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However, to the best of our knowledge, no work has quantified the impact of event-level data incompleteness in spatio-temporal prediction on each ethnicity. In this paper, we propose a model to quantify the impact of recorded crime data incompleteness on ethnic disparities in policing. We also examine whether these disparities depend on where in the city data are missing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We introduce a neighbourhood-level method that allocates PP prediction errors (false positives as potential over-policing, false negatives as potential under-policing) to ethnic groups in proportion to their share of the neighbourhood population. Using Chicago crime records (2022\u20132024), census data, and HAGEN (a state-of-the-art spatial-temporal PP model), we explore data incompleteness by reducing the original crime data at varying ratios in two settings: (i) 25\u2013100% reductions in neighbourhoods with low racial diversity; (ii) 50% or 100% reductions in communities dominated by a single ethnicity (Hispanic, Non-Hispanic Black, Non-Hispanic White, or Non-Hispanic Asian). Enforcement bias outcomes are then compared across ethnicities and scenarios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Removing records from a single-ethnicity-majority community generally reduces predicted over-policing and increases predicted under-policing for that ethnicity across the whole study area, with Asian-majority scenarios a consistent exception. In low-diversity neighbourhoods, greater missingness amplifies prediction bias against Non-Hispanic Black residents specifically: predicted over-policing falls and predicted under-policing rises, with the opposite holding for other groups. The largest overall shifts occur when data are removed from Black-majority communities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Incomplete crime records could propagate into substantial, ethnically unequal disparities in model-predicted policing exposure, whose direction depends on where missingness concentrates. The findings motivate auditing missingness and its demographic distribution as a prerequisite for deploying event-level PP systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40163-026-00306-9","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T21:08:45Z","timestamp":1792012125000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["How crime data incompleteness shapes ethnic disparities in predictive policing: evidence from Chicago"],"prefix":"10.1186","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4620-221X","authenticated-orcid":false,"given":"Mengqin K.","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9900-5108","authenticated-orcid":false,"given":"Federico","family":"Liberatore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0349-9057","authenticated-orcid":false,"given":"Alun","family":"Preece","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,14]]},"reference":[{"key":"306_CR1","doi-asserted-by":"crossref","unstructured":"Akpinar, N.-J., De-Arteaga, M., & Chouldechova, A. 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