Mackay and Oldford’s perspective, discussed in the preceding section, is a little too narrow for many complex real-world situations. It also tends to focus on problems that have scientific research focus rather than the broader range of practical problems that the GIS analyst is likely to encounter.
The approach we describe in the following subsections is intended to provide a strong framework for undertaking projects that have a significant geospatial component. For example, the framework might be used to guide the process of preparing a response to an initial government-initiated RFI (request for information). It might subsequently used to help guide the development of a full-scale costed proposal in response to an RFP (request for proposals), or to address a subset of a larger project within which the geospatial element is a small element. Alternatively, the PPDAC methodology can be applied to problems in which the collection and analysis of particular datasets is the central task, as may be the case with primary environmental, socio-economic or epidemiological research.
PPDAC is a flexible and dynamic methodology, not a rigid set of procedures or forms, and thus may be applied at several stages of a project. An example ‘case study’ of the use of the PPDAC approach is provided on the accompanying website under the section entitled RESOURCES. This case study examines the hypothetical response to an RFP covering noise mapping of urban areas (in order to produce maps of the type illustrated in Figure 3‑4, showing noise levels in Augsburg, Germany). The example is illustrative only and is not based on actual application of the PPDAC methodology to a specific project that has been implemented. However, it draws on a wide range of materials relating to this topic that have been developed at a national and international level.
Source: Accon & DataKustnik GmbH, Germany
In respect of problems that the GIS user is likely to meet there are a number of reasons for treating spatial problems as ‘special’. These include the following:
•Spatial analysis is particularly concerned with problems that have an explicit spatial context, and frequently data at one location is not independent of data at other locations (see Sections 2.2, Spatial Relationships and 2.3, Spatial Statistics). Indeed such associations (spatial correlations) are the norm, especially for measurements taken at locations that are near to one another. Identifying and analyzing such patterns is often the goal of analysis, at least at the early stages of investigation
•Many problems must be considered in a spatio-temporal context rather than simply a spatial context. Time of day/week/month/year may have great relevance to obtaining an understanding of particular spatial problems, especially those of an environmental nature and problems relating to infrastructure usage and planning
•The theoretical foundations of statistics rely on a set of assumptions and sampling procedures that are often more applicable to experimental datasets than purely observational data. Very few problems addressed by spatial analysis fall into the category of truly experimental research
•Often the purpose of spatial analysis is not merely to identify pattern, but to construct models, if possible by gaining an understanding of process. In some instances this will involve the development of new models, whereas in others it will involve the use and/or development of existing models (see further, Section 3.4, Geospatial analysis and model building, and the discussion on Agent-based modeling in Section 8.2.6, Explanation or prediction?)
•Spatial patterns are rarely if ever uniquely determined by a single process, hence spatial analysis is often the start of further investigations into process and model building, and rarely an end in itself. Where explicitly spatial factors, such as distance or contiguity, are identified as important or significant, the question why? must follow: is the problem under consideration directly or indirectly affected by purely spatial factors; or is the spatial element a surrogate for one or more explanatory variables that have not be adequately modeled or are unobtainable?
•Spatial datasets are often provided by third parties, such as national mapping agencies, census units, third party data vendors and "open" sources. Metadata provided with such material may or may not provide adequate information on the quality, accuracy, consistency, completeness and provenance of the information. In many areas of spatial research these elements are pre-determined, although they are often augmented by corporate datasets (e.g. customer databases, crime incident records, medical case details, automatically tracked event data) or field research (e.g. georeferenced collection of soil samples or plant locations, market research exercises, bathymetric surveys, remote-sensed data etc.)
Each of these factors serves to distinguish spatial analysis from analysis in other disciplines, whilst at the same time recognizing the considerable similarities and overlap of methodologies and techniques.
In the following subsections we examine each step of the PPDAC model in the context of spatial analysis. In this “revised” version of the PPDAC approach the Plan stage is much broader than in Mackay and Oldford’s model. In their approach the Plan stage focuses largely on the data collection procedures to be adopted. In our case it covers all aspects of project planning, including preparation for data acquisition and analysis, and considerations of feasibility within given project constraints.
For many problems that arise in spatial analysis there will be multiple instances of the process described, especially for the data and analysis stages, where several different but related datasets are to be studied in order to address the problem at hand. Finally, in instances where the geospatial analyst is simply presented with the data and asked to carry out appropriate analyses, it is essential that the context is first understood (i.e. Problem, Plan, Data and Conclusions) even if these cannot be influenced or revisited.