dc.contributor.author |
Adams, B |
en |
dc.contributor.author |
Gahegan, Mark |
en |
dc.contributor.editor |
Miller, JA |
en |
dc.contributor.editor |
OSullivan, D |
en |
dc.contributor.editor |
Wiegand, N |
en |
dc.coverage.spatial |
Montreal, Canada |
en |
dc.date.accessioned |
2017-05-24T05:31:28Z |
en |
dc.date.issued |
2016-01-01 |
en |
dc.identifier.citation |
9th International Conference on Geographic Information Science (GIScience), Montreal, Canada, 27 Sep 2016 - 30 Sep 2016. Editors: Miller JA, OSullivan D, Wiegand N . Lecture Notes in Computer Science: GIScience 2016: Geographic Information Science. Springer. 9927: 243-258. 01 Jan 2016 |
en |
dc.identifier.isbn |
978-3-319-45737-6 |
en |
dc.identifier.issn |
0302-9743 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/33036 |
en |
dc.description.abstract |
The intrinsic connection between place, space, and time in narrative texts is the subject of chronotopic literary analysis. We take the notion of the chronotope and apply it to exploratory analysis of unstructured big data. Exploratory chronotopic data analysis provides a data-driven perspective on how place, space, and time are connected in large, crowdsourced text collections. In this study, we processed the English Wikipedia text to find all co-occurrences of named places and dates and discovered that times are linked to places in a large majority of cases. We analyzed these millions of connections between places and dates and discovered a number of interesting trends. Because of the scale of the data involved, we suggest that chronotopic data analysis will lead to the development of new data models and methods for geographic information science and related fields, such as digital humanities. |
en |
dc.publisher |
Springer |
en |
dc.relation.ispartof |
9th International Conference on Geographic Information Science (GIScience) |
en |
dc.relation.ispartofseries |
Lecture Notes in Computer Science: GIScience 2016: Geographic Information Science |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.sherpa.ac.uk/romeo/issn/0302-9743/ |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.subject |
Science & Technology |
en |
dc.subject |
Technology |
en |
dc.subject |
Physical Sciences |
en |
dc.subject |
Computer Science, Artificial Intelligence |
en |
dc.subject |
Computer Science, Information Systems |
en |
dc.subject |
Computer Science, Theory & Methods |
en |
dc.subject |
Geosciences, Multidisciplinary |
en |
dc.subject |
Computer Science |
en |
dc.subject |
Geology |
en |
dc.subject |
Place |
en |
dc.subject |
Time |
en |
dc.subject |
Chronology |
en |
dc.subject |
Historical geographic information science |
en |
dc.subject |
Big data |
en |
dc.subject |
Volunteered geographic information |
en |
dc.subject |
SPACE |
en |
dc.subject |
TIME |
en |
dc.subject |
ISSUES |
en |
dc.subject |
WEB |
en |
dc.title |
Exploratory Chronotopic Data Analysis |
en |
dc.type |
Conference Item |
en |
dc.identifier.doi |
10.1007/978-3-319-45738-3_16 |
en |
pubs.begin-page |
243 |
en |
pubs.volume |
9927 |
en |
dc.description.version |
AM - Accepted Manuscript |
en |
dc.rights.holder |
Copyright: Springer |
en |
pubs.end-page |
258 |
en |
pubs.finish-date |
2016-09-30 |
en |
pubs.publication-status |
Published |
en |
pubs.start-date |
2016-09-27 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Proceedings |
en |
pubs.elements-id |
543385 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
School of Computer Science |
en |
dc.identifier.eissn |
1611-3349 |
en |
pubs.record-created-at-source-date |
2017-05-24 |
en |