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Title: Dynamically-Spaced Geo-Grid Segmentation for Weighted Point Sampling on a Polygon Map Layer

Abstract

Geo-grid algorithms divide a large polygon area into several smaller polygons, which are important for studying or executing a set of operations on underlying topological features of a map. The current geo-grid algorithms divide a large polygon in to a set of smaller but equal size polygons only (e.g. is ArcMaps Fishnet). The time to create a geo-grid is typically proportional to number of smaller polygons created. This raises two problems - (i) They cannot skip unwanted areas (such as water bodies, given about 71% percent of the Earth's surface is water-covered); (ii) They are incognizant to any underlying feature set that requires more deliberation. In this work, we propose a novel dynamically spaced geo-grid segmentation algorithm that overcomes these challenges and provides a computationally optimal output for borderline cases of an uneven polygon. Our method uses an underlying topological feature of population distributions, from the LandScan Global 2016 dataset, for creating grids as a function of these weighted features. We benchmark our results against available algorithms and found our approach improves geo-grid creation. Later on, we demonstrate the proposed approach is more effective in harvesting Points of Interest data from a crowd-sourced platform.


Citation Formats

Sims, Kelly M., Thakur, Gautam, Sparks, Kevin A., Urban, Marie L., Rose, Amy N., and Stewart, Robert N. Dynamically-Spaced Geo-Grid Segmentation for Weighted Point Sampling on a Polygon Map Layer. United States: N. p., 2018. Web.
Sims, Kelly M., Thakur, Gautam, Sparks, Kevin A., Urban, Marie L., Rose, Amy N., & Stewart, Robert N. Dynamically-Spaced Geo-Grid Segmentation for Weighted Point Sampling on a Polygon Map Layer. United States.
Sims, Kelly M., Thakur, Gautam, Sparks, Kevin A., Urban, Marie L., Rose, Amy N., and Stewart, Robert N. Sun . "Dynamically-Spaced Geo-Grid Segmentation for Weighted Point Sampling on a Polygon Map Layer". United States. https://www.osti.gov/servlets/purl/1470886.
@article{osti_1470886,
title = {Dynamically-Spaced Geo-Grid Segmentation for Weighted Point Sampling on a Polygon Map Layer},
author = {Sims, Kelly M. and Thakur, Gautam and Sparks, Kevin A. and Urban, Marie L. and Rose, Amy N. and Stewart, Robert N.},
abstractNote = {Geo-grid algorithms divide a large polygon area into several smaller polygons, which are important for studying or executing a set of operations on underlying topological features of a map. The current geo-grid algorithms divide a large polygon in to a set of smaller but equal size polygons only (e.g. is ArcMaps Fishnet). The time to create a geo-grid is typically proportional to number of smaller polygons created. This raises two problems - (i) They cannot skip unwanted areas (such as water bodies, given about 71% percent of the Earth's surface is water-covered); (ii) They are incognizant to any underlying feature set that requires more deliberation. In this work, we propose a novel dynamically spaced geo-grid segmentation algorithm that overcomes these challenges and provides a computationally optimal output for borderline cases of an uneven polygon. Our method uses an underlying topological feature of population distributions, from the LandScan Global 2016 dataset, for creating grids as a function of these weighted features. We benchmark our results against available algorithms and found our approach improves geo-grid creation. Later on, we demonstrate the proposed approach is more effective in harvesting Points of Interest data from a crowd-sourced platform.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {7}
}

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