META TOPICPARENT |
name="SoftwareCodes" |
## Matlab Spatial Statistics Toolbox 1.1
Below is the detail information:
DEVELOPED |
1999 |
AUTHOR |
R. Kelley Pace LREC Chair of Real Estate E.J. Ourso College of Business Administration Louisiana State University Baton Rouge, LA 70803 (225)-388-6256 FAX: (225)-334-1227 kelley@spatial-statistics.com
Ronald Barry Associate Professor of Statistics Department of Mathematical Sciences University of Alaska Fairbanks, Alaska 99775-6660 (907)-474-7226 FAX: (907)-474-5394 FFRPB@uaf.edu |
PLATFORM |
The toolbox requires Matlab 5.0 or later. Unfortunately, previous editions of Matlab did not contain the Delaunay command and others needed for the toolbox. The total installation takes around 15 Mb. The routines have been tested on PC compatibles ? the routines should run on other platforms, but have not been tested on non-PC compatibles. |
PURPOSE |
The spatial statistics toolbox provides maximum likelihood estimation and likelihood-based inference for a variety of models (with a heavy emphasis upon lattice models). The toolbox particularly excels at spatial estimation with large data sets. |
FUNCTIONS |
Specifically, the software can estimate simultaneous spatial autoregressions (SAR), conditional spatial autoregressions (CAR), mixed regressive spatially autoregressive estimates as well as other lattice models. Spatial weight matrix can be calculated for very large datasets (> 100 000 points) It can be based upon nearest neighbours (symmetric or asymmetric) and Delaunay triangles (symmetric). The Delaunay spatial weight matrix leads to a concentration matrix or a variance-covariance matrix that depends upon only one-parameter (a , the autoregressive parameter). In contrast, the nearest neighbour concentration matrices or variance-covariance matrices depend upon three parameters (a , the autoregressive parameter; m, the number of neighbours; and r , which governs the rate weights decline with the order of the neighbours with the closest neighbour given the highest weighting, the second closest given a lower weighting, and so forth).
Computation of the log-determinants for a grid of autoregressive parameters (prespecified by the routine) can also be done. Computing the log-determinants is the slowest step but only needs to be done once for most problems (the same applies to creating the spatial weight matrix). |
CODES |
Matlab codes available |
TIP |
Papers and links to other related Matlab codes can be found on the web site of Kelley Pace |
HOMEPAGE |
www.spatial-statistics.com/software_index.htm |
-- TWikiAdminUser - 2010-06-25 |