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

考虑以下iMOV将使其可行的场景：一名公众报告了街头抢劫，或者可能是入室盗窃或暴力袭击。地址和犯罪细节都被记录下来。计算机系统将这些信息与犯罪发生地点的特征以及该地区其他已报告的犯罪相结合。专门的犯罪分析人员通过与地图互动来处理这些材料，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，有助加强地理资料，从而详细分析有关罪行的特点。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所有犯罪的特征进行比较。这些比较的结果导致提出了一些可能的罪犯和他们可能的居住地点。通过对系统的询问，分析员提醒当地的警察巡逻队观察一个区域，寻找罪犯，并特别注意三个最有可能的嫌疑人，并记录下他们目前的住所。这种罪行的比较，对罪犯的推断，他们的居住地点和犯罪活动模式，所有这些都包括从有限的信息中生成数据集，然后挖掘这些数据，为继续调查提供有效的方向。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。通过将这些数据的分析与地理信息系统结合起来，可以极大地改善警察工作的许多方面，包括预防犯罪、调查，甚至为法庭准备案件。因此，警方调查领域为开发具有更广泛适用性的软件工具提供了令人兴奋的工具和数据，例如市场研究或公共秩序管理。因此，该项目允许：灵活的数据管理，使来自不同来源的数据能够有效地结合起来，例如，犯罪信息与罪犯记录信息。这些信息通常由警察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。基于数据和文本挖掘的案例比较分析。这允许将犯罪与共同罪犯联系起来。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%，但要有效地利用这些发现，需要一个互动系统。3.对可能的嫌疑人进行优先排序。一些已发表的研究表明，有可能将犯罪地点作为确定可能嫌疑人居住地点的基础，并将其作为搜索和优先考虑嫌疑人的过滤器。有了更多的人口和土地使用资料，就有可能在一般地理资料所提供的参数之外，改进嫌疑犯的搜索参数。5.追踪和顺序分析还可以开发预测模型，使警方能够预测回家的路线和未来犯罪的可能地点。6.为了提高这些制度的效力，必须考虑到犯罪活动的基本比率。这也将为从目前许多犯罪制图的粗粒度模型转向处理个别罪犯行为所需的细粒度模型提供基础。该系统最有效的实现将是实时数据采集和快速推理。因此，将发展这一制度，以确保这些程序能够及时和准确地结合起来。

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.