Advancing the Understanding of Immigration, Crime, and Crime Reporting at the Local Level with a Synthetic Population, United States, 2019

This study investigated the complex relationship between unauthorized immigration and crime at the local level. Through a mix of data fusion, synthetic population modeling, and detailed crime reporting from selected jurisdictions, the study sought to produce nuanced insights to challenge prevailing assumptions about immigration and crime, ultimately aiding in informed policy-making and resource allocation.
This study employed crime and crime reporting data from ten jurisdictions across the United States paired with synthetic data which estimated the unauthorized immigrant population. This research aimed to provide an in-depth analysis at the census tract level. Analyses focused on unauthorized immigration and its correlation with drug, property, and violent crime rates, while accounting for crime reporting in traditional and emerging immigrant destinations along with sites with low foreign populations.

本研究针对地方层面非法移民与犯罪之间的复杂关联展开探究。本研究结合数据融合、合成人口建模技术，以及选定管辖区域的详细犯罪报案数据，旨在生成精细化的研究洞察，以挑战当前关于移民与犯罪的主流假设，最终为循证政策制定与资源配置提供助力。
本研究采用了美国境内10个管辖区域的犯罪及犯罪报案数据，并搭配用于估算非法移民人口的合成数据集。本研究旨在开展人口普查街区层面的深度分析，分析重点聚焦非法移民与毒品犯罪、财产犯罪及暴力犯罪率的相关性，同时纳入传统移民目的地、新兴移民目的地以及外籍人口稀少地区的犯罪报案情况作为控制变量。