Dataset Information

Combining difference and equivalence test results in spatial maps.

ABSTRACT:

Background

Regionally partitioned health indicator values are commonly presented in choropleth maps. Policymakers and health authorities use them among others for health reporting, demand planning and quality assessment. Quite often there are concerns whether the health situation in certain areas can be considered different or equivalent to a reference value.

Results

Highlighting statistically significant areas enables the statement that these areas differ from the reference value. However, this approach does not allow conclusions which areas are sufficiently close to the reference value, although these are crucial for health policy making as well. In order to overcome this weakness a combined integration of statistical difference and equivalence tests into choropleth maps is suggested and the approach is exemplified with health data of Austrian newborns.

Conclusions

The suggested method will improve the interpretability of choropleth maps for policymakers and health authorities.

SUBMITTER: Waldhoer T

PROVIDER: S-EPMC3032638 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Combining difference and equivalence test results in spatial maps.

Waldhoer Thomas T Heinzl Harald H

International journal of health geographics 20110110

<h4>Background</h4>Regionally partitioned health indicator values are commonly presented in choropleth maps. Policymakers and health authorities use them among others for health reporting, demand planning and quality assessment. Quite often there are concerns whether the health situation in certain areas can be considered different or equivalent to a reference value.<h4>Results</h4>Highlighting statistically significant areas enables the statement that these areas differ from the reference value ...[more]

PMID: 21219612

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Project description:Learning and generalization in spatial domains is often thought to rely on a "cognitive map", representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share common computational principles, and what are the implications for behavior? Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks. Participant behavior indicated sensitivity to both spatial and conceptual distance, and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel. The same Gaussian Process model best captured human search decisions and judgments in both domains, and could simulate realistic learning curves, where we found equivalent levels of generalization in spatial and conceptual tasks. At the same time, we also find characteristic differences between domains. Relative to the spatial domain, participants showed reduced levels of uncertainty-directed exploration and increased levels of random exploration in the conceptual domain. Participants also displayed a one-directional transfer effect, where experience in the spatial task boosted performance in the conceptual task, but not vice versa. While confidence judgments indicated that participants were sensitive to the uncertainty of their knowledge in both tasks, they did not or could not leverage their estimates of uncertainty to guide exploration in the conceptual task. These results support the notion that value-guided learning and generalization recruit cognitive-map dependent computational mechanisms in spatial and conceptual domains. Yet both behavioral and model-based analyses suggest domain specific differences in how these representations map onto actions.

Dataset Information

Combining difference and equivalence test results in spatial maps.

Background

Results

Conclusions

Publications

Combining difference and equivalence test results in spatial maps.

Similar Datasets

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