UCL  IRIS
Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at http://www.ucl.ac.uk/finance/research/post_award/post_award_contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
Mapping Spatio-Temporal Patterns of Disabled People in emergencies: A Bayesian approach
  • Publication Type:
    Conference
  • Authors:
    Bantis A, Haworth J, Holloway C, Twigg J
  • Published proceedings:
    Proceedings of the 13th International Conference on GeoComputation
  • Name of conference:
    GeoComputation 2015 Conference
  • Conference place:
    Texas, Dallas, USA
  • Conference start date:
    20/05/2015
  • Conference finish date:
    23/05/2015
Abstract
Emergency management can greatly benefit from understanding the spatiotemporal distribution of individual population groups as this will optimise the allocation of resources and personnel needed in case of an emergency caused by a disaster. This is especially true for people with a disability as they tend to be overlooked by emergency officials. This is generally approached statically using census data,not taking into account the dynamics of disabled peoples concentrations throughout space-time as exhibited in large metropolitan areas such as London. Transport data collected by automatic fare collection methods (such as Transport for London's Oyster card scheme) combined with accessibility covariates (number of opportunities/destinations within an areal unit) have the potential of being a good source for describing the distribution of this concentration. The aim of this study is to explore these datasetsfor use within the scope as described above. The paper attempts to model the distribution using discrete spatio-temporal variation methods. More specifically, it uses Poisson spatio-temporal generalised linear models built within a Bayesian hierarchical modelling framework, ranging from simple to more complexones, while taking into account the spatio-temporal interactions that emerge between the space-time units. The performance of the resulting models in terms of their ability to explain the effects of the covariates as well as predicting future disabled peoples counts were compared relative to each other using the deviance information criterion and posterior predictive check criterion. Analysis of the results revealed a distinct spatiotemporal pattern of disabled users for Oyster card datasets, which deviates from the transportation habits of the rest of population. The effect of the chosen covariates diminishes as model's complexity increases, giving rise to patterns that could potentially be explained by including sociological aspects in the models.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers
Author
Dept of Civil, Environ &Geomatic Eng
Author
Dept of Computer Science
Author
Dept of Civil, Environ &Geomatic Eng
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by