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Introduction and terminology
In this Guide we address the full spectrum of spatial analysis and associated modeling techniques that are provided within currently available and widely used geographic information systems (GIS) and associated software. Collectively such techniques and tools are often now described as geospatial analysis, although we use the more common form, spatial analysis, in most of our discussions.
The term ‘GIS’ is widely attributed to Roger Tomlinson and colleagues, who used it in 1963 to describe their activities in building a digital natural resource inventory system for Canada (Tomlinson 1967, 1970). The history of the field has been charted in an edited volume by Foresman (1998) containing contributions by many of its early protagonists. A timeline of many of the formative influences upon the field is provided in Longley et al. (2015, p20). The research makes the unassailable point that the success of GIS as an area of activity has been driven by the success of its applications in solving real world problems.
In order to cover such a wide range of topics, this Guide has been divided into a number of main sections or chapters. These are then further subdivided, in part to identify distinct topics as closely as possible, facilitating the creation of a web site from the text of the Guide. Hyperlinks embedded within the document enable users of the web and PDF versions of this document to navigate around the Guide and to external sources of information, data, software, maps, and reading materials.
Chapter 2 provides an introduction to spatial thinking, described by some as “spatial literacy”, and addresses the central issues and problems associated with spatial data that need to be considered in any analytical exercise. In practice, real-world applications are likely to be governed by the organizational practices and procedures that prevail with respect to particular places. Not only are there wide differences in the volume and remit of data that the public sector collects about population characteristics in different parts of the world, but there are differences in the ways in which data are collected, assembled and disseminated (e.g. general purpose censuses versus statistical modeling of social surveys, property registers and tax payments). Data collected by the private sector, often as a result of the use of services that automatically gather information on events, locations and individuals, present a further challenge (see Chapter 9 for an extended discussion of this issue).
There are also differences in the ways in which different data holdings can legally be merged and the purposes for which data may be used — particularly with regard to health and law enforcement data. Finally, there are geographical differences in the cost of geographically referenced data. Some organizations, such as the US Geological Survey, are bound by statute to limit charges for data to sundry costs such as media used for delivering data while others, such as most national mapping organizations in Europe, are required to exact much heavier charges in order to recoup much or all of the cost of data creation. Analysts may already be aware of these contextual considerations through local knowledge, and other considerations may become apparent through browsing metadata catalogs. GIS applications must by definition be sensitive to context, since they represent unique locations on the Earth’s surface.
This initial discussion is followed in Chapter 3 by an examination of the methodological background to GIS analysis. Initially we examine a number of formal methodologies and then apply ideas drawn from these to the specific case of spatial analysis. A process known by its initials, PPDAC (Problem, Plan, Data, Analysis, Conclusions) is described as a methodological framework that may be applied to a very wide range of spatial analysis problems and projects. We conclude Chapter 3 with a brief discussion on model-building, with particular reference to the various types of model that can be constructed to address geospatial problems.
Subsequent Chapters present the various analytical methods supported within widely available software tools. The majority of the methods described in Chapter 4 (Building blocks of spatial analysis) and many of those in Chapter 6 (Surface and field analysis) are implemented as standard facilities in modern commercial GIS packages such as ArcGIS, MapInfo, Manifold, TNTMips and Intergraph (now part of Hexagon Geospatial). Many are also provided in more specialized GIS products such as Idrisi, GRASS, QGIS and ENVI. Note that GRASS and QGIS (which includes GRASS in its download kit) are OpenSource. A number of software tools described and illustrated in this book have since been superseded or are no longer available, but their underlying principles and methods remain applicable to the analytical processes examined.
In addition we discuss a number of more specialized tools, designed to address the needs of specific sectors or technical problems that are otherwise not well-supported within the core GIS packages at present. Chapter 5, which focuses on statistical methods, and Chapter 7 and Chapter 8 which address Network and Location Analysis, and Geocomputation, are much less commonly supported in GIS packages, but may provide loose- or close-coupling with such systems, depending upon the application area. In all instances we provide detailed examples and commentary on software tools that are readily available. The final Chapter (Chapter 9) addresses issues associated with so-called Big Data.
As noted above, throughout this Guide examples are drawn from and refer to specific products — these have been selected purely as examples and are not intended as recommendations. Extensive use has also been made of tabulated information, providing abbreviated summaries of techniques and formulas for reasons of both compactness and coverage. These tables are designed to provide a quick reference to the various topics covered and are, therefore, not intended as a substitute for fuller details on the various items covered. We provide limited discussion of 2D and 3D mapping facilities, and the support for digital globe formats (e.g. KML and KMZ), which is increasingly being embedded into general-purpose and specialized data analysis toolsets. These developments confirm the trend towards integration of geospatial data and presentation layers into mainstream software systems and services, both terrestrial and planetary (see, for example, the KML images of Mars DEMs developed by Google as part of the Google Earth project).
Just as all datasets and software packages contain errors, known and unknown, so too do all books and websites, and the authors of this Guide expect that there will be errors despite our best efforts to remove these! Some may be genuine errors or misprints, whilst others may reflect our use of specific versions of software packages and their documentation. Inevitably with respect to the latter, new versions of the packages that we have used to illustrate this Guide will have appeared even before publication, so specific examples, illustrations and comments on scope or restrictions may have been superseded. In all cases the user should review the documentation provided with the software version they plan to use, check release notes for changes and known bugs, and look at any relevant online services (e.g. user/developer forums and blogs on the web) for additional materials and insights.
The web version of this Guide may be accessed via the associated Internet site: www.spatialanalysisonline.com. The contents and sample sections of the PDF version may also be accessed from this site. In both cases the information is regularly updated. The Internet is now well established as society’s principal mode of information exchange and most GIS users are accustomed to searching for material that can easily be customized to specific needs. Our objective for such users is to provide an independent, reliable and authoritative first port of call for conceptual, technical, software and applications material that addresses the panoply of new user requirements.