Using OpenStreetMap Data for Urban Spatial Analysis: Crowdsourced Geography for Research
I participated in the “Using OpenStreetMap Data for Urban Spatial Analysis” event hosted by Temple University Libraries as part of their Mapping Series. The workshop introduced OpenStreetMap (OSM) as a free, open, and collaborative geospatial data platform and demonstrated how crowdsourced geographic data can be used for urban research and spatial analysis. The session covered the OSM data model (nodes, ways, relations, and tags), the use of OverpassQL for querying targeted urban datasets such as roads, buildings, amenities, and land use, and techniques for extracting and visualizing OSM data using tools like Overpass Turbo, Overpass-ultra, and QGIS. Through live demonstrations and hands-on exercises, I learned how to search and evaluate OSM features, construct spatial queries, and import results into GIS software for mapping and analysis.
I gained practical experience with the OpenStreetMap ecosystem, including understanding how geographic features are structured and tagged, writing basic OverpassQL queries, and transforming raw OSM data into usable spatial layers. I also learned how crowdsourced data can support research questions in urban planning, transportation, and community analysis, as well as best practices for contributing edits back to OSM using the iD editor.
This workshop strengthened my technical foundation in geospatial data analysis and open-source GIS tools, which are directly relevant to my interest in data science, urban analytics, and applied research. The skills I developed—querying large spatial datasets, evaluating data quality, and visualizing urban patterns. Therefore, this supports my long-term goal of working in roles that combine data analysis with real-world problem solving in cities and communities.