Endmember representation of human geography layers

Buck, Andrew; Zare, Alina; Keller, James M.; Popescu, Mihail

doi:10.1109/cibd.2014.7011520

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Cited by 2 publications

(2 citation statements)

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“…The study of human geography is important for several application areas, including, for example, preparing for disaster response and relief, identifying medically underserved areas, and so on. Buck et al [164] proposed a VAT-based approach to summarizing various human-related factors that can be mapped for a geographic region for visualization and easy understanding by domain experts. To combine various human geographic factors for a region, a human geography data cube (consisting of spatial dimensions of the region and one human-geographic dimension) was created.…”

Section: Human Geographymentioning

confidence: 99%

Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of Algorithms

Kumar

Bezdek

2020

IEEE Syst. Man Cybern. Mag.

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Section: Human Geographymentioning

confidence: 99%

Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of Algorithms

Kumar

Bezdek

2020

IEEE Syst. Man Cybern. Mag.

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“…1 depicts the Human geographic data cube with human geography layers of Missouri with a size of 1,127,957 vectors. A companion paper [6] explores the concept of human hyperspectral unmixing and the meanings of endmembers in this new domain. Fig.…”

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confidence: 99%

A human geospatial predictive analytics framework with application to finding medically underserved areas

Keller

Buck

Zare

et al. 2014

2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD)

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Human geography is a concept used to indicate the augmentation of standard geographic layers of information about an area with behavioral variations of the people in the area. In particular, the actions of people can be attributed to both local and regional variations in physical (i.e., terrain) and human (e.g., income, political, cultural) variables. In this paper, we study the utility of a human geographic data cube coupled with computational intelligence as a means to predict conditions across a geographic area. This becomes a Big data problem. In this sense, we are using genotype information to predict phenotype states. We demonstrate the approach on the prediction of medically underserved areas in Missouri.

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Endmember representation of human geography layers

Cited by 2 publications

References 11 publications

Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of Algorithms

Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of Algorithms

A human geospatial predictive analytics framework with application to finding medically underserved areas

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