An Interactive Modus Operandi Visualisation System Integrating Geographical Information For Suspect Prioritisation and Investigation Management(iMOV)

考虑以下iMOV将使可行的场景：一名公众报告街头抢劫，或者可能是入室盗窃或暴力袭击。地址和犯罪细节被记录下来。一个计算机系统将这些信息与犯罪发生地点和该地区其他报告的犯罪的特点结合起来。专门的犯罪分析师通过与地图互动来处理这些材料，该地图显示了袭击地区的其他犯罪和已知罪犯。有关罪犯作案手法的背景资料和一套相关的分析功能使这一地理信息得到加强，从而能够对犯罪的显著特点进行详细分析。使用数据和文本挖掘技术，将这种犯罪的行为特征和任何法医信息与许多不同警察数据库中的所有犯罪特征进行比较。这些比较的结果导致一些可能的罪犯和他们可能的居住地点的建议。通过询问系统，分析员提醒当地警察巡逻队观察罪犯所在的区域，并特别注意三个最有可能的嫌疑人，并记录他们目前的居住地。这种罪行比较、对罪犯、其居住地点和犯罪活动模式的推断，都包括从有限的信息中生成数据集，然后挖掘这些数据，以提供有效的指导，继续进行调查。数据是文本的，半结构化的，没有一致的上下文或参数，通常是部分的，可靠性有限。通过将对这些数据的分析与地理信息系统相结合，就有巨大的潜力改进警察工作的许多方面，包括预防犯罪、调查，甚至是为法庭准备案件。因此，警方的调查工作提供了一个令人振奋的工具和数据，以开发更广泛适用的软件工具，例如市场研究或公共秩序管理。该项目因此可以：1.灵活的数据管理，以便有效地结合来自不同来源的数据，例如有关犯罪的信息和有关罪犯记录的信息。这些通常被警方以不同的形式存储在不同的系统中，并且可能对相同的概念使用不同的术语。2.基于数据和文本挖掘的比较案例分析。这允许将罪行与普通罪犯联系起来。申请人最近对数量和严重犯罪的研究表明，使用简单的参数可以建立非常准确的联系，在某些情况下高于80%，但要有效地使用这些发现，需要一个互动系统。3.优先考虑可能的嫌疑人。一些已发表的研究表明，可以使用犯罪地点作为确定嫌疑人可能居住地点的基础，并将其作为搜索嫌疑人和确定嫌疑人优先顺序的过滤器。4.随着人口统计和土地使用信息的增加，有可能在一般地理信息之外改进嫌疑人的搜索参数。5.线索和顺序分析还可以开发预测模型，使警察能够预测回家的路线和未来犯罪的可能地点。6.为了提高这些系统的能力，必须考虑到犯罪活动的基本比率。这也将为从当前犯罪地图的粗粒度建模转向处理个别犯罪者行为所需的细粒度建模提供基础。7.该系统最有效的实施将是实时数据收集和快速推理。因此，将开发这一系统，以确保这些程序能够及时和准确地整合。

Consider the following scenario which iMOV will make feasible:A member of the public reports a street robbery, or it may be a burglary or violent attack. The address and crime details are recorded. A computer system integrates this information with characteristics of the locale where the crime took place and other reported crimes in the area. Dedicated crime analysts work with this material by interacting with a map that shows other crimes and known offenders in the area of the assault. This geographical information is enhanced by background information on offenders' modus operandi and a set of related analysis functions, enabling a detailed analysis of the distinctive characteristics of the offence to be carried out. The behavioural signature of this crime and any forensic information, is compared with the signature of all crimes in a number of different police databases using data and text mining techniques. The results of these comparisons lead to the proposal of a number of possible offenders and their likely residential locations. By interrogating the system the analyst alerts a local police patrol to observe an area for the criminal and to pay particular attention to three most likely suspects with a note of where they are currently living. This comparison of offences, the drawing of inferences about offenders, their residential locations and their patterns of criminal activity, all consist of generating data sets from limited information and then mining that data for productive directions to continue the investigations. The data is textual, semi-structured, does not have consistent context or parameters, and is typically partial and of limited reliability. By tying analysis of this data into geographical information systems there is enormous potential for improving many aspects of police work, including crime prevention, investigation and even preparing a case for court. The domain of police investigations therefore provides an exciting vehicle and data to develop software tools, of wider applicability, say to market research or public order management.The project thus allows:1.Flexible Data Management so that data from different sources can be effectively combined, for example, information on offences with that on offender records. These are typically stored in different forms on different systems by the police, and may use varying terms for the same concepts.2.Comparative case analysis based on data and text mining. This allows offences to be linked to a common offender. Recent research, by the applicants, on volume as well as serious crime, has demonstrated that remarkably accurate links can be made using simple parameters, higher than 80% in some cases, but to use these findings effectively an interactive system is needed. 3.Prioritisation of likely suspects. A number of published studies have shown the possibility of using offence location as a basis for identifying possible suspects' residential locations and using this as a filter for searching for and prioritising suspects.4.With added demographical and land-use information it is possible to refine the search parameters for suspects beyond those available from the general geographical information. 5.Trail and sequential analyses also allows predictive models to be developed that will allow police to anticipate routes home and the likely location of future offences. 6.To improve the power of these systems it is essential to take account of base rates of criminal activity. This will also provide a basis for moving from the coarse-grain modelling of much current crime mapping to the fine-grain necessary for dealing with the actions of individual offenders.7.The most effective implementation of this system will be with real-time data collection and rapid inference. The system will therefore be developed to ensure that such processes can be integrated in a timely and accurate manner.