IntroductionThe development of GIS was the result of the analysis of spatial data (Goodchild and Robert 2003) similarly GIS advanced the management of data with spatial references. Consequently, the foundation of GIS is spatial analysis because it involves operations such as transformations, manipulations, and other methods applicable to GIS to improve data values. This in turn will encourage decisions, highlighting patterns or trends that are not easily identifiable and anomalies. The process of spatial analysis involves transforming raw data into useful information. The main focus 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 through the use of ground instruments, satellite sensors, census data, past documents or 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, the human eye and the brain constitutes an excellent detector of anomalies on maps and cartographic images. Therefore spatial analysis will be approached as a continuum of methods from simple to complex, for example looking at the map, to requiring complex software and complicated mathematical understanding. Basically these are various methods used to examine an object with varying results in response to the object changing its position. Furthermore, spatial analysis is inductive, deductive, or normative, revealing implicit information in 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.... .. middle of paper ......10) . Conclusion In this assignment, spatial analysis was defined as “the set of methods used in which the object results change when the object changes its position” (Longley et al. 2005). Various spatial analysis models, i.e., queries, transformations, measures, and spatial interpolation, were discussed. In the area of ​​transform buffering, point in polygon and polygon superposition some operations were discussed. Under measurement, distance and length measurements as well as slope and exposures were discussed. Finally, as part of spatial interpolation, Theissen polygons, inverse distance weighting and kriging were worked out. To conclude, spatial data analytics and data analytics in general is an ever-evolving activity in GIS due to the increasing complexity of user query requests and the goal of meeting these needs (Heywood et al. 2006).