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Greenspaces are crucial for enhancing mental and physical health. Recent research has shifted from static methods of assessing exposure to greenspaces, based on fixed locations, to dynamic approaches that account for individual mobility. These dynamic evaluations utilize advanced technologies like GPS tracking and remote sensing to provide more precise exposure estimates. However, little work has been conducted to compare dynamic and static exposure assessments and the effect of individual mobility on these evaluations. This study delves into how greenspaces around homes and workplaces, along with mobility patterns, affect dynamic greenspace exposure in Hong Kong. Data was collected from 787 participants in four communities in Hong Kong using GPS, portable sensors, and surveys. Using multiple statistical tests, our study revealed significant variations in participants' daily mobility patterns across socio-demographic and temporal factors. Further, using linear mixed-effects models, we identified complex and statistically significant interactions between participants' static greenspace exposure and their mobility patterns. Our findings suggest that individual mobility patterns significantly modify the relationship between static and dynamic greenspace exposure and play a critical role in explaining socio-demographic and temporal context differences in the relationship between static and dynamic greenspace exposure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals' geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method., Methods: This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios., Results: The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual's home and all the other daily activity locations., Conclusions: This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing.

The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions from traffic based on GPS sampled data, have not sufficiently considered vehicle activities and may have led to erroneous estimations. By adopting the analytical construct of the space-time path in time geography, this study proposes methods that more accurately estimate and visualize vehicle energy consumption/emissions based on analysis of vehicles' mobile activities ( MA ) and stationary activities ( SA ). First, we build space-time paths of individual vehicles, extract moving parameters, and identify MA and SA from each space-time path segment (STPS). Then we present an N-Dimensional framework for estimating and visualizing fuel consumption/emissions. For each STPS, fuel consumption, hot emissions, and cold start emissions are estimated based on activity type, i.e., MA , SA with engine-on and SA with engine-off. In the case study, fuel consumption and emissions of a single vehicle and a road network are estimated and visualized with GPS data. The estimation accuracy of the proposed approach is 88.6%. We also analyze the types of activities that produced fuel consumption on each road segment to explore the patterns and mechanisms of fuel consumption in the study area. The results not only show the effectiveness of the proposed approaches in estimating fuel consumption/emissions but also indicate their advantages for uncovering the relationships between fuel consumption and vehicles' activities in road networks., Competing Interests: The authors declare no conflict of interest.

Physical inactivity among children is on the rise. Active transport to school (ATS), namely walking and cycling there, adds to children's activity level. Little is known about how exposures along actual routes influence children's transport behavior. This study examined how natural and built environments influence mode choice among Dutch children aged 6-11 years. 623 school trips were tracked with global positioning system. Natural and built environmental exposures were determined by means of a geographic information system and their associations with children's active/passive mode choice were analyzed using mixed models. The actual commuted distance is inversely associated with ATS when only personal, traffic safety, and weather features are considered. When the model is adjusted for urban environments, the results are reversed and distance is no longer significant, whereas well-connected streets and cycling lanes are positively associated with ATS. Neither green space nor weather is significant. As distance is not apparent as a constraining travel determinant when moving through urban landscapes, planning authorities should support children's ATS by providing well-designed cities., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Geographers play important roles in public health research, particularly in understanding healthcare accessibility, utilisation, and individual healthcare experiences. Most accessibility studies have benefited from the increased sophistication of geographic information systems (GIS). Some studies have been enhanced with semi-structured in-depth interviews to understand individual experiences of people as they access healthcare. However, few accessibility studies have explicitly utilised individual in-depth interview data in the construction of new GIS accessibility measures. Using mixed methods including GIS analysis and individual data from semi-structured in-depth interviews, we offer satisfaction-adjusted distance as a new way of conceptualising accessibility in GIS. Based on fieldwork in a predominantly lower-income community in Columbus, Ohio (USA), we find many residents felt neighbourhood healthcare facilities offered low-quality care, which suggested an added perceived distance as they attempt to access high-quality healthcare facilities. The satisfaction-adjusted distance measure accounts for the perceived distance some residents feel as they search for high-quality healthcare in lower-income urban neighbourhoods. In moving beyond conventional GIS and re-conceptualising accessibility in this way, we offer a more realistic portrayal of the issues lower-income urban residents face as they attempt to access high-quality healthcare facilities. The work has theoretical implications for conceptualising healthcare accessibility, advances the mixed-methodologies literature, and argues for a more equitable distribution of high-quality healthcare in urban neighbourhoods.

Spatiotemporal kriging (STK) is recognized as a fundamental space-time prediction method in geo-statistics. Spatiotemporal regression kriging (STRK), which combines space-time regression with STK of the regression residuals, is widely used in various fields, due to its ability to take into account both the external covariate information and spatiotemporal autocorrelation in the sample data. To handle the spatiotemporal non-stationary relationship in the trend component of STRK, this paper extends conventional STRK to incorporate it with an improved geographically and temporally weighted regression (I-GTWR) model. A new geo-statistical model, named geographically and temporally weighted regression spatiotemporal kriging (GTWR-STK), is proposed based on the decomposition of deterministic trend and stochastic residual components. To assess the efficacy of our method, a case study of chlorophyll-a (Chl-a) prediction in the coastal areas of Zhejiang, China, for the years 2002 to 2015 was carried out. The results show that the presented method generated reliable results that outperform the GTWR, geographically and temporally weighted regression kriging (GTWR-K) and spatiotemporal ordinary kriging (STOK) models. In addition, employing the optimal spatiotemporal distance obtained by I-GTWR calibration to fit the spatiotemporal variograms of residual mapping is confirmed to be feasible, and it considerably simplifies the residual estimation of STK interpolation. [ABSTRACT FROM AUTHOR]

Advanced Traveler Information Systems (ATIS) require efficient information retrieval and updating in a dynamic environment at different geographical scales. ATIS applications are useful in yielding a better utilization of the […]

This article reviews conventional methods applied in current urban metabolism studies. Based on the limitations of these conventional methods, it highlights two urgent methodological needs for urban metabolism research: the need for using different spatial and temporal scales and the need for addressing issues of sustainable development. In order to meet these urgent needs, we propose a research framework based on 3D geovisualization. The article argues that GIS and visualization can play an important role in enhancing the transparency and comprehensibility of the results of urban metabolism studies. Furthermore, it is also an effective platform for investigating urban metabolism at various spatial and temporal scales. Specifically, introducing the various speeds of flows and incorporating the differences in the rhythm of these flows will be helpful. GIS and visualization can help to translate analysis results into urban policy suggestions. [ABSTRACT FROM AUTHOR]

In recent decades, decreasing physical activity has emerged as one of the major issues affecting human health since people increasingly engaged in sedentary behavior in their homes and workplaces. In physical activity research, using GPS trajectories and advanced GIS methods has a potential for greatly enhancing our understanding of the association between objectively measured moderate and vigorous physical activity and physical and social environments. Relying only on objectively measured physical activity intensity, however, ignores the role of different places and types of physical activity on people's health outcomes. The aim of this study is to propose an approach to classifying physical activity in free-living conditions for physical activity research using published smartphone accelerometer data. Random forest and gradient boosting are used to predict jogging, walking, sitting, and standing. Generated training models based on the two classifiers are tested on accelerometer data collected from the smartphones of two subjects in free-living conditions. GPS trajectories with predicted physical activity labels are visually explored on a map to offer new insight on the assessment of the predicted results of daily activities and the identification of any difference in the results between random forest and gradient boosting. The findings of this study indicate that random forest and gradient boosting enable accurate physical activity classification in free-living conditions. GPS trajectories linked with predicted labels on a map assist the visual exploration of the erroneous prediction in daily activities including in-vehicle activities. [ABSTRACT FROM AUTHOR]

This study aims to empirically demonstrate the necessity to consider both the spatiotemporal variability of air pollution and individual daily movement patterns in exposure and health risk assessment. It compares four different types of exposure estimates generated by using (1) individual movement data and hourly air pollution concentrations; (2) individual movement data and daily average air pollution data; (3) residential location and hourly pollution levels; and (4) residential location and daily average pollution data. These four estimates are significantly different, which supports the argument that ignoring the spatiotemporal variability of environmental risk factors and human mobility may lead to misleading results in exposure assessment. Additionally, three-dimensional (3D) geovisualization presented in the paper shows how person-specific space-time context is generated by the interactions between air pollution and an individual, and how the different individualized contexts place individuals at different levels of health risk. [ABSTRACT FROM AUTHOR]

This article introduces the background and contents of this forum, which includes six articles based on presentations in the opening and closing plenary sessions of the featured theme on “Context and Uncertainty in Geography and GIScience” at the 2017 American Association of Geographers (AAG) annual meeting in Boston. The purpose of the featured theme was to explore and deepen our understanding of the spatiotemporal uncertainties in the contextual influences on human behavior, practice, and experience. The articles in this forum focus on specific themes concerning context and uncertainty in geographic and GIScience research. They provide a helpful discussion of the complexity and challenges of specific issues through different theoretical and methodological perspectives. [ABSTRACT FROM AUTHOR]

Increasingly, ‘place’, including physical and geographical characteristics as well as social meanings, is recognized as an important factor driving individual and community health risks. This is especially true among marginalized populations in low and middle income countries (LMIC), whose environments may also be more difficult to study using traditional methods. In the NIH-funded longitudinal study Mapa de Salud, we employed a novel approach to exploring the risk environment of female sex workers (FSWs) in two Mexico/U.S. border cities, Tijuana and Ciudad Juárez. In this paper we describe the development, implementation, and feasibility of a mix of quantitative and qualitative tools used to capture the HIV risk environments of FSWs in an LMIC setting. The methods were: 1) Participatory mapping; 2) Quantitative interviews; 3) Sex work venue field observation; 4) Time-location-activity diaries; 5) In-depth interviews about daily activity spaces. We found that the mixed-methodology outlined was both feasible to implement and acceptable to participants. These methods can generate geospatial data to assess the role of the environment on drug and sexual risk behaviors among high risk populations. Additionally, the adaptation of existing methods for marginalized populations in resource constrained contexts provides new opportunities for informing public health interventions. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the authors discuss various reports within the issue on topics including the positions of moving objects, spatiotemporal modeling of dynamic geographic event, and spatiotemporal Big Data.

Constructing and renovating urban parks is an effective strategy for high-density cities to satisfy residents' needs for recreation in nature. Using Hong Kong as an example of a high-density city, the purpose of this study was to examine the associations of park and neighbourhood environments with park-based physical activity (PA) among children and examine the moderating effect of neighbourhood income in their relationships. A cross-sectional study was conducted in 32 urban parks in Hong Kong. Park-based PA and the park and neighbourhood environments were measured using systematic observation tools and the geographic information system. Neighbourhood income was extracted from the 2011 Hong Kong Population Census data on median household income. A total of 7,753 children were observed using urban parks for PA during the study. Children's park-based PA was positively associated with the quality of supporting amenities, park safety, neighbourhood walkability, and neighbourhood quality, while the associations of park-based PA with the diversity of active facilities and park size were not significant. In addition, neighbourhood income moderated the associations of park-based PA with the diversity of active facilities, the quality of supporting amenities, neighbourhood walkability, and neighbourhood quality. These findings provide empirical evidence on the moderating role of neighbourhood socioeconomic status on the associations between park and neighbourhood environments and children's park-based PA in a high-density city. With children's needs for using parks for PA, our findings are beneficial for urban planners to renovate existing urban parks to enhance their utility for children living in high-density cities. [ABSTRACT FROM AUTHOR]

The uncertain geographic context problem (UGCoP), first articulated by Kwan (2012; The uncertain geographic context problem. Annals of the Association of American Geographers, 102 (5), 958–968), refers to the problem that findings about the effects of area-based contextual variables on individual behaviors or outcomes may be affected by how contextual units (e.g., neighborhoods) are geographically delineated and the extent to which these areal units deviate from the true geographic context. It is a significant methodological problem because it means that analytical results can differ for different delineations of contextual units even if everything else is the same. Drawing upon Kwan (2012) and recent social science studies (especially environmental health and neighborhood effects research), this article further elaborates on the nature of the UGCoP and explores how recent advances in geographical information system (GIS) and geospatial technologies can help address the problem. It discusses possible means for mitigating the UGCoP, especially with regard to the collection of detailed individual space–time data with global positioning systems, construction of individual activity spaces, and the use of qualitative and web-based GIS to capture people's activity locations and everyday experiences. It also discusses the challenges for future research that seeks to address the UGCoP. [ABSTRACT FROM AUTHOR]

In classical time geography, an individual travel path is composed of a chain of visits, with each visit being a flexible activity between two fixed activities at two known stations. In reality, individuals tend to carry out trips with much variation and complexity, with multipurpose trips being a prominent and pervasive phenomenon. There is limited research to date on multipurpose trips in time-geographic analysis by geographic information system (GIS) scientists, or more specifically, multiple flexible activities between two fixed stations. To fill this gap, this article proposes four models for identifying the choice set with multiple flexible activities under space–time constraints. The models are derived through set-theoretic formalism based on the concept of trip chaining. The structure of the four models establishes a theoretical framework for conceptualizing trip-chaining behaviour with respect to the fixity of activities and the number of fixed stations as destinations or origins. They provide fundamental and rigorous apparatus for studying complex individual activity–travel patterns in many applied contexts when multipurpose trips are involved. This article also describes implementation of the models with a real transportation network as a way of validation. [ABSTRACT FROM AUTHOR]

This research seeks to contribute to advancing qualitative methodologies at the intersection of qualitative geographic information systems (GIS), narrative analysis, 3D GIS-based time-geographic methods, and computer-aided qualitative data analysis. The approach to GIS-based narrative analysis developed in the study, called “geo-narrative,” is based on extending current GIS capabilities for the analysis and interpretation of narrative materials such as oral histories, life histories, and biographies. The three central elements in this approach are (1) narrative analysis as the qualitative approach; (2) 3D GIS-based time-geographic methods as the representational framework; and (3) 3D-VQGIS as the GIS-based computer-aided qualitative data analysis component. A case example based on a study of the lives of the Muslim women in Columbus, Ohio, after 11 September 2001 is used to illustrate the approach. [ABSTRACT FROM AUTHOR]

Since the attacks on the World Trade Center in New York City and the Pentagon in Washington, DC on 11 September 2001, Muslims or Muslim-looking people in the USA have experienced a significant increase in hostility and hate violence. The anti-Muslim hate crimes have affected the lives of these people of color in significant ways. In this article I seek to recover part of the post-September 11 experiences of American Muslims that were obfuscated by the dominant anti-Muslim master narrative, which conflated the Islamic faith with terrorism and constructed all Muslims as dangerous anti-American outsiders. I explore a way of telling stories about these experiences using the expressive power of geospatial technologies. Using the experiences of a Muslim woman in Columbus (Ohio, USA) as an example, I describe how the technological spaces afforded by geographical information systems (GIS) may be used to illuminate the impact of the fear of anti-Muslim hate violence on the daily lives of Muslim women and to help articulate their emotional geographies in the post-September 11 period. [ABSTRACT FROM AUTHOR]

A series of travel simulators have been developed in the past two decades under the Intelligent Transportation Systems (ITS) umbrella. They have addressed issues such as reactions to advisory radio and variable message signs, use of navigation systems, route diversion, and mode choice. The objective of this paper is to present the design and implementation of a different kind of travel simulator. GABRIEL (Gis Activity-Based tRavel sImuLator) has as a foundation the activity-based approach and makes use of geographic information systems (GIS) as a development environment. The simulation scenario consists of a commute trip where two activities take place. En-route to the first destination, congestion occurs and subjects are requested to take action based on a set of alternatives. The simulator provides re-routing, destination substitution, dynamic geographic information and real-time information to aid users in their decision-making process. As a result it helps subjects in developing their ability to adapt given a particular scenario and allow researchers in understanding trip making, activity rescheduling, and the decision-making process from a comprehensive perspective. [ABSTRACT FROM AUTHOR]

Reflects on the implications of the critical discourse on geographic information systems (GIS) in the 1990s for feminist geographic research. GIS methods; Limitations of the critical discourse on GIS; Forms of feminist engagement with GIS.

Although recent studies of individual accessibility have used detailed representations of urban street networks, unrealistic measures of travel time based on assumptions about constant travel speeds through the network were often used. Utilizing constant travel times does not allow for daily congestion and assumes that the effects of congestion are uniform throughout the city and affect all people equally. This research measures individual space-time accessibility in order to show that the incorporation of locally specific travel times within a street network allows a significant increase in the ability to realistically evaluate individual accessibility within cities. The results show that the accessibility of individuals within cities is not homogenous, and neither does access to employment or shopping opportunities vary according to common expectations about urban form and human behavior. Instead, the role of distance in predicting accessibility variations within cities is quite limited. This article also shows that incorporating time into accessibility measures in the form of congestion and business hours leads to additional (and highly spatially uneven) reductions in accessibility, revealing that the temporal dimension is very important to accurately assessing individual accessibility. [ABSTRACT FROM AUTHOR]

Abstract. In recent years, comprehensive geographic data sets of metropolitan areas and individual-level, georeferenced data are becoming more available to social scientists. At the same time, tools for performing spatial analysis in a GIS environment have also become more available. These developments provide many new opportunities for the analysis and theoretical understanding of disaggregate human spatial behavior. This paper examines how these developments may enable the researcher to represent complex urban and cognitive environments more realistically, and to overcome the limitations of aggregate spatial data framework. It explores their implications for the theoretical and methodological development in geography and other social science disciplines. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various reports within the issue on topics including the application of geographical information system (GIS) in health researches, healthcare accessibility, and avian influenza virus.

In recent years, with the growing accessibility of abundant contextual emotion information, which is benefited by the numerous georeferenced user-generated content and the maturity of artificial intelligence (AI)-based emotional computing technics, the emotion layer of human–environment relationship is proposed for enriching traditional methods of various related disciplines such as urban planning. This paper proposes the geographic information system (GIS)-based emotional computing concept, which is a novel framework for applying GIS methods to collective human emotion. The methodology presented in this paper consists of three key steps: (1) collecting georeferenced data containing emotion and environment information such as social media and official sites, (2) detecting emotions using AI-based emotional computing technics such as natural language processing (NLP) and computer vision (CV), and (3) visualizing and analyzing the spatiotemporal patterns with GIS tools. This methodology is a great synergy of multidisciplinary cutting-edge techniques, such as GIScience, sociology, and computer science. Moreover, it can effectively and deeply explore the connection between people and their surroundings with the help of GIS methods. Generally, the framework provides a standard workflow to calculate and analyze the new information layer for researchers, in which a measured human-centric perspective onto the environment is possible. [ABSTRACT FROM AUTHOR]

Introduces a series of articles pertaining to the relation between feminist geography and geographic information systems (GIS). Use of GIS in feminist research and practice; Limitations of GIS; Role of GIS in feminist geography; Implications of GIS for gender equality.

Much research on three-dimensional (3D) indoor geographic information systems (GIS) to date has been focused on 3D topological modeling in the context of emergency management and response. Besides emergency situations, however, little is known about other human activities and the effective use and retrieval of semantically relevant information about such activities based on route analysis in complex buildings regarding 3D indoor GIS. This study proposes a location-based service (LBS) using ontology-based semantic queries with a focus on the indoor activities in a university context. An ontology model called ‘University activity ontology’ is designed with regard to the indoor activities at a university for sharing, managing and querying data semantically. In particular, reasoning rules are created for semantic queries to retrieve and provide information about places relevant to a destination with keywords given by users. A 3D network-based topological data model is generated by connecting a road network model and indoor topological network model to calculate the shortest path from an outdoor/indoor location to an indoor destination of interest selected by users among suggested choices. For the implementation, a location-based GIS application is developed based on the Android operating system (OS) with interactive two-dimensional (2D) and 3D visualization. [ABSTRACT FROM AUTHOR]