Entropy error model of planar geometry features in GIS

Li, Dajun; Guan, Yunlan; Jian-ya, Gong; Daosheng, Du

doi:10.1007/bf02826749

Cited by 4 publications

(6 citation statements)

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“…It also supports that uncertainty might be best modeled at the point level, even for lines and polygons as suggested by Shi and Liu () and Dajun et al. (). There needs to be further exploration into EBGM to link properties of the data and the analysis to EBGM classes.…”

Section: Discussionsupporting

confidence: 83%

“…While polygons are topologically different from lines, they are still collections of points and lines. The various shapes of polygon error-bands vary in the same ways as lines (see Figures 6h and 6i; Leung et al 2004b, Dajun et al 2003, Love et al 2007, and line versus wedge types of segmented error-band geometry.…”

Section: The Error-band Geometry Modelmentioning

confidence: 92%

“…More specific to spatial confidence intervals was research on error/epsilon bands (Perkal ; Chrisman ; Blakemore ), s‐bands (Shi and Ehlers ; Shi ) and the g‐donut (Dajun et al. ). Others discussed using systems that store the measurements themselves and not the derived data (i.e.…”

Section: Review Of Modeling Error and Uncertainty Researchmentioning

confidence: 99%

“…Early research in what is today GIS uncertainty focused on hard computing, for example probability theory (Arthurs 1965), adjustment of observed data (Mikhail and Ackermann 1976), evidence theory (Shafer 1976), and spatial statistics (Cressie 1993). More specific to spatial confidence intervals was research on error/epsilon bands (Perkal 1956;Chrisman 1982;Blakemore 1984), s-bands (Shi and Ehlers 1993;Shi 1994) and the g-donut (Dajun et al 2003). Others discussed using systems that store the measurements themselves and not the derived data (i.e.…”

Section: Review Of Modeling Error and Uncertainty Researchmentioning

confidence: 99%

“…Dutton (1992) used Monte Carlo simulation to model the error distribution of geometric objects, but the above methods were not useful in a GIS. Shi and Liu (2000) and Dajun et al (2003), using strictly geometric methods, developed the g-band and g-donut, respectively, which estimate error at points or vertices along a line to compute the uncertainty of that line, as opposed to using the line itself. Shi (1994), used a similar approach (s-band), but modeled thematic and positional uncertainty.…”

Section: Review Of Modeling Error and Uncertainty Researchmentioning

confidence: 99%

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A Review and Evaluation of Uncertainty Classification and the Error‐Band Geometry Model

Foy

Carstensen

Prisley

et al. 2014

Transactions in GIS

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Advances in computer technologies have improved the quality of maps, making map comparison and analysis easier, but uncertainty and error still exist in GIS when overlaying geographic data with multiple or unknown confidence levels. The goals of this research are to review current geospatial uncertainty literature, present the Error‐Band Geometry Model (EBGM) for classifying the size and shape of spatial confidence intervals for vector GIS data, and to analyze the interpretability of the model by looking at how people use metadata to classify the uncertainty of geographic objects. The results from this research are positive and provide important insight into how people interpret maps and geographic data. They suggest that uncertainty is more easily interpreted for well defined point data and GPS data. When data is poorly defined, people are unable to determine an approach to model uncertainty and generate error‐bands. There is potential for using the EBGM to aid in the development of a GIS tool that can help individuals parameterize and model spatial confidence intervals, but more research is needed to refine the process by which people use the decision tree. A series of guiding questions or an “uncertainty wizard” tool that helps one select an uncertainty modeling approach might improve the way people apply this model to real‐world applications.

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Section: Discussionsupporting

confidence: 83%

Section: The Error-band Geometry Modelmentioning

confidence: 92%

Section: Review Of Modeling Error and Uncertainty Researchmentioning

confidence: 99%

Section: Review Of Modeling Error and Uncertainty Researchmentioning

confidence: 99%

Section: Review Of Modeling Error and Uncertainty Researchmentioning

confidence: 99%

See 3 more Smart Citations

A Review and Evaluation of Uncertainty Classification and the Error‐Band Geometry Model

Foy

Carstensen

Prisley

et al. 2014

Transactions in GIS

View full text Add to dashboard Cite

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Influence of conversion on the location of points and lines: The change of location entropy and the probability of a vector point inside the converted grid point

Chen

2018

ISPRS Journal of Photogrammetry and Remote Sensing

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Development and validation of a sensitive entropy-based measure for the water maze

Maei

Zaslavsky

Wang

et al. 2009

Front. Integr. Neurosci.

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In the water maze, mice are trained to navigate to an escape platform located below the water's surface, and spatial learning is most commonly evaluated in a probe test in which the platform is removed from the pool. While contemporary tracking software provides precise positional information of mice for the duration of the probe test, existing performance measures (e.g., percent quadrant time, platform crossings) fail to exploit fully the richness of this positional data. Using the concept of entropy (H), here we develop a new measure that considers both how focused the search is and the degree to which searching is centered on the former platform location. To evaluate how H performs compared to existing measures of water maze performance we compiled five separate databases, containing more than 1600 mouse probe tests. Random selection of individual trials from respective databases then allowed us to simulate experiments with varying sample and effect sizes. Using this Monte Carlo-based method, we found that H outperformed existing measures in its ability to detect group differences over a range of sample or effect sizes. Additionally, we validated the new measure using three models of experimentally induced hippocampal dysfunction: (1) complete hippocampal lesions, (2) genetic deletion of αCaMKII, a gene implicated in hippocampal behavioral and synaptic plasticity, and (3) a mouse model of Alzheimer's disease. Together, these data indicate that H offers greater sensitivity than existing measures, most likely because it exploits the richness of the precise positional information of the mouse throughout the probe test.

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Entropy error model of planar geometry features in GIS

Cited by 4 publications

References 0 publications

A Review and Evaluation of Uncertainty Classification and the Error‐Band Geometry Model

A Review and Evaluation of Uncertainty Classification and the Error‐Band Geometry Model

Influence of conversion on the location of points and lines: The change of location entropy and the probability of a vector point inside the converted grid point

Development and validation of a sensitive entropy-based measure for the water maze

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