Tag: code

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Data

OSMnx 2.0 Beta

OSMnx v2.0.0 is targeted for release later in 2024. This major release includes some breaking changes (including removing previously deprecated functionality) that are not backwards compatible with v1. See the migration guide and reference paper for details.

The first beta pre-release is out now, and testers are needed. If you use OSMnx, you can help test it by installing the latest pre-release. Create a virtual environment then run: pip install --pre osmnx

For more, check out the migration guide and reference paper.

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Tech

Outlook to Google Calendar Sync

Ah, the travails of academia. Like many universities, USC uses Microsoft Outlook as its email and calendar provider. This presents some integration challenges for those of us, like me, who use Google Calendar everywhere else in life. It’s effectively impossible to sync an Outlook Calendar with a Google Calendar, so I had to juggle between both when trying to schedule anything. Chaos ensues.

So, I created a script to perform an ongoing one-way sync from my USC Microsoft Office 365 Outlook calendar to my personal Google calendar, handling new, updated, and deleted events. I had to develop my own solution because Microsoft/Google inexplicably can’t get their own acts together. For example, you can publish your Outlook calendar’s ICS URL and add it to Google, but it only syncs roughly once per day so you miss any new appointments in the meantime. Microsoft Flow used to work (clumsily) for syncing, but even their official recipes are now broken. So I had to roll my own.

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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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Data

New Article on Computational Notebooks

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.

Categories
Tech

Getting Started with Python

Piedmont, California street network created in Python with OSMnx, networkx, matplotlibThis is a guide for absolute beginners to get started using Python. Since releasing OSMnx a few weeks ago, I’ve received a lot of comments from people who would love to try it out, but don’t know where to begin with Python. I’ll demonstrate how to get Python up and running on your system, how to install packages, and how to run code.

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