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Pforte, Lars, Brunsdon, Chris, Cahalane, Conor and Charlton, Martin (2017) Data imputation in a short-run space-time series: A Bayesian approach. Environment and Planning B: Planning and Design, 45 (5). pp. 864-887. ISSN 1472-3417
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Abstract
This paper discusses a project on the completion of a database of socio-economic indicators across the European Union for the years from 1990 onward at various spatial scales. Thus the database consists of various time series with a spatial component. As a substantial amount of the data was missing a method of imputation was required to complete the database. A Markov Chain Monte Carlo approach was opted for. We describe the Markov Chain Monte Carlo method in detail. Furthermore, we explain how we achieved spatial coherence between different time series and their observed and estimated data points.
| Item Type: | Article |
|---|---|
| Keywords: | Time series; Markov Chain Monte Carlo; data imputation; |
| Academic Unit: | 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: | 11351 |
| Identification Number: | 10.1177/0265813516688688 |
| Depositing User: | Conor Cahalane |
| Date Deposited: | 16 Oct 2019 16:21 |
| Journal or Publication Title: | Environment and Planning B: Planning and Design |
| Publisher: | SAGE Publications |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/11351 |
| 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 |
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