Identifying spatio-temporal hotspots of human activity that are popular non-work destinations

Abstract

The improved temporal and spatial granularity of data now available from current information technologies offers an opportunity to study previously unexplored dimensions of the relationship between built environment and social outcomes. Within the field of urban studies, an old question worth revisiting with these new technologies is how to best trace the spatial boundaries that circumscribe a place or location to explore non-work activity. In this study, we explore a data-driven definition of places as units of analysis that can be used to explore non-work activity in Singapore. Such a definition of place characterizes an urban space in terms of its concentration of activity and the topology of the built environment–features that are especially important to urban planners. We utilize available smartphone data to develop a systematic framework to identify locations with a concentrated human presence. Using a cylinder moving over a grid representing Singapore, we scan aggregated smartphones locational requests (by time and cell), identifying areas with atypical high concentrations at a given time. Our tool identified 93 places with a concentrated human presence. Direct observation of six of these places at the selected times in conjunction with additional transportation and population data indicated that the topology of commercial establishments provided a strong approximation of non-work activity at a given time and place. Having established the relevance of commercial establishments in approximating non-work activity, then points-of-interest data within the 93 derived places are used to propose a typology of commercial patches, based on their spatial configuration. Nine metrics of the geometry and topology of patches of establishments, such as compacity and their dependence on proximity to shopping malls, were developed. These combined variables revealed more temporal and spatial variety within locations than had previously been recognized. The most popular places for non-work activity were densely configured with various commercial sub-spaces or patches appealing to different lifestyles and income groups. This study suggests that a location/place can be best defined as a highly detailed, multi-faceted, and always evolving area of activity rather than as a fixed location with temporal and unmovable boundaries. Suggesting a dynamic redefinition of location/place that builds on other recent work, this work offers potential contributions to locational models for non-work activity.

Keywords

Non-work travel pattern detection cell phone data machine learning transportation Density-Based Spatial Clustering and Application with Noise clustering

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