The University of Tokyo · Social Sciences
Professor Yuji Yoshimura's research lab specializes in urban analytics and behavioral data science, focusing on the spatial and temporal patterns of human behavior in urban and cultural environments. The lab leverages large-scale, anonymized digital trace data—such as Bluetooth, bank card transactions, and museum visitor flows—to study urban dynamics, commercial activity, and visitor experiences in museums and cities. Key research directions include spatial clustering, urban diversity, and the application of data mining techniques like association rule mining to understand human mobility and consumption behavior. The lab bridges urban planning, computational social science, and data analytics to derive actionable insights for city management and cultural institutions.
Figures are computed from collected data and may differ slightly.
Museums often suffer from so-called ‘hypercongestion’, wherein the number of visitors exceeds the capacity of the physical space of the museum. This can potentially be detrimental to the quality of visitors' experiences, through disturbance by the behavior and presence of other visitors. Although this situation can be mitigated by managing visitors' flow between spaces, a detailed analysis of visitor movement is required to realize fully and apply a proper solution to the problem. In this paper
As is often believed that the more centrally located a shop, the higher its sales volume, this paper analyzed relationships between the spatial clustering of retail stores, their respective transaction volumes, and the urban street networks to determine whether, and to what extent, the accessibility and density of a store’s location was correlated with its transaction volume. While this hypothesis is widely accepted, its veracity is underexplored and rarely validated using large-scale empirical
Art museum professionals traditionally rely on observations and surveys to enhance their knowledge of visitor behavior and experience. However, these approaches often produce spatially and temporally limited empirical evidence and measurements. Only recently has the ubiquity of digital technologies revolutionized the ability to collect data about human behavior. Consequently, the greater availability of large-scale datasets based on quantifying visitors' behavior provides new opportunities to ap
In this article, we introduce the method of urban association rules and its uses for extracting frequently appearing combinations of stores that are visited together to characterize shoppers’ behaviors. The Apriori algorithm is used to extract the association rules (i.e. if -> result) from customer transaction datasets in a market-basket analysis. An application to our large-scale and anonymized bank card transaction dataset enables us to output linked trips for shopping all over the city: th
This study attempts to formally quantify Jane Jacob’s notion of urban diversity and examine whether greater diversity actually contributes economic benefits to a neighborhood. Focusing on the number and types of stores at the street level, we use the Shannon–Weaver index to quantify commercial diversity. We then compare the obtained degrees of diversity with store sales volumes obtained through credit card transaction data aggregated in the neighborhood divided into a 200-m grid. The results of
These results suggest that the effect of tolterodine on micturition is gender-specific, suppressing water consumption and urine production in female but not male rats, and decreasing bladder volume. There is a possibility that the reported clinical effects of tolterodine arise through the suppression of fluid consumption.
Art Museums traditionally employ observations and surveys to enhance their knowledge of visitors' behavior and experience. However, these approaches often produce spatially and temporally limited empirical evidence and measurements. Only recently has the ubiquity of digital technologies revolutionized the ability to collect data on human behavior. Consequently, the greater availability of large-scale datasets based on quantifying visitors' behavior provides new opportunities to apply computation
This paper focuses on a planning method for an iterative transportation task by cooperative mobile robots. This task requires the generation of appropriate robot paths and the formation of groups of cooperating robots. In order to realize efficient transportation, the planning architecture consisting of “Path-Generation Phase” and “Strategy-Making Phase” is proposed. The former phase generates robot paths from global environmental information and produces a graph network from the derived robot p
This paper discusses the concepts of aesthetic and nonaesthetic in streetscapes. The nonaesthetic refers to physical elements and spatial compositions that make the aesthetic emerge differently depending on the observer who sees them. Using Ashihara’s (1979) methodology for evaluating aesthetic townscapes and Sibley’s (1959) concept of aesthetic properties, this paper proposes a quantification of the nonaesthetic of streetscapes, focusing on streets in Ginza, Tokyo, and examining the spatial clu
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