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Measuring urban vitality through human mobility patterns
Understanding how people move and use places in the city represents a relevant theme in urbanism research. However, quantitatively measuring these spatial phenomena has been sometimes overlooked, possibly because of the arduous process of collecting detailed data for extensive urban contexts. The availability of large spatial data sets containing human mobility information at a higher spatiotemporal resolution represents an unprecedented op¬portunity to enhance the understanding of the phenomena. However, despite the promising results in current work, this progress has not been directly applied in urbanism research: new data and quantitative methods can be integrated into the spatial analysis to test exist¬ing theories and unveil new spatial characteristics. This research explores this opportunity, adopting a computational method to quantitatively measure long-standing spatial qualities, such as urban vitality, related to the human activity and the usage of places over time. Employing up-to-date techniques from quantitative methods and data science, it investigates the spatiotemporal patterns of human mobility and their relationship with the spatial distribution of activities in the city of London. Results unveil the variations of temporal pat¬terns of human activity and the presence of people in places, which are critical to evaluate the vitality of the urban space. Results also show that it is possible to measure spatial qualities and classify urban space according to features that are not strictly related to the morphology of the built environment. The approach proposed in this research enhances the methods of analysis available to urbanism research, aiming at integrating new spatial information and quantitative technique into the standard procedures employed in urban analysis and planning. This work also contributes to the transdisciplinary research about urban dynamics, strengthening the overall understanding of cities.
Measuring urban vitality through human mobility patterns
Understanding how people move and use places in the city represents a relevant theme in urbanism research. However, quantitatively measuring these spatial phenomena has been sometimes overlooked, possibly because of the arduous process of collecting detailed data for extensive urban contexts. The availability of large spatial data sets containing human mobility information at a higher spatiotemporal resolution represents an unprecedented op¬portunity to enhance the understanding of the phenomena. However, despite the promising results in current work, this progress has not been directly applied in urbanism research: new data and quantitative methods can be integrated into the spatial analysis to test exist¬ing theories and unveil new spatial characteristics. This research explores this opportunity, adopting a computational method to quantitatively measure long-standing spatial qualities, such as urban vitality, related to the human activity and the usage of places over time. Employing up-to-date techniques from quantitative methods and data science, it investigates the spatiotemporal patterns of human mobility and their relationship with the spatial distribution of activities in the city of London. Results unveil the variations of temporal pat¬terns of human activity and the presence of people in places, which are critical to evaluate the vitality of the urban space. Results also show that it is possible to measure spatial qualities and classify urban space according to features that are not strictly related to the morphology of the built environment. The approach proposed in this research enhances the methods of analysis available to urbanism research, aiming at integrating new spatial information and quantitative technique into the standard procedures employed in urban analysis and planning. This work also contributes to the transdisciplinary research about urban dynamics, strengthening the overall understanding of cities.
Measuring urban vitality through human mobility patterns
Sulis, Patrizia (author)
2019-08-28
Doctoral thesis, UCL (University College London).
Theses
Electronic Resource
English
DDC:
710
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