Tag: projection

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Data

What’s New With OSMnx, Part 2

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.

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Categories
Data

Street Network Orientation

OSMnx is a Python package for easily downloading and analyzing street networks anywhere in the world. Among other analyses, we can use it to explore street network orientation. That is, what are the bearings and spatial orientations of the streets in the network? All of the code for this example is in this GitHub notebook. First we use OSMnx to download the street network of Moraga, California, a small town in the hills just east of Berkeley:

Moraga, California street network OSMnx OpenStreetMap Python

OSMnx automatically calculates all of the streets’ bearings. Specifically it calculates the compass bearing from each directed edge’s origin node u to its destination node v. Now we can visualize these bearings, binned together as a histogram to get a sense of the relative frequency of the streets’ spatial orientations. Or better yet, we can project that histogram as a polar plot to match the compass bearings:

Moraga, California street network orientation edge bearings polar plot OSMnx OpenStreetMap Python

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Categories
Academia

New Article: OSMnx in CEUS

My article “OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks” was published in the journal Computers, Environment and Urban Systems earlier this month. OSMnx is a Python package that lets you download a street network anywhere in the world at any scale with a single line of code, then analyze or visualize it with one more line of code.

OSMnx: Figure-ground diagrams of one square mile of each street network, from OpenStreetMap, made in Python with matplotlib, geopandas, and NetworkX

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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 dataCheck out the journal article about OSMnx.

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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Categories
Data

R-tree Spatial Indexing with Python

r-tree spatial index with python geopandas: Thumbnail of Walnut Creek, California city boundary and street intersections inside and outside city limits Check out the journal article about OSMnx, which implements this technique.

A spatial index such as R-tree can drastically speed up GIS operations like intersections and joins. Spatial indices are key features of spatial databases like PostGIS, but they’re also available for DIY coding in Python. I’ll introduce how R-trees work and how to use them in Python and its geopandas library. All of my code is in this notebook in this urban data science GitHub repo.

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Categories
Data

Mapping Everywhere I’ve Ever Been in My Life

I recently wrote about visualizing my Foursquare check-in history and mapping my Google location history, and it inspired me to mount a more substantial project: mapping everywhere I’ve ever been in my life (!!). I’ve got 4 years of Foursquare check-ins and Google location history data. For everything pre-smart phone, I typed up a simple spreadsheet of places I’d visited in the past and then geocoded it with the Google Maps API. All my Python and Leaflet code is available in this GitHub repo and is easy to re-purpose to visualize your own location history.

I’ll show the maps first, then run through the process I followed, below. First off, I used Python and matplotlib basemap to create this map of everywhere I’ve ever been:

Location History World Map, data from Foursquare and Google, made with Python matplotlib basemap

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