Spatial Analysis

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The development of GIS was a result of spatial data analysis (Goodchild and Robert 2003) in the same token GIS has advanced the management of spatially referenced data. The foundation of GIS as a result is spatial analysis because it involves operations such as transformations, manipulations and other methods that are applicable to GIS to improve the data values. In turn this will encourage decisions, exposing patterns or trends not easily identifiable and anomalies. The process of spatial analysis involves the transforming of raw data into useful information. The main focus of spatial data analysis is the division of information of data analysis, where georeferenced object contains important information (Good and Robert 2003). Features on the Earth’s surface are measured directly through the use of ground instruments, satellites sensors, census data, documents or past maps (Demers 2000). The most important of these is cartographic objects on which cartographic analysis can be performed to obtain useful data. The combination of the latter and the human eye as well as brain form an excellent detector of anomalies on maps as well as cartographic imagery. Therefore spatial analysis will be approached as a continuum of simple to complex methods e.g. looking at map to requiring complex software’s and complicated mathematical understanding. Basically it is various methods used to examine an object with changing results in response to the object changing its location. In addition, spatial analysis is inductive, deductive or normative, revealing implicit to explicit information.
Examples of a form of spatial analysis
In response to the outbreak of cholera in major industrial cities in the early 1850s Dr. John Snow used the O...

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In this assignment spatial analysis was defined as the “set of methods used where the results of the object change when the object changes its location” (Longley et al. 2005). The various models of spatial analysis was discussed namely queries, transformations, measures and spatial interpolation. Under transformation buffering, point-in-polygon, polygon overlay are some operations that were discussed. Under measure the distance and length measurements were discussed as well as slope and aspects. Finally under the subject of spatial interpolation Theissen polygons, inverse-distance weighting and kriging were elaborated on. To conclude, spatial data analysis and just data analysis in general is an ever evolving in GIS due to the increasing complexity of interrogation queries demands by used and the objective to meet these needs (Heywood et al. 2006).
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