Can urban vitality be seen? Video analytics of social interaction and land use

Social interaction is a powerful indicator of the vitality and quality of urban public spaces. In areas such as urban squares, the frequency, duration, and spatial distribution of interpersonal encounters reflect how effectively a space facilitates social interactions within the urban land system. However, these interactions—often brief and subtle—are difficult to observe and quantify in dynamic environments using conventional planning tools, limiting the proper management and design of urban spaces. To address this gap, this study proposes a human-centred, AI-based video analytic approach using computer vision techniques to detect and quantify social interactions. Interactions are classified into four categories—transient, sustained, long-term, and gathering—based on proximity and temporal thresholds, and are analysed in relation to spatial configurations, such as seating, greenery, and visual landmarks. A Social Interaction Intensity Index (SIII) is introduced to evaluate the capacity of different spatial zones to foster social activity, based on identified interaction points and their duration, normalized by area for fair comparison across zones. Rather than interpreting behaviour semantically, the index provides an objective and scalable indicator of how effectively urban land supports social functions and public life. This framework bridges data-driven sensing and human-centred land use policymaking, enabling planners and decision-makers to identify socially underperforming zones, guide spatial reconfiguration, and promote inclusive, evidence-based regeneration strategies that uphold humanistic values in public space design. • Computer vision enables rule-based automatic detection to quantify social interactions in public spaces. • A Social Interaction Intensity Index (SIII) compares vibrancy across different spatial zones. • Interaction types and their spatiotemporal patterns are linked to key urban features. • Provides a data-driven foundation for human-centred public space design and land use optimization.

Authors

Institutions

Publication Details

Journal
Land Use Policy
Published
2025-10-18
DOI
https://doi.org/10.1016/j.landusepol.2025.107818
Citations
1
Primary Topic
Urban Green Space and Health
Type
article
Field-Weighted Citation Impact
1.20
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Can urban vitality be seen? Video analytics of social interaction and land use

Yasir Ali, Asya Natapov, Wenjie Chen
1 citations
Land Use Policy
Urban Green Space and Health
1.20
article

Can urban vitality be seen? Video analytics of social interaction and land use

Yasir Ali, Asya Natapov, Wenjie Chen
article en
1 citations

Abstract

Social interaction is a powerful indicator of the vitality and quality of urban public spaces. In areas such as urban squares, the frequency, duration, and spatial distribution of interpersonal encounters reflect how effectively a space facilitates social interactions within the urban land system. However, these interactions—often brief and subtle—are difficult to observe and quantify in dynamic environments using conventional planning tools, limiting the proper management and design of urban spaces. To address this gap, this study proposes a human-centred, AI-based video analytic approach using computer vision techniques to detect and quantify social interactions. Interactions are classified into four categories—transient, sustained, long-term, and gathering—based on proximity and temporal thresholds, and are analysed in relation to spatial configurations, such as seating, greenery, and visual landmarks. A Social Interaction Intensity Index (SIII) is introduced to evaluate the capacity of different spatial zones to foster social activity, based on identified interaction points and their duration, normalized by area for fair comparison across zones. Rather than interpreting behaviour semantically, the index provides an objective and scalable indicator of how effectively urban land supports social functions and public life. This framework bridges data-driven sensing and human-centred land use policymaking, enabling planners and decision-makers to identify socially underperforming zones, guide spatial reconfiguration, and promote inclusive, evidence-based regeneration strategies that uphold humanistic values in public space design. • Computer vision enables rule-based automatic detection to quantify social interactions in public spaces. • A Social Interaction Intensity Index (SIII) compares vibrancy across different spatial zones. • Interaction types and their spatiotemporal patterns are linked to key urban features. • Provides a data-driven foundation for human-centred public space design and land use optimization.

Land Use PolicyVol. 159
Loughborough University (GB)
Openalex Percentile: Top 17%
Urban Green Space and Health
1.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.