Methods of study
Method 1: Using standard landscape indices
This illustrative study of how spatial pattern analysis can be conducted within GIS, makes use of the spatial analysis tools within Patch Analyst which is integrated within ArcView GIS (Elkie et al., 1999). This means that a range of spatial statistics are calculated for a particular ArcView theme or view from within the GIS. The output from the analysis is a table of spatial statistics.
Method 2: Using spatial autocorrelation
The approach makes use of geostatistics and applies a local measure of spatial autocorrelation based on the statistic Geary's C. The spatial autocorrelation approach examines spatial variability and pattern within a moving window that passes across the raw spectral data.
Measures of spatial autocorrelation work by examining how objects at one location are similar to objects located nearby, that is, they look for spatial dependence in the data (similarity as a function of distance).
If features situated close together have similar attribute information, then the pattern in the data can be described as exhibiting positive autocorrelation. When features close together are more dissimilar in attribute value than features further away, pattern in the data is negatively autocorrelated. Zero autocorrelation exists when attributes or their values are independent of location.
Issues of scale are addressed by altering the distance over which spatial dependence is calculated (ie the window of analysis) and the cell size.
