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

SBIR Phase I: Algorithms and Visualization Techniques for the Detection of Geographic Aberrations in Crime (GIS)
SBIR 第一阶段:犯罪地理畸变检测算法和可视化技术 (GIS)
批准号:
0637589
负责人:
M. Cecelia Buchanan
金额:
$10.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-01-01 至 2007-06-30

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中文摘要
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英文摘要
This Small Business Technology Transfer Phase I research project tests the feasibility of software tools that leverage spatial statistics to enable police personnel to test their theories of criminality against data collected in the day-today activities of policing. Specifically, the research will validate the feasibility 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 analyze crime spikes, or unusual aberrations that occur in concentrated geographic areas, is an innovation in policing which holds the potential to enhance the organizational capacity of police departments across the country. The project will also study the development of a software interface that enables everyday crime analysts, police officers, and police captains to perform spatial analysis of crime by applying spatial statistics to test 'hunches'. In addition to this product's obvious market, i.e. law enforcement, there are applications in all levels of government. In addition, there is a market in 'special' law enforcement agencies such as The Department of Homeland Security, the Coast Guard or Military Police. Of the roughly 250 municipalities with populations of over 100,000 people, each has police departments that would find this system of use. The tools will assist police personnel to do a better job, and the efficiency gains will result in better policing and other societal benefits. Because 'Hunches' are not limited to policing, the algorithms and technologies developed in this research project will be applicable to other datasets that have the same sort of informational pattern - points of time occurring in space and time, such as consumer buying patterns or epidemiology.
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