A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets

Openshaw, Stan; Charlton, Martin; Wymer, Colin; Craft, Alan

doi:10.1080/02693798708927821

Cited by 414 publications

(195 citation statements)

References 10 publications

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“…Exploratory spatial data analysis (ESDA) was a new and exciting field in 1992, building on the improved interactive graphics capabilities that became available first in the Macintosh and in Unix workstations in the 1980s, and later in the PC. Exploratory analysis also included the data-driven approach being advocated by Openshaw (Openshaw et al, 1987;Openshaw, Charlton, and Craft, 1988;Openshaw, Cross, and Charlton, 1990), which saw the search for pattern as an activity that could be essentially independent of any theoretical framework. Today we might find echoes of the same strategy in the use of techniques borrowed from artificial intelligence, including neural nets and self-organizing maps (Fischer, 1998;Fischer and Leung, 1998;Skupin and Hagelmann, 2005).…”

Section: Synopsis Of the 1992 Argumentsmentioning

confidence: 99%

Whose Hand on the Tiller? Revisiting “Spatial Statistical Analysis and GIS”

Goodchild

2008

Perspectives on Spatial Data Analysis

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Anselin and Getis argue in their 1992 paper "Spatial statistical analysis and GIS" that the development of a toolbox of spatial analytic techniques should be directed by the scientists whose work defines the need for such a toolbox. The field of GIS has changed fundamentally since 1992 as a result of new technical developments, including a general move away from the map as the defining metaphor, the influence of the Internet and the World Wide Web, and changes in the practice of software engineering. Science as a whole has also changed, towards a more collaborative model that is more dependent on computational infrastructure. The impacts of space on the methodology of science are also better understood. The Anselin and Getis paper was remarkably prescient in its identification of the major issues that continue to affect the relationship between spatial analysis and GIS. Institutional issues continue to frame the relationship between GIS and spatial analysis, and are best addressed through partnerships.

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Section: Synopsis Of the 1992 Argumentsmentioning

confidence: 99%

Whose Hand on the Tiller? Revisiting “Spatial Statistical Analysis and GIS”

Goodchild

2008

Perspectives on Spatial Data Analysis

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“…Public health, spatial statistics and geographical information systems (GIS) have contributed more recently to spatial epidemiology, creating an emphasis on interdisciplinary collaboration and knowledge translation (Moore and Carpenter, 1999;Elliott and Wartenberg, 2004;Wang et al, 2006;Beale et al, 2008). A commonly used methodology, and one that has been greatly enhanced by these linkages, is spatial cluster analysis (Openshaw et al, 1987;Besag and Newell, 1991). Defined by the Center for Disease Control and Prevention (CDC) as "an unusual aggregation, real or perceived, of health events that are grouped together in time and space", a cluster can occur in several health classifications and data types; population-based (e.g.…”

Section: Introductionmentioning

confidence: 99%

A scoping review of spatial cluster analysis techniques for point-event data

et al. 2013

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Abstract. Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

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“…In these methods, the region is scanned by a rectangular or circular window to detect any anomaly in disease occurrence or intensity. Examples of scan methods are Openshaw's GAM (Openshaw et al (1987)), Besag and Newell's method to detect clusters of size k, which comprise of regions containing exactly k observed cases (Besag and Newell (1991)), and Kulldorff & Nagarwalla's scan statistic (Kulldorff and Nagarwalla (1995)). In literature, despite the lack of a comprehensive comparison of many available geographical disease clustering tests, an empirical comparison was performed by Kulldorff et al (2003) using spatial scan statistic, the maximized excess events test, and the nonparametric M statistic.…”

Section: Introductionmentioning

confidence: 99%

Segregation indices for disease clustering

Ceyhan

2013

Statistics in Medicine

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Spatial clustering has important implications in various fields. In particular, disease clustering is of major public concern in epidemiology. In this article, we propose the use of two distance-based segregation indices to test the significance of disease clustering among subjects whose locations are from a homogeneous or an inhomogeneous population. We derive their asymptotic distributions and compare them with other distance-based disease clustering tests in terms of empirical size and power by extensive Monte Carlo simulations. The null pattern we consider is the random labeling (RL) of cases and controls to the given locations. Along this line, we investigate the sensitivity of the size of these tests to the underlying background pattern (e.g., clustered or homogenous) on which the RL is applied, the level of clustering and number of clusters, or differences in relative abundances of the classes. We demonstrate that differences in relative abundance has the highest impact on the empirical sizes of the tests. We also propose various non-RL patterns as alternatives to the RL pattern and assess the empirical power performance of the tests under these alternatives. We illustrate the methods on two real-life examples from epidemiology.

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A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets

Cited by 414 publications

References 10 publications

Whose Hand on the Tiller? Revisiting “Spatial Statistical Analysis and GIS”

Whose Hand on the Tiller? Revisiting “Spatial Statistical Analysis and GIS”

A scoping review of spatial cluster analysis techniques for point-event data

Segregation indices for disease clustering

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