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Time geography was conceptualized in the 1960s when the technology was very different from what we have today. Conventional time-geographic concepts therefore were developed with a focus on human activities and interactions in physical space. We now live in a smart, connected, and dynamic world with human activities and interactions increasingly taking place in virtual space enabled by modern information and communications technology. Coupled with recent advances in sensing and mobile technologies, it is now feasible to collect human dynamics data in both physical and virtual spaces with unprecedented spatial and temporal details in the so-called Big Data era. The Big Data era brings both opportunities and challenges to time geography. While the unprecedented data collected in the Big Data era can serve as useful data sources to time-geographic research, we also notice that some classical concepts in time geography are insufficient to properly handle human dynamics in today’s hybrid physical–virtual world in many cases. This paper first discusses the evolving human dynamics enabled by technological advances to illustrate different types of hybrid physical–virtual space performed through internet applications, digital twins, and augmented reality/virtual reality/metaverse. We then review the classical time-geographic concepts of constraints, space–time path, space–time prism, bundle, project/situation, and diorama in a hybrid physical–virtual world to discuss potential extensions of some classical time-geographic concepts to bolster human dynamics research in today’s hybrid physical–virtual world.

Advancements of information, communication and location-aware technologies have made collections of various passively generated datasets possible. These datasets provide new opportunities to understand human mobility patterns at a low cost and large scale. This study presents a home-based approach to understanding human mobility patterns based on a large mobile phone location dataset from Shenzhen, China. First, we estimate each individual’s “home” anchor point, and a modified standard distance ( S D ′ ) is proposed to measure the spread of each individual’s activity space centered at this “home” anchor point. We then derive aggregate mobility patterns at mobile phone tower level to describe the distance distribution of S D ′ for people who share the same “home” anchor point. A hierarchical clustering algorithm is performed and the spatial distributions of the derived clusters are analyzed to highlight areas with similar aggregate human mobility patterns. The results suggest that 43 % of the population sample travelled within a short distance ( S D ′ ≤ 1 km ) during the 13-day study period while 23.9 % of them were associated with a large activity space ( S D ′ ≥ 5 km ). The geographical differences of people’s mobility patterns in Shenzhen are evident. Areas with a large proportion of people who have a small activity space mainly locate in the northern part of Shenzhen such as Baoan and Longgang districts. In the southern part where the economy is highly developed, the percentage of people with a larger activity space is higher in general. The findings could offer useful implications on policy and decision making. The proposed approach can also be used in other studies involving similar spatiotemporal datasets for travel behavior and policy analysis.

Motor vehicle accidents are one of the most prevalent causes of traumatic injury in patients needing transport to a trauma center. Arrival at a trauma center within an hour of the accident increases a patient’s chances of survival and recovery. However, not all vehicle accidents in Tennessee are accessible to a trauma center within an hour by ground transportation. This study uses the anti-covering location problem (ACLP) to assess the current placement of trauma centers and explore optimal placements based on the population distribution and spatial pattern of motor vehicle accidents in 2015 through 2019 in Tennessee. The ACLP models seek to offer a method of exploring feasible scenarios for locating trauma centers that intend to provide accessibility to patients in underserved areas who suffer trauma as a result of vehicle accidents. The proposed ACLP approach also seeks to adjust the locations of trauma centers to reduce areas with excessive service coverage while improving coverage for less accessible areas of demand. In this study, three models are prescribed for finding optimal locations for trauma centers: (a) TraCt: ACLP model with a geometric approach and weighted models of population, fatalities, and spatial fatality clusters of vehicle accidents; (b) TraCt-ESC: an extended ACLP model mitigating excessive service supply among trauma center candidates, while expanding services to less served areas for more beneficiaries using fewer facilities; and (c) TraCt-ESCr: another extended ACLP model exploring the optimal location of additional trauma centers.

While the literature clearly acknowledges that individuals may experience different levels of segregation across their various socio-geographical spaces, most measures of segregation are intended to be used in the residential space. Using spatially aggregated data to evaluate segregation in the residential space has been the norm and thus individual’s segregation experiences in other socio-geographical spaces are often de-emphasized or ignored. This paper attempts to provide a more comprehensive approach in evaluating segregation beyond the residential space. The entire activity spaces of individuals are taken into account with individuals serving as the building blocks of the analysis. The measurement principle is based upon the exposure dimension of segregation. The proposed measure reflects the exposure of individuals of a referenced group in a neighborhood to the populations of other groups that are found within the activity spaces of individuals in the referenced group. Using the travel diary data collected from the tri-county area in southeast Florida and the imputed racial–ethnic data, this paper demonstrates how the proposed segregation measurement approach goes beyond just measuring population distribution patterns in the residential space and can provide a more comprehensive evaluation of segregation by considering various socio-geographical spaces.

The rapidly developing economy and growing urbanization in China have created the largest rural-to-urban migration in human history. Thus, a comprehensive understanding of the pattern of rural flight and its prevalence and magnitude over the country is increasingly important for sociological and political concerns. Because of the limited availability of internal migration data, which was derived previously from the decennial population census and small-scale household survey, we could not obtain timely and consistent observations for rural depopulation dynamics across the whole country. In this study, we use aggregate location-aware data collected from mobile location requests in the largest Chinese social media platform during the period of the 2016 Chinese New Year to conduct a nationwide estimate of rural depopulation in China (in terms of the grid cell-level prevalence and the magnitude) based on the world's largest travel period. Our results suggest a widespread rural flight likely occurring in 60.2% (36.5%-81.0%, lower-upper estimate) of rural lands at the grid cell-level and covering ~1.55 (1.48-1.94) million villages and hamlets, most of China's rural settlement sites. Moreover, we find clear regional variations in the magnitude and spatial extent of the estimated rural depopulation. These variations are likely connected to regional differences in the size of the source population, largely because of the nationwide prevalence of rural flight in today's China. Our estimate can provide insights into related investigations of China's rural depopulation and the potential of increasingly available crowd-sourced data for demographic studies.

The Symposium on Human Dynamics Research, first organized at the 2015 AAG Meeting in Chicago, celebrated its 10th anniversary at the 2024 AAG Meeting in Honolulu, marking a decade of transformative advancements in the field. Over the past decade, the focus of human dynamics research has shifted from traditional spatial-temporal analyses to sophisticated modeling of human behavior in a hybrid physical-virtual world. This evolving field now examines the intricate interdependencies between physical and digital environments, addressing critical issues such as urban resilience, public health, social equity, and community sustainability. The symposium emphasized the growing importance of interdisciplinary collaboration, advanced data-driven analytical platforms, and innovative theoretical frameworks to better understand human interactions across these spaces. As human dynamics continue to shape global urban systems, these advancements are pivotal for future research and real-world problem-solving, offering novel insights into the interconnectedness of mobility, technology, and societal well-being in a rapidly changing world.

Mobile phone location data is a newly emerging data source of great potential to support human mobility research. However, recent studies have indicated that many users can be easily re-identified based on their unique activity patterns. Privacy protection procedures will usually change the original data and cause a loss of data utility for analysis purposes. Therefore, the need for detailed data for activity analysis while avoiding potential privacy risks presents a challenge. The aim of this study is to reveal the re-identification risks from a Chinese city's mobile users and to examine the quantitative relationship between re-identification risk and data utility for an aggregated mobility analysis. The first step is to apply two reported attack models, the top N locations and the spatio-temporal points, to evaluate the re-identification risks in Shenzhen City, a metropolis in China. A spatial generalization approach to protecting privacy is then proposed and implemented, and spatially aggregated analysis is used to assess the loss of data utility after privacy protection. The results demonstrate that the re-identification risks in Shenzhen City are clearly different from those in regions reported in Western countries, which prove the spatial heterogeneity of re-identification risks in mobile phone location data. A uniform mathematical relationship has also been found between re-identification risk (x) and data (y) utility for both attack models: y = -axb+c, (a, b, c>0; 0

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