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AbstractAdvanced techniques in Geographic Information Systems (GIS) currently provide one of the most promising approaches to investigating the health impacts of green space. The GIS solution of deriving causally relevant green space exposures still faces challenges from the arbitrary determination of the contextual unit size. This paper presents an in-depth and rigorously defined analytical framework to illustrate the effect of buffer and to find the optimal buffer radius. We employed a cross-sectional study with 980 participants in Hong Kong to validate our analytics. The home locations, socio-demographic attributes, and self-reported health statuses were collected from questionnaires. Residence-based green space exposures were derived using an exhaustive range of buffer radii and fine-grained remote sensing data. Participants’ overall health was modeled through logistic regression to validate our analytics. Our results clearly indicate the U-shaped p-value curves along the gradient of buffer radii, which illustrates the optimal buffer sizes in pertinent geographic contexts. We also observed two independent ranges of optimal buffer sizes. Our work elicited the effect of buffer size on green space exposure measures and essential implications for a range of health geography and environmental health studies that require accurate green space exposure measures. [ABSTRACT FROM AUTHOR]

Community roads are crucial for efficient navigation in residential areas. However, current navigation maps often lack comprehensive coverage of these roads. To address this issue, we present a Human-Flow Model (HFM) to identify roads within residential communities by utilizing abundant low-frequency trajectory data from human movements. First, the HFM leverages human movement density in residential zones to estimate the likelihood of road existence. Using the probability distribution of human movement and neighboring building footprints, we construct a Human-Flow Probability Field (HFPF), which serves as a distribution representation for modeling human movement within densely populated built-up areas. Then, the flow paths are extracted from the HFPF using hydrological analysis techniques, which facilitates the identification of main paths and smaller branches within the community road network. Finally, the road network is refined using morphological methods. Our model was tested using six residential communities located in Wuhan, China. It consistently outperformed other methods by detecting more roads with higher accuracy, especially intricate branches. By incorporating flow semantics, our model capitalizes on sparse trajectory data to enhance the fine-scale community road networks. This improvement enhances the last-mile navigation experience in sustainable cities, contributing to overall urban mobility and convenience for residents. [ABSTRACT FROM AUTHOR]

Temporal contexts are essential for the derivation of causally relevant environmental exposure in environmental and public health studies. We argue that the proper temporal contexts need further emphasis in the mobility-oriented research paradigm and articulate this issue using a study in Hong Kong. We use people's exposure to outdoor artificial light at night (ALAN) and green space as two essential examples. We recruited 208 participants from two representative communities in Hong Kong and derived their mobility-oriented environmental exposures from high-resolution remote sensing data and Google Street View imagery. We employed one-standard-deviational ellipses to quantitatively represent participants' activity spaces, and we further used participants' seven-day Global Positioning System trajectories to derive their spatiotemporally weighted exposures to green space and outdoor ALAN in different temporal contexts. Multiple t tests were used to examine the disparities in activity spaces and measured exposures in different temporal contexts, and these exposure measurements were then used to predict people's health outcomes. We found that people's activity spaces are significantly different in size between day and night, for both weekdays and weekends, and in both geographic contexts. We further observed that improper temporal contexts could lead to significantly different environmental exposure levels and causally irrelevant modeling results. [ABSTRACT FROM AUTHOR]

This study advances the measurement of community social context by introducing the daily dynamic perspective to promote a better understanding of the relationship between community social context and community attachment. It measured the social context averaging or polarization (SCAP) effect of communities every 3 h using census and cell phone data and investigated residents' community attachment in 71 communities in Guangzhou, China. There are three findings. First, the social contexts of many communities varied during the day, either moving toward the mean of the whole city or away from the mean. Distinct patterns exist during work hours, evening hours, and night hours. Second, considering variations in social contexts during the evening hours significantly enhances the explanation of the heterogeneity in residents' community attachment. Third, variations in social contexts are more likely to influence residents' attachment in old blocks and migrant communities, and communities that may have gated sub-units are less likely to be influenced. The study advocates that context dynamics be taken as a new dimension of community indicators in place perception studies. The study is also instructive in targeting space and time that deserve special attention in community governance practice. [ABSTRACT FROM AUTHOR]

The rapid development of information and communications technology (ICT) in recent years leads to the emergence of cyber space and results in the fragmentation of human activities to some extent. Nevertheless, few studies focused on the exploration of fragmentation, the new characteristic of human activities, partially due to the limited access to proper datasets. In this work, massive Call Detail Records (CDR) data and Uniform Resource Locator (URL) data from millions of mobile phone users are used to extract the activity fragmentation in both cyber and physical spaces. The relationships between activity fragmentation in the two spaces and built environment factors are further examined at different geographical scales. The results show that: 1) the spatial distributions of physical human activity fragmentation (PHAF) and cyber human activity fragmentation (CHAF) are inversely correlated; 2) compared with PHAF, CHAF are less associated with the built environment, probably because cyber activities are less restricted in time and space; 3) CHAF are more influenced by physical fragmentations and the relationships are stable across different cites, indicating that cyber space cannot be completely detached from physical space; and 4) the impact of built environment on CHAF and PHAF are different across different cities. The findings not only shed deeper light on activity fragmentation, but also enlighten the future exploration of its association with both cyber and physical spaces. [ABSTRACT FROM AUTHOR]

In practice, the ride-hailing platform generally provides two products: express services and ridesplitting services, and driver resources are usually shared across these two products on the supply side, i.e., hybrid ride-hailing mode. This study tackles the order dispatch problem with considering flexible one-to-three matching strategy under hybrid modes. In this problem, if a driver serves express requests, only one order can be matched with; if a driver serves ridesplitting requests, this driver can be matched with at most three orders simultaneously or sequentially. Considering one-to-three matching, en-route matching, and multiple passengers per order, this problem is formulated as a multi-objective integer programming, a hierarchical approach is adopted to address it. Using the ride-hailing trip data, we examine characteristics of this problem, such as the impact of service priority and the benefits of one-to-three matching. Finally, some beneficial management implications are proposed to improve the operation of the ride-hailing service. [ABSTRACT FROM AUTHOR]

Using individual-level data collected from two communities in Hong Kong, this study proposes a significant association rule mining method to identify the complex associations between individual socioeconomic characteristics and perceived air pollution in people's daily life. It defines a measure, namely the rule inequality index, to assess the social inequality in perceived exposure to air pollution in both residential and visited neighborhoods. The results indicate that the associations between individual socioeconomic characteristics and perceived air pollution are not always consistent over communities, nor are the value ranges of perceived air pollution. Further, the tendency of different social groups to perceive high levels of air pollution can differ considerably depending on whether they are in their residential or visited neighborhoods. We also find that social groups based on different socioeconomic variables typically experience varying degrees of neighborhood effects on the associated social equalities. Our findings emphasize the importance of considering nonstationary associations and human mobility in research on social inequality related to mobility-dependent environmental exposure. [ABSTRACT FROM AUTHOR]

This study seeks to examine the possible impacts of sociodemographic factors, individual mobility patterns, and daily activities on individual exposure to COVID-19 risk when assessed by different risk measures. Taking Hong Kong as the study area, we first model the risk of COVID-19 using a density-based approach and a network-based approach and reveal the differences in the spatial distributions of COVID-19 transmission risk they obtained. Then, using two-day individual Global Positioning System trajectory data and travel diaries collected from individuals in two communities, we measure individual exposure to COVID-19 transmission risk based on their mobility patterns and reveal the disparities in COVID-19 risk exposure among residents of different demographic groups and neighborhoods when conducting different activities. This study reveals the differences in the spatial patterns of COVID-19 transmission risk when the risk is conceptualized by density and network. It demonstrates the power of a spatial network approach in enhancing our understanding of individual exposure to infectious diseases. This study also advances the existing literature by exploring COVID-19 risk exposure, considering individual daily mobility and addressing the uncertain geographic context problem in analyzing individual COVID-19 risk exposure. [ABSTRACT FROM AUTHOR]

Land subsidence, a common geological phenomenon in deltaic regions, poses significant risks to infrastructures, environments, and human lives. Monitoring and understanding land subsidence are crucial for establishing resilient, adaptive, and sustainable environments. In this study, a robust multi-temporal interferometric synthetic aperture radar (MTInSAR) method was employed to monitor land subsidence in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) using 541 Sentinel-1 SAR images. Spatial auto- and cross-correlation analysis were applied to select the most reasonable explanatory variables from nine candidates in each city of the GBA. City-level geographically weighted regression (GWR) models were then constructed to explore the spatially nonstationary effects of natural and human-induced factors. The findings revealed subsidence velocities ranging from 0 to 83.7 mm/yr in the GBA from 2015 to 2021, with the dominant factors affecting subsidence varying among regions. Natural and human-related factors accounted for 51.13% and 48.87%, respectively. Further analysis of representative subsidence areas highlights the importance of continuous monitoring of ground deformation using MTInSAR, region-specific land subsidence mitigation strategies, and regular maintenance of urban infrastructure. These insights are valuable for policymakers and urban planners in comprehending the complex processes underlying land subsidence and informing decision-making processes for sustainable and resilient urban development. [ABSTRACT FROM AUTHOR]

This study examines the job-access inequity between the richest 10% and poorest 40% transit-based workers across space (i.e. central city, the inner-ring/outer-ring suburb) and race (i.e. white, black and Hispanic) in Chicago. The results indicate that there are job-access inequities across both space and race. In terms of job-access inequity across race, there are more job-access inequities for whites and blacks than for Hispanics. In terms of job-access inequity across space, the central city has the least cross-race inequities while the outer-ring suburb has the most cross-race inequities. Overall, job-access inequities are more serious across space than across race. [ABSTRACT FROM AUTHOR]

Human interaction with indoor objects constantly changes indoor space and its navigability. Spatial subdivision models that delineate navigable space become a crucial prerequisite for indoor navigation. However, existing spatial subdivision models do not fully capture the dynamic changes of the indoor context and have limitations in identifying precise navigable space, thereby reducing the accuracy and efficiency of indoor navigation. This study proposes a novel interactive semantic model (ISM) that consists of an interactive semantic base (ISB) and empirical rules to accurately determine the navigability of the reshaped functional spaces (F-Spaces) of indoor objects. First, two-level F-Spaces (fine-grained resource F-Space and coarse-grained structure F-Space) are defined to express multi-granularity interactive semantics for delineating heterogeneous F-Spaces. Second, empirical rules are established through an extensible multi-dimensional semantics classification framework to determine each F-Space's navigability. Lastly, a navigable F-Space generation scheme is designed by considering the adaptive navigability of the two-level F-Spaces. Simulated experiments show that the proposed model can generate precise and efficient dynamic navigable spaces. This study reduces the cognitive burden of human agents when facing indoor dynamic navigation, thereby improving the spatial experience of navigation. [ABSTRACT FROM AUTHOR]

The widespread use of personal geospatial data raises serious geoprivacy concerns for sharing these data, which may limit the reproducibility of research findings. One widely used method for securely sharing confidential geospatial information is applying geomasking techniques before sharing. Geomasking may reduce the usability of the data. Thus, researchers need to strike a balance between privacy protection and analytical accuracy. Although many geomasking methods have been proposed, there is no systematic evaluation of these methods or guidance on which method to use and how to apply it properly. To address this gap, we evaluate eight geomasking methods with simulated geospatial data with various spatial patterns and investigate their performance on privacy protection and analytical accuracy. We propose not only a set of preliminary guidelines for applying the proper geomasking methods when using different spatial analysis methods but also an evaluation framework for assessing geomasking methods for other spatial analysis methods. The findings will help researchers to properly apply geomasking for sensitive geospatial data and thus promote data sharing and interdisciplinary collaboration while protecting personal geoprivacy. [ABSTRACT FROM AUTHOR]

Based on different conceptualizations and measures of individual-level environmental exposure, this study examines how the uncertain geographic context problem (UGCoP) and the neighborhood effect averaging problem (NEAP) might affect the assessment of COVID-19 risk. Using the COVID-19 data on an open-access government Web site and the individual-level activity data of sixty confirmed COVID-19 cases (infected persons) in Hong Kong, we first represent COVID-19 risk environments using case-based and venues-based high-risk locations. The COVID-19 risk of each of the sixty selected cases is then evaluated by three approaches based on their exposures to the case-based or venues-based risk environments: the mobility-based approach, the residence-based approach, and the activity space–based approach. The results indicate that the UGCoP and the NEAP exist in the assessment of COVID-19 risk, which has significant implications: Ecological COVID-19 studies need to address the uncertainties due to the UGCoP and the NEAP by considering people's daily mobility. Otherwise, ignoring peoples' daily mobility and its interactions with complex and dynamic COVID-19 risk environments could lead to misleading results and misinform government nonpharmaceutical intervention measures. [ABSTRACT FROM AUTHOR]

Spatial flow co-location patterns (FCLPs) are important for understanding the spatial dynamics and associations of movements. However, conventional point-based co-location pattern discovery methods ignore spatial movements between locations and thus may generate erroneous findings when applied to spatial flows. Despite recent advances, there is still a lack of methods for analyzing multivariate flows. To bridge the gap, this paper formulates a novel problem of FCLP discovery and presents an effective detection method based on frequent-pattern mining and spatial statistics. We first define a flow co-location index to quantify the co-location frequency of different features in flow neighborhoods, and then employ a bottom-up method to discover all frequent FCLPs. To further establish the statistical significance of the results, we develop a flow pattern reconstruction method to model the benchmark null hypothesis of independence conditioning on univariate flow characteristics (e.g. flow autocorrelation). Synthetic experiments with predefined FCLPs verify the advantages of our method in terms of correctness over available alternatives. A case study using individual home-work commuting flow data in the Chicago Metropolitan Area demonstrates that residence- or workplace-based co-location patterns tend to overestimate the co-location frequency of people with different occupations and could lead to inconsistent results. [ABSTRACT FROM AUTHOR]

In many central districts in cities across the world, different types of stores form clusters resulting from the benefits of spatial agglomeration. To precisely analyze co-location relationships in a micro-scale space, this study develops a new statistical method by addressing the limitations of the ordinary cross K function method. The objectives of this paper are, first, to formulate an exact statistical method for analyzing co-location along streets in a central district constrained by a street network; second, to implement this statistical method in computational procedures. Third, this method is extended to the analysis of repulsive-location, i.e. phenomena of stores locating repulsively among different types of stores. Fourth, the paper shows a graph-theoretic diagram illustrating the spatial structure of stores in a central district consisting of bilateral, unilateral co-location and repulsive-location. Last, the proposed method is applied to eight different types of stores in a trendy district in Tokyo. The results show that the method is useful for revealing the spatial structure consisting of co-location and repulsive-location in the central district. [ABSTRACT FROM AUTHOR]

This research examines how people subjectively perceive the disclosure risk of a map using original data collected in an online survey with 856 participants. The results indicate that perceived disclosure risk increases as the amount of locational information displayed on a map increases. Compared to point-based maps, perceived disclosure risk is significantly lower for kernel density maps, convex hull maps, and standard deviational ellipse maps. The results also revealed that perceived disclosure risk is affected by map scale and the presence of information of other people on a map. For geomasking methods, perceived disclosure risk decreases as aggregation level increases and as relocation distance increases. However, aggregation methods (point to polygon) are more effective in preventing the re-identification of individuals when compared to relocation methods (point to point). Lastly, the perceived disclosure risk of a map that displays socially-vulnerable people is significantly higher than that of a map that displays non-vulnerable groups. Specifically, a map displaying the private locations of elementary school students has the highest perceived disclosure risk. Based on the results, a set of geoprivacy protection guidelines for mapping people's private locations to minimize people's perceived disclosure risk is proposed. Implications for mapping infectious diseases like the COVID-19 are also discussed. [ABSTRACT FROM AUTHOR]

The neighborhood effect averaging problem (NEAP) can be a serious methodological problem that leads to erroneous assessments when studying mobility-dependent exposures (e.g., air or noise pollution) because people's daily mobility could amplify or attenuate the exposures they experienced in their residential neighborhoods. Specifically, the NEAP suggests that individuals' mobility-based exposures tend toward the mean level of the participants or population of a study area when compared to their residence-based exposures. This research provides an in-depth examination of the NEAP and how the NEAP is associated with people's daily mobility through an assessment of individual exposures to ground-level ozone using the activity-travel diary data of 2,737 individuals collected in the Los Angeles metropolitan statistical area. The results obtained with exploratory analysis (e.g., a scatterplot and histograms) and spatial regression models indicate that the NEAP exists when assessing individual exposures to ozone in the study area. Further, high-income, employed, younger, and male participants (when compared to low-income, nonworking, older, and female participants) are associated with higher levels of neighborhood effect averaging because of their higher levels of daily mobility. Finally, three-dimensional interactive geovisualizations of the space-time paths and hourly ozone exposures of seventy-one selected participants who live in the same neighborhood corroborate the findings obtained from the spatial regression analysis. [ABSTRACT FROM AUTHOR]

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This study examined how contextual areas defined and operationalized differently may lead to different exposure estimates. Substance users' exposures to environmental stress (in terms of two variables: community social economic status and crime) were assessed from global positioning systems (GPS) data. Participants were 47 outpatients with substance use disorders admitted for methadone maintenance at a research clinic in Baltimore, Maryland. From 35.2 million GPS tracking points, we compared 7 different methods for defining activity space. The different methods yielded different exposure estimates, which would lead to different conclusions in studies using only one method. These results have important implications for future research on the effect of contextual influences on health behaviors and outcomes: whether a study observes any significant influence of an environmental factor on health may depend on what contextual units are used to assess individual exposure. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on the topic of uncertainties in geographic information science (GIScience) and geography caused by errors in geospatial data measurement.

This article draws on recent studies to argue that researchers need to be attentive to the limits of the neighborhood effect as conventionally understood. It highlights the complexities of contextual influences and the challenges in accurately representing and measuring individual exposures to those influences. Specifically, it discusses the idiosyncratic and multidimensional nature of contextual effects, the temporal complexities of contextual influences, the frame dependence of exposure measures, selective mobility bias, and publication bias in neighborhood effects research. It also discusses how contextual uncertainties could be mitigated in future research (e.g., through collecting and using high-resolution space-time data and moving toward frame-independent exposure measures with results that are not affected by how data are organized with respect to space and time). [ABSTRACT FROM AUTHOR]

In this research, we match web-based activity diary data with daily mobility information recorded by GPS trackers for a sample of 709 residents in a 7-day survey in Beijing in 2012 to investigate activity satisfaction. Given the complications arising from the irregular time intervals of GPS-integrated diary data and the associated complex dependency structure, a direct application of standard (spatial) panel data econometric approaches is inappropriate. This study develops a multi-level temporal autoregressive modelling approach to analyse such data, which conceptualises time as continuous and examines sequential correlations via a time or space-time weights matrix. Moreover, we manage to simultaneously model individual heterogeneity through the inclusion of individual random effects, which can be treated flexibly either as independent or dependent. Bayesian Markov chain Monte Carlo (MCMC) algorithms are developed for model implementation. Positive sequential correlations and individual heterogeneity effects are both found to be statistically significant. Geographical contextual characteristics of sites where activities take place are significantly associated with daily activity satisfaction, controlling for a range of situational characteristics and individual socio-demographic attributes. Apart from the conceivable urban planning and development implications of our study, we demonstrate a novel statistical methodology for analysing semantic GPS trajectory data in general. [ABSTRACT FROM AUTHOR]

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]

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]

Drawing on examples from human mobility research, I argue in this article that the advent of big data has significantly increased the role of algorithms in mediating the geographic knowledge production process. This increased centrality of algorithmic mediation introduces much more uncertainty to the geographic knowledge generated when compared to traditional modes of geographic inquiry. This article reflects on important changes in the geographic knowledge production process associated with the shift from using traditional "small data" to using big data and explores how computerized algorithms could considerably influence research results. I call into question the much touted notion of data-driven geography, which ignores the potentially significant influence of algorithms on research results, and the fact that knowledge about the world generated with big data might be more an artifact of the algorithms used than the data itself. As the production of geographic knowledge is now far more dependent on computerized algorithms than before, this article asserts that it is more appropriate to refer to this new kind of geographic inquiry as algorithm-driven geographies (or algorithmic geographies) rather than data-driven geography. The notion of algorithmic geographies also foregrounds the need to pay attention to the effects of algorithms on the content, reliability, and social implications of the geographic knowledge these algorithms help generate. The article highlights the need for geographers to remain attentive to the omissions, exclusions, and marginalizing power of big data. It stresses the importance of practicing critical reflexivity with respect to both the knowledge production process and the data and algorithms used in the process. [ABSTRACT FROM AUTHOR]

This introductory piece sets the context for the special issue and explains its rationale. It offers a series of reflections on the rise of the mobilities turn and its relations with preexisting research traditions, most notably transportation geography. Rather than placing different approaches in opposition and favoring one over others, we contend that all need to be seen as situated, partial, and also generative modes of abstraction. Each of these approaches makes mobility exist in specific and ultimately simplified and selective ways. In addition, we argue that geography as a pluralistic discipline will benefit from further conversations between modes of conceptualizing, theorizing, and examining mobility. We outline five lines along which such conversations can be structured: conceptualizations and analysis, inequality, politics, decentering and decolonization, and qualifying abstraction. The article concludes with discussion on three fruitful directions for future research on mobility. [ABSTRACT FROM AUTHOR]

The ability to replicate, or reproduce, research is fundamental to the scientific process. Research combining a variety of georeferenced data is spreading rapidly across scientific domains and international borders. This suggests a growing potential for the use and integration of new and existing data sets to create new multi-disciplinary scientific collaborations. Yet, the unique characteristics of georeferenced data present special challenges to such collaborations. These data are highly identifiable when presented in maps and other visualizations or when combined with sensor data or other related geospatial data sets. The potential opportunities of collaboration may thus be constrained by the need to protect the locational privacy (geoprivacy) and confidentiality of subjects in research using georeferenced data. This paper reviews the obstacles to and potential methods for sharing georeferenced data in order to support a growing and dynamic geospatial research community and build capacity for data-intensive research across the social and environmental sciences. The development and implementation of a geospatial virtual data enclave methodology is proposed as an innovative and viable solution to share and archive georeferenced data among researchers while protecting the geoprivacy of research subjects and the confidentiality of these data. The ability to share confidential geospatial data among researchers is crucial to ensuring replicability of scientific research, and to enable researchers to verify and build upon the research of others. [ABSTRACT FROM PUBLISHER]

The recent growth in smokeless tobacco (ST) consumption has raised questions about consumer risk perceptions of ST products, especially in high-risk vulnerable populations. This qualitative study examined risk perceptions of ST among adolescent and adult users and nonusers in Ohio Appalachia. Focus groups and interviews were held with adolescents (n = 53;Mage = 17 years) and adults (n = 63;Mage = 34 years) from four Ohio Appalachian counties. Participants were asked about their perceptions of ST-related health risks, ST safety, and the relative safety of ST compared with cigarettes. Transcriptions were coded independently by two individuals. Overall, participants were knowledgeable about health problems from ST use (e.g., oral cancers, periodontal disease). Nearly all participants stated that ST use is not safe; however, there was disagreement about its relative safety. Some perceived all tobacco products as equally harmful; others believed that ST is safer than cigarettes for either the user or those around the user. Disagreements about ST relative safety may reflect mixed public health messages concerning the safety of ST. Comprehensive consumer messages about the relative safety of ST compared with cigarettes are needed. Messages should address the effect of ST on the health of the user as well as those exposed to the user. [ABSTRACT FROM PUBLISHER]

ABSTRACT Demand for service in location modelling is often evaluated based on the spatial proximity of fixed and static reference locations of demand (e.g. home) to a facility, which ignores person-specific activity-travel patterns and the temporal changes in demand for service throughout the day. To address these limitations, this study draws upon recent developments in space-time measures of individual accessibility to explore the spatial and temporal structures of demand by considering individuals' space-time constraints and impact of existing urban structures. Based on a time-geographic framework, eight space-time demand measures were developed and compared with three conventional location-based demand measures for 12 hospitals through an empirical study conducted in Columbus, Ohio. The results show that geographic proximity between clients' home and facilities may not be an effective indicator for service demand, and conventional demand measures tend to underestimate potential demand for service in most situations. The study concludes that space-time demand measures that take into account people's activity-travel patterns in space-time would lead to better estimation of demand for service in most cases. [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.

Many fundamental notions in geographic and social science research still tend to be conceptualized largely in static spatial terms, ignoring how our understanding of the issues we study can be greatly enriched through the lenses of time and human mobility. This article revisits three such notions: racial segregation, environmental exposure, and accessibility. It argues for the need to expand our analytical focus from static residential spaces to other relevant places and times in people's everyday lives. Mobility is an essential element of people's spatiotemporal experiences, and these complex experiences cannot be fully understood by just looking at where people live. As many social scientists are interested in studying segregation, environmental exposure, and accessibility, geographers can contribute to advancing temporally integrated analysis of these issues through careful examination of people's everyday experiences as their lives unfold in space and time. Interdisciplinary research along this line could have a broad impact on many disciplines beyond geography. [ABSTRACT FROM PUBLISHER]

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]

Any study that examines the effects of area-based attributes on individual behaviors or outcomes faces another fundamental methodological problem besides the modifiable areal unit problem (MAUP). It is the problem that results about these effects can be affected by how contextual units or neighborhoods are geographically delineated and the extent to which these areal units deviate from the true geographic context. The problem arises because of the spatial uncertainty in the actual areas that exert the contextual influences under study and the temporal uncertainty in the timing and duration in which individuals experienced these contextual influences. Using neighborhood effects and environmental health research as a point of departure, this article clarifies the nature and sources of this problem, which is referred to as the uncertain geographic context problem (UGCoP). It highlights some of the inferential errors that the UGCoP might cause and discusses some means for mitigating the problem. It reviews recent studies to show that both contextual variables and research findings are sensitive to different delineations of contextual units. The article argues that the UGCoP is a problem as fundamental as the MAUP but is a different kind of problem. Future research needs to pay explicit attention to its potential confounding effects on research results and to methods for mitigating the problem. [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 article, as part of this forum that celebrates the 100th volume of the Annals of the Association of American Geographers, reviews the cumulative contribution of this journal to the advancement of methods, models, and geographic information systems (GIS) from 1911 to 2010. Using data in an Endnote bibliographic database and the ISI Citation Database, I analyze the trends of contributions in these areas in the journal over the past century. I conduct a content analysis on these articles in the journal and identify several enduring and prominent themes in them. I also highlight research directions that are particularly promising for future studies. This article concludes that the cumulative contribution of the Annals to advancing research in methods, models, and GIS over the past century is enormous. Since the restructuring of the Annals into four sections in 2001, the Methods, Models, and GIS section has played a pivotal role in significantly increasing the proportion of method papers in the journal, reversing a downward trend that had been in place since the 1970s. [ABSTRACT FROM AUTHOR]

Criticisms of quantitative geography have a long history in our discipline. Since the emergence of early Marxist geography, the advent of new theoretical traditions has been accompanied by sustained and sometimes vigorous critiques of spatial analysis. In this introductory article to the second focus section on critical quantitative geographies, we discuss the accuracy of some of the criticisms about the use(s) to which spatial analysis is put and about the philosophical presumptions underpinning quantitative geographies. We argue that spatial analysis and critical geographies should not be considered mutually exclusive of one another—although this does not mean that numbers and critical geographies are entirely comfortable with one another. We suggest that critical geographies cannot dispense with numbers and visualizations based on numerical information. We hope that the articles collected here will inspire geographers to engage with numbers and statistics to challenge inequality and imagine alternative futures. [ABSTRACT FROM AUTHOR]

Although many have questioned the adequacy of quantitative methods for addressing issues of concern in critical geographies, such as social justice and inequality, many have argued that quantification can potentially make rich contributions to understanding and addressing these issues. In light of the recent attempts to reassert the critical potential and positive role of quantitative geography, we suggest in this introductory article for the Focus Section that the antagonism between critical and quantitative geographies is not beneficial to the discipline. We highlight some promising developments in modern quantitative geography and reflect on the ways in which the critical-quantitative binary can be at least partially eclipsed. We emphasize that knowledge in quantitative methods is essential for deciphering and challenging regressive political agendas, now often supported by numbers and quantitative analysis. Quantitative geography, when integrated with a critical sensibility and used appropriately, can be a powerful tool for fostering progressive social and political change. [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]

Drawing upon relevant literature, this article reviews research on socially sustainable urban transportation (SSUT). Arguing that the social dimensions of sustainability are more often implied rather than explicit, this article subsequently considers how urban transportation influences the achievement of social sustainability in urban regions. Socially sustainable urban transportation is implied in the literature through the elements of social equity, social exclusion, and quality of life. Several well-established research areas provide potential theoretical, methodological, and applied insight into the development of future socially sustainable urban transportation research agendas, including: urban form, location- and individual-based accessibility, and city/regional planning for sustainability. [ABSTRACT FROM AUTHOR]

The widespread use of mobile communications is leading to new practices in family life and social life, and these changes have significant implications for the study of urban travel. Because of the adoption of new modes of space-time coordination, changing time use and increasing mobility, changing use of existing urban nodes, the blurring of boundaries between home and work, the importance of social networks and social capital, and the shift to person-to-person connectivity, the spatial structure and processes of interaction among individuals have become much more complicated in this age of mobile communications. Static spatial frameworks based on fixed points (e.g., home or workplace) and distances among them are no longer adequate for understanding urban travel. The study of urban travel now needs new conceptualizations and new methodologies. [ABSTRACT FROM AUTHOR]

Building on earlier contributions to feminist understanding of geospatial technologies (GT), I seek to further develop feminist perspectives on GT along new directions. I argue that an attention to the importance of affect (feelings and emotions) and the performative nature of GT practices offers a distinctive critical edge to feminist work on GT. I emphasize the need for GT practitioners to contest the dominant meanings and uses of GT, and to participate in struggles against the oppressive or violent effects of these technologies. I argue that only when emotions, feelings, values, and ethics become an integral part of our geospatial practices can we hope that the use of GT will lead to a less violent and more just world. [ABSTRACT FROM AUTHOR]

Reports on the development of geography from canonical geography to hybrid geographies. Two major rifts in geography with lasting effect; Division of the social-cultural geography; Attempts to create a unified identity for geography based on a singular and purified vision.

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]

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.

A postscript to the December 2010 issue of the journal is presented. The authors reflect on the contributions that the journal has made to the field of American geography since its debut in 1911 and on the work of the Association of American Geographers since its foundation in 1904. They discuss a project that is designed to digitize the entire collection of the journal and examine the work of the geographers who contributed to the journal in its early days. It explores the impact that interdisciplinary research had on geography and discusses other changes that have occurred in the field.

An introduction is presented in which the author discusses various reports within the issue on topics including the geographies of health, well-being, and disease.

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.