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1、 Page1of35For office use onlyT1_T2_T3_T4_Team Control NumberProblem ChosenBFor office use onlyF1_F2_F3_F4_Research on the Location and Time of Serial CriminalsFebruary 22,2010AbstractTwisted souls and antisocial spirits have resulted in serial murders. In order to prevent such crimes which cause gre
2、at harm to public security, the paper has established effective early warning systems and efficient detection programs by using mathematical models so that we can reduce the number of potential victims significantly. Warning preparations: First, the paper makes a statistical analysis of the victims
3、personality characterictics (such as physical characteristics, clothing, occupations, etc.), locations and time of the criminal offenses, then the paper has a Principal Factor Analysis of those factors so that we can rule out the obvious or non-intrinsic factors. The principal factors obtained above
4、 are the characteristics of the crime targets in the serial murders. According to the conclusion, early warnings are given to the vulnerable groups and residents with the same situation, residence and daily schedule so as to strengthen their vigilance. Scheme One: The Establishment of Geographical P
5、rofile Based on Time-Series Analysis ModelAccording to the statistics of the locations of serial crimes, the paper predicts the distance between adjacent cases via GPS (Global Positioning System) and establishes a time series analysis model. The paper uses SPSS software to predict the distance betwe
6、en the possible locations of the next crime and the past crime scenes. The data of the serial murders committed by Peter Sutcliffe and of the serial robberies in Dallas are used to verify the model: at 95% confidence level, the data obtained tally with the real circumstances.Scheme Two: The Establis
7、hment of Geographical Profile Based on the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Fuzzy Matter-Element AnalysisBased on a large number of crime statistics, the paper has established the Adaptive Network-Based Fuzzy Inference System (ANFIS) model and the Subtractive Clustering Init
8、ializing Fuzzy Inference System (SCIFIS) model. The crime data in Dallas from 1992 to 2007 are taken as the sample to verify the accuracy of the ANFIS model for the prediction of the distribution of the citys high crime areas and the areas where similar criminal cases take place. Then, based on resu
9、lts of neural network training, the crime areas obtained are made further screening. The complex element matrix is established by using Fuzzy Matter-Element Analysis. Thus the associated vector is generated through the relationship among the three elements of matter-element. The possible locations o
10、f the next crime are screened through the comparison of correlation. The paper predicts the serial robberies in Dallas and compares the data with the facts using the model reaching 95% confidence level. Therefore, the model is also reasonable and reliable. The prediction of the time of crime: Based
11、on the time series formula, the paper predicts the time of the next crime by commensurability of ternary, quaternion and quintuple and the existing time of criminal offenses.The criminal is predicted over time and space based on the geographical Profile generated by the two schemes mentioned and tim
12、e prediction method mentioned above. In this way, the case solving rate can be significantly increased, which is also helpful to build a harmonious society. Keywords: Principal Factor Analysis; Time-series Analysis; Adaptive Network- Based Fuzzy Inference System; Fuzzy Matter-Element Analysis; Comme
13、nsurability;Executive SummaryIn view of the increasing criminal rate in the world, the polices final goal is to increase their case solving rate. Based on this, a model is established under the premise of serial criminal cases, which is used to predict the time and location of the criminal cases. Th
14、ere are two schemes in the model, which can be used to predict the geographical Profile and one method to predict the time of the crime. The first scheme can predict the circular region of the next crime by using the Time Series Analysis Model and further reduces the predicted region by using the Pr
15、incipal Factor Analysis method. The second scheme can predict the next possible location of similar cases by using the Adaptive Network-based Fuzzy Inference System. Then a Fuzzy Matter-element Analysis is made of the common characteristics of the victims in the serial crimes so that the prediction region can be further reduced. While predicting the geographical files, the commensurability of ternary, quaternion and quintuple is used to determine the time of the next criminal offense. In this way, the suspected serial crim
