Telehealth can reduce travel barriers to oncology, yet its impact depends on both digital connectivity and the geography of care. We present an open, reusable dataset that characterizes two critical components of telehealth infrastructure for cancer care, accessible oncologists and sufficient and affordable internet, at the ZIP Code Tabulation Area level across the United States. The resource integrates population-weighted fixed broadband measures and 5G coverage, internet subscription as an affordability proxy, geocoded oncologist practice sites with full-time-equivalent capacity, and a national origin-destination matrix of road travel times. From these inputs we compute spatial accessibility for in-person care by two-step floating catchment area method (2SFCA) and telehealth-enabled care by two-step virtual catchment area method (2SVCA) at 45-120-minute thresholds. We support transparency by releasing the source and intermediate indicators, the final accessibility scores, and a replicable 2SFCA/2SVCA workflow. Anticipated uses include benchmarking infrastructure across states and metropolitan areas, analyses of disparities by rurality and area deprivation, subsidy simulations, and rapid replication in new diseases or providers contexts.

Maternal healthcare accessibility is a key determinant of maternal and newborn outcomes, yet the United States continues to experience disproportionately high maternal mortality rates compared with other high-income countries. Efforts to address this problem are hampered by substantial spatial disparities, especially in large states like Florida. Existing methodologies for evaluating healthcare access, such as the widely used Generalized Two-Step Floating Catchment Area (G2SFCA) method, may not accurately capture real-world circumstances because they often rely on assumed, uniform parameters that overlook contextual heterogeneity in travel behavior. However, maternal patients in different geographies experience drastically different transportation barriers and varying tolerance for distance and travel times, underscoring the need for more granular, area-specific modeling. This study proposes a data-driven Variable Catchment 2SFCA (V2SFCA) framework to estimate maternal healthcare accessibility across Florida. Leveraging observed patient flow data, we employed gravity models to empirically calibrate distance decay functions and separately defined catchment thresholds specific to each area type. These data-driven, area-specific parameters enable the framework to more accurately reflect behavioral heterogeneity in maternal healthcare utilization. Applied to Florida, the model reveals substantial accessibility disparities across the four area types, including metropolitan, micropolitan, small town, and rural. It also demonstrates improved behavioral realism compared with conventional approaches, offering actionable insights for equitable maternal care planning and resource allocation.

The purpose of this survey study was to comprehensively examine if various types of screen time differed between children with overweight or obesity (OWOB; ≥ 85th BMI percentile) vs. normal weight (NW: < 85th BMI percentile) in a largely socioeconomically disadvantaged population. 739 parent proxies of children aged 5-11 years (M = 9.27, SD = 1.49) mostly enrolled in Medicaid (83.9%) in a United States (US) southern state responded to an online questionnaire called the Movement Behavior Questionnaire - Child (MBQ-C; open version). Eight items of the MBQ-C separately measured passive and interactive screen time on weekdays and weekend days. The survey also gathered parent-reported child weight and height, along with their sociodemographic characteristics. Compared to children with NW (M = 294.5 ± 7.2 min/day), children with OWOB (M = 364.3 ± 10.6 min/day) reported greater amount of total screen time (MDiff = 69.8 min/day, 95% CI = [45.0-94.6], p < 0.001). Of the sample, 24.9% met the sedentary screen time guidelines of no more than 2 hours/day favoring children with NW (OR = 0.59, 95% CI [0.39, 0.86], p = 0.008). The difference of screen time between weight-status groups was greatest in passive screen time, particularly on weekdays (MDiff = 63.2 min/day, 95% CI = 47.6-78.7, p < 0.001). Demographic factors did not significantly moderate the relationship between screen time and weight status. Most of the disadvantaged children failed to meet the screen time guidelines. Children with OWOB reported higher screen time, particularly passive screen time on weekdays. These findings suggest the need for tailored interventions to not only curb overall screen time but also mitigate specific types of screen time behaviors on specific days for children with OWOB (i.e., passive screen use on weekdays).

Spatiotemporal kernel density estimation (STKDE) extends traditional KDE by integrating spatial and temporal kernels to capture dynamic clustering patterns of events. Widely used in crime analysis, STKDE enables predictive mapping of “hotspots” that vary across both space and time. Enhancements such as cyclic STKDE (cSTKDE) further improve accuracy by modeling periodic crime rhythms. Case studies from Baton Rouge demonstrate STKDE's superiority over spatial‐only methods in forecasting burglary and robbery hotspots. Ongoing developments include real‐time, network‐based, and machine‐learning‐integrated STKDE, with applications extending to epidemiology and urban safety.

The maximum accessibility equality problem (MAEP) is a spatial planning framework prioritizing equitable access to public services, addressing disparities rather than merely improving efficiency. Unlike traditional location–allocation models, MAEP minimizes inequality in accessibility across regions and populations. A two‐step optimization model (2SO) is proposed: the first locates facilities based on coverage or proximity; the second allocates service capacities to reduce access inequality, measured by indices like the two‐step floating catchment area (2SFCA). The equity objective is modeled through quadratic programming to minimize weighted variance in access levels. A case study from rural health care and one from urban electric vehicle infrastructure in China show the model's potential to enhance both fairness and service outcomes. The entry also discusses future challenges, such as integrating dynamic demand, behavioral responses, multimodal access, and balancing equity–efficiency tradeoffs. The MAEP provides a robust, adaptable approach for aligning infrastructure planning with social equity and sustainability goals.

Background/objectives:Social determinants of health (SDOHs) may affect children's health and health behaviors. This study aimed to understand the relationship between health behaviors and SDOHs in a child population from under-resourced communities [i.e., Medicaid eligible or enrolled, overweight or with obesity (OWOB), predominantly Black or African Americans]. Methods:Following a stratified sampling strategy, parent proxies (N = 311) completed an online survey to measure participants (5-12 years old) health behaviors and SDOHs including socioeconomic status [SES; Area Deprivation Index (ADI), household income], living conditions, and food insecurity. Results:Participants with OWOB showed greater screen time than normal weight (NW) children. Health behaviors (i.e., physical activity, screen time, sleep, dietary behavior) generally favored the higher SES group (considering household income and ADI). SDOHs (as a variant) correlated with health behaviors (the other variant; Canonical r = 0.27, p < 0.05). Of the SDOHs, household chaos negatively correlated with regular bedtime routines in both weight status groups (NW: r = -0.19, p < 0.05; OWOB: r = -0.24, p < 0.01). Adverse living conditions and greater food insecurity were associated with more screen time and, unexpectedly associated with more physical activity (r ranged from 0.19 to 0.22, p < 0.05) in NW participants. Conclusions:The findings unraveled differences in health behaviors by weight status and SDOHs. SDOHs showed significant correlations with health behaviors, and these correlations were slightly greater in NW children. Weight status plays an important role in the relationship between health behaviors and SDOHs among children from under-resourced communities.

Open Earth observation (EO) archives and cloud processing engines have expanded EO-enabled environmental analytics. However, reproducible and replicable integration between cloud remote-sensing workflows and local geoinformation modelling remains difficult for interdisciplinary users, particularly across multiple tools and scripts. We present KNIME extensions for Google Earth Engine (GEE) and geospatial analytics: open no/low-code visual components that expose core GEE objects as executable nodes and enable interoperability between cloud and local computation. This democratizes end-to-end EO analytics by coupling scalable cloud processing with KNIME's data-science ecosystem for validation and reporting. Three reusable templates demonstrate (1) land-cover mapping, (2) heat-exposure and healthcare-accessibility burden screening, and (3) multimodal GenAI place-type inference from imagery and building data. The software turns script-centric pipelines into transparent, versioned components that support reproducibility and replicability (R&R) and reinforce FAIR, end-to-end environmental workflows.

Telehealth accessibility refers to the potential for individuals to obtain health‐care services through digital communication technologies. It depends on the availability and affordability of broadband internet, as well as individual capacity to use digital tools. This entry outlines the background and public health significance of telehealth, defines key components of accessibility, and presents the two‐step virtual catchment area (2SVCA) model as a method to measure virtual access. Empirical evidence from national and international studies highlights the spatial and demographic variations in telehealth accessibility. Populations in rural areas and economically disadvantaged communities often face compounded challenges. The entry emphasizes the need for improved infrastructure, policy support, and digital inclusion strategies to promote equitable telehealth access.

Delineating Hospital Service Areas (HSAs) is critical for healthcare resource allocation and policymaking. However, existing methods struggle to simultaneously capture patient flow patterns, spatial contiguity, and multiple capacity constraints. Traditional spatial clustering fails to integrate flow networks effectively, while standard Graph Neural Networks (GNNs) often lack the capability to strictly enforce capacity constraints. Even the Spatially constrained Leiden (ScLeiden) algorithm, which couples flow and adjacency, often yields communities with near-zero inflow. We propose a novel framework, SGCN-MST, integrating physics-informed graph learning with constraint-based regionalization. It leverages a physics-informed GNN to simulate patient flow as a spatial diffusion process, explicitly capturing the spatial decay of interactions. The resulting “interaction-aware” embedding is fed into a spatial Minimum Spanning Tree (MST) using a Depth-First Search (DFS) strategy to dynamically balance modularity optimization with multiple constraints. Applied to the Florida inpatient database, our model reveals a nested, hierarchical spatial structure where large regional referral centers coexist with compact local communities, closely mirroring functional medical hierarchies. Comparative analysis shows that SGCN-MST provides a more balanced and policy-ready solution than baselines such as ScLeiden and Region2Vec when localization, contiguity, and capacity feasibility must be considered jointly. This study provides a statistically robust and administratively practical tool for health geography.

BACKGROUND:Childhood obesity remains a pressing public health concern, with significant long-term implications for children's growth and development, as well as an increased risk for various diseases. In recent years, the obesity prevalence among children has shown a concerning rise, particularly in the Deep South (e.g., Louisiana) exhibiting higher rates compared to other regions of the United States. Meanwhile, environmental factors play a critical role in shaping children's health behaviors and outcomes. There has been an increasing body of research on the relation between obesogenic environment and health, but far less focused on child health. METHODS:This study provides a comprehensive measurement of obesogenic environments for children in Louisiana covering park accessibility, walkability, and food environment at the ZIP Code area level. Geographic information system (GIS) analyses revealed significant urban-rural disparities and heterogeneity by race and socioeconomic deprivation. A three-way analysis of variance (ANOVA) further revealed the main and interaction effects of urbanicity, Black population percentage, and area deprivation index (ADI) on obesogenic environment indicators. RESULTS:Regarding the disparities of walkability, not only the individual factors including urbanicity, Black population percentage, and ADI significantly affect walkability, but also the interaction terms among these factors, including both two-way and three-way interactions, are significant. Urban-rural differences and ADI levels contribute to disparities in the food environment. But no significant disparities are observed in park accessibility across different subgroups. CONCLUSION:While park accessibility disparities are limited, the food environment varies significantly by urbanicity and ADI level, walkability shows complex socio-spatial interactions. The study sheds light on targeted public health interventions and urban planning to close the gaps in obesogenic environments for children's health.

This paper examines the travel behaviors of hand-foot-and-mouth disease (HFMD) patients in Nanchang City in central China. Based on the HFMD patients’ hospital visitation data from the Center of Disease Control (CDC) of Nanchang in 2018, a spatial network of patient-to-hospital trip flows is constructed. A Geographic Information Systems (GIS) automated network community detection method, termed ‘ScLeiden’, is utilized to delineate the study area into six hospital service areas (HSAs) to represent distinctive health care markets. Patients’ travel patterns across these HSAs are compared to highlight the geographic disparity. In two HSAs anchored by major hospitals in the regions, the volume of patients increased up to a travel range and then declined, and thus formed a single peak in the trip volume distribution curve across travel time. Each of the remaining four HSAs exhibited two or more peaks in their trip volume distribution curves. The patterns reflected the split choices of patients for the largest Children Hospital in the region, the second-tier county hospital, or others, which were likely to be stratified by their economic affordability, transportation means, and possible health literacy. The study provides valuable insights into the delineation of HSAs and the unique patients’ travel behaviors in China.

Accurately identifying and analyzing cross-boundary regional cooperation remains challenging due to administrative constraints and data limitations. This study utilizes high-resolution human mobility data, network community scanning (NCS), and association rule mining to examine trans-provincial cooperation across 369 Chinese cities, leveraging Location-Based Services (LBS) data from 1.3 billion users. The findings indicate that border-adjacent cooperation dominates trans-provincial interactions in China, while non-adjacent cooperation is embedded within broader cooperative networks formed through adjacent ties reinforcing the interwoven nature of cross-administrative collaborations. Additionally, emerging cooperative clusters extend beyond officially designated urban agglomerations, revealing unrecognized regional synergies not yet captured in planning frameworks. These results highlight discrepancies between actual collaboration linkages and government-defined regional plans, underscoring the need for adaptive, data-driven policymaking and offering practical insights for regional planning and governance.

This study employs the network community scanning (NCS) method to analyze inter-city human flow network patterns using the most recent 2023 Baidu mobility data in China. Specifically, it uses the Community Multiresolution Overlap Scan (CMOS) technique to define regions comparable to the recently government designated urban agglomerations (UAs) and other administrative units (i.e., economic regions and provincial units) so that the delineated and targeted regions overlap most. The results reveal a larger spatial context for multiple UAs to interact as well as a centric structure to detect within each UA. By comparing designated UAs and the NCSderived comparable regions, it largely validates the rationale of UAs. More in-depth examination reveals discrepancies on a finer scale. Based on community affiliation index (CAI) and PageRank centrality, the centric structure analysis of designated UAs identifies whether an UA is absent of any central city, led by a dominant center, or jointly anchored by multiple centers. The efficient network delineation algorithm supported by timely mining of big data enables policy makers to examine the hierarchical structure of cities in real time and make necessary adjustments.

This study employs an innovative multi-constraint Monte Carlo simulation method to estimate suppressed county-level cancer counts for population subgroups and extend the downscaling from county to ZIP Code Tabulation Areas (ZCTA) in the U.S. Given the known cancer counts at a higher geographic level and larger demographic groups at the same geographic level as constraints, this method uses the population structure as probability in the Monte Carlo simulation process to estimate suppressed data entries. It not only ensures consistency across various data levels but also accounts for demographic structure that drives varying cancer risks. The 2016-2020 cancer incidence data from the Utah Cancer Registry is used to validate our approach. The method yields results with high precision and consistency across the full urban-rural continuum, and significantly outperforms several machine-learning models such as Random Forest and Extreme Gradient Boosting.

Telehealth has been promoted as a solution to spatial healthcare access barriers, yet its role in addressing cancer care disparities remains uncertain, particularly in the context of digital divides in broadband availability and affordability. This study assessed spatial and telehealth accessibility to cancer care across 33,499 ZIP Code Tabulation Areas (ZCTA) in the United States using the two-step floating catchment area (2SFCA) and two-step virtual catchment area (2SVCA) methods, respectively. Incorporating physician locations, cancer incidence, travel time based on transportation networks, and broadband coverage and subscription rates, we found that accessibility declined from urban to rural areas and was lower in ZCTA with greater socioeconomic deprivation. Areas with higher proportions of Black and Hispanic populations showed modestly higher access scores, yet a three-way interaction among rurality, deprivation, and racial ethnic composition revealed compounded disadvantages. Telehealth reduced but did not eliminate these gaps, highlighting how its reliance on digital infrastructure may both alleviate and intensify disparities in cancer care access.

Hand foot mouth disease (HFMD) is one of the widespread transmissible diseases that target preschool children, especially in urban regions in East Asia. Based on the mobile app data, a GIS automated regionalization method is used to define regions of various urbanicity levels. The variability of HFMD patients' travel behaviors across these regions are examined by the complementary cumulative distribution functions (CCDFs). The travel burden in Nanchang increased as the patients' residences moved toward regions of more rurality. Specifically, more urbanized regions near the city center (i.e., urban core and its surrounding urban) enjoyed better access to the healthcare service by spending an average of about 17-minute drive time. Patients residing in less urbanized regions such as suburbs and downtowns of surrounding rural counties either chose a nearby hospital or journeyed far to the region's central Children's Hospital, with average elevated travel time of 25 and 22 min, respectively. Those in rural areas travelled an average of 36 min, the longest among all regions. The study also finds a unique dual-peak pattern across two travel ranges in the study area due to the patients' dilemma between pursuing high-quality hospitals and accommodating travel burden. Such a pattern mandates the adoption of a segmented regression approach to deriving the best-fitting CCDFs in most regions except for the rural.

Persistent disparities in access to maternal healthcare across the United States, particularly in rural and underserved communities, have resulted in poor maternal outcomes. Traditional statistical methods, such as Quadratic Programming (QP), have been utilized for healthcare resource reallocation, but they struggle with dynamic, multi-objective geographic optimization problems. This study presents a Reinforcement Learning (RL)-based framework to optimize maternity care resource distribution. We follow the Maximal Accessible Equality Problem (MAEP), aiming to enhance spatial equality by reducing the weighted accessibility variance. We integrate the Two-Step Floating Catchment Area (2SFCA) method for measuring maternity care accessibility and leverage Proximal Policy Optimization (PPO) to dynamically reallocate obstetric facilities across counties in Florida. To account for real-world complexities, we tested our RL framework under three scenario objectives: minimizing distance, obstetric bed supply preservation, and prioritizing underserved counties. Results show the proposed framework effectively reduces accessibility variance by 31.7-49.3%. These findings highlight the potential of RL in offering scalable, data-driven solutions to support equitable maternity health care, benefiting practical applications for implementing actionable health policy decision making.

Importance Patients often travel for cancer care, yet the extent to which patients cross state lines for cancer care is not well understood. This knowledge can have implications for policies that regulate telehealth access to out-of-state clinicians. Objective To quantify the extent of cross-state delivery of cancer services to patients with cancer. Design, Setting, and Participants This cross-sectional study analyzed fee-for-service Medicare claims data for beneficiaries (aged ≥66 years) with a diagnosis of breast, colon, lung, or pancreatic cancer between January 1, 2017, and December 31, 2020. Analyses were performed between January 1 and July 30, 2024. Exposure Patient rurality. Main Outcomes and Measures The primary outcome of interest was receipt of cancer care across state lines. Frequencies of cancer services (surgery, radiation, and chemotherapy) were summarized by cancer type in relation to in-state vs out-of-state receipt of care based on state of residence for Medicare beneficiaries. Cross-state delivery of cancer services was also quantified by adjacent vs nonadjacent states and overall between-state flows for service utilization. Results The study included 1 040 874 Medicare beneficiaries with cancer. The mean (SD) age of the study population was 76.5 (7.4) years. Most patients were female (68.2%) and urban residing (78.5%); one-quarter (25.9%) were aged between 70 and 74 years. In terms of race and ethnicity, 7.0% of patients identified as Black, 3.4% as Hispanic, and 85.5% as White. Overall, approximately 6.9% of cancer care was delivered across state lines, with the highest proportion (8.3%) occurring for surgical care, followed by radiation (6.7%) and chemotherapy (5.6%) services. Out of all cross-state care, 68.4% occurred in adjacent states. Frequency of cross-state cancer care increased with patient rurality. Compared with urban-residing patients, isolated rural-residing patients were 2.5 times more likely to cross state lines for surgical procedures (18.5% vs 7.5%), 3 times more likely to cross state lines for radiation therapy services (16.9% vs 5.7%), and almost 4 times more likely to cross state lines for chemotherapy services (16.3% vs 4.2%). Conclusions and Relevance In this cross-sectional study of Medicare claims data, a notable proportion of cancer services occurred across state lines, particularly for rural-residing patients. These results highlight the need for cross-state telehealth policies that recognize the prevalence of care delivery from geographically distant specialized oncology services.

Urban business districts frequently evolve in ways that deviate from initial expectations due to business dynamics and urban development. Although numerous scholars have investigated these districts, a crucial premise is often overlooked: business districts are neither confined nor static regions. Existing delineation methods, such as government documents and cognitive maps, inadequately capture their spatial complexities. Therefore, this study focuses on Huaqiangbei, a classical business district, and proposes a framework to analyze the evolution of its spatial scope and business semantics (the collective understanding of its functional attributes). Toponym co-occurrences between Huaqiangbei and name of POI were extracted from web pages to delineate the spatial scopes of Huaqiangbei using KDE and the fuzzy set method, while business semantics were identified through TF-IDF algorithm. Then the changes in spatial scopes and business semantics with multiple time series were traced. The findings reveal that Huaqiangbei's vague region differs from its officially planned boundary, exhibiting a dynamic and multi-level core-peripheral structure. Additionally, core regions feature "Electronics Hypermarket" as the dominant business semantic and present diverse semantic combination patterns. The district's evolution is intricately linked to Shenzhen's urban development, reflecting its growth trajectory. This study offers an innovative perspective for comprehending business district development, offering valuable insights for optimizing business services and governance policies, thereby enhancing urban consumption and promoting high-quality development.