1. Lecture 6 Implementing Spatial Analysis
6-1 What is Spatial Analysis?
Goodchild – set of analytical methods which requires access both to the attributes of the objects under study and to their locational information.
Openshaw – spatial analysis is comparable to spatial statistics; impacts from the development of quantitative and statistical geography.
2018/11/12
Jun Liang, UNC

2. 6-1 What is Spatial Analysis? (Cont.)
Definition from Google Search:
The process of modeling, examining, and interpreting model results. Spatial analysis is the process of extracting or creating new information about a set of geographic features. Spatial analysis is useful for evaluating suitability and capability, for estimating and predicting, and for interpreting and understanding. In GIS there are four traditional types of spatial analysis: spatial overlay and contiguity analysis, surface analysis, linear analysis, and raster analysis. (volusia.org/gis/glossary.htm)
The process of modeling, examining, and interpreting model results. Spatial analysis is useful for evaluating suitability and capability, for estimating and predicting, and for interpreting and understanding. There are four traditional types of spatial analysis: topological overlay and contiguity analysis, surface analysis, linear analysis, and raster analysis. (
2018/11/12
Jun Liang, UNC

3. 6-1 What is Spatial Analysis? (Cont.)
A way of analysing data that explicitly incorporates information about location as well about attribute. This term may be used almost interchangeably with geographical data analysis. (hds.essex.ac.uk/g2gp/gis/sect101.asp)
The examination of the spatial pattern of natural and human-made phenomena using numerical analysis and statistics (
The term "spatial analysis" encompasses a wide range of techniques for analyzing, computing, visualizing, simplifying, and theorizing about geographic data. Methods of spatial analysis can be as simple as taking measurements from a map or as sophisticated as complex geocomputational procedures based on numerical analysis. Spatial analysis is statistical description or explanation of either locational or attribute information or both (Goodchild, 1987). From Fischer, et al (1996) and Chou (1997) the spatial analysis include techniques such as spatial querying, point-in-polygon operation, buffering, overlaying, intersection, dissolving, proximity analysis, etc. (
2018/11/12
Jun Liang, UNC

4. 6-2 Modified Area Unit Problem (MAUP)
MAUP arises because of different types and levels of aggregation can produce wholly different representations of geographical phenomena.
GIS has the capability to reorganize data rapidly to examine the sensitivity of different zoning systems and thus can assist spatial statisticians.
Examples:
Several levels of aggregation of demographic data.
Different levels aggregation of census data.
2018/11/12
Jun Liang, UNC

5. Jun Liang, Geography @ UNC
6-2 MAUP (Cont.)
Two different MAUP effects: scale effect and zonal effect.
The scale effect is the variation in numerical results that occurs due to the number of zones used in an analysis. 16 32 8 4 2 10 10 14
9.3
2018/11/12
Jun Liang, UNC

6. Jun Liang, Geography @ UNC
6-2 MAUP (Cont.)
The zonation effect is the variation in numerical results arising from the grouping of small areas into larger units. 16 32 8 4 2 10
18.7
3.3
11.3
2018/11/12
Jun Liang, UNC

7. Jun Liang, Geography @ UNC
6-3 Boundary Problems
Geographical study areas are usually bounded in ways that do not correspond with the effects of spatial processes.
For example, features outside study area may actually affected the process greatly/significantly. How we can detect this in our research/GIS applications?
2018/11/12
Jun Liang, UNC

8. 6-4 Spatial Interpolation
Spatial interpolation is a very important spatial analytical technique for business and service planning. GIS software offers different spatial interpolations for many operations. Examples?
Convert a grid theme into a surface contour.
Create a cost surface from sample points.
2018/11/12
Jun Liang, UNC

9. 6-4 Spatial Interpolation (Cont.)
Typical Interpolation methods used in ArcGIS:
The Inverse Distance Weighted (IDW) interpolator assumes that each input point has a local influence that diminishes with distance. It weights the points closer to the processing cell greater than those farther away.
The Spline interpolator is a general purpose interpolation method that fits a minimum-curvature surface through the input points. Conceptually, it is like bending a sheet of rubber to pass through the points, while minimizing the total curvature of the surface.
Kriging is an advanced interpolation procedure that generates an estimated surface from a scattered set of points with z values. Kriging is based on the regionalized variable theory that assumes that the spatial variation in the phenomenon represented by the z values is statistically homogeneous throughout the surface; that is, the same pattern of variation can be observed at all locations on the surface. This hypothesis of spatial homogeneity is fundamental to the regionalized variable theory.
2018/11/12
Jun Liang, UNC