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Using theft incidents in Washington, D.C., this study quantifies how three common preprocessing strategies\u2014record deletion, centroid aggregation, and random perturbation\u2014affect kernel density estimation (KDE) hotspot prediction under varying data quality and spatial supports. Monte Carlo simulations introduce uncertainty into 5%\u201350% of training incidents and apply each strategy across six preprocessing units (street, tract, neighborhood, and 100\/300\/500\u2009m grids) under long\u2010 and short\u2010horizon rolling\u2010window validation. Performance is assessed with the Prediction Accuracy Index (PAI) and a pixel\u2010wise F1 score. Deletion is scale\u2010invariant but lowers performance through information loss. Relocation\u2010based strategies perform well under fine supports but deteriorate as preprocessing units become coarser relative to the KDE bandwidth. Sensitivity tests using 100\/300\/500\u2009m bandwidths confirm these patterns, with marked degradation around \ud835\udf14\u224830% in the present setting.<\/jats:p>","DOI":"10.1111\/tgis.70282","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T05:47:54Z","timestamp":1779083274000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Impact of Geocoding Uncertainty on Crime Hotspot Prediction"],"prefix":"10.1111","volume":"30","author":[{"given":"Zengli","family":"Wang","sequence":"first","affiliation":[{"name":"Jiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing Hohai University  Nanjing China"},{"name":"College of Geography and Remote Sensing Hohai University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4673-3572","authenticated-orcid":false,"given":"Ke","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Hohai University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering Hohai University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4861-7439","authenticated-orcid":false,"given":"Yongnian","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Hohai University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,17]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2016.1159684"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097\u20100258(19990315)18:5<497::AID\u2010SIM45>3.0.CO;2\u2010#"},{"key":"e_1_2_11_4_1","volume-title":"Interactive Spatial Data Analysis","author":"Bailey T. 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