{"id":33,"date":"2026-02-19T17:16:26","date_gmt":"2026-02-19T22:16:26","guid":{"rendered":"https:\/\/community.mis.temple.edu\/kwilliams\/blog\/points\/using-openstreetmap-data-for-urban-spatial-analysis-crowdsourced-geography-for-research\/"},"modified":"2026-02-19T17:16:26","modified_gmt":"2026-02-19T22:16:26","slug":"using-openstreetmap-data-for-urban-spatial-analysis-crowdsourced-geography-for-research","status":"publish","type":"points","link":"https:\/\/community.mis.temple.edu\/kwilliams\/points\/using-openstreetmap-data-for-urban-spatial-analysis-crowdsourced-geography-for-research\/","title":{"rendered":"Using OpenStreetMap Data for Urban Spatial Analysis: Crowdsourced Geography for Research"},"content":{"rendered":"
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This workshop taught me how OpenStreetMap (OSM) works as a huge, community-built map that researchers can actually use for real urban analysis. I learned how to move around the OSM ecosystem and pull out the exact kinds of city data you\u2019d need for a project, like road networks, building footprints, land use areas, and amenities (schools, hospitals, stores, transit stops, etc.). We practiced different ways to download that data and talked about how to judge whether it\u2019s reliable, like checking how complete an area is, looking at edit history, and paying attention to common quality issues (missing tags, outdated features, or inconsistent labeling). I also learned the basics of editing OSM directly, so you\u2019re not just taking data from it, you can improve it by fixing mistakes, adding missing places, and updating features. Finally, we went into how you can bring OSM data into GIS software and use it for spatial analysis, like studying accessibility, land use patterns, transportation networks, and neighborhood-level differences. The big takeaway for me is that OSM isn\u2019t just a map; it\u2019s an up-to-date dataset you can use for research, and contributing back helps make it better for everyone.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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