Lai, Juntao;
(2019)
Urban Place Profiling Using Geo-Referenced Social Media Data.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Achieving a better understanding of urban places benefits a variety of applications, such as urban planning, marketing, tourism, etc., hence it is a long-standing research topic. With the development of internet and mobile technologies, increasing number of individuals are contributing their place-related local knowledge through geo-referenced social media (GSM). This provides us new opportunities to obtain an accurate, detailed and dynamic understanding of urban places in a convenient and efficient way. However, place is a vague and abstract concept that is perceived and communicated by people, therefore it is difficult to formalise place information in digital systems. Furthermore, there are many issues in the extraction, organisation and analysis of place-related information from the disorganised local knowledge in GSM data, such as extracting local place names, estimating vague boundaries, inferring and representing their characteristics, especially in the complex and dynamic urban context. This research first defines a concept of place profile, as a structural information set with four elements, name, location, activities and time. A framework integrating spatial, temporal and semantic analysis is then designed to extract and formalise the information in GSM data regarding to these place elements. Two different approaches of associating GSM data to places are developed and presented, corresponding to the spatial and platial perspectives: the location-led approach which associates data to pre-defined spatial units, and the innovative name-led approach that associates data to place names. It is hoped that the place-profiling theory, the research framework and technical innovations presented in this thesis, can help to advance the formalisation of place in modern digital information systems, and benefit future urban place related studies and applications.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Urban Place Profiling Using Geo-Referenced Social Media Data |
| Event: | UCL (University College London) |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2019. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10072281 |
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