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Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demographic structures. While some current population synthesizers consider the structural relationships among agents from the same household, there remains room for refinement in this domain, which could potentially introduce biases in subsequent disease transmission simulations. In response, this study unveils a novel methodology for generating synthetic populations tailored for infectious disease transmission simulations. By integrating insights from microsample-derived household structures, we employ a heuristic combinatorial optimizer to recalibrate these structures, subsequently yielding synthetic populations that faithfully represent agent structural relationships. Implementing this technique, we successfully generated a spatially-explicit synthetic population encompassing over 17 million agents for Shenzhen, China. The findings affirm the method's efficacy in delineating the inherent statistical structural relationship patterns, aligning well with demographic benchmarks at both city and subzone tiers. Moreover, when assessed against a stochastic agent-based Susceptible-Exposed-Infectious-Recovered model, our results pinpointed that variations in population synthesizers can notably alter epidemic projections, influencing both the peak incidence rate and its onset. Author summary: Exploring the intricacies of infectious disease transmission becomes profoundly insightful with tools that can model complex, nonlinear interactions. Among these, agent-based models stand out, primarily due to their ability to mirror not just expansive populations but also detailed individual-level interactions. While synthetic populations act as a vital surrogate for the real-world demographic when comprehensive datasets are elusive, their creation isn't without challenges. Present synthesizers, though acknowledging intra-household relationships, still have nuances left unexplored. Addressing this, our research presents a pioneering method tailored for crafting synthetic populations, especially for disease spread simulations. This methodology, grounded in data from genuine household structures, utilizes an optimization strategy to calibrate these relationships effectively. Applying this, we meticulously constructed a synthetic population for Shenzhen, China, encompassing over 17 million agents. Our results underscored the technique's prowess in accurately emulating population structures, adhering commendably to demographic metrics on both city and subzone scales. Crucially, our findings also illuminated that the specific choice of synthesizer can have a profound bearing on epidemic simulations, affecting crucial attributes like the peak and timing of disease incidence. [ABSTRACT FROM AUTHOR]

Exploring the spatiotemporal characteristics of park visitors and the "push and pull" factors that shape this mobility is critical to designing and managing urban parks to meet the demands of rapid urbanization. In this paper, 56 parks in Shenzhen were studied in 2019. First, cell phone signaling data were used to extract information on visitors' departure locations and destination parks. Second, the bivariate Moran's I and bivariate local Moran's I (BiLISA) methods were used to identify the statistical correlation between the factors of the built environment and the park recreation trips. Finally, linear regression models were constructed to quantify the factors influencing the attractiveness of the park. Our study showed the following: (1) Recreation visitors at large parks varied significantly among population subgroups. Compared with younger adults, teenagers and older adults traveled lower distances and made fewer trips, and in particular, older adults of different genders differed significantly in park participation. (2) Recreational trips in large parks were related to the functional layout of the built environment around their residence. In areas with rich urban functions (e.g., southern Shenzhen), trips to large parks for leisure are more aggregated. (3) The findings reinforce the evidence that remote sensing data for urban vegetation can be an effective factor in characterizing park attractiveness, but the explanatory power of different vegetation data varies widely. Our study integrated the complementary human activity and remote sensing data to provide a more comprehensive understanding of urban park use and preferences. This will be important for future park planning. [ABSTRACT FROM AUTHOR]

Tourism networks are an important research part of tourism geography. Despite the significance of transportation in shaping tourism networks, current studies have mainly focused on the "daily behavior" of urban travel at the expense of tourism travel, which has been regarded as an "exceptional behavior". To fill this gap, this study proposes a framework for exploring the spatial and temporal characteristics of urban tourism travel by taxi. We chose Shenzhen, a densely populated mega-city in China with abundant tourism resources, as a case study. First, we extracted tourist trips from taxi trajectories and used kernel density estimation to analyze the spatial aggregation characteristics of tourist trip origins. Second, we investigated the spatial dependence of tourist trips using local spatial autocorrelation analysis (Getis-Ord Gi*). Third, we explored the correlations between the tourist trip origins and urban geographic contextual factors (e.g., catering services and transportation facilities) using a geographically weighted regression model. The results show the following: (1) the trends between the coverage of tourist travel networks and the volume of tourist trips are similar; (2) the spatial interaction intensity of urban tourism has grouping and hierarchical characteristics; and (3) the spatial distribution of tourist trips by taxi is uneven and influenced by the distribution of urban morphology, tourism resources, and the preferences of taxi pick-up passengers. Our proposed framework and revealed spatial and temporal patterns have implications for urban tourism traffic planning, tourism product development, and tourist flow control in tourist attractions. [ABSTRACT FROM AUTHOR]

Investigating physical encounters among individuals is important for various applications such as infectious disease modeling and friend recommendation. As enclosed spaces, public transit systems (e.g., buses and metros) in densely populated areas are locations where physical encounters occur numerously. Currently, encounter networks in bus systems have been investigated with the help of smart card data (SCD); however, no attempt has been made toward the metro systems, which is more challenging as the travel behaviors of metro passengers are complex but not recorded in the SCD in detail. This study proposed a novel framework for investigating physical encounters of individuals in urban metro systems with SCD. First, we developed a method to match passengers to specific trains, which can allow the segmentation of individual trips inside a metro system. Second, we proposed an approach to measuring the encounter frequencies and durations of each passenger pair by synthesizing their encounter behaviors in not only the train space, but also the entering/exiting space and the transfer space. Finally, using the SCD of Shenzhen, China, we analyzed the physical encounter patterns at a population scale, and demonstrated the potential of applying the encounter network to trace the spread of infectious diseases. Overall, this study provided a framework for evaluating physical encounters in metro systems with SCD, and revealed the underlying physical encounter patterns in the metro system of a metropolitan city, which is of considerable application value. • We made the first attempt to investigate physical encounters in urban metro systems. • We developed a method to segment passenger trips inside a metro system. • We revealed the statistical properties of physical encounters in a metro system. • We applied the derived encounter network in tracing the spread of infectious disease. [ABSTRACT FROM AUTHOR]

The significance of urban metro stations extends beyond their roles as transport nodes in a city. Their surroundings are usually well-developed and attract a lot of human activities, which make the metro station areas important cognitive places characterized by vague boundaries and rich semantics. Current studies mainly define metro station areas based on an estimation of walking distance to the stations (e.g., 700 m) and investigate these areas from the perspectives of transportation and land use instead of as cognitive places perceived by the crowd. To fill this gap, this study proposes a novel framework for extracting and understanding the cognitive regions of urban metro stations based on points of interest (POIs). First, we extract the cognitive regions of metro stations based on co-occurrence patterns of the stations and their surrounding POIs on web pages by proposing a cohesive approach combined of spatial clustering, web page extraction, knee-point detection, and polygon generation techniques. Second, we identify the semantics of metro stations based on POI types inside the regions using the term frequency-inverse document frequency (TF-IDF) method. In total 166 metro stations along with more than one million POIs in Shenzhen, China are utilized as data sources of the case study. The results indicate that our proposed framework can well detect the place characteristics of urban metro stations, which enriches the place-based GIS research and provides a human-centric perspective for urban planning and location-based-service (LBS) applications. • We made the first attempt to investigate urban metro stations as cognitive places in cities. • We proposed a novel approach for extracting the cognitive regions of metro stations based on their surrounding POIs. • We introduced a TF-IDF method to identify the semantics of metro station areas based on their inside POI types. • This study enriches place-based GIS research and provides a human-centric perspective for urban planning and LBS applications. [ABSTRACT FROM AUTHOR]