Spatiotemporal Association Research Articles

A novel AQI forecasting method based on fusing temporal correlation forecasting with spatial correlation forecasting

Air is an essential natural resource, and the Air Quality Index (AQI) is an important indicator visually reflecting air quality. Accurate AQI prediction is critical for controlling air pollution. This study proposes a new spatio-temporal correlation hybrid prediction model ST-EXMG-AE-XGBoost. The process of building this model includes three stages. In the first stage, Pearson correlation analysis is performed on different AQI monitoring stations to capture their temporal correlation characteristics. The prediction models T-ELM, T-XGBoost, and T-MLP are built in turn. In the second stage, GAT captures the spatial correlation characteristics, and the prediction model S-GAT is made. In the third stage, the prediction results of T-ELM, T-XGBoost, T-MLP, and S-GAT are learned, and features are reconstructed using the AE autoencoder. In contrast, the spatio-temporal association prediction is performed using the XGBoost method afterward. Through the prediction study of historical data from AQI monitoring stations in Shunyi District, Beijing, the following conclusions can be drawn: (a) compared with deep learning models such as LSTM and TCN, the classical models such as ELM, MLP, and XGBoost still have good application value with high prediction accuracy and short training time; (b) compared with single prediction models, the proposed temporal correlation prediction methods T-ELM, T-MLP, and T-XGBoost further improve the AQI prediction effect; (c) the use of GAT method can effectively explore the spatial connectivity characteristics between different AQI monitoring stations; (d) the proposed spatio-temporal correlation hybrid prediction model ST-EXMG-AE-XGBoost performs the best among all the models involved and has good application prospects.

Diisocyanates influence models of atopic dermatitis through direct activation of TRPA1.

We recently used EPA databases to identify that isocyanates, most notably toluene diisocyanate (TDI), were the pollutant class with the strongest spatiotemporal and epidemiologic association with atopic dermatitis (AD). Our findings demonstrated that isocyanates like TDI disrupted lipid homeostasis and modeled benefit in commensal bacteria like Roseomonas mucosa through disrupting nitrogen fixation. However, TDI has also been established to activate transient receptor potential ankyrin 1 (TRPA1) in mice and thus could directly contribute to AD through induction of itch, rash, and psychological stress. Using cell culture and mouse models, we now demonstrate that TDI induced skin inflammation in mice as well as calcium influx in human neurons; each of these findings were dependent on TRPA1. Furthermore, TRPA1 blockade synergized with R. mucosa treatment in mice to improve TDI-independent models of AD. Finally, we show that the cellular effects of TRPA1 are related to shifting the balance of the tyrosine metabolites epinephrine and dopamine. This work provides added insight into the potential role, and therapeutic potential, or TRPA1 in the pathogenesis of AD.

Emplacement ages and petrogenesis of the Sunchang and Namwon granitoids, South Korea

We present new sensitive high-resolution ion microprobe (SHRIMP) U-Pb zircon ages for two adjacent plutons (Sunchang and Namwon) along the Middle Jurassic Honam Shear Zone, South Korea, and report their major- and trace-element and Sr-Nd-Pb isotopic compositions to constrain the emplacement ages and petrogenesis of the plutons. The Sunchang foliated granites yield U-Pb ages of 186–178 Ma, suggesting long-lasting magmatic activity. The emplacement age of the Namwon pluton (192–180 Ma) indicates that it was broadly coeval with or slightly older than the Sunchang pluton. Some zircon grains contain ancient (Neoarchean to Paleo-Proterozoic) inherited cores. The REE patterns of the granitoids are roughly U-shaped with no strong negative Eu anomalies and show arc-like enrichments in LILEs and depletion in Nb or Ta, similar to I-type granites. The Sunchang granites yield initial 87Sr/86Sr ratios of 0.71543–0.71675, εNd(t) values of −29.0 to −26.0, and 206Pb/204Pb ratios of 18.44–18.90, and the Namwon granitoids yield initial 87Sr/86Sr ratios of 0.71229–0.71652, εNd(t) values of −27.5 to −20.9, and 206Pb/204Pb ratios of 17.93–18.23. The granitoids have adakite-like elevated Sr/Y (25–78) and La/Yb (15–66) ratios but are potassic, which is inconsistent with slab-derived melts. Furthermore, they have relatively low MgO at a given SiO2 content compared with high-SiO2 adakites and low Mg# (30–48) and Ni (<5 ppm), Co (<8 ppm) and Cr (<52 ppm) contents. They resemble C-type adakites, which form by the partial melting of thickened lower continental crust with residual garnet and amphibole. Underplating mafic magma from the mantle wedge or shear heating may have been the source of heat for the crustal melting. The close spatiotemporal association of these C-type adakitic rocks suggests a genetic link between them. The theory that the massive to weakly foliated Namwon granitoids were emplaced post-tectonically needs to be revised, as the Namwon and Sunchang granitoids formed during the same magmatic episode in the regional-scale shear zone.

Decoupled spatiotemporal patterns of avian taxonomic and functional diversity

Each year, seasonal bird migration leads to an immense redistribution of species occurrence and abundances,1,2,3 with pervasive, though unclear, consequences for patterns of multi-faceted avian diversity. Here, we uncover stark disparities in spatiotemporal variation between avian taxonomic diversity (TD) and functional diversity (FD) across the continental US. We show that the seasonality of species richness expectedly3 follows a latitudinal gradient, whereas seasonality of FD instead manifests a distinct east-west gradient. In the eastern US, the temporal patterns of TD and FD are diametrically opposed. In winter, functional richness is highest despite seasonal species loss, and the remaining most abundant species are amassed in fewer regions of the functional space relative to the rest of the year, likely reflecting decreased resource availability. In contrast, temporal signatures for TD and FD are more congruent in the western US. There, both species and functional richness peak during the breeding season, and species' abundances are more regularly distributed and widely spread across the functional space than during winter. Our results suggest that migratory birds in the western US disproportionately contribute to avian FD by possessing more unique trait characteristics than resident birds,4,5 while the primary contribution of migrants in the eastern US is through increasing the regularity of abundances within the functional space relative to the rest of the year. We anticipate that the uncovered complexity of spatiotemporal associations among measures of avian diversity will be the catalyst for adopting an explicitly temporal framework for multi-faceted biodiversity analysis.

Exploratory Spatio-Temporal Data Analysis (ESTDA) of Dengue and its association with climatic, environmental, and sociodemographic factors in Punjab, India

Routine health data is rich in information but underutilised for research in Low and Middle-Income countries. The present study was carried out to understand spatiotemporal patterns of dengue and its association with risk factors using routine data from health and allied sectors. ESTDA included estimation of dengue incidence rates, time series features and correlation coefficients up to the sub-district level. Scatter plots and correlation coefficients were used to identify relationships between covariates. Dengue incidence in 2015–19 was 47.76, 33.64, 52.03, 49.71, and 33.36 per 100,000, respectively, with a mean (SD) age of 34.33 (16.78) years and the majority being males (63.94%). Dengue had significant cross-correlation, non-linear relationships, and spatio-temporal associations with climatic, environmental, and socio-demographic risk factors. Significant autocorrelation of dengue occurrence was present at a lag of one month with seasonal patterns. The reproducible open-source algorithms add value to existing Routine Health Information Systems, and the findings will enable the development of Spatio-temporal models in future research. The research was done using R version 4.1.0.

Geospatial Modeling of Health, Socioeconomic, Demographic, and Environmental Factors with COVID-19 Incidence Rate in Arkansas, US

The COVID-19 pandemic has posed numerous challenges to human society. Previous studies explored multiple factors in virus transmission. Yet, their impacts on COVID-19 are not universal and vary across geographical regions. In this study, we thoroughly quantified the spatiotemporal associations of 49 health, socioeconomic, demographic, and environmental factors with COVID-19 at the county level in Arkansas, US. To identify the associations, we applied the ordinary least squares (OLS) linear regression, spatial lag model (SLM), spatial error model (SEM), and multiscale geographically weighted regression (MGWR) model. To reveal how such associations change across different COVID-19 times, we conducted the analyses for each season (i.e., spring, summer, fall, and winter) from 2020 to 2021. We demonstrate that there are different driving factors along with different COVID-19 variants, and their magnitudes change spatiotemporally. However, our results identify that adult obesity has a positive association with the COVID-19 incidence rate over entire Arkansas, thus confirming that people with obesity are vulnerable to COVID-19. Humidity consistently negatively affects COVID-19 across all seasons, denoting that increasing humidity could reduce the risk of COVID-19 infection. In addition, diabetes shows roles in the spread of both early COVID-19 variants and Delta, while humidity plays roles in the spread of Delta and Omicron. Our study highlights the complexity of how multifactor affect COVID-19 in different seasons and counties in Arkansas. These findings are useful for informing local health planning (e.g., vaccine rollout, mask regulation, and testing/tracing) for the residents in Arkansas.

Aedes aegypti in Southern Brazil: Spatiotemporal Distribution Dynamics and Association with Climate and Environmental Factors.

In Brazil, the mosquito Aedes (Stegomyia) aegypti is considered the main vector of the dengue, chikungunya, and Zika arbovirus transmission. Recent epidemiological studies in southern Brazil have shown an increase in the incidence of dengue, raising concerns over epidemiological control, monitoring, and surveys. Therefore, this study aimed at performing a historical spatiotemporal analysis of the Ae. aegypti house indices (HI) in southern Brazil over the last 19 years. As vector infestation was associated with climatic and environmental variables, HI data from the Brazilian Ministry of Health, climate data from the Giovanni web-based application, and environmental data from the Mapbiomas project were used in this study. Our results showed an expressive increase in the number of HI surveys in the municipalities confirming the vector presence, as compared to those in 2017. Environmental variables, such as urban infrastructure, precipitation, temperature, and humidity, were positively correlated with the Ae. aegypti HI. This was the first study to analyze Ae. aegypti HI surveys in municipalities of southern Brazil, and our findings could help in developing and planning disease control strategies to improve public health.

Spatiotemporal graph-based analysis of land cover evolution using remote sensing time series data

Earth observation technology has improved the detection of land cover changes. However, current pixel-based change detection methods cannot adequately describe the evolutionary process and spatiotemporal association of geographic entities. Therefore, we developed a method for analyzing the processes and patterns of land cover evolution based on spatiotemporal graphs. First, a spatiotemporal graph was generated from a time series of land cover maps according to the spatial and temporal relationships between land cover objects, as defined by spatial adjacency and temporal transition, respectively. Subsequently, structural characteristics, such as the spatial roles, adjacency type, temporal transitions and evolution trajectories, were derived from the spatiotemporal graph to describe and analyze the evolution of land cover. Finally, this method was applied to analyze land cover evolution in Fujian Province, China, from 2001 to 2019. The proposed method not only completely preserves the spatial adjacency and temporal transition details among land cover objects in a spatiotemporally unified graph framework but also extracts evolution-related spatiotemporal structural characteristics. This study provides a reliable scientific basis for analyzing the consistency of long-term land cover dynamics and has practical value for other geographic applications.

Spatiotemporal dynamic decoupling states of eco-environmental quality and land-use carbon emissions: A case study of Qingdao City, China

Spatiotemporal dynamic association between eco-environmental quality and land-use carbon emissions (LCE) is an indispensable part of regional and city planning and land management and a necessary condition for maintaining sustainable development. With the increasing awareness of the importance of sustainable development, quantifying the relationship between eco-environmental quality and LCE needs to be improved. Based on raster datasets of carbon emissions from fossil fuels, remotely sensed environmental variables, and land-use and land-cover change, this study aimed to combine an improved model of LCE, remote sensing ecological index (RSEI), and decoupling theory along with the help of the Google Earth Engine (GEE) and geographical information system platforms to explore the spatiotemporal patterns and trends of the eco-environmental quality and LCE over Qingdao City in the coastal economic belt of China for 2005–2019. There are three significant findings in this study. First, during 2005–2019, RSEI in the study area showed an overall upward trend (0.4365 in 2005 and 0.5378 in 2019), with a significant difference in the spatial distribution. Second, during 2005–2019, the total LCE value showed an increasing trend (4.028 million tonnes (Mt) in 2005 and 7.929 Mt. in 2019), with the study area acting as a carbon source. Third, the study area exhibited the four decoupling states of mainly weak decoupling (WD), expansive negative decoupling (END), strong decoupling (SD), and strong negative decoupling (SND). Finally, space management policies should be formulated based on different spatiotemporal characteristics and decoupling states to achieve carbon emission reductions and high-quality development.

Joint Data Association, Spatiotemporal Bias Compensation and Fusion for Multisensor Multitarget Tracking

Spatiotemporal bias compensation and data association are two essential prerequisites for correct data fusion. Neglect of either in multisensor systems prevents the algorithms from successful application. In this paper, a joint data association, bias compensation and fusion method is presented for multitarget tracking in asynchronous multisensor systems with spatiotemporal biases. The time stamp interval is used to model the state transition, while the difference between the measurement and time stamp interval is regarded as temporal bias and augmented in the state vector as well as spatial bias. The measurements are then formulated as a function of both target states and spatiotemporal biases, where the unknown temporal bias is used to align measurements and target states. Based on the formulation, the joint data association and bias compensation problem is converted into the classical data association and filtering problem in a unified Bayesian framework, without requirement of iterative optimization procedure between data association and bias estimation. The data association is formulated as a 2-D assignment problem and is solved using the generalized Auction algorithm. For each track-measurement pair in association results, the unscented Kalman filter is used to handle the measurement to produce estimates of target states and spatiotemporal bias simultaneously. Furthermore, a bias fusion approach with feedback is presented to fuse the spatiotemporal bias estimates from multiple targets to improve the bias compensation performance and solve the correlation problems therein. Simulation results demonstrate the effectiveness of the proposed method.

STAMP: Spatio-Temporal Association Mapping of Proteins.

We describe a tool, Spatio-Temporal Association Mapping of Proteins (STAMP), for identifying protein interactomes via proximity labeling. For a proof-of-principle study, we use cytidine 5'-triphosphate synthase (CTPS) as an example. CTPS, a metabolic enzyme, forms filamentous structures termed cytoophidia in various tissues. We apply STAMP to a variety of developmental stages and tissues in Drosophila including adult ovaries. Using a cell-specific GAL4 driver, we verify that TurboID can biotinylate the bait protein CTPS, making possible the identification of protein-protein interactions (PPIs) in individual cells. Using the wild-type and mutant CTPS as bait proteins, STAMP results in two distinct sets of proximate proteomes. Our results suggest that STAMP is a feasible tool to catch in vivo PPIs in situ at a defined spatiotemporal resolution.

Ecosystem services assessment from capacity to flow: A review

Ecosystem services are the basis for human survival and development and act as a bridge between nature and society. In recent years, ecosystem service flows have become the focus and primary challenge of ecosystem service research, with development in research efforts toward quantification and spatialization. This review explains the necessity of ecosystem service flows, discusses the progress of research in this area over the last decade, and compares the theoretical and representative research approaches from two conceptual perspectives on ecosystem service flows. One of these approaches focuses on processes, treating ecosystem service flows as the spatiotemporal association between ecological service provision areas and service benefit areas, and the other emphasizes final utility, considering ecosystem service flows as the actual supply of ecosystem services to beneficiaries in different areas. On this basis, the research gaps in theoretical principles, assessment methods, and applications are summarized, and future research perspectives on ecosystem service flows are proposed, with a focus on optimizing ecosystem service benefits and promoting cooperative development of systemic human–nature relationships.

Next location recommendation: a multi-context features integration perspective

Next location recommendation aims to mine users’ historical trajectories to predict their potentially preferred locations in the next moment. Although previous studies have explored the idea of incorporating location or social contextual information for recommendation, they still suffer from several major limitations: (1) not fully considering the semantic associations between locations, (2) not considering the heterogeneity in preferences of socially linked users, (3) not fully utilizing contextual information from distinctive sources to further improve the recommendation performance. In this paper, we propose a novel multi-context-based next location recommendation model that incorporates location context, trajectory context, and social context to obtain comprehensive users’ preferences while allowing for interactions between contexts. Specifically, we first develop an efficient method combining both high-order location graphs and location semantic graphs to characterize subtle associations between locations. Then we explore the social contextual information and introduce the location subgraph which considers heterogeneous preferences among friends. Finally, we use the LSTM and geo-dilated LSTM to capture the spatio-temporal associations between users’ trajectories and integrate various contextual information to improve model performance. Extensive experiments on three real datasets show that our model has superior results in the next location recommendation task over other baselines.

Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes

A local version of spatio-temporal log-Gaussian Cox processes is proposed by using Local Indicators of Spatio-Temporal Association (LISTA) functions plugged into the minimum contrast procedure, to obtain space as well as time-varying parameters. The new procedure resorts to the joint minimum contrast fitting method to estimate the set of second-order parameters. This approach has the advantage of being suitable in both separable and non-separable parametric specifications of the correlation function of the underlying Gaussian Random Field. Simulation studies to assess the performance of the proposed fitting procedure are presented, and an application to seismic spatio-temporal point pattern data is shown.

An event-based model and a map visualization approach for spatiotemporal association relations discovery of diseases diffusion

Infectious disease diffusion is inherently a complex spatiotemporal phenomenon. Simplifying this complexity to discover the associated structure of the city is of great importance. However, existing approaches mainly focus on distance property in geographic space to examine randomness, dispersion, or clustered structure of the disease distribution. While, the outbreak continuously changes its properties, shapes, or locations. Regardless of this adjacency-based structure, there may be associated spatial units that exhibit similar behaviors towards the outbreak fluctuations in a city. To reveal these characteristics, this research proposes a novel event-based spatiotemporal model, mining associated areas in space and time simultaneously. This model was applied to the cases rate of COVID-19 at the ZIP Code level in New York City. The results showed that the proposed approach could sufficiently address the spatiotemporal association relationships. To better understand the discovered associations, a map visualization approach is introduced, allowing recognition of these association relations at a glance. This approach develops a deep understanding of the spatiotemporal structure of the outbreak and better manifests the association and cause-and-effect relations between ZIP Code areas. The results provide good assets for the construction of healthy resilient cities with the function of preventing epidemic crises in the future.

Do large‐scale associations in birds imply biotic interactions or environmental filtering?

AbstractAimThere has been a wide interest in the effect of biotic interactions on species' occurrences and abundances at large spatial scales, coupled with a vast development of the statistical methods to study them. Still, evidence for whether the effects of within‐trophic‐level biotic interactions (e.g. competition and heterospecific attraction) are discernible beyond local scales remains inconsistent. Here, we present a novel hypothesis‐testing framework based on joint dynamic species distribution models and functional trait similarity to dissect between environmental filtering and biotic interactions.LocationFrance and Finland.TaxonBirds.MethodsWe estimated species‐to‐species associations within a trophic level, independent of the main environmental variables (mean temperature and total precipitation) for common species at large spatial scale with joint dynamic species distribution (a multivariate spatiotemporal delta model) models. We created hypotheses based on species' functionality (morphological and/or diet dissimilarity) and habitat preferences about the sign and strength of the pairwise spatiotemporal associations to estimate the extent to which they result from biotic interactions (competition, heterospecific attraction) and/or environmental filtering.ResultsSpatiotemporal associations were mostly positive (80%), followed by random (15%), and only 5% were negative. Where detected, negative spatiotemporal associations in different communities were due to a few species. The relationship between spatiotemporal association and functional dissimilarity among species was negative, which fulfils the predictions of both environmental filtering and heterospecific attraction.Main conclusionsWe showed that processes leading to species aggregation (mixture between environmental filtering and heterospecific attraction) seem to dominate assembly rules, and we did not find evidence for competition. Altogether, our hypothesis‐testing framework based on joint dynamic species distribution models and functional trait similarity is beneficial in ecological interpretation of species‐to‐species associations from data covering several decades and biogeographical regions.

Malaria prevalence and forest cover change in Kenya: a geospatial analysis

BackgroundThe relationship between forest cover loss and infectious disease risk needs further investigation, particularly in sub-Saharan Africa, and with regards to the effect to vulnerable populations—namely, children. The proposed research investigates the links between forest cover change and malaria. We aimed to identify spatiotemporal associations between deforestation and key children's health outcomes in sub-Saharan Africa nations affected by deforestation. Key aims were identifying: the amount of forest cover change most associated with malaria incidence changes; how these factors affect particularly vulnerable populations; what the most notable mechanisms were linking forest cover change to malaria incidence changes; how these findings informed the current theoretical understanding of the relationship between forest cover change and infectious disease risk; and identifying policy solutions that could avert any observed negative outcomes. MethodsWe propose regression analyses to investigate forest cover data and malaria incidence and paediatric (aged 2–10 years) prevalence data from selected US Agency for International Development's Demographic and Health Survey (DHS) datasets to assess the association between malaria disease and forest cover change. The research will use DHS-derived cluster sites along the human–wildlife interface in Kenya as geographical variables. The anticipated outcomes would show the relationship between forest cover change and malaria incidence and paediatric prevalence, while allowing for further mechanistic investigation. To measure forest cover change, a Normalized Vegetation Index and Enhanced Vegetation Index will be used. Malaria incidence and paediatric prevalence by cluster site is quantified in the DHS dataset. We plan to run logistical, multiple, and longitudinal regression analyses plotting forest cover change versus malaria incidence and paediatric prevalence at the cluster level at 5-year time points from 2000 to 2020. FindingsThis study is in the process of being completed, thus the findings are currently incomplete, bbut preliminary data will be available at the Planetary Health Alliance Annual Meeting in October 2022. We expect to show an association between substantial forest cover loss and higher malaria burden. The expected results would provide a basis and further opportunity for research to explore the causal relationship between these data. InterpretationThe relationship between forest cover loss and human health in areas along the human–wildlife interface is poorly understood. This research proposes an investigation into ecological determinants of of malarial disease. Although the connection between deforestation and infectious diseases is becoming more clear, additional data that expounds upon the ecological burden of disease for malaria in Kenya, and children as a vulnerable population, is needed in the literature. Understanding the relationship between forest cover and malarial disease might allow for the investigation of mechanistic properties that could further the evidence for a casual relationship between specific factors. FundingNational Academy of the Sciences, University of Wisconsin-Madison Global Health Institute Graduate Research Award.

Spatiotemporal association of carbon dioxide emissions in China's urban agglomerations

The reduction of carbon dioxide (CO2) emissions and sustainable development in low-carbon ways are of great significance to urban agglomerations. However, few studies are exploring the relationship between CO2 emissions and socioeconomic development at city levels from the perspective of clusters of regions. Based on the open data of inventory for anthropogenic CO2 emissions, nighttime light data, and population dataset as a proxy for the socioeconomic development levels of urban agglomerations, we used Mann–Kendall trend test, Tapio decoupling analysis, and spatial autocorrelation analysis to explore the spatiotemporal association of CO2 emissions and the impact of socioeconomic development on emissions in the nineteen urban agglomerations in China. Findings showed that the growth of CO2 emissions in China was primarily concentrated in urban agglomerations. The CO2 emissions in eastern coastal and northern urban agglomerations were much higher than those in other areas, while the emissions in western urban agglomerations were the lowest. The periodic characteristics of CO2 emissions were consistent with China's five-year development plan. Urban agglomerations in the early stage from 2000 to 2002 or with developed and stable industrial structures tended to achieve decoupling. High-high (HH) clusters of socioeconomic development with CO2 emissions were mainly distributed in urban agglomerations of the Beijing-Tianjin-Hebei region (BTH), the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), Yangtze River Delta (YRD), Huhhot-Baotou-Ordos-Yulin (HBOY), Shandong Peninsula (SP), and Central Shanxi (CS). Most of the clusters except those in HBOY shrunk from 2000 to 2010 and remained relatively stable from 2010 to 2019. These urban agglomerations should promote synergistic emission reduction. High-low (HL) clusters mostly appeared in central cities with a high socioeconomic level and surrounding cities with low CO2 emissions s, i.e., in urban agglomerations of Chengdu-Chongqing region (CC), the Beibu Gulf (BG), and Lanzhou-Xining (LX). These urban agglomerations with prominent polarization phenomena should adhere to regional overall coordination and thus minimize total regional costs of CO2 emission reduction. The results could provide references for the synergistic reduction of CO2 emissions and the coordinated development in urban agglomerations.

Impact of Socioeconomic Deprivation on the Local Spread of COVID-19 Cases Mediated by the Effect of Seasons and Restrictive Public Health Measures: A Retrospective Observational Study in Apulia Region, Italy.

The aim of this study was to investigate the spatiotemporal association between socioeconomic deprivation and the incidence of COVID-19 and how this association changes through the seasons due to the existence of restrictive public health measures. A retrospective observational study was conducted among COVID-19 cases that occurred in the Apulia region from 29 February 2020 to 31 December 2021, dividing the period into four phases with different levels of restrictions. A generalized estimating equation (GEE) model was applied to test the independent effect of deprivation on the incidence of COVID-19, taking into account age, sex, and regional incidence as possible confounding effects and covariates, such as season and levels of restrictions, as possible modifying effects. The highest incidence was in areas with a very high deprivation index (DI) in winter. During total lockdown, no rate ratio between areas with different levels of DI was significant, while during soft lockdown, areas with very high DI were more at risk than all other areas. The effects of social inequalities on the incidence of COVID-19 changed in association with the seasons and restrictions on public health. Disadvantaged areas showed a higher incidence of COVID-19 in the cold seasons and in the phases of soft lockdown.

Spatio-temporal association mining of intercity PM2.5 pollution: Hubei Province in China as an example.

The complex interaction between emissions, meteorology, and atmospheric chemistry makes accurate predictions of particulate pollution difficult. Advanced data mining techniques can reveal potential laws, providing new possibilities for understanding the evolution and causes of air pollution. Based on the Granger method and block modeling analysis, this paper explored the intercity spillover effects of hourly PM2.5 in Hubei Province, China, to determine the specific role (i.e., overflow, limited overflow, bilateral, inflow, and limited inflow) of each city on regional pollution formation. Furthermore, a dynamic Apriori algorithm considering time-lag effects was used to mine the spatio-temporal associations of extreme PM2.5 pollution events among different cities. Results suggest that the northern and central cities with high-level PM2.5 concentration in Hubei have a significant spillover effect, whereas the eastern and southern cities generally play a role as the sink of pollutants. Based on the association rules of extreme PM2.5 pollution, four main pollutant transport channels were excavated and well matched with the trajectories extracted by the atmospheric model. This paper provides new insights for exploring the interaction of intercity particulate pollution, which is a supplement and cross-validation of the model results.