Time geography in a hybrid physical-virtual world.

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.

Assessing Trauma Center Accessibility for Healthcare Equity Using an Anti-Covering Approach.

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.

Relative space-based GIS data model to analyze the group dynamics of moving objects.

The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects' relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5-50% those of the absolute GIS method for the same function of these three scenarios. It's better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service.

An Invisible Salient Landmark Approach to Locating Pedestrians for Predesigned Business Card Route of Pedestrian Navigation.

Visual landmarks are important navigational aids for research into and design of applications for last mile pedestrian navigation, e.g., business card route of pedestrian navigation. The business card route is a route between a fixed origin (e.g., campus entrance) to a fixed destination (e.g., office). The changing characteristics and combinations of various sensors' data in smartphones or navigation devices can be viewed as invisible salient landmarks for business card route of pedestrian navigation. However, the advantages of these invisible landmarks have not been fully utilized, despite the prevalence of GPS and digital maps. This paper presents an improvement to the Dempster⁻Shafer theory of evidence to find invisible landmarks along predesigned pedestrian routes, which can guide pedestrians by locating them without using digital maps. This approach is suitable for use as a "business card" route for newcomers to find their last mile destinations smoothly by following precollected sensor data along a target route. Experiments in real pedestrian navigation environments show that our proposed approach can sense the location of pedestrians automatically, both indoors and outdoors, and has smaller positioning errors than purely GPS and Wi-Fi positioning approaches in the study area. Consequently, the proposed methodology is appropriate to guide pedestrians to unfamiliar destinations, such as a room in a building or an exit from a park, with little dependency on geographical information.

An estimate of rural exodus in China using location-aware data.

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.

Re-Identification Risk versus Data Utility for Aggregated Mobility Research Using Mobile Phone Location Data.

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

Who are the children using hospice care?

PURPOSE: The purpose was to examine the characteristics of children who use hospice care. DESIGN AND METHODS: Using the Andersen Model of Health Services Use, California Medicaid administrative databases were analyzed to describe the characteristics of 76 children in hospice. RESULTS: The predisposing, enabling, and need characteristics of children were identified. Children who used hospice were a diverse group with community resources that enabled them to access care while presenting with serious health needs. Children enrolled in hospice were more likely older (15-20 years of age), resided nearer a pediatric hospice, and had a serious health condition such as neuromuscular disease with multiple comorbidities. PRACTICE IMPLICATIONS: With this knowledge, pediatric nurses can improve their clinical practice by targeting conversations with families and children most in need of hospice care.

Measuring segregation: an activity space approach.

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.