Digital behavior spatial model: Integrating Takunai and Google Maps API for behavioral-based urban space analysis in the Kupang Waterfront Area, Indonesia

Abstract

The rapid expansion of urbanization, accompanied by the growing implementation of smart city initiatives, has generated an increasing demand for data-driven approaches capable of documenting and interpreting actual human behavior within urban environments. Conventional techniques for behavioral observation, including questionnaire-based surveys and manual behavioral mapping, frequently encounter limitations associated with temporal coverage, scalability, continuity of observation, and potential observer bias. In response to these challenges, this study proposes a Digital Behavior Spatial Model (DBSM) that integrates Takunai, a mobile-based spatial-temporal tracking application, with Google Maps API to reconstruct urban spatial configurations from behavioral traces generated through real-world human activities. The research was undertaken in the Kupang Bay waterfront area, Indonesia, using GPS trajectories, timestamps, visitation frequencies, movement paths, and location-based service data collected throughout a fourteen-day observation period. A combination of spatial-temporal analysis, density mapping, activity-node extraction, movement-corridor analysis, and spatial-network analysis was employed to identify recurring patterns of urban activity and movement. The findings demonstrate the emergence of multiple activity nodes, dominant movement corridors, and a semi-polycentric spatial configuration characterized by strong recreational-commercial interactions along the waterfront. Pronounced temporal variations were also identified, demonstrating a process of spatial reorganization between daytime economic activities and nighttime recreational and social functions. These findings indicate that urban space is dynamically produced through collective behavioral interactions rather than being determined exclusively by formal planning regulations and predetermined spatial classifications. The proposed DBSM contributes theoretically to the fields of Digital Urbanism and Complex Adaptive Systems while providing practical implications for smart city development, continuous urban monitoring, evidence-based urban design, digital urban governance, and adaptive spatial planning.