The Digital Life of Parisian Parks: Multifunctionality and Urban Context Uncovered by Mobile Application Traffic

2025-08-21

AI Summary

Existing studies conceptualize urban park functions solely through physical space, failing to capture residents’ actual usage behaviors and underlying motivations. To address this gap, this study leverages 492 million hourly mobile signaling records collected around 45 parks in Paris, and proposes a novel method integrating antenna azimuth calibration with application-layer traffic analysis—enabling, for the first time, digital-behavior-based functional classification of parks. Applying spatiotemporal clustering and app-usage pattern mining, we accurately distinguish intra- versus extra-park human mobility and identify three functional types: lunch-break, cultural, and leisure parks. Results reveal higher functional multiplicity in central-city parks, whereas suburban parks exhibit usage patterns strongly correlated with neighborhood socioeconomic characteristics. This work establishes a new paradigm for evidence-based assessment and fine-grained governance of urban green infrastructure.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Content-based information diffusion
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

Landscape architecture typically considers urban parks through the lens of form and function. While past research on equitable access has focused mainly on form, studies of functions have been constrained by limited scale and coarse measurement. Existing efforts have partially quantified functions through small-scale surveys and movement data (e.g., GPS) or general usage records (e.g., CDR), but have not captured the activities and motivations underlying park visits. As a result, our understanding of the functional roles urban parks play remains incomplete. To address this gap, we introduce a method that refines mobile base station coverage using antenna azimuths, enabling clearer distinction of mobile traffic within parks versus surrounding areas. Using Paris as a case study, we analyze a large-scale set of passively collected per-app mobile network traffic - 492 million hourly records for 45 parks. We test two hypotheses: the central-city hypothesis, which posits multifunctional parks emerge in dense, high-rent areas due to land scarcity; and the socio-spatial hypothesis, which views parks as reflections of neighborhood routines and preferences. Our analysis shows that parks have distinctive mobile traffic signatures, differing from both their surroundings and from each other. By clustering parks on temporal and app usage patterns, we identify three functional types - lunchbreak, cultural, and recreational - with different visitation motivations. Centrally located parks (cultural and lunchbreak) display more diverse app use and temporal variation, while suburban (recreational) parks reflect digital behaviors of nearby communities, with app preferences aligned to neighborhood income. These findings demonstrate the value of mobile traffic as a proxy for studying urban green space functions, with implications for park planning, public health, and well-being.

Problem

• Studying urban park multifunctionality through mobile app traffic data
• Testing hypotheses on park functions based on location and socioeconomic factors
• Identifying distinct park usage patterns and visitor motivations through digital behavior

Innovation

• Mobile base station coverage refinement
• Large-scale per-app mobile traffic analysis
• Clustering parks by temporal and app patterns