Development of data-driven approaches to crime and traffic safety (DDACTS): case study for the city of Regina

在传统的执法系统下，执法人员和城市工程师通常会等到一个地点被确定为犯罪或交通碰撞的问题区域后才采取执法行动。执法取决于警察的直觉和经验，以及对公众呼吁的回应。这种方法显然是被动的，而不是主动的。相比之下，拟议的“发展数据驱动的犯罪和交通安全方法（DDACTS）”战略是基于数据驱动的科学分析。DDACTS是复杂的、以情报为导向的、具有预测性的。该系统的目标区域，因为预计会有大量的犯罪和碰撞，执法将更有效，并有助于预防犯罪和碰撞。里贾纳拟议研究项目的主要目标有四个方面：1)引入/建立一个新的，数据驱动和分析执法计划，旨在同时主动减少未来犯罪/碰撞的预期数量，2)特别考虑天气对犯罪和碰撞的影响，3)开发一个原型移动执法通信系统（例如黑莓应用程序），以协助现场执法人员，4)评估DDACTS如何影响里贾纳的犯罪和碰撞的数量和类型。通过集中数据支持的情报和分析，以预防为基础的主动执法可能比目前的被动方法更具成本效益，特别是如果它可以同时减少犯罪和碰撞。由于基于地理信息系统的先进地理空间和地质统计技术的发展，现在有可能预测和确定特定类型的犯罪和碰撞重叠的热点，并显示它们如何随时间、一周中的哪一天和季节变化。拟议的流动执法通讯系统将透过执法人员的手提装置，实时通知现场执法人员有关热点的情况，使他们能够采取更有效的执法策略，并保护公众免受可预防的罪行和碰撞的伤害。

Under traditional enforcement systems, enforcement officers and city engineers typically wait until a location is identified as a problem area for crimes or traffic collisions before taking law enforcement action. Enforcement depends on officers' intuition and experience, and responding to calls from the public. This approach is clearly reactive rather than proactive. In contrast, the proposed "Development of Data-Driven Approaches to Crime and Traffic Safety (DDACTS)" strategy is based on data-driven, scientific analysis. DDACTS is sophisticated, intelligence-led, and predictive. The system targets areas where, because high numbers of crimes and collisions are predicted, enforcement will be more efficient and can contribute to crime and collision prevention. The main goal of the proposed research program in Regina is four fold: 1) introduce/establish a new, data-driven and analytical enforcement program designed to simultaneously and proactively reduce the expected number of future crimes/collisions, 2) specifically consider the impact of weather on crimes and collisions, 3) develop a prototype mobile enforcement communication system (e.g., BlackBerry App) to assist field enforcement officers, and 4) evaluate how DDACTS impacts the number and type of crimes and collisions in Regina. Proactive enforcement based on prevention through focused data supported intelligence and analysis is likely to be far more cost effective than the current reactive approach, especially if it can be designed to reduce crimes and collisions simultaneously. Due to the development of GIS based advanced geospatial and geostatistical technologies, it is now possible to predict and identify hotspots where particular types of crimes and collisions overlap, and to show how they vary by time of day, day of the week, and season. The proposed mobile enforcement communication system will inform field enforcement officers of hotspots in real-time via officers' handheld devices enabling officers to conduct more effective enforcement tactics and to protect the public from preventable crimes and collisions.