Extreme snowfall events significantly affect human dynamics, challenging transportation safety. Current research on inferring mobility patterns post-snowfall is hindered by scale-free network connectivity, abrupt contextual changes, intricate mechanisms of spatial interaction, and multivariate feature handling. This paper proposes the Weather-Informed Mobility Network (WIMN), a graph deep learning model designed to infer the post-snowfall human movement flows within mobility networks. WIMN treats each region of interest as a spatially-embedded graph and has three characteristics: (i) a geo-attraction block informed by the gravity law, recalibrating the spatial-wise importance of locations and interactions; (ii) an Encoder-Decoder structure where Encoder learns stable spatial dependencies and Decoder captures the snow-induced dynamic changes; and (iii) a Pareto-inspired hub selection, processing locations hierarchically according to their degree centrality within the network context. Applied to the Twin Cities Metropolitan Area, Minnesota, USA, WIMN outperformed baseline methods by up to 18.5% in inferring the redistribution of human mobility flows. Additionally, WIMN demonstrated spatial explainability across the learned deep weights, snowfall intensity, and flow reductions. These findings highlight WIMN’s effectiveness in understanding short-term human dynamics after extreme weather events, with potential applications in aiding transportation planning and disaster response.

Census data, as a traditional data source of resident socio-demographics, provides valuable information for decision-makers, researchers, and the public. While numerous efforts have been made to develop more comprehensive data products based on census datasets, most approaches treat census units as static and independent entities, overlooking their interactions. In this paper, we introduce the “visitor census” dataset, a semantically enriched census that incorporates human visitations extracted from large-scale mobile positioning data. We identified and validated the potential home locations of 3.58 million anonymous mobile phone users across seven U.S. metropolitan statistical areas in July 2021 and utilized home detection results to enrich the socio-demographic profile of the places users visited. The proposed data generation framework is adaptive, allowing future integration of diverse socio-demographic features at varying spatial and temporal scales. Overall, this visitor-based census represents an effort to enrich resident-based census knowledge by incorporating mobilities and spatial interactions in human digital traces, bridging the gap between aggregated and individual analysis, as well as between conventional census and mobile phone data.

Spatial flows between consecutive distribution snapshots describe how one configuration evolves into the next. Such panel flows are inferable, albeit challenging, from observations over space and time. Current flow generation models focus on cross-sectional scenarios and neglect the temporal nature of flows. The panel gravity model, although interpretable, relies on linear assumptions and overlooks the change of geographic contexts. Our work introduces a Deep Spatial Evolution Network (DSEN) to infer panel flows, i.e. the spatial evolution between two snapshots of spatial distributions. DSEN incorporates a cross-event context learner to encode the contextual features, and a gravity-informed spatial evolution decoder to learn latent evolutionary features. Using a device-level mobile positioning dataset in the Twin Cities Metropolitan Area, Minnesota, U.S., DSEN achieves a 14.0% correlation improvement and a 15.3% error reduction compared to baselines in inferring human flows during the 2021 Christmas holiday, while explaining evolution processes and flow directionalities via deep features. Further experiments across sampling ratios and unseen events demonstrated the robustness and generalizability of our model, respectively. Deep flow inference for panel data holds promise for advancing mobility studies at the GeoAI frontier, expanding access to flow data, and informing solutions to pressing human-environment challenges in future cities.

1. Recent decades have witnessed the rise of volunteered geographic information (VGI) (Goodchild 2007) to be a significant phenomenon in GIScience and beyond as it offers unprecedented opportunitie...

Urban spaces, though often perceived as discrete communities, are shared by various functional and social groups. Our study introduces a graph-based physics-aware deep learning framework, illuminating the intricate overlapping nature inherent in urban communities. Through analysis of individual mobile phone positioning data at Twin Cities metro area (TCMA) in Minnesota, USA, our findings reveal that 95.7 % of urban functional complexity stems from the overlapping structure of communities during weekdays. Significantly, our research not only quantifies these overlaps but also reveals their compelling correlations with income and racial indicators, unraveling the complex segregation patterns in U.S. cities. As the first to elucidate the overlapping nature of urban communities, this work offers a unique geospatial perspective on looking at urban structures, highlighting the nuanced interplay of socioeconomic dynamics within cities.

<p>Some evidence suggests that pediatric sarcomas have both shared and distinct genetic profiles; however, large-scale efforts to characterize germline genetic susceptibility across these malignancies are limited by their rarity. We evaluated the role of common and rare variants in the genetic etiology of the more frequent pediatric sarcomas: osteosarcoma (OS); Ewing sarcoma (ES); and rhabdomyosarcoma (RMS), subcategorized into embryonal (ERMS) and alveolar (ARMS).</p>

1. With the prevalence of positioning services, such as the Global Positioning Service (GPS), an enormous amount of trajectory data has been collected. This has provided researchers with opportunit...

<h3>Importance</h3><p>Most regulated medical devices enter the US market via the 510(k) regulatory submission pathway, wherein manufacturers demonstrate that applicant devices are “substantially equivalent” to 1 or more “predicate” devices (legally marketed medical devices with similar intended use). Most recalled medical devices are 510(k) devices.</p><h3>Objective</h3><p>To examine the association between characteristics of predicate medical devices and recall probability for 510(k) devices.</p><h3>Design, Setting, and Participants</h3><p>In this exploratory cross-sectional analysis of medical devices cleared by the US Food and Drug Administration (FDA) between 2003 and 2018 via the 510(k) regulatory submission pathway, linear probability models were used to examine associations between a 510(k) device’s recall status and characteristics of its predicate medical devices. Public documents for the 510(k) medical devices were collected using FDA databases. A text extraction algorithm was applied to identify predicate medical devices cited in 510(k) regulatory submissions. Algorithm-derived metadata were combined with 2003-2020 FDA recall data.</p><h3>Exposures</h3><p>Citation of predicate medical devices with certain characteristics in 510(k) regulatory submissions, including the total number of predicate medical devices cited by the applicant device, the age of the predicate medical devices, the lack of similarity of the predicate medical devices to the applicant device, and the recall status of the predicate medical devices.</p><h3>Main Outcomes and Measures</h3><p>Class I or class II recall of a 510(k) medical device between its FDA regulatory clearance date and December 31, 2020.</p><h3>Results</h3><p>The sample included 35 176 medical devices, of which 4007 (11.4%) were recalled. The applicant devices cited a mean of 2.6 predicate medical devices, with mean ages of 3.6 years and 7.4 years for the newest and oldest, respectively, predicate medical devices. Of the applicant devices, 93.9% cited predicate medical devices with no ongoing recalls, 4.3% cited predicate medical devices with 1 ongoing class I or class II recall, 1.0% cited predicate medical devices with 2 ongoing recalls, and 0.8% cited predicate medical devices with 3 or more ongoing recalls. Applicant devices citing predicate medical devices with 3 or more ongoing recalls were significantly associated with a 9.31–percentage-point increase (95% CI, 2.84-15.77 percentage points) in recall probability compared with devices without ongoing recalls of predicate medical devices, or an 81.2% increase in recall probability relative to the mean recall probability. A 1-SD increase in the total number of predicate medical devices cited by the applicant device was significantly associated with a 1.25–percentage-point increase (95% CI, 0.62-1.87 percentage points) in recall probability, or an 11.0% increase in recall probability relative to the mean recall probability. A 1-SD increase in the newest age of a predicate medical device was significantly associated with a 0.78–percentage-point decrease (95% CI, 1.29-0.30 percentage points) in recall probability, or a 6.8% decrease in recall probability relative to the mean recall probability.</p><h3>Conclusions and Relevance</h3><p>This exploratory cross-sectional study of 510(k) medical devices cleared by the FDA between 2003 and 2018 demonstrated significant associations between 510(k) submission characteristics and recalls of medical devices. Further research is needed to understand the implications of these associations.</p>

Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.

<p>We examined the associations between lung function and incident dementia and cognitive decline in 12,688 participants of the ARIC study who provided lung function measurements in 1990-1992. Cognitive tests were administered up to seven times, and dementia was ascertained through 2019. We used shared parameter models to jointly model proportional hazard models and linear mixed-effect models to estimate lung function-associated dementia rate and cognitive change, respectively. Higher forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) were associated with reduced dementia rate (n=2452 developed dementia); hazard ratios per 1L higher FEV1 and FVC were 0.79 (95%CI: 0.71-0.89) and 0.81 (95%CI: 0.74-0.89), respectively. Each 1L higher FEV1 and FVC was associated with 0.08 (95%CI: 0.05-0.12) SD and 0.05 (95%CI: 0.02-0.07) SD attenuation of 30-year cognitive decline, respectively. One percent higher FEV1/FVC was associated with 0.008 (95% CI: 0.004-0.012) SD less cognitive decline. We observed statistical interaction between FEV1 and FVC, suggesting that cognitive declines depended on values of specific FEV1 and FVC (as compared to FEV1, FVC, or FEV1/FVC% models that suggested linear incremental associations). Our findings may have important implications for reducing burden of cognitive decline that is attributable to environmental exposures and associated lung function impairment.</p>

Geographical units densely connected by human movements can be treated as a geospatial community. Detecting geospatial communities in a mobility network reveals key characteristics of human movements and urban structures. Recent studies have found communities can be overlapping in that one location may belong to multiple communities, posing great challenges to classic disjoint community detection methods that only identify single-affiliation relationships. In this work, we propose a Geospatial Overlapping Community Detection (GOCD) framework based on graph generation models and graph-based deep learning. GOCD aims to detect geographically overlapped communities regarding the multiplex connections underlying human movements, including weak and long-range ties. The detection process is formalized as deriving the optimized probability distribution of geographic units' community affiliations in order to generate the spatial network, i.e., the most reasonable community affiliation matrix given the observed network structure. Further, a graph convolutional network (GCN) is introduced to approach the affiliation probabilities via a deep learning strategy. The GOCD framework outperformed existing baselines on non-spatial benchmark datasets in terms of accuracy and speed. A case study of mobile positioning data in the Twin Cities Metropolitan Area (TCMA), Minnesota, was presented to validate our model on real-world human mobility networks. Our empirical results unveiled the overlapping spatial structures of communities, the overlapping intensity for each CBG, and the spatial heterogeneous structure of community affiliations in the Twin Cities. CCS CONCEPTS • Networks → Topology analysis and generation; Network mobility; • Human-centered computing → Ubiquitous and mobile devices.

Geographic phenomena are considered complex due to the heterogeneous nature of spatial dependencies. It is impossible to specify a universal law described in statistical or physical languages that can perfectly characterize a real-world geographic process and explain how it forms certain observed patterns. Traditional spatial analytics based on strict statistical principles, strong assumptions, or classic computation workflows are facing great challenges and opportunities when embracing the explosive growth of geospatial data and recent technical innovations. Here, we highlight the promises of Intelligent Spatial Analytics (ISA), a new set of spatial analytical approaches based on spatially explicit deep neural networks with more flexible data representation, modules for complex spatial dependence, weaker model prior assumptions, and hence the enhanced ability to predict/explain unknowns. Three essential topics in spatial analysis, i.e., geostatistics, spatial econometrics, and flow analytics are elaborated as examples in the vision of ISA. We also discuss challenging issues of ISA as an invitation to explore deeper linkages between machine/deep learning and spatial analysis at the frontier of Geospatial Artificial Intelligence. CCS CONCEPTS • Applied computing → Environmental sciences; • Information systems → Geographic information systems; • Computing method-ologies → Model verification and validation.

Traffic flow prediction, as one of the prominent tasks in intelligent transportation systems, is challenging due to underlying complex spatiotemporal characteristics. Consideration of historical spatial and temporal dependencies is essential for the traffic prediction of a geographic unit for a future time period. Existing works mainly adopted graphs to represent the irregular layout of spatial units, where nodes are signal of spatial units and edges are link strengths between units. For contemporary deep learning based spatiotemporal prediction tasks, the temporal dependence can be well modeled via convolution neural network or recurrent neural network, and spatial dependence features are commonly captured using graph convolution networks. However, classic graph structures cannot fully represent the complex nature of spatial relationships in transportation networks, because the spatial pattern of a location might be influenced by multiple sets of contextual information simultaneously, while a graph edge can only describe the linkage between two nodes. In addition, most existing models ignore the synchronous dependence between temporal and spatial features, leading to a mismatch between the temporal and spatial features of a location. Based on such problems, a hypergraph-based deep learning model, namely synchronous hypergraph convolutional network (SHGCN), is proposed to better capture the complex relationships between spatial and temporal knowledge. A novel synchronous hypergraph cell (SH-Cell) is designed based on LSTM cells integrated in the form of a Seq2seq architecture. Then, we construct dynamic hypergraphs to capture the synchronous spatiotemporal dependence adaptively using SH-Cells. Experimental results demonstrate the superiority of SHGCN over well-known benchmarks on two real-world publicly-available traffic datasets. This research provides new insights for improving the traffic flow prediction accuracy and understanding complex spatiotemporal relationships towards a more reliable urban traffic management.. 2022. SHGCN: A hypergraph-based deep learning model for spatiotemporal traffic flow prediction.

Since the summer of 2020, the rate of coronavirus cases in the United States has been higher in rural areas than in urban areas, raising concerns that patients with coronavirus disease 2019 (COVID-...

Introduction: Obstructive sleep apnea (OSA) is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. There is strong clinical and epi-demio...

<p> <italic>Background:</italic> While rotavirus vaccine programs effectively protect against severe rotavirus gastroenteritis, rotavirus vaccine strains have been identified in the stool of vaccinated children and their close contacts suffering from acute gastroenteritis. The prevalence of vaccine strains, the emergence of vaccine-derived strains and their role in acute gastroenteritis are not well studied. </p>