eprintid: 1480750
rev_number: 35
eprint_status: archive
userid: 608
dir: disk0/01/48/07/50
datestamp: 2016-04-17 10:18:51
lastmod: 2021-09-20 00:13:03
status_changed: 2017-05-10 09:12:57
type: article
metadata_visibility: show
creators_name: Li, S
creators_name: Dragicevic, S
creators_name: Castro, FA
creators_name: Sester, M
creators_name: Winter, S
creators_name: Coltekin, A
creators_name: Pettit, C
creators_name: Jiang, B
creators_name: Haworth, J
creators_name: Stein, A
creators_name: Cheng, T
title: Geospatial big data handling theory and methods: A review and research challenges
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F44
keywords: Science & Technology, Physical Sciences, Technology, Geography, Physical, Geosciences, Multidisciplinary, Remote Sensing, Imaging Science & Photographic Technology, Physical Geography, Geology, Big data, Geospatial, Data handling, Analytics, Spatial modeling, Review, VISUAL ANALYTICS, GEOGRAPHICAL INFORMATION, DATA VISUALIZATION, HEAD/TAIL BREAKS, SPATIAL DATA, CLASSIFICATION, STATISTICS, SYSTEMS, MODEL, GIS
note: Copyright © 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future.
date: 2016-05
date_type: published
publisher: ELSEVIER SCIENCE BV
official_url: http://doi.org/10.1016/j.isprsjprs.2015.10.012
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
article_type_text: Review
verified: verified_manual
elements_id: 1094644
doi: 10.1016/j.isprsjprs.2015.10.012
lyricists_name: Cheng, Tao
lyricists_name: Haworth, James
lyricists_id: TCHEN23
lyricists_id: JHAWO13
actors_name: Haworth, James
actors_name: Laslett, David
actors_id: JHAWO13
actors_id: DLASL34
actors_role: owner
actors_role: impersonator
full_text_status: public
publication: ISPRS Journal of Photogrammetry and Remote Sensing
volume: 115
pagerange: 119-133
pages: 15
issn: 1872-8235
citation:        Li, S;    Dragicevic, S;    Castro, FA;    Sester, M;    Winter, S;    Coltekin, A;    Pettit, C;                 ... Cheng, T; + view all <#>        Li, S;  Dragicevic, S;  Castro, FA;  Sester, M;  Winter, S;  Coltekin, A;  Pettit, C;  Jiang, B;  Haworth, J;  Stein, A;  Cheng, T;   - view fewer <#>    (2016)    Geospatial big data handling theory and methods: A review and research challenges.                   ISPRS Journal of Photogrammetry and Remote Sensing , 115    pp. 119-133.    10.1016/j.isprsjprs.2015.10.012 <https://doi.org/10.1016/j.isprsjprs.2015.10.012>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1480750/1/Haworth.pdf