18 Facts About Spatial analysis

Spatial analysis includes a variety of techniques, many still in their early development, using different analytic approaches and applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures.

Classification of the techniques of spatial analysis is difficult because of the large number of different fields of research involved, the different fundamental approaches which can be chosen, and the many forms the data can take.

Spatial analysis began with early attempts at cartography and surveying.

Spatial analysis confronts many fundamental issues in the definition of its objects of study, in the construction of the analytic operations to be used, in the use of computers for analysis, in the limitations and particularities of the analyses which are known, and in the presentation of analytic results.

Spatial analysis dependence is the spatial relationship of variable values or locations (for themes defined as objects, such as cities).

Spatial analysis dependence is measured as the existence of statistical dependence in a collection of random variables, each of which is associated with a different geographical location.

Spatial analysis dependence is of importance in applications where it is reasonable to postulate the existence of corresponding set of random variables at locations that have not been included in a sample.

Spatial analysis dependency is the co-variation of properties within geographic space: characteristics at proximal locations appear to be correlated, either positively or negatively.

Spatial analysis dependency leads to the spatial autocorrelation problem in statistics since, like temporal autocorrelation, this violates standard statistical techniques that assume independence among observations.

Spatial analysis association is the degree to which things are similarly arranged in space.

Spatial analysis sampling involves determining a limited number of locations in geographic space for faithfully measuring phenomena that are subject to dependency and heterogeneity.

Spatial analysis data comes in many varieties and it is not easy to arrive at a system of classification that is simultaneously exclusive, exhaustive, imaginative, and satisfying.

Multivariable Spatial analysis allows a change of variables, transforming the many variables of the census, usually correlated between themselves, into fewer independent "Factors" or "Principal Components" which are, actually, the eigenvectors of the data correlation matrix weighted by the inverse of their eigenvalues.

Local spatial autocorrelation statistics provide estimates disaggregated to the level of the spatial analysis units, allowing assessment of the dependency relationships across space.

Complex adaptive systems theory as applied to spatial analysis suggests that simple interactions among proximal entities can lead to intricate, persistent and functional spatial entities at aggregate levels.

Spatial analysis includes a large variety of statistical techniques that apply to data that vary spatially and which can vary over time.

Geospatial and Hydrospatial analysis goes beyond 2D and 3D mapping operations and spatial statistics.

The increasing ability to capture and handle geographic data means that spatial analysis is occurring within increasingly data-rich environments.