Integrative Urbanism: Using Social Media to Map Activity Patterns for Decision-Making Assessment.
In the context of digital spatial analysis and modeling urban space and processes, this article presents a methodology to update and operationalize Jan Gehl’s traditional observations on activities people engage in public urban space. We aim to show how shared (big) data can help to understand contemporary urban processes and retool urban planning and management for the common good.
The article details how newly computed analyses, such as Shannon-Wiener Index of complexity of activities as well as gravity and centrality indexes, can be implemented to study the experiential qualities of public spaces and development opportunities of urban spaces and neighborhoods. The proposed method is tested in the city of Turku in Finland, where an interactive interface called Turku Open Platform is used by developers and stakeholders, integrating these analytics to decision-making and public discussions.
The so-called human behavior or city social dynamics or practices are not exclusively determined by the morphology of the place or its function, but they have an anthropological basis. Social needs (need for security, for openness, of play, for isolation and encounter, etc.) are anthropological requirements generated and developed socially. In this context, structure, function and form are not sufficient for the generation of social relations, but they can only favor it. By measuring these social needs stored in online social media servers, a new layer of the city is defined and thus, it is available for analysis and eventually intervention. This whole process constitutes the city as a hybrid space that can only be fully comprehended by analyzing the layers of information beyond the spatial form. A great part of this information is registered in online servers, and it is rated and reviewed by apps and social media users. This could be understood as a sample of human behavior or social dynamics and practices to which one can access by mining API data.
Re-organising both location-based social media data, statistical sources and configurational spatial analysis, the presented method unearths emerging activity patterns across scales from local to regional, shifting focus from the traditional functional analysis of urban space towards understanding activities and, thus, the human perspective of use, practices and new agencies.