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Ongoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science. Responding to the identified research gap, a case study that hybridizes different smart data to understand pedestrian jaywalking as a reflection of urban spaces that need further improvement is presented. Finally, some major research challenges and directions are also highlighted.

With many people’s life and work more and more Internet-dependent, we yet have to face the persistent digital divide. In the U.S., funding programs in the past allocated resources to incumbent Internet Service Providers (ISP) who failed to connect the rural and low-income communities. This is largely due to an information mismatch - the financially strong ISPs do not necessarily have the vision to connect the marginalized community. This project sets out to build a multidimensional profile database of ISPs that emphasizes the potential of serving the underserved or unserved areas. By applying the Textual Entailment Natural Language Processing algorithm, we infer the vision, goals, and identity of more than 1,000 ISPs using a large number of self-description texts from their websites. Then we created an ISP typology based on the socio-economic profiles of the service areas of these ISPs and their business vision. Deviating from the traditional bifurcating definition of “for-profit” and “non-profits” business, this ISP typology illustrates evident geographical, social, and service quality level division of the current Internet services in the U.S. Planners and policymakers could use this typology to create specific funding programs that effectively address the current digital divide.

As the living tissue connecting urban places, streets play significant roles in driving city development, providing essential access, and promoting human interactions. Understanding street activities and how these activities vary across different streets is critical for designing both efficient and livable streets. However, current street classification frameworks primarily focus on either streets’ functions in transportation networks or their adjacent land uses rather than actual activity patterns, resulting in coarse classifications. This research proposes an activity-based street classification framework to categorize street segments based on their temporal street activity patterns, which is derived from high-resolution de-identified and privacy-enhanced mobility data. We then apply the proposed framework to 18,023 street segments in the City of Boston and reveal 10 distinct activity-based street types (ASTs). These ASTs highlight dynamic street activities on streets, which complements existing street classification frameworks, which focus on the static or transportation characteristics of the street segments. Our results show that a street classification framework based on temporal street activity patterns can identify street categories at a finer granularity than current methods, which can offer useful implications for state-of-the-art urban management and planning. In particular, we find that our classification distinguishes better those streets where crime is more prevalent than current functional or contextual classifications of streets.

Research on the relationship between space and social interaction has focused on indoor spaces, such as museums and offices. However, empirical evidence on the connection between the intensity of social interaction and outdoor public spaces is still lacking. Applying machine learning algorithms to a 9-hour time-lapse video of an urban park, we decipher the effects of two spatial features, edges, and landmarks, on visitors’ activities. We identified dynamic visitor groups in the videos through a graph-based method and mapped the clustering pattern of interaction activities over time and space. In parallel, we used a computer vision algorithm to delineate fixed objects (notably the harbourfront, landside park boundary, a carousel, four benches, three pavilions, and four heart-shaped seating) and dynamic edges (formed by moveable furniture as park visitors repositioned them) onsite. We found that dynamic edges formed by moveable furniture and the fixed edge of a visual landmark consistently attracted more social interaction and group activities. In designing public spaces that encourage group activities, urban planners and designers can leverage the combination of fixed objects and flexible furniture to maximise the choices for visitors and curate a more engaging public open space.

The experience of walking through a city is influenced by amenities and the visual qualities of its built environment. This paper uses thousands of pedestrian trajectories obtained from GPS signals to construct a desirability index for streets in Boston. We create the index by comparing the actual paths taken by pedestrians with the shortest path between any origin-destination pairs. The index captures pedestrians' willingness to deviate from their shortest path and provides a measure of the scenic and experience value provided by different parts of the city. We then use computer vision techniques combined with georeferenced data to measure the built environment of streets. We show that desirable streets have better access to public amenities such as parks, sidewalks, and urban furniture. They are also sinuous, visually enclosed, have less complex facades, and have more diverse business establishments. These results further our understanding of the value that the built environment brings to pedestrians, enhancing our capacity to design more lively and functional environments.

Crime and perception of safety are two intertwined concepts affecting the quality of life and the economic development of a society. However, few studies have quantitatively examined the difference between the two due to the lack of granular data documenting public perceptions in a given geographic context. Here, by applying a pre-trained scene understanding algorithm, we infer the perception of safety score of streetscapes for census block groups in the city of Houston using a large number of Google Street View images. Then, using this inferred perception of safety, we create “perception bias” categories for each census block group. These categories capture the level of mismatch between people’s visually perceived safety and the actual crime rates. This measure provides scalable guidance in deciphering the relationship between the built environment and crime. Finally, we construct a series of models to examine the “perception bias” with static and dynamic urban factors, including socioeconomic features (e.g., unemployment rate and ethnic compositions), urban diversity (e.g., number and diversity of Points of Interest), and urban livelihood (i.e., hourly count of visitors). Analytical and numerical results suggest that the association between characteristics of urban space and “perception bias” over crime could be paradoxical. On the one hand, neighborhoods with a higher volume of day-time visitors appear more likely to be safer than it looks (low crime rate and low safety score). On the other hand, those with a higher volume of night-time visitors are likely to be more dangerous than it looks (high crime rate). The findings add further knowledge to the long-recognized relationship between built environment and crime as well as highlight the perception of safety in cities, which in turn enhances our capacity to design urban management strategies that prevent the emergence of extreme “perception bias”.