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Spatial data refer to data that are geographically referenced and represent phenomena that are located in space. More specifically, spatial data refer to data that not only have the values or attributes related to the phenomena of interest but also the geographical or locational information of the observations. While aspatial data analysis uses only the former, spatial data analysis uses both. In a broad sense, spatial data analysis is the quantitative study of spatial data (Bailey & Gatrell, 1995). Spatial analysis is sometimes used interchangeably with spatial data analysis, geographic analysis, spatial information analysis, and geographic information analysis in the existing literature. While these terms refer to different things and have different foci, the boundary among them is somehow not clear and not completely agreed upon among researchers from different disciplines. For the purposes of this book, we understand spatial analysis as being composed of spatial data analysis and geographic analysis. The spatial regression models and methods are a specific set of tools for spatial data analysis.

Spatial data refer to data that are referenced geographically and represent phenomena located in space.

Spatial data analysis describes, models, and explains spatial data, from which we can make inferences about the phenomena under study and make predictions for areas where observations have not been sampled (Bailey & Gatrell, 1995). A spatial data analysis is conducted instead of aspatial data analysis if the data have spatial information and the spatial arrangements in the data or in the interpretation of the results are given explicit consideration. In particular, spatial data analysis is about using statistical methods to analyze spatial data; in the existing literature, this is often referred to as spatial statistics.

Spatial data analysis describes, models, and explains spatial data and enables us to make inferences and predictions.

Geographic analysis (or geographic information analysis or spatial information analysis) examines spatial data locations, attributes, and feature relations using geographic analysis techniques, and it also extracts or creates new information from spatial data (O’Sullivan & Unwin, 2010). Examples of geographic analysis are spatial overlay, spatial interpolation, network analysis, three-dimensional analysis, geocoding, terrain analysis, and others by using GIS software (e.g., ESRI) that uses GIS and remote sensing images. Geographic analysis methods and tools have been developed mostly by geographers, geologists, and environmental scientists and have been used by researchers from a wide range of fields.

Geographic analysis extracts or creates new information from spatial data and examines spatial data locations, attributes, and feature relations.