Enhancing urban vitality: integrating traditional metrics with big data and socio-economic insights

Abstract

A city is an intricate system where interactions between transport, land use, the environment, and the population occur at various scales. This complexity makes it challenging to predict and govern these interactions. However, big data on human activity patterns allows researchers to discover dynamic, temporary patterns in the activity landscape and understand the choreographies of people's behavior to enhance urban areas' vitality through planning. In this article, we hypothesized that a higher diversity of urban spatio-functional and socio-economic features indicates higher urban vitality in Tallinn, Estonia. We explored multi-sourced indexes to interpret this formation of urban vitality using complex agent variables of location, cluster, diversity, and similar actors generating self-organizing patterns of urban life. We used functional and morphological components and socio-economic data identified as traditional, `slow' vitality measures (SM), and mobile phone location data as dynamic metrics (DM), respectively. We analyzed them in a geographic information system (GIS) environment to measure the types of spatial configurations, temporal variation of vital places, and their correlation. The results indicate a positive correlation (r=0.5116) between the slow metrics and the high mobile phone activity. These correlations demonstrate that cell phone data provides a detailed and accurate view of people's daily rhythms and choreographies. The diversity indicators offer a new method to interpret urban vitality in cities and make planning decisions that support its emergence.