Modelling urban vibrancy with mobile phone and OpenStreetMap data

Area of Science:

• Urban Studies
• Human Geography
• Computational Social Science

Background:

• Urban vibrancy, defined by human activity and interaction, is crucial for city livability.
• Traditional urban studies face data collection challenges regarding human behavior and urban features.
• Novel data sources are needed to accurately assess urban vibrancy and its drivers.

Purpose of the Study:

• To investigate the relationship between urban features and urban vibrancy using novel data.
• To determine if different urban features contribute to vibrancy differently across various age groups.
• To explore the role of 'third places' in fostering urban social interactions and vibrancy.

Main Methods:

• Utilized OpenStreetMap data for urban features and Italian census data for demographic information.
• Employed spatial models to quantitatively analyze the correlation between urban features and vibrancy.
• Integrated mobile phone data with crowdsourced urban features for a comprehensive analysis.

Main Results:

• Confirmed a strong association between specific urban features and measurable urban vibrancy.
• Identified 'third places' (social hubs) as critical contributors to urban vibrancy.
• Observed variations in the impact of urban features on vibrancy across different age demographics.

Conclusions:

• Mobile phone data combined with crowdsourced urban features offer a powerful approach to understanding urban vibrancy.
• Urban planning should prioritize the development and accessibility of social spaces to enhance city vibrancy.
• Future research can leverage these methods to explore urban dynamics and improve city design.