CrimeStat III : A Spatial Statistics Program for the Analysis of Crime Incident Locations (Version 3.0) Ned Levine.

Format:

Datafile

Contributor:

Inter-university Consortium for Political and Social Research.

Series:

ICPSR (Series) ; 2824.

ICPSR ; 2824

Language:

English

Genre:

Academic theses.

Physical Description:

1 online resource.

Place of Publication:

Ann Arbor, Mich. : Inter-university Consortium for Political and Social Research [distributor], 2005.

System Details:

Mode of access: World Wide Web.

data file

Summary:

CrimeStat is a full-featured Windows-based spatial statistics program that was written in Visual C++ and uses a graphical interface with database and expanded statistical functions. The purpose of this program is to provide supplemental statistical tools to aid law enforcement agencies and criminal justice researchers in their crime mapping efforts. This program interfaces with geographic information systems (GIS) and can be linked with the crime mapping efforts of police departments, such as the Baltimore County Police Department, for which CrimeStat was originally developed. CrimeStat can read ASCII, dBASE (III or IV), and ArcView Shape (SHP) files directly. In addition to printing tables, CrimeStat can write graphical objects to the ArcView, Atlas GIS, and MapInfo GIS programs and can write interpolation files to these programs and to the Surfer for Windows and ArcView Spatial Analyst programs. The calculating algorithms, particularly for distances, are multithreading, which allows them to take advantage of multiple processors. CrimeStat also has Dynamic Data Exchange (DDE) capabilities so that it can be accessed from within another program. CrimeStat III version 3.0 includes statistical routines for seven categories of spatial statistics: (1) the spatial distribution of incidents, such as the mean center, center of minimum distance, standard deviational ellipse, directional mean, convex hull, Moran's I spatial autocorrelation index, and a Moran correlogram, (2) properties of distances between incidents, including nearest neighbor analysis, linear nearest neighbor analysis, Ripley's K statistic, and network distance along with direct and indirect distance, (3) hot spot analyses, such as hierarchical nearest neighbor clustering, K-means clustering, and local Moran statistics, (4) single-variable kernel density estimation for producing a surface or contour estimate o... Cf.: http://dx.doi.org/10.3886/ICPSR02824

Notes:

Title from ICPSR DDI metadata of 2006-09-15.

OCLC:

61147974

Access Restriction:

Restricted for use by site license.