2026-07-27T21:43:39Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:9386 2024-03-12T11:57:39Z ddc:004 open_access

Dynamically-Spaced Geo-Grid Segmentation for Weighted Point Sampling on a Polygon Map Layer (Short Paper) Sims, Kelly Thakur, Gautam Sparks, Kevin Urban, Marie Rose, Amy Stewart, Robert geofence geo-grid quadtree points of interest (POI) volunteered geographic information (VGI) 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Kelly Sims and Gautam Thakur and Kevin Sparks and Marie Urban and Amy Rose and Robert Stewart 2018 Is Part Of LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GISCIENCE.2018.58 urn:nbn:de:0030-drops-93860 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.58 eng https://creativecommons.org/licenses/by/3.0/legalcode