Spatial analysis refers to a process that relies upon both exploratory and confirmatory techniques to answer important questions and enhance decision making with spatial data. This includes approaches to identify patterns and processes, detect outliers and anomalies, test hypotheses and theories, and generate spatial data and knowledge. Data qualify as “spatial” when their location is known and it has the potential to impact the outcome of an analysis. Most often, this space is tied to the geographic domain and concerns the Earth’s surface or subsurface. However, spatial data also exist within different scales and contexts, including nano- and picoscale processes in cellular electrophysiology and subatomic physics, among many others. When locational information is given about a particular piece of data, researchers in the field of spatial analysis can use that data to calculate statistical and mathematical relationships regarding time and space. If the data do not include locational information, such as a list of bicycle parts, spatial analysis would not be necessary. In fact, unless the data have some sort of locational information, spatial analysis is not possible. This article provides a foundation for exploring some of the most important works in spatial analysis. The General Overviews section provides readers with many of the most common and important techniques used in spatial analysis. Important Reference Resources are then discussed, followed by an overview of popular Journals that publish work pertaining to spatial analysis techniques and their applications. The two most common application areas for spatial analysis techniques, Gis and Remote Sensing, are then discussed, as are their respective software packages. The final section includes a more detailed overview of spatial analysis Techniques and their associated subdomains.
Spatial data analysis and the use of maps in scientific health articles
