Analysing and visualising areal crime data. A case study of residential burglary in San Francisco, USA

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

The automatic identification and prioritisation of criminal networks from police crime data

The identification of criminal networks is not a routine exploratory process within the current practice of the law enforcement authorities; rather it is triggered by specific evidence of criminal activity being investigated. A network is identified when a criminal comes to notice and any associates who could also be potentially implicated would need to be identified if only to be eliminated from the enquiries as suspects or witnesses as well as to prevent and/or detect crime. However, an identified network may not be the one causing most harm in a given area.. This paper identifies a methodology to identify all of the criminal networks that are present within a Law Enforcement Area, and, prioritises those that are causing most harm to the community. Each crime is allocated a score based on its crime type and how recently the crime was committed; the network score, which can be used as decision support to help prioritise it for law enforcement purposes, is the sum of the individual crime scores.

Training guide for hate crime data collection : Uniform Crime Reporting.

Shipping list no.: 2004-0278-P.

Iowa Uniform Crime Report, 2004

The National Uniform Crime Reporting System began with 400 cities representing 20 million inhabitants in 43 states on January 1st, 1930. Since the establishment of the Uniform Crime Reporting Program, the volume, diversity, and complexity of crime steadily increased while the UCR program remained virtually unchanged. Recognizing the increasing need for more in-depth statistical information and the need to improve the methodology used for compiling, analyzing, auditing, and publishing the collected data, an extensive study of the Uniform Crime reports was undertaken. The objective of this study was to meet law enforcement needs into the 21st century. The result of the study was NIBRS (National Incident Based Reporting System). Adoption of the NIBRS system took place in the mid 1980’s and Iowa began organizational efforts to implement the system. Conversion to IBR (Incident Based Iowa Uniform Crime Reporting) was completed January 1, 1991, as part of a national effort to implement incident based crime reporting, coordinated by the Federal Bureau of Investigation and the Bureau of Justice Statistics of the U.S. Department of Justice. Iowa was the fifth state in the nation to be accepted as a certified “reporting state” of incident based crime data to the national system.

Iowa Uniform Crime Report, 2005

The National Uniform Crime Reporting System began with 400 cities representing 20 million inhabitants in 43 states on January 1st, 1930. Since the establishment of the Uniform Crime Reporting Program, the volume, diversity, and complexity of crime steadily increased while the UCR program remained virtually unchanged. Recognizing the increasing need for more in-depth statistical information and the need to improve the methodology used for compiling, analyzing, auditing, and publishing the collected data, an extensive study of the Uniform Crime reports was undertaken. The objective of this study was to meet law enforcement needs into the 21st century. The result of the study was NIBRS (National Incident Based Reporting System). Adoption of the NIBRS system took place in the mid 1980’s and Iowa began organizational efforts to implement the system. Conversion to IBR (Incident Based Iowa Uniform Crime Reporting) was completed January 1, 1991, as part of a national effort to implement incident based crime reporting, coordinated by the Federal Bureau of Investigation and the Bureau of Justice Statistics of the U.S. Department of Justice. Iowa was the fifth state in the nation to be accepted as a certified “reporting state” of incident based crime data to the national system.

Iowa Uniform Crime Report, 2006

The National Uniform Crime Reporting System began with 400 cities representing 20 million inhabitants in 43 states on January 1st, 1930. Since the establishment of the Uniform Crime Reporting Program, the volume, diversity, and complexity of crime steadily increased while the UCR program remained virtually unchanged. Recognizing the increasing need for more in-depth statistical information and the need to improve the methodology used for compiling, analyzing, auditing, and publishing the collected data, an extensive study of the Uniform Crime reports was undertaken. The objective of this study was to meet law enforcement needs into the 21st century. The result of the study was NIBRS (National Incident Based Reporting System). Adoption of the NIBRS system took place in the mid 1980’s and Iowa began organizational efforts to implement the system. Conversion to IBR (Incident Based Iowa Uniform Crime Reporting) was completed January 1, 1991, as part of a national effort to implement incident based crime reporting, coordinated by the Federal Bureau of Investigation and the Bureau of Justice Statistics of the U.S. Department of Justice. Iowa was the fifth state in the nation to be accepted as a certified “reporting state” of incident based crime data to the national system.

Iowa Uniform Crime Report, 2007

The National Uniform Crime Reporting System began with 400 cities representing 20 million inhabitants in 43 states on January 1st, 1930. Since the establishment of the Uniform Crime Reporting Program, the volume, diversity, and complexity of crime steadily increased while the UCR program remained virtually unchanged. Recognizing the increasing need for more in-depth statistical information and the need to improve the methodology used for compiling, analyzing, auditing, and publishing the collected data, an extensive study of the Uniform Crime reports was undertaken. The objective of this study was to meet law enforcement needs into the 21st century. The result of the study was NIBRS (National Incident Based Reporting System). Adoption of the NIBRS system took place in the mid 1980’s and Iowa began organizational efforts to implement the system. Conversion to IBR (Incident Based Iowa Uniform Crime Reporting) was completed January 1, 1991, as part of a national effort to implement incident based crime reporting, coordinated by the Federal Bureau of Investigation and the Bureau of Justice Statistics of the U.S. Department of Justice. Iowa was the fifth state in the nation to be accepted as a certified “reporting state” of incident based crime data to the national system.

Iowa Uniform Crime Report, 2008

The National Uniform Crime Reporting System began with 400 cities representing 20 million inhabitants in 43 states on January 1st, 1930. Since the establishment of the Uniform Crime Reporting Program, the volume, diversity, and complexity of crime steadily increased while the UCR program remained virtually unchanged. Recognizing the increasing need for more in-depth statistical information and the need to improve the methodology used for compiling, analyzing, auditing, and publishing the collected data, an extensive study of the Uniform Crime reports was undertaken. The objective of this study was to meet law enforcement needs into the 21st century. The result of the study was NIBRS (National Incident Based Reporting System). Adoption of the NIBRS system took place in the mid 1980’s and Iowa began organizational efforts to implement the system. Conversion to IBR (Incident Based Iowa Uniform Crime Reporting) was completed January 1, 1991, as part of a national effort to implement incident based crime reporting, coordinated by the Federal Bureau of Investigation and the Bureau of Justice Statistics of the U.S. Department of Justice. Iowa was the fifth state in the nation to be accepted as a certified “reporting state” of incident based crime data to the national system.

Iowa Uniform Crime Report, 2009

The National Uniform Crime Reporting System began with 400 cities representing 20 million inhabitants in 43 states on January 1st, 1930. Since the establishment of the Uniform Crime Reporting Program, the volume, diversity, and complexity of crime steadily increased while the UCR program remained virtually unchanged. Recognizing the increasing need for more in-depth statistical information and the need to improve the methodology used for compiling, analyzing, auditing, and publishing the collected data, an extensive study of the Uniform Crime reports was undertaken. The objective of this study was to meet law enforcement needs into the 21st century. The result of the study was NIBRS (National Incident Based Reporting System). Adoption of the NIBRS system took place in the mid 1980’s and Iowa began organizational efforts to implement the system. Conversion to IBR (Incident Based Iowa Uniform Crime Reporting) was completed January 1, 1991, as part of a national effort to implement incident based crime reporting, coordinated by the Federal Bureau of Investigation and the Bureau of Justice Statistics of the U.S. Department of Justice. Iowa was the fifth state in the nation to be accepted as a certified “reporting state” of incident based crime data to the national system.

Identifying the socioeconomic determinants of crime in Spanish provinces

In this paper we study, having as theoretical reference the economic model of crime (Becker, 1968; Ehrlich, 1973), which are the socioeconomic and demographic determinants of crime in Spain paying attention on the role of provincial peculiarities. We estimate a crime equation using a panel dataset of Spanish provinces (NUTS3) for the period 1993 to 1999 employing the GMMsystem estimator. Empirical results suggest that lagged crime rate and clear-up rate are correlated to all typologies of crime rate considered. Property crimes are better explained by socioeconomic variables (GDP per capita, GDP growth rate and percentage of population with high school and university degree), while demographic factors reveal important and significant influences, in particular for crimes against the person. These results are obtained using an instrumental variable approach that takes advantage of the dynamic properties of our dataset to control for both measurement errors in crime data and joint endogeneity of the explanatory variables

Identifying the socioeconomic determinants of crime in Spanish provinces

In this paper we study, having as theoretical reference the economic model of crime (Becker, 1968; Ehrlich, 1973), which are the socioeconomic and demographic determinants of crime in Spain paying attention on the role of provincial peculiarities. We estimate a crime equation using a panel dataset of Spanish provinces (NUTS3) for the period 1993 to 1999 employing the GMMsystem estimator. Empirical results suggest that lagged crime rate and clear-up rate are correlated to all typologies of crime rate considered. Property crimes are better explained by socioeconomic variables (GDP per capita, GDP growth rate and percentage of population with high school and university degree), while demographic factors reveal important and significant influences, in particular for crimes against the person. These results are obtained using an instrumental variable approach that takes advantage of the dynamic properties of our dataset to control for both measurement errors in crime data and joint endogeneity of the explanatory variables

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis