IntroductionThe development of GIS is the result of the analysis of spatial data (Goodchild and Robert 2003), in the same way that GIS has advanced the management of spatially referenced data. The foundation of GIS is therefore spatial analysis as it involves operations such as transformations, manipulations and other methods applicable to GIS to improve data values. In turn, this will encourage decisions, revealing hard-to-identify patterns or trends as well as anomalies. The process of spatial analysis involves transforming raw data into useful information. The main objective of spatial data analysis is the information division of data analysis, where the georeferenced object contains important information (Good and Robert 2003). Earth's surface characteristics are measured directly using ground-based instruments, satellite sensors, census data, documents, or past maps (Demers 2000). The most important of these are map objects on which map analysis can be performed to obtain useful data. The combination of the latter and the human eye as well as the brain forms an excellent anomaly detector on maps as well as cartographic imagery. Therefore, spatial analysis will be approached as a continuum from simple to complex methods, for example examining maps requiring complex software and complicated mathematical understanding. Basically, these are various methods used to examine an object with changing results in response to the changing location of the object. Additionally, spatial analysis is inductive, deductive, or normative, revealing implicit or explicit information. Examples of a form of spatial analysis In response to the cholera epidemic in large industrial cities in the early 1850s, Dr. John Snow used the O.... .. middle of article......10).ConclusionIn this assignment, spatial analysis was defined as "the set of methods used where the results of the object changes when the object changes location” (Longley et al. 2005). The different spatial analysis models were discussed, namely queries, transformations, measurements and spatial interpolation. Under transformation buffering, point in polygon and polygon overlay, some operations have been discussed. Under measurement, distance and length measurements were discussed as well as slope and aspects. Finally, under the theme of spatial interpolation, Theissen polygons, inverse distance weighting and kriging were developed. To conclude, spatial data analysis and data analysis in general are constantly evolving in GIS due to the increasing complexity of query queries requested by users and the goal of meeting these needs ( Heywood et al.. 2006).