Spatial Analysis

by

Tony H. Grubesic, Jake R. Nelson

• LAST REVIEWED: 01 December 2017
• LAST MODIFIED: 31 March 2016
• DOI: 10.1093/obo/9780199874002-0125

Introduction

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.

General Overviews

A vast body of literature exists regarding spatial analysis and its associated techniques. Efforts to summarize this literature and provide a typological overview of its core theories and techniques include the early works Bunge 1966 and Bailey and Gatrell 1995. With the advent of quantitative geography, Cliff and Ord 1981 provides statistically oriented, technique-specific reviews. Similarly, Cressie 1993 provides an excellent and more current foundation that is heavily focused on spatial statistical techniques, particularly for points, lattice structures, and raster data forms. The more recent works Unwin 1996; Fotheringham, et al. 2000; and O’Sullivan and Unwin 2010 acknowledge the complementarities between spatial analysis and the increasing use of geographic information systems (Gis). Finally, important works in domain-specific techniques are highlighted in Waller and Gotway 2004, with emphasis on the field of public health and spatial epidemiology.

• Bailey, Trevor C., and Anthony C. Gatrell. Interactive Spatial Data Analysis. Harlow, UK: Longman Scientific & Technical Essex, 1995.

  This book offers a data-driven approach to spatial data analysis. Each of the techniques is presented with illustrative examples and accompanying data sets to provide the reader with a stronger comprehension of the approaches being used. Broad sections include point pattern analysis, univariate area analysis, multivariate area analysis, and network analysis.

• Bunge, William. Theoretical Geography. Vol. 1. Lund, Sweden: University of Lund, 1966.

  In many respects, this is the book that kicked off the quantitative revolution in geography. Bunge uses geometry as a tool to describe patterns, reason about their processes, and translate both pattern and process to theory. Mathematical work on map projections, overlay, shape, geodesics, sampling, topology, and central place theory are also covered.

• Cliff, Andrew David, and J. Keith Ord. Spatial Processes: Models & Applications. London: Pion, 1981.

  This book was written as a follow up to Cliff and Ord 1973 (cited under Statistical) on autocorrelation. The authors added additional material to the spatial autocorrelation discussion and included more detail and valuable discussion on point pattern analysis, spatial correlograms, and hypothesis testing.

• Cressie, Noel. Statistics for Spatial Data. New Jersey: John Wiley, 1993.

  DOI: 10.1002/9781119115151

  Revised in 2015, this book provides an excellent foundation both to the theory and to the application of spatial statistics. This text provides a comprehensive guide to analyzing geostatistical data, lattice data, and point patterns for use in science and engineering.

• Fotheringham, Stewart A., Chris Brunsdon, and Martin Charlton. Quantitative Geography: Perspectives on Spatial Data Analysis. London and Thousand Oaks, CA: SAGE, 2000.

  This book covers a range of spatial analytical methods and their development, including exploratory spatial data analysis (ESDA), measurement of local relationships, point pattern analysis, spatial regression, geostatistics, statistical inference, and spatial interaction modeling. The breadth of topics makes this a great resource for students and practitioners.

• O’Sullivan, David, and David Unwin. Geographic Information Analysis. Hoboken, NJ: John Wiley, 2010.

  DOI: 10.1002/9780470549094

  This recent text, consisting of twelve chapters, covers a wide range of analytical techniques within geographic information science and also discusses data structures, a unique topic not often covered. The book is well organized and easily digestible for nonexperts, but provides enough detail to satisfy those already immersed in spatial analytics.

• Unwin, David J. “Gis, Spatial Analysis and Spatial Statistics.” Progress in Human Geography 20.4 (1996): 540–551.

  DOI: 10.1177/030913259602000408

  This paper provides a broad overview of common spatial analysis techniques used within geographic information systems, backed by many referenced works. Recent approaches to spatial analysis are discussed with a logical progression through point pattern analysis, local statistics, and an examination of the influence of Gis on the development of spatial statistics.

• Waller, Lance A., and Carol A. Gotway. Applied Spatial Statistics for Public Health Data. Hoboken, NJ: John Wiley, 2004.

  DOI: 10.1002/0471662682

  This book is structured as a primer for researchers interested in using spatial analysis and/or data in the domains of epidemiology, biostatistics, and public health. Spatial autocorrelation, spatial clustering, data uncertainty, and other techniques highly relevant to these emerging fields are covered.