Dillon Mahmoudi
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Digital Growth Machine Bibliography
On 1 March 2022, we moderated a series of sessions at the Annual Meeting of the AAG on the Digital Growth Machine. Each session yielded a number of suggested “further readings” which we collected here. As promised, here are the citations culled from the chat. Special thanks to Luis Alvarez León and Jovanna Rosen, who…
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Digital Growth Machine Sessions at the AAG
Feeding the Digital Growth Machine: Data, the city, and the urban process under digital capitalism Sponsored by the Digital Geography Specialty Group and the Urban Geography Specialty Group Organizers: Joe Gallagher (UMBC), Alicia Sabatino (UMBC), Evan Thomas (UMBC), Dillon Mahmoudi (UMBC), and John G Stehlin (UNCG) The growth machine has proven a durable concept for thinking about the capture of urban governance/policymaking by a class…
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Downloading National Block Group and Tract data using tidycensus
Getting national census data, with related geometry data to analyze, is more difficult than it should be. The various tools that have been released by Census Bureau are geared toward “advanced casual users” and not those doing spatial analysis. Older tools, like Data Ferrett, have been phased out while newer tools, like data.census.gov, are still…
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Academic Writing using Zotero, Sublime Text and Latex
There are a lot of different options for writing. For collaborating, I prefer passing Word documents around or using Google Docs or even Office 365. However, my preferred method is writing in latex (or LaTeX) with some caveats. The biggest helper for me is the integration of a tool called write-good which highlights passive voice…