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Drug and Substance Abuse Point of Interest (POI) conflation and spatial attribute enrichment framework
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DOI：10.1080/15230406.2026.2632858.png)
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Abstract
En 中文
Point of Interest (POI) data is essential for accurate spatial analysis of pressing healthcare issues such as Drug and Substance Abuse (DSA). However, retrieving accurate healthcare POI information remains complicated. POI conflation integrates data from multiple sources to enhance spatial attribute quality and coverage. This study proposes a multi-step framework for healthcare POI conflation, including POI data collection from Location-Based Services (LBS), geographic and spatial attributes collection, calculating similarity across datasets, POI matching, spatial attributes enrichment, manual labeling, quality control, and deployment. We tested this framework on a DSA use case in California, USA. Our automated approach was able to detect 11,936 unique POIs related to this healthcare tag. Of the locations used for use case validation, 33% were common with the commercial benchmark POI dataset used by Safegraph. Out of the 11,936 total POIs, we were only able to find 4535 (38%) that were not in the commercial benchmark dataset and relevant to the use case. Moreover, our results indicate that spatial attributes fill rates are 32% at the geometry building level and 98% at the Census block group (CBG) level. We conclude that using LBS can provide POIs with relevance and spatial attributes similar to commercial datasets.
Keywords:
Healthcare Location-Based Services (LBS)
geospatial analysis
automated pipeline
data mining
data integration
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