Brunsdon, Chris and Comber, Alexis (2020) Big issues for big data: challenges for critical spatial data analytics. Journal of Spatial Information Science, 21. pp. 89-98. ISSN 1948-660X

Preview

Download (179kB) | Preview

Abstract

In this paper we consider some of the issues of working with big data and big spatial data and highlight the need for an open and critical framework. We focus on a set of challenges underlying the collection and analysis of big data. In particular, we consider 1) inference when working with usually biased big data, challenging the assumed inferential superiority of data with observations, n, approaching N, the population n -> N. We also emphasise 2) the need for analyses that answer questions of practical significance or with greater emphasis on the size of the effect, rather than the truth or falsehood of a statistical statement; 3) the need to accept messiness in your data and to document all operations undertaken on the data because of this, in support of openness and reproducibility paradigms; and 4) the need to explicitly seek to understand the causes of bias, messiness etc in the data and the inferential consequences of using such data in analyses, by adopting critical approaches to spatial data science. In particular we consider the need to place individual data science studies in a wider social and economic contexts, along with the role of inferential theory in the presence of big data, and issues relating to messiness and complexity in big data.

Item Type:	Article
Additional Information:	Copyright (c) 2020 Chris Brunsdon, Alexis Comber. This work is licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/
Keywords:	big data; inference; CDS; messy data; network data;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG Faculty of Social Sciences > Geography Faculty of Social Sciences > Research Institutes > Maynooth University Social Sciences Institute, MUSSI
Item ID:	14713
Identification Number:	https://doi.org/10.5311/JOSIS.2020.21.625
Depositing User:	Prof. Chris Brunsdon
Date Deposited:	24 Aug 2021 15:35
Journal or Publication Title:	Journal of Spatial Information Science
Publisher:	University of Maine
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

Repository Staff Only(login required)

Item control page

Downloads

Origin of downloads