Using spatiotemporal distribution of geocoded Twitter data to predict US county-level health indices Nguyen, Thin, Larsen, Mark, O'Dea, Bridianne, Nguyen, Hung, Nguyen, Duc Thanh, Yearwood, John, Phung, Dinh, Venkatesh, Svetha and Christensen, Helen 2018, Using spatiotemporal distribution of geocoded Twitter data to predict US county-level health indices, Future generation computer systems, pp. 1-9, doi: 10.1016/j.future.2018.01.014. Attached Files Name Description MIMEType Size Downloads Title Using spatiotemporal distribution of geocoded Twitter data to predict US county-level health indices Author(s) Nguyen, Thin orcid.org/0000-0003-3467-8963 Larsen, Mark O'Dea, Bridianne Nguyen, Hung Nguyen, Duc Thanh Yearwood, John orcid.org/0000-0002-7562-6767 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Christensen, Helen Journal name Future generation computer systems Start page 1 End page 9 Total pages 9 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2018 ISSN 0167-739X Keyword(s) Mining spatial and temporal data Statistical features Spatio-temporal features Cluster computing Large-scale parallel and distributed implementation Apache Spark Notes In press Language eng DOI 10.1016/j.future.2018.01.014 Indigenous content off Field of Research 0805 Distributed Computing 0806 Information Systems HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2018, Elsevier Persistent URL http://hdl.handle.net/10536/DRO/DU:30111467 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment Centre for Pattern Recognition and Data Analytics (PRADA) Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 17 Jul 2018, 14:15:19 EST Wed, 18 Jul 2018, 05:00:35 EST Tue, 04 Sep 2018, 06:03:11 EST Wed, 05 Sep 2018, 11:04:04 EST Wed, 05 Sep 2018, 11:09:25 EST Wed, 05 Sep 2018, 11:10:05 EST Fri, 07 Dec 2018, 15:29:35 EST Thu, 07 Mar 2019, 09:26:49 EST Thu, 18 Jul 2019, 11:29:58 EST Fri, 18 Oct 2019, 13:18:32 EST Fri, 18 Oct 2019, 13:19:05 EST Wed, 22 Jan 2020, 12:10:24 EST Wed, 03 Jun 2020, 15:22:09 EST Sat, 11 Jul 2020, 02:31:15 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 398 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Tue, 17 Jul 2018, 14:15:19 EST

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