Measuring “third places”: Comparing neighborhood data for cognitive health research

INTRODUCTION: Neighborhood “third places” are increasingly studied as contextual determinants of cognitive health, yet the reliability of geospatial datasets is poorly understood. METHODS: We evaluated Advan Research, Data Axle, FourSquare, and the National Establishment Time Series (NETS) across five categories: cafes/coffee shops, civic/social organizations, libraries, performing arts/museums, and recreation centers/gyms. Bayesian multilevel logistic regression models estimated locational accuracy and categorical validity for 13,168 coder ratings of 4876 unique businesses. Qualitative content analysis examined reflections from 18 coders. RESULTS: Advan showed high locational accuracy (≥95%), with Data Axle and FourSquare performing well for most categories and NETS the lowest. Inaccuracies stemmed from outdated or incorrect addresses and non-fixed locations. Advan, Data Axle, and FourSquare performed moderately well for categorization (70% to 97%) and NETS the worst. Misclassification reflected ambiguous purposes, misleading names, and uncertainty around third places. DISCUSSION: Study-specific dataset selection, triangulation, cleaning, error calibration, and clearer third place conceptualization are critical to strengthen neighborhood-based dementia research.