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

Descartes (formerly called IRIS) is a software system designed to support visual exploration of spatially referenced data, e.g. demographic, economical, or cultural information about geographical objects or locations such as countries, districts, or cities. Descartes offers two integrated services: automated presentation of data on maps, and facilities to interactively manipulate these maps. Automated mapping is enabled by incorporating generic knowledge on map design into the system. Descartes selects suitable presentation methods according to characteristics of the variables to be analysed and relationships among those variables if more than one were selected simultaneously. The cartographic knowledge of Descartes allows non-cartographers to receive proper presentations of their data, and the automation of map construction helps the users to save valuable time that can better be used for data analysis and problem-solving. Exploratory data analysis requires highly interactive, dynamic data displays. We s...

http://geoanalytics.net/and/papers/ijgis99.pdf

Interactive maps for visual data exploration

Current software tools for visualization of spatio-temporal data, on the one hand, utilize the opportunities provided by modern computer technologies, on the other hand, incorporate the legacy from the conventional cartography. We have considered existing visualization-based techniques for exploratory analysis of spatio-temporal data from two perspectives: (1) what types of spatio-temporal data they are applicable to; (2) what exploratory tasks they can potentially support. The technique investigation has been based on an operational typology of spatio-temporal data and analytical tasks we specially devised for this purpose. The result of the study is a structured inventory of existing exploratory techniques related to the types of data and tasks they are appropriate for. This result is potentially helpful for data analysts—users of geovisualization tools: it provides guidelines for selection of proper exploratory techniques depending on the characteristics of data to analyze and the goals of analysis. At the same time the inventory as well as the suggested typology of tasks could be useful for tool designers and developers of various domain-specific geovisualization applications. The designers can, on the one hand, see what task types are insufficiently supported by the existing tools and direct their creative activities towards filling the gaps, on the other hand, use the techniques described as basic elements for building new, more sophisticated ones. The application developers can, on the one hand, use the task and data typology in the analysis of potential user needs, on the other hand, appropriately select and combine existing tools in order to satisfy these needs.

http://geogra.uah.es/patxi/Andrienko03_spatio_temp_visualziation.pdf

Exploratory spatio-temporal visualization: an analytical review

Our research goal was to determine which choropleth classification methods are most suitable for epidemiological rate maps. We compared seven methods using responses by fifty-six subjects in a two-part experiment involving nine series of U.S. mortality maps. Subjects answered a wide range of general map-reading questions that involved individual maps and comparisons among maps in a series. The questions addressed varied scales of map-reading, from individual enumeration units, to regions, to whole-map distributions. Quantiles and minimum boundary error classification methods were best suited for these general choropleth map-reading tasks. Natural breaks (Jenks) and a hybrid version of equal-intervals classing formed a second grouping in the results, both producing responses less than 70 percent as accurate as for quantiles. Using matched legends across a series of maps (when possible) increased map-comparison accuracy by approximately 28 percent. The advantages of careful optimization procedures in chorop...

Evaluation of Methods for Classifying Epidemiological Data on Choropleth Maps in Series

Spatial decision support is one of the central functions ascribed to Geographical Information Systems (GIS). One of the foci of developing decision support capabilities of GIS has been the integration of maps with multiple criteria decision models. Progress in this area has been slow due to a limited role played by maps as decision support tools. In this paper we present new prototype spatial decision support tools emphasising the role of maps as a source of structure in multiple criteria spatial decision problems. In these tools the role of map goes beyond the mere display of geographic decision space and multicriterion evaluation results. Maps becomes a 'visual index' through which the user orders decision options, assigns priorities to decision criteria, and augments the criterion outcome space by map-derived heuristic knowledge. As the additional means of structuring multicriterion spatial decision problems we present an experimental use of data mining, integrated with dynamic maps and multiple criter...

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Map-centred exploratory approach to multiple criteria spatial decision making

This paper investigates how concepts from exploratory data analysis, dynamic graphics, and statistical visualization may be applied to the study of geo-referenced data. The first part describes the use of commercially available computer programs with data stored as irregularly spaced points or in raster (grid) format. The second part presents results from an operational prototype that operates on data stored as polygons and includes some novel features that extend cluster analysis into the spatial domain.

E. Bruce MacDougall

Papers

Exploratory Analysis, Dynamic Statistical Visualization, and Geographic Information Systems