Importance Of Spatial Data Analysis

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1. Introduction
Longely et al (2005) state that there are many possible ways of defining spatial analysis but at the end all the definitions express the basic idea that information on locations is essential. Analysis carried out without knowledge of locations is not spatial analysis (Longely et al, 2005). Spatial data analysis (SDA) is a set of techniques created to support a spatial perspective on data (Goodchild et al, 1992). SDA can be differentiated from other forms of analysis by definition. It might be defined as a set of techniques whose results are dependent on the locations of the objects or events being analyzed, requiring access to both the locations and the attributes of objects (Goodchild, 1987; Goodchild et al, 1992). Spatial analysis is the heart of GIS because it includes all of the transformations, manipulations, and methods that can be applied to geographic data to add value to them. In a nutshell, spatial analysis is the process by which raw data is turned into useful information, in scientific discovery and decision making (Longely et al, 2005). A geographical information system (GIS) provides a powerful collection of tools for the management and visualization of spatial data. These tools are more influential when they are integrated with methods for spatial data analysis (Krivoruchko and Gotway, ). Bailey and Gatrell (1995) distinguish between spatial phenomena using the basic GIS operations such as spatial query, join, buffering, and layering and spatial data analysis as the application of statistical theory and techniques to the modeling of spatially referenced data, which is the discipline of spatial statistics. ArcGIS spatial analyst provides powerful spatial modeling and analysis features. GIS ...

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...considered to be that of the contained rain gauge station. The amount of rain falling on each polygon can be calculated as the amount recorded by the rain gauge multiplied by the area of the polygon (Aronoff, 1989)
b) Inverse-Distance Weighting
Inverse distance weighted (IDW) interpolation is a method that enforces the condition that the estimated value of a point is influenced more by nearby known points than by those farther away (Chang, 2010). Inverse distance weighted estimates cell values by averaging the values of sample data points in the vicinity of each cell. The closer a point is to the center of the cell being estimated, the more influence it has in the averaging process (ESRI, 2001).
c) Kriging
Kriging is a geostatistical method for spatial interpolation. Kriging has the ability to assess the quality of prediction with estimated prediction errors.

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