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Title:Data-intensive spatial pattern discovery based on generalized spatial point representations
Author(s):Gao, Yizhao
Director of Research:Wang, Shaowen
Doctoral Committee Chair(s):Wang, Shaowen
Doctoral Committee Member(s):Kwan, Mei-Po; Li, Bo; McLafferty, Sara
Department / Program:Geography & Geographic InfoSci
Discipline:Geography
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):CyberGIS
GeoComputation
Geographic Information Science
Movement Analysis
Spatial Analysis and Modeling
Abstract:Geospatial big data consisting of records at the individual level or with fine spatial resolutions, such as geo-referenced social media posts and movement records collected using GPS, provide tremendous opportunities to understand complex geographic phenomena and their space-time dynamics. Such data have been widely used in many real-world applications, such as event detection and population migration analyses. These applications require not only efficient data handling and processing capabilities, but also innovative data models and analytical approaches that satisfy application-specific requirements. The aim of this dissertation research is to establish a suite of innovative methods for analyzing geospatial big data that can be modeled as generalized spatial points while addressing the following key research questions: how to estimate the spatial and spatiotemporal patterns of geographic phenomena from geospatial big data based on spatial point models? How to compare these patterns to gain insights into complex geographic phenomena? How to estimate the computational intensity of the methods? How can cyberGIS be advanced to resolve the computational intensity? Specifically, novel methods are designed in this dissertation research to exploit spatial data characteristics, innovate spatial point pattern analytics, and resolve computational intensity through high-performance spatial algorithms. Such methods are evaluated in the context of several real-world applications, including event detection from social media data and spatial movement pattern detection. Experiment results demonstrated that fine-scale spatial patterns can be revealed from geospatial big data using the proposed approaches. Novel cyberGIS software capabilities are also created as a result of this dissertation research.
Issue Date:2018-07-13
Type:Text
URI:http://hdl.handle.net/2142/101698
Rights Information:Copyright 2018 Yizhao Gao
Date Available in IDEALS:2018-09-27
2020-09-28
Date Deposited:2018-08


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