Using OpenStreetMap point-of-interest data to model urban change-A feasibility study

Area of Science:

• Geographic Information Science
• Urban Informatics
• Spatial Data Analysis

Background:

• Volunteered Geographic Information (VGI) like OpenStreetMap (OSM) significantly enhances spatial data collection.
• The quality of OSM's Point-of-Interest (POI) data, crucial for urban studies, remains under-examined.
• Assessing POI data quality is vital for leveraging VGI in environmental and urban research.

Purpose of the Study:

• To evaluate the accuracy, coverage, and trend-worthiness of OpenStreetMap (OSM) Point-of-Interest (POI) data.
• To compare OSM POI data quality against another VGI source with editorial control.
• To explore the utility of OSM POI data, particularly its temporal aspects, for urban science research.

Main Methods:

• Compared OSM POI data with Foursquare data using label similarity and positional proximity to assess accuracy and coverage.
• Analyzed the trend-worthiness of OSM POI data for coffee shops in Manhattan.
• Applied spatio-temporal statistical models to correlate changes in coffee shop POIs with housing price fluctuations.

Main Results:

• OSM POI data demonstrates reasonable accuracy and coverage when compared to editorially controlled VGI.
• The temporal dynamics of OSM POI data (changesets) show potential for tracking urban change.
• A correlation was observed between the number of coffee shops and home prices in specific urban areas.

Conclusions:

• OpenStreetMap (OSM) POI data, while imperfect, is a valuable resource for urban science.
• The temporal aspect of OSM POI data (changesets) is particularly useful for studying urban dynamics.
• OSM data can be effectively utilized to drive research on urban change and inform urban planning.