A Novel Framework for Spatiotemporal POI Analysis

Urban landscapes are rapidly evolving, integrating diverse Points of Interest (POIs) to accommodate city dwellers’ needs, highlighting the necessity for efficient analytical frameworks. This study presents a preprocessing framework using KNIME, known for its user-friendly interface and robust data management, for efficient POI preprocessing. We integrated the Advan mobility dataset with the Census dataset, allowing us to consider both the POI features of the location and the characteristics of the people using these POIs. We also introduce two novel POI features that link visitors to POIs: median dwell time at POIs and visitor travel distance from home to POI, to deepen our understanding of POI dynamics. We used the framework to conduct spatiotemporal analyses across 31 POI categories, identifying significant temporal variations linked to daily human behaviour within these categories. For our spatial analysis case study, due to healthcare disparities across various geographic divisions in the US, we selected the outpatient care services category of POIs for deeper analysis. The study underscores a significant correlation between our two novel features and variances in both geographic (land area) and demographic (population density) aspects across nine US divisions. This research makes a substantial contribution to urban studies, providing a solid framework for POI analysis and introducing other influential features along with visitors’ check-in data for examining POIs in cities.