EconPapers    
Economics at your fingertips  
 

The Analysis of Big Data on Cites and Regions - Some Computational and Statistical Challenges

Laurie A. Schintler and Manfred Fischer

No 2018/08, Working Papers in Regional Science from WU Vienna University of Economics and Business

Abstract: Big Data on cities and regions bring new opportunities and challenges to data analysts and city planners. On the one side, they hold great promise to combine increasingly detailed data for each citizen with critical infrastructures to plan, govern and manage cities and regions, improve their sustainability, optimize processes and maximize the provision of public and private services. On the other side, the massive sample size and high-dimensionality of Big Data and their geo-temporal character introduce unique computational and statistical challenges. This chapter provides overviews on the salient characteristics of Big Data and how these features impact on paradigm change of data management and analysis, and also on the computing environment.

Keywords: massive sample size; high-dimensional data; heterogeneity and incompleteness; data storage; scalability; parallel data processing; visualization; statistical methods (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-geo, nep-pay and nep-ure
Date: 2018-10-28
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://epub.wu.ac.at/6637/ original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wiw:wus046:6637

Access Statistics for this paper

More papers in Working Papers in Regional Science from WU Vienna University of Economics and Business Welthandelsplatz 1, 1020 Vienna, Austria.
Bibliographic data for series maintained by WU Library ().

 
Page updated 2020-01-11
Handle: RePEc:wiw:wus046:6637