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Spatial Data Mining Analytical Environment for Large Scale Geospatial Data

Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users.

Identiferoai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-3356
Date16 December 2016
CreatorsYang, Zhao
PublisherScholarWorks@UNO
Source SetsUniversity of New Orleans
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceUniversity of New Orleans Theses and Dissertations

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