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Area of Science:

  • Geographic Information Science
  • Behavioral Science
  • Data Science

Background:

  • Behavioral scientists require accurate point-of-interest (POI) labels for mobile device location data.
  • Volunteered geographic information (VGI), like OpenStreetMap (OSM), offers cost-effective annotation but data quality is debated.
  • Limited evidence exists on VGI's suitability for personal mobility data annotation.

Purpose of the Study:

  • To assess the performance of OpenStreetMap (OSM) point-of-interest (POI) data for annotating personal mobility data.
  • To compare OSM's annotation accuracy against participant-provided labels and commercial geodatabases (Foursquare, Google Maps).
  • To provide empirical evidence for using OSM in the semantic enrichment of mobile location data research.

Main Methods:

  • Developed a Python package to extract POI geometric and semantic data from OpenStreetMap (OSM).
  • Annotated year-long smartphone location data from 93 participants in the United States using the OSM package.
  • Evaluated annotation performance against participant-provided labels and compared with Foursquare and Google Maps.

Main Results:

  • OpenStreetMap (OSM) data achieved the best overall performance in semantic annotation across eight place categories.
  • OSM labeled 81% of places with an average F1 score of 0.65, demonstrating its utility for mobility data.
  • While OSM excelled overall, Foursquare and Google Maps showed advantages in annotating specific place categories.

Conclusions:

  • This study provides empirical support for utilizing OpenStreetMap (OSM) data for semantic enrichment in mobile location data research.
  • OSM offers a cost-effective and efficient alternative to commercial databases for annotating personal mobility patterns.
  • Recommendations are provided for future implementations of OSM in behavioral science research utilizing location data.