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An ontology-based Modelling framework for detailed spatio-temporal population estimation

An ontology-based Modelling framework for detailed spatio-temporal population estimation
An ontology-based Modelling framework for detailed spatio-temporal population estimation
The whereabouts of population changes over short time scales as people go about their daily lives. A requirement for very detailed population estimates that reflects this variation has been recognised for decades, with myriad application areas that could benefit from this in the public, research and commercial domains. Yet there remains a lack of suitable, extensible and transferrable methods for estimating population at the fine spatial and temporal scales of detail required for these applications. Such population estimation requires the integration of data from diverse sources including core geographic, statistical and the new and emerging sources from sensors and the internet. This integration includes creating appropriate linkages between the spatial, temporal and attribute data domains where these are related. Semantic web technologies provide a simple data model for the integration of such diverse data. Ontologies provide the ability to formalise the relationships between these data and make inferences through those defined relationships. This thesis presents a framework, or structure, into which new, evolving and alternative data can be worked with the goal of generating population estimates at very fine spatial (address level) and temporal (continuous) detail. The three-part modelling framework presented here integrates population in the spatial, temporal and attribute domains to estimate population counts at the level of addresses, on a continuous temporal scale. This thesis introduces, for the first time, the foundations of a semantic web-based modelling solution to this problem in the population estimation domain.
University of Southampton
King, Rebecca
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King, Rebecca
0e803521-f8d6-4603-9bfa-4946fb6d96c3
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f

King, Rebecca (2019) An ontology-based Modelling framework for detailed spatio-temporal population estimation. University of Southampton, Doctoral Thesis, 360pp.

Record type: Thesis (Doctoral)

Abstract

The whereabouts of population changes over short time scales as people go about their daily lives. A requirement for very detailed population estimates that reflects this variation has been recognised for decades, with myriad application areas that could benefit from this in the public, research and commercial domains. Yet there remains a lack of suitable, extensible and transferrable methods for estimating population at the fine spatial and temporal scales of detail required for these applications. Such population estimation requires the integration of data from diverse sources including core geographic, statistical and the new and emerging sources from sensors and the internet. This integration includes creating appropriate linkages between the spatial, temporal and attribute data domains where these are related. Semantic web technologies provide a simple data model for the integration of such diverse data. Ontologies provide the ability to formalise the relationships between these data and make inferences through those defined relationships. This thesis presents a framework, or structure, into which new, evolving and alternative data can be worked with the goal of generating population estimates at very fine spatial (address level) and temporal (continuous) detail. The three-part modelling framework presented here integrates population in the spatial, temporal and attribute domains to estimate population counts at the level of addresses, on a continuous temporal scale. This thesis introduces, for the first time, the foundations of a semantic web-based modelling solution to this problem in the population estimation domain.

Text
Rebecca King PhD thesis final copy - Version of Record
Available under License University of Southampton Thesis Licence.
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Published date: August 2019

Identifiers

Local EPrints ID: 431111
URI: https://eprints.soton.ac.uk/id/eprint/431111
PURE UUID: 87ac4cb7-e81e-4091-88aa-3928309c5109
ORCID for Rebecca King: ORCID iD orcid.org/0000-0003-3953-3413
ORCID for Nicholas Gibbins: ORCID iD orcid.org/0000-0002-6140-9956
ORCID for David Martin: ORCID iD orcid.org/0000-0003-0397-0769

Catalogue record

Date deposited: 23 May 2019 16:30
Last modified: 24 May 2019 00:39

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