Preface Readership Acknowledgements Introduction Part I. The Context for Spatial Data Analysis: 1. Spatial data analysis: scientific and policy context 2. The nature of spatial data Part II. Spatial Data: Obtaining Data And Quality Issues: 3. Obtaining spatial data through sampling 4. Data quality: implications for spatial data analysis Part III. The Exploratory Analysis of Spatial Data: 5. Exploratory analysis of spatial data 6. Exploratory spatial data analysis: visualisation methods 7. Exploratory spatial data analysis: numerical methods Part IV. Hypothesis Testing in the Presence of Spatial Autocorrelation: 8. Hypothesis testing in the presence of spatial dependence Part V. Modeling Spatial Data: 9. Models for the statistical analysis of spatial data 10. Statistical modeling of spatial variation: descriptive modeling 11. Statistical modeling of spatial variation: explanatory modeling Appendices References Index.

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Spatial Data Analysis: Theory and Practice

Neighbourhood effects on health: does it matter where you draw the boundaries?

▪ Abstract We review literature that uses spatial analytic tools in contexts where Geographic Information Systems (GIS) is the organizing system for health data or where the methods discussed will likely be incorporated in GIS-based analyses in the future. We conclude the review with the point of view that this literature is moving toward the development and use of systems of analysis that integrate the information geo-coding and data base functions of GISystems with the geo-information processing functions of GIScience. The rapidity of this projected development will depend on the perceived needs of the public health community for spatial analysis methods to provide decision support. Recent advances in the analysis of disease maps have been influenced by and benefited from the adoption of new practices for georeferencing health data and new ways of linking such data geographically to potential sources of environmental exposures, the locations of health resources and the geodemographic characteristics of ...

Public Health, GIS, and Spatial Analytic Tools

Regionalization is a classification procedure applied to spatial objects with an areal representation, which groups them into homogeneous contiguous regions This paper presents an efficient method for regionalization The first step creates a connectivity graph that captures the neighbourhood relationship between the spatial objects The cost of each edge in the graph is inversely proportional to the similarity between the regions it joins We summarize the neighbourhood structure by a minimum spanning tree (MST), which is a connected tree with no circuits We partition the MST by successive removal of edges that link dissimilar regions The result is the division of the spatial objects into connected regions that have maximum internal homogeneity Since the MST partitioning problem is NP‐hard, we propose a heuristic to speed up the tree partitioning significantly Our results show that our proposed method combines performance and quality, and it is a good alternative to other regionalization methods fou

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Efficient regionalization techniques for socio‐economic geographical units using minimum spanning trees

Regionalization is to divide a large set of spatial objects into a number of spatially contiguous regions while optimizing an objective function, which is normally a homogeneity (or heterogeneity) measure of the derived regions. This research proposes and evaluates a family of six hierarchical regionalization methods. The six methods are based on three agglomerative clustering approaches, including the single linkage, average linkage (ALK), and the complete linkage (CLK), each of which is constrained with spatial contiguity in two different ways (i.e. the first-order constraining and the full-order constraining). It is discovered that both the Full-Order-CLK and the Full-Order-ALK methods significantly outperform existing methods across four quality evaluations: the total heterogeneity, region size balance, internal variation, and the preservation of data distribution. Moreover, the proposed algorithms are efficient and can find the solution in O(n 2log n) time. With such data scalability, for the first time it is possible to effectively regionalize large data sets that have 10 000 or more spatial objects. A detailed comparison and evaluation of the six methods are carried out with the 2004 US presidential election data.

Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP)

The paper describes SAGE, a software system that can undertake exploratory spatial data analysis (ESDA) held in the ARC/INFO geographical information system. The aims of ESDA are described and a simple data model is defined associating the elements of ‘rough’ and ‘smooth’ with different attribute properties. The distinction is drawn between global and local statistics. SAGE's region building and adjacency matrix modules are described. These allow the user to evaluate the sensitivity of results to the choice of areal partition and measure of interarea adjacency. A range of ESDA techniques are described and examples given. The interaction between the table, map and graph drawing windows in SAGE is illustrated together with the range of data queries that can be implemented based on attribute values and locational criteria. The paper concludes with a brief assessment of the contribution of SAGE to the development of spatial data analysis.

Exploratory Spatial Data Analysis

This paper considers issues associated with the construction of regions as part of a programme of exploratory spatial data analysis in the case of what Cressie (1991) refers to as “lattice data”. Lattice data arise where a study area has been partitioned into a set of zones or regions attached to each of which is a vector that describes the set of attributes for that zone. The focus of this paper will be the analysis of health data so the attributes in question may be health related but may also include demographic, socio-economic and environmental attributes.

Regionalisation Tools for the Exploratory Spatial Analysis of Health Data

This paper provides a description of SAGE, a software package linked to the Arc/Info GIS that can be used to undertake spatial statistical analysis of area based data. The paper is written from the perspective of the user who wishes to undertake exploratory and confirmatory spatial data analysis. The paper discusses design aspects of the package and also the statistical analysis philosophy underlying its contents. The paper describes the statistical analyses which SAGE can perform and details on how it performs them together with some illustrative examples. Detail on visualisation aspects of SAGE are discussed in a separate paper (Haining et al. 2000). The wider contribution of the paper is to build on earlier developments in this area and identify the needs of software packages if they are to enable users to implement effective spatial statistical analysis.

Jingsheng Ma

Papers

Exploratory Spatial Data Analysis

Regionalisation Tools for the Exploratory Spatial Analysis of Health Data

Designing and implementing software for spatial statistical analysis in a GIS environment