Geographic Information Systems and Medical Geography: Toward a New Synergy

Sui, Daniel Z.

doi:10.1111/j.1749-8198.2007.00027.x

Cited by 18 publications

(13 citation statements)

References 138 publications

(180 reference statements)

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“…For instance, in the health sciences where geocoding often creates the underlying data used in studies determining the link between location and a certain health outcome or activity pattern (Rushton et al. 2006, Sui 2007), numerous reports have shown that this practice would introduce serious bias which could invalidate results and/or conclusions drawn from the remaining data (e.g. Krieger et al.…”

Section: Introductionmentioning

confidence: 99%

Improving Geocode Accuracy with Candidate Selection Criteria

Goldberg

Cockburn

2010

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Geocoding systems typically use more than one geographic reference dataset to improve match rates and spatial accuracy, resulting in multiple candidate geocodes from which the single "best" result must be selected. Little scientific evidence exists for formalizing this selection process or comparing one strategy to another, leading to the approach used in existing systems which we term the hierarchy-based criterion: place the available reference data layers into qualitative, static, and in many cases, arbitrary hierarchies and attempt a match in each layer, in order. The first non-ambiguous match with suitable confidence is selected and returned as output. This approach assumes global relationships of relative accuracy between reference data layers, ignoring local variations that could be exploited to return more precise geocodes. We propose a formalization of the selection criteria and present three alternative strategies which we term the uncertainty-, gravitationally-, and topologically-based strategies. The performance of each method is evaluated against two ground truth datasets of nationwide GPS points to determine any resulting spatial improvements. We find that any of the three new methods improves on current practice in the majority of cases. The gravitationally-and topologicallybased approaches offer improvement over a simple uncertainty-based approach in cases with specific characteristics.

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Section: Introductionmentioning

confidence: 99%

Improving Geocode Accuracy with Candidate Selection Criteria

Goldberg

Cockburn

2010

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“…The original rate for each area may be unstable if its base population (denominator) is small, as observed in public health data analysis (see Sui (2007) for a review on related problems). In other words, the rates for small areas within a homogeneous region may exhibit spurious variation.…”

Section: Methodological Improvements To Redcapmentioning

confidence: 99%

Automatic Region Building for Spatial Analysis

Guo

Wang

2011

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High-resolution spatial data have become increasingly available with modern data collection techniques and efforts. However, it is often inappropriate to use the default geographic units to perform spatial analysis due to unstable estimates with small areas (e.g. cancer rates for census blocks or tracts). Regionalization is aggregating small units into relatively larger areas while optimizing a homogeneity measure (such as the sum of squared differences). For exploratory spatial analysis, regionalization may help remove spurious data variation through aggregation and discover hidden patterns in data (such as areas of unusually high cancer rates). Towards this goal, this research introduces several improvements to a recent group of regionalization methods -REDCAP (Guo 2008) and conducts evaluation experiments with synthetic data sets to assess and compare the capability of regionalization methods for exploratory spatial analysis. One of the major improvements is the integration of a local empirical Bayes smoother (EBS) with the regionalization methods. We generate a large number of synthetic data sets with controlled spatial patterns to evaluate the performance of both new and existing methods. Evaluation results show that the new methods (integrated with EBS) perform significantly better than their original versions and other methods (including the EBS method on its own) in terms of detecting the true patterns in the synthetic data sets.

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“…We perform the analysis in Stata software and describe how to do so. This example is illustrative to the different sample survey analysis approaches and should be substantively stimulating to readers interested in a variety of human geographic research agendas and especially to those in medical and health geography, a currently growing A Comparison of Design-Based and Model-Based Analysis of Sample Surveys 469 area of research (for example, see Kolivras 2006;Langford and Higgs 2006;Strait 2006;Xu et al 2006;Sui 2007;Wheeler 2007).…”

Section: A Comparison Of Design-based and Model-based Analysis Of Sammentioning

confidence: 98%