a Computational Notebook.” It reflects on the use of Jupyter notebooks in applied data science research, pedagogy, and practice, and it uses the OSMnx examples repository as an example.

From the abstract:

Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively conduct analytics and disseminate reproducible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tutorials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download urban data and model street networks for various study sites, compute network indicators, visualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks help introduce methods to new users and help researchers reach broader audiences interested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban planning, analytics, and related geocomputation disciplines should continue into the future.

For more, check out the article.