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The COVID-19 pandemic has resulted in cities implementing lockdown measures, causing unprecedented disruption (e.g. school/shop/office closures) to urban life often extending over months. With the spread of COVID-19 now being relatively contained, many cities have started to ease their lockdown restrictions by phases. Following the phased recovery strategy proposed by the UK government following the first national lockdown, this paper utilises Greater London as its case study, selecting three main reopening measures (i.e., schools, shops and hospitality reopening). This paper applies sentiment analysis and topic modelling to explore public opinions expressed via Twitter. Our findings reveal that public attention towards the reopening measures reached a peak before the date of policy implementation. The attitudes expressed in discussing reopening measures changed from negative to positive. Regarding the discussed topics related to reopening measures, we find that citizens are more sensitive to early-stage reopening than later ones. This study provides a time-sensitive approach for local authorities and city managers to rapidly sense public opinion using real-time social media data. Governments and policymakers can make use of the framework of sensing public opinion presented herein and utilise it in leading their post-lockdown cities into an adaptive, inclusive and smart recovery.

Crowdsourced data such as social media data, points of interest and geotagged images has attracted the attention of urban researchers, as it provides first-hand information regarding human activities, perception and the interaction with the built environment, helping researchers illuminate a richer sense of what cities are all about from data itself. Despite the appealing potential of crowdsourced data in answering urban-related questions, there are challenges inherent to the whole process of transforming immense data into accurate and actionable insights in the urban domain, which overshadow the benefits of this type of data in well-designed cases of use. This thesis focuses on leveraging crowdsourced data in sensing urban dynamics by implementing advanced machine learning techniques to deal with hitherto unsolved challenges. The main aim is to contribute to the current knowledge on crowdsourced data-driven studies for better management and planning of cities. The thesis starts with a systematic review of crowdsourced data-driven studies in the context of urban research in terms of concepts, data sources, applications and methods evidenced in the literature. The thesis further elaborates on the challenges of crowdsourced data mining and highlights the potential of state-of-the-art machine learning techniques in handling those challenges. An analytical framework of crowdsourced data-intensive studies for sensing urban dynamics is then formulated. Following the framework, this thesis then demonstrates its application by carrying out three empirical studies of the Greater London – sensing urban function from points of interest data, sensing activity pattern from location-based social media data, and sensing public opinion from social media data. These studies illustrate the capability of machine learning techniques in dealing with the challenges of crowdsourced data-driven studies in the context of urban research. Finally, this thesis discusses the implications of the three studies, summarised as evidence-based decision making for planning and near real-time urban management for governance.

The penetration of devices integrated with location-based services and internet services has generated massive data about the everyday life of citizens and tracked their activities happening in cities. Crowdsourced data, such as social media data, POIs data and collaborative websites, generated by the crowd, has become fine-grained proxy data of urban activity and widely used in research in urban studies. However, due to the heterogeneity of data types of crowdsourced data and the limitation of previous studies mainly focusing on a specific application, a systematic review of crowdsourced data mining for urban activity is still lacking. In order to fill the gap, this paper conducts a literature search in the Web of Science database, selecting 226 highly related papers published between 2013 and 2019. Based on those papers, the review firstly conducts a bibliometric analysis identifying underpinning domains, pivot scholars and papers around this topic. The review also synthesises previous research into three parts: main applications of different data sources and data fusion; application of spatial analysis in mobility patterns, functional areas and event detection; application of socio-demographic and perception analysis in city attractiveness, demographic characteristics and sentiment analysis. The challenges of this type of data are also discussed in the end. This study provides a systematic and current review for both researchers and practitioners interested in the applications of crowdsourced data mining for urban activity., This research is funded by a scholarship from the China Scholarship Council