1. Health and disease are inherently geographic. They emerge, spread, and are experienced through the interactions among individual behaviors, environmental exposures, mobility patterns, and social...

With the acceleration of the population aging trend in numerous countries worldwide, particularly in China, healthcare services become increasingly vital. The accessibility of healthcare facilities is typically influenced by multiple spatial and non-spatial factors, including population size, transportation modes and the reputation of healthcare services. Previous studies have mostly overlooked the perceptions of patients. However, residents’ sentimental perceptions are crucial for accessibility analysis, especially in the prevalent context of seeking medical treatment across provinces and cities. Considering the above factors, a new model named Sentiment-Enhanced two-step floating catchment area (S-E2SFCA) based on the 2SFCA method was proposed in this research, which employs social media dataset and sentiment analysis to measure the accessibility of hierarchical healthcare facilities across cities and provinces, including secondary and tertiary hospitals. The research findings not only demonstrated the effectiveness of integrating social media data in assessing facility capacity from a perceptual perspective but also revealed the current status and existing issues in healthcare facilities accessibility of the Yangtze River Delta (YRD) region through a case study. These findings could also support the relative regional policy making and promote the balanced spatial distribution of healthcare facilities.

Advanced 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.

This study focuses on optimizing the location of electric vehicle (EV) charging stations in the state of Georgia using geospatial big data. With the growing concern about climate change and the need for a transition to sustainable energy, the adoption of EVs is rapidly increasing, necessitating the development of a robust charging infrastructure. We use historical data and predictive modeling to forecast EV adoption by 2030, assessing future charging demand through travel survey data to understand spatial and temporal travel patterns. A Mixed-Integer Programming (MIP) approach, combined with a genetic algorithm, is employed to optimize the layout of charging stations, balancing multiple objectives such as minimizing the number of stations, maximizing coverage, ensuring social equity, and balancing urban-rural distribution. This framework aims to support efficient and equitable development of charging infrastructure in Georgia, ultimately facilitating the wider adoption of electric vehicles.

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.

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.

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.

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.

Uncertainty and context pose fundamental challenges in GIScience and geographic research. Geospatial data are imbued with errors (e.g. measurement and sampling) and various types of uncertainty tha...

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.

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.

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.

In recent years, a variety of geospatial technologies have made it possible to collect and assemble huge amounts of spatiotemporal data about a wide range of physical and human phenomena. These tec...

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.

This research is motivated by the need for 3D GIS data models that allow for 3D spatial query, analysis and visualization of the subunits and internal network structure of ‘micro‐spatial environments’ (the 3D spatial structure within buildings). It explores a new way of representing the topological relationships among 3D geographical features such as buildings and their internal partitions or subunits. The 3D topological data model is called the combinatorial data model (CDM). It is a logical data model that simplifies and abstracts the complex topological relationships among 3D features through a hierarchical network structure called the node‐relation structure (NRS). This logical network structure is abstracted by using the property of Poincaré duality. It is modelled and presented in the paper using graph‐theoretic formalisms. The model was implemented with real data for evaluating its effectiveness for performing 3D spatial queries and visualization.

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 limited costly transportation arteries and providing value-added traveler information. Many ATIS applications are built on the functionalities provided by Geographical Information Systems (GIS), which often cannot meet extra requirements like real-time response. We investigate GIS-based systems in ATIS and propose a system architecture based on GIS and distributed database technology. Issues on data modeling, data representation, storage and retrieval, data aggregation, and parallel processing of queries are discussed. This paper introduces a distributed system architecture for ATIS based on recent technology. It presents new data models for information representation and proposes data shipping for efficient query processing and function shipping for reducing communication overhead. The paper also examines the use of a network of computers for solving complex problems more timely and privacy protection for sensitive data.