Let's start with a definition of spatial statistics. The GIS dictionary (http://gisgeography.com/gis-dictionary-definition-glossary/) defines spatial statistics as the field of study concerning statistical methods that use space and spatial relationships (such as distance, area, volume, length, height, orientation, centrality, and/or other spatial characteristics of data) directly in their mathematical computations. Spatial statistics are used for a variety of different types of analyses, including pattern analysis, shape analysis, surface modeling and surface prediction, spatial regression, statistical comparisons of spatial datasets, statistical modeling and prediction of spatial interaction, and more. The many types of spatial statistics include descriptive, inferential, exploratory, geostatistical, and econometric statistics.
Spatial statistics are applicable across a wide range of environmental disciplines, including agriculture, geology, soil science, hydrology, ecology, oceanography, forestry, meteorology, and climatology, among others. Many socio-economic disciplines including epidemiology, crime analysis, real estate, planning, and others also benefit from spatial statistical analysis.
Spatial statistics can give answers to the following questions:
- How are the features distributed?
- What is the pattern created by the features?
- Which are the clusters?
- How do patterns and clusters of different variables compare to one another?
- What is the relationship between sets of features or values?
