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Discovering and Accessing Sub-national Statistics and Geospatial Data of East Asian Countries: Trends and Obstacles

dc.contributor.authorYang, Jungwon
dc.date.accessioned2019-08-14T20:17:38Z
dc.date.available2019-08-14T20:17:38Z
dc.date.issued2015-01-07
dc.identifier.citationIASSIST Quarterly, Vol 38 No 1 (2015): Spring 2014, pp.18-23en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/150625
dc.description.abstractThe increasing use of geographic information systems (GIS), combined with the wider availability of sub-national statistics, has recently opened up new possibilities for more interdisciplinary academic research in social sciences. Social science researchers have become more and more interested in combining geographic analysis, traditional quantitative and statistical methods in order to test hypotheses and present arguments in more effective ways. However, discovering, accessing, and using the international geospatial data and statistics is still a challenge for the researchers. As the Organisation for Economic Co-operation and Development (OECD) Global Science Forum report (2013) noted, information about the existence of micro-data and the availability of re-use is often difficult to find. Language barriers, as well as legal, cultural, and technological obstacles often exacerbate the difficulties of re-using the discovered data. In this paper, I will review types of geospatial data and sub-national statistics of East Asian countries have recently developed via central and local governments and academic institutions. Also, I will discuss the obstacles researchers encountered while using such data in their research.en_US
dc.language.isoen_USen_US
dc.publisherIASSISTen_US
dc.subjectstatistics, geospatial data, East Asia, data discovery and accessen_US
dc.titleDiscovering and Accessing Sub-national Statistics and Geospatial Data of East Asian Countries: Trends and Obstaclesen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelInformation and Library Science
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumLibrary, University of Michiganen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/150625/1/IASSISTQuarterlyvol381_yang.pdf
dc.identifier.sourceIASSIST Quarterlyen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-6287-297Xen_US
dc.description.filedescriptionDescription of IASSISTQuarterlyvol381_yang.pdf : Journal Article
dc.identifier.name-orcidYang, Jungwon; 0000-0001-6287-297Xen_US
dc.owningcollnameLibrary (University of Michigan Library)


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