SBIR Phase II: Algorithms and Visualization Techniques for the Detection of Geographic Aberrations in Crime (GIS)

这一小企业创新研究第二阶段项目将进一步开发HunchLab -利用空间统计的软件工具，使警务人员能够根据日常警务活动中收集的数据检验其犯罪理论。之前的第一阶段项目证明了开发HunchLab作为一套创新的软件工具的可行性，该软件工具可以搜索警察部门的历史数据，搜索犯罪分析师提出的理论或“预感”所预期的地理畸变，并应用空间统计来确认或否认假设。预防犯罪是一项比简单地查明事件或逮捕并相应地部署资源更为复杂的任务。发现和分析犯罪和混乱地理模式变化的能力是警务工作的一项创新，有可能提高全国各地警察部门的组织能力。第二阶段项目将完善应用程序并构建额外的功能，包括针对不同用户类型的替代工作流，扩展警报基础设施，并构建文本挖掘功能。该产品将影响的明显部门是各级政府的执法部门。此外，成功的结果将影响联邦执法机构和区域犯罪分析联盟。大约有250个城市的人口超过10万，每个城市都有警察部门，他们会发现这个系统的用处。这些工具将帮助成千上万的警察每天更好地完成工作。这种效率将导致更好的警务，这意味着罪犯将被更有效地抓获。犯罪分子造成的损害远远超过其活动直接造成的财产和医疗费用。也许更重要的是，这项研究将为执法以外的其他领域的其他产品奠定基础。在第一阶段原型中开发的算法和技术可以转移到其他数据集，这些数据集展示了类似的点模式过程-具有明确空间和时间属性的事件。我们的第一阶段过程证明了在执法以外的领域，包括欺诈检测，真实的房地产，销售和公共卫生的实质性效用。第二阶段的工作计划包括使用其他数据集进行测试，以完善软件应满足这些其他市场的需求。

This Small Business Innovative Research (SBIR) Phase II project will further develop HunchLab -- software tools that leverage spatial statistics to enable police personnel to test their theories of criminality against data collected in the day-to-day activities of policing. The preceding Phase I project proved the feasibility of developing HunchLab as a set of innovative software tools that scour the historic data of a police department, search for geographic aberrations expected by the theories or 'hunches' put forth by crime analysts, and apply spatial statistics to confirm or deny the supposition. Preventing crime is a more sophisticated task than simply mapping incidents or arrests and deploying resources accordingly. The ability to detect and analyze changes in the geographic patterns of crime and disorder is an innovation in policing which holds the potential to enhance the organizational capacity of police departments across the country. This Phase II project will refine the application and build additional functionality, including alternate workflows for different user types, expanding the alert infrastructure, and building text mining capabilities.The obvious sector that this product will impact is law enforcement at all levels of government. Additionally the successful outcome will impact federal law enforcement agencies and regional crime analysis consortia. There are roughly 250 municipalities with over 100,000 people in them, and these each have police departments that would find this system of use. The tools will be helping thousands of police officers do their jobs better every day. This efficiency will result in better policing, meaning that criminals will be caught more effectively. Criminals cause damage far in excess of the property and medical costs directly attributable to their activity. Perhaps more importantly, the research will form the basis for other products that operate in realms other than law enforcement. The algorithms and technologies developed in the Phase I prototype are transferable to other datasets that demonstrate similar point pattern processes - events with explicit spatial and temporal attributes. Our Phase I process demonstrated a substantial utility in domains other than law enforcement including fraud detection, real estate, sales and public health. The Phase II work plan includes testing with other data sets to refine that software should address these other markets.