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，然后才采取执法行动。执法取决于官员的直觉和经验，并回应公众的呼吁。这种做法显然是被动的，而不是主动的。相比之下，拟议的“制定数据驱动的犯罪和交通安全办法”战略是以数据驱动的科学分析为基础的。DDR2是复杂的，智能主导的，预测性的。该系统针对的是一些地区，因为预计这些地区会发生大量犯罪和碰撞事件，执法工作将更加有效，并有助于预防犯罪和碰撞。里贾纳拟议研究计划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，旨在同时和主动减少未来犯罪/碰撞的预期数量，2）特别考虑天气对犯罪和碰撞的影响，3）开发一个原型移动的执法通信系统（例如，黑莓应用程序），以协助现场执法人员，和4）评估如何DDBACK影响的数量和类型的犯罪和碰撞在里贾纳。通过有重点的数据支持的情报和分析，在预防的基础上积极主动地执法，可能比目前的被动办法更具成本效益，特别是如果这种办法能够同时减少犯罪和碰撞。由于基于GIS的先进地理空间和地理统计技术的发展，现在可以预测和确定特定类型犯罪和碰撞重叠的热点，并显示它们如何随一天中的时间，一周中的几天和季节而变化。拟议的移动的执法通讯系统会透过执法人员的手提装置，即时通知现场执法人员有关热点的情况，使执法人员能采取更有效的执法策略，并保护市民免受可预防的罪案和碰撞事故的影响。

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.