Understanding urban infrastructure via big data: the case of Belo Horizonte
Aksel Ersoy and
Klaus Chaves Alberto
Regional Studies, Regional Science, 2019, vol. 6, issue 1, 374-379
Abstract:
One major impact of the global economic crisis is the way it has deepened inequalities around the world. Infrastructure remains essential within this debate as it provides wider health, economic and environmental benefits for society beyond the conventional calculations of cash returns. With the potential exploration of big data, cities now face challenges as well as opportunities to use a series of static and dynamic datasets. Big data methods are offering new opportunities to design decision-making models for urban planning and management. The combination of social media, census, sensors and traditional data gives a new perspective to solve modern urban challenges through a holistic and inclusive approach. Nevertheless, the BOLD methods are relatively new and have not been applied in the context of urban infrastructure. This paper explores whether BOLD methods can help one reconceptualize urban infrastructure not only with technical and operational characteristics but also with social values in the context of the Global South. To demonstrate, Belo Horizonte, Brazil, is used as a case study.
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://hdl.handle.net/10.1080/21681376.2019.1623068 (text/html)
Access to full text is restricted to subscribers.
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:taf:rsrsxx:v:6:y:2019:i:1:p:374-379
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rsrs20
DOI: 10.1080/21681376.2019.1623068
Access Statistics for this article
Regional Studies, Regional Science is currently edited by Alasdair Rae
More articles in Regional Studies, Regional Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().