This thesis introduces a framework for modelling the social dynamic of an urban landscape from multiple and disparate real-time datasets. It seeks to bridge the gap between artificial simulations of human behaviour and periodic real-world observations. The approach is data-intensive, adopting open-source programmatic and visual analytics. The result is a framework that can rapidly produce contextual insights from samples of real-world human activity – behavioural data traces. The framework can be adopted standalone or integrated with other models to produce a more comprehensive understanding of people-place experiences and how context affects behaviour. The research is interdisciplinary. It applies emerging techniques in cognitive and spatial data sciences to extract and analyse latent information from behavioural data traces located in space and time. Three sources are evaluated: mobile device connectivity to a public Wi-Fi network, readings emitted by an installed mobile app, and volunteered status updates. The outcome is a framework that can sample data about real-world activities at street-level and reveal contextual variations in people-place experiences, from cultural and seasonal conditions that create the ‘social heartbeat’ of a landscape to the arrhythmic impact of abnormal events. By continuously or frequently sampling reality, the framework can become self-calibrating, adapting to developments in land-use potential and cultural influences over time. It also enables ‘opportunistic’ geographic information science: the study of unexpected real-world phenomena as and when they occur. The novel contribution of this thesis is to demonstrate the need to improve understanding of and theories about human-environment interactions by incorporating context-specific learning into urban models of behaviour. The framework presents an alternative to abstract generalisations by revealing the variability of human behaviour in public open spaces, where conditions are uncertain and changeable. It offers the potential to create a closer representation of reality and anticipate or recommend behaviour change in response to conditions as they emerge
