Spatial Analysis Tools
The AMOEBA clustering method finds irregularly shaped, contiguous clusters in spatial datasets. The code is provided as an ArcToolbox, and the scripting is done in Python. I have not had time to write documentation yet, but it is somewhat self-explanatory. A demonstration dataset is provided.
AMOEBA ArcToolbox (AMOEBAToolbox.zip)
Point Pattern Analysis or PPA is a C program that performs a number of spatial analysis routines on univariate spatial data. These include a number of point pattern analysis techniques, such as nearest neighbor methods and Ripley's K. The program also computes a number of local and global spatial autocorrelation statistics (I,c,G). In all cases, a three column ASCII file (x,y,z) is required. A spatial weights matrix may be used to compute Moran's I and Geary's c. Please download the manual for a complete listing and an example application of each method. Additionally, the Getis filtering method can be computed in the DOS/Windows version of the program (not in manual).
The program was developed by Dong Mei Chen, Arthur Getis, and Jared Aldstadt. It is freely available from this website. Source code is available upon request.
PPA Manual (Word Documents) (manual.zip)
DOS/Windows Executable (PPA.zip)
There is also a web interface to PPA maintained by Andy Long at Northern Kentucky University.
The ArcGIS template file contains a set of point pattern analysis and spatial autocorrelation statistics. These routines were used in the Center for the Spatially Integrated Social Sciences workshops entitled "Spatial Analysis in a GIS Environment." These scripts require ArcGIS 8.x or higher, and were written by Jared Aldstadt. The exercises were also written by Jared Aldstadt, and are largely based on previous exercises by Lauren Scott.
ArcGIS Template for performing some simple Spatial Analysis Routines (PPA.mxt.zip)
Exercise that Explain the use of the ArcGIS Template file (exercises.zip)
A zipped folder containing the template, exercises, and exercise data (student.zip)
A template with code for global autocorrelation stats and bivariate K functions (PPA2.mxt.zip).
This page is provided by:
Jared Aldstadt
geojared@buffalo.edu
Disclaimer: The
contents and link identifiers of this web page are not monitored, reviewed, nor
endorsed by the State University of New York at