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Background: Severe acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear.Methods: We used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows.Results: The presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destination-based spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78% of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54% with a 1% increase in urban income, and intraregional flows increased by 8.35%. In contrast, the total interregional flows decreased by 3.38% with a 1% increase in rural income, and intraregional flows declined by 2.29%. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows.Conclusions: Our results confirm that the SARS input-output flows presented significant geographic spatial heterogeneity and spatial effects. Income differences were the major cause of the flows between pairs of regions. Railway capacity, PGDP, and urban rate also played important roles. These findings provide valuable information for the Chinese government to control the future spread of nationwide epidemics. [ABSTRACT FROM AUTHOR]

At the current stage, the service performance analysis of BeiDou satellite navigation system (BDS) can provide important references for BDS applications. Accuracy, integrity, availability, and continuity are important indicators of the service capability of the satellite navigation system. In this paper, the relationships among them are analyzed firstly. Secondly, for describing accuracy, position dilution of precision (PDOP) is selected and the quantitative calculation formulas of its availability and continuity are established. Then PDOP of BDS in Asia-Pacific region is simulated and analyzed, the results show that the average availability and continuity of PDOP in the service area during 24 h are up to 99.60% and 0.9947/h, respectively. Thirdly, this paper has a brief introduction to weighted least square algorithm of receiver autonomous integrity monitoring (RAIM) and analyzes the relationships among the probability of false alert, the probability of missed detection, threshold, the minimum detectable error and the number of visible satellites. The threshold and the minimum detectable error will increase with the increasing number of visible satellites when given the false alert probability and missed detection probability, respectively. Finally, the quantitative calculation formula of RAIM availability and continuity is presented and RAIM availability and continuity are simulated and analyzed as well, the results show that the average availability and continuity in the service area are up to 99.95% and 0.9984/h, respectively. [ABSTRACT FROM AUTHOR]

Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method was based on the spatial statistic trinity theory, where the statistical error is determined by the population properties, the condition of the sample, and the method of estimation. In our study, the spatial association of the variables was quantified by the covariance and the ratio of air quality data between stations, resulting in linear unbiased estimates of the missing data. STPI-BSHADE was compared with two widely used statistical methods, inverse distance weighting (IDW) and Kriging. Theoretically, IDW and Kriging are short of the capacity of using the heterogeneous characteristics of the population and remedying the sample bias. Empirically, the accuracy of the STPI-BSHADE method was assessed using hourly particulate matter 2.5 data, collected from May 13 to December 31, 2014, in the Beijing-Tianjin-Hebei areas, where air quality presents spatial heterogeneity. The experimental results also demonstrated that STPI-BSHADE significantly outperformed the traditional methods. [ABSTRACT FROM AUTHOR]

The multi-constellation is anticipated to enhance navigation performance by providing abundant satellites in view. However, with this comes the necessity of considering simultaneous fault events in integrity risk assessment when applying the receiver autonomous integrity monitoring. Meanwhile, the integrity monitoring efficacy remains vulnerable to the uncertainty of prior fault probability in multi-constellation navigation. In this regard, this paper proposes a Bayesian approach for the multi-constellation navigation at the user segment to strengthen the evaluation robustness of integrity risk. We bound the Bayesian posterior probability of fault hypotheses that comprise multiple concurrent faults (satellite or constellation faults) without assuming an optimistic prior fault distribution. This posterior probability bound leads to an associated Bayesian integrity risk. Following that, a fault-tolerant estimate accounting for multiple faults and nominal biases is derived to minimize the Bayesian integrity risk. Finally, we establish a Bayesian fault-tolerant protection level to bound the position estimate error and assess the integrity availability, resisting the uncertainty in the prior fault probability. Results validate the robustness improvement the proposed Bayesian approach fulfills compared with the advanced receiver autonomous integrity monitoring in four cases. In addition, the Bayesian approach can lower the protection level at the cost of an offset from the least-square estimate while maintaining the same position accuracy. [ABSTRACT FROM AUTHOR]

In this paper, BiOIO 3 and Bi 2 O 4 nanosheet monomers were synthesized and used to prepare several BiOIO 3 /Bi 2 O 4 composites by an ultrasonic impregnation method. The resulting photocatalytic materials were characterized by XRD, SEM, TEM, XPS, FTIR, PL, UV–Visible diffuse reflectance spectroscopy (DRS), and BET. The photocatalytic activity of the BiOIO 3 /Bi 2 O 4 composites was evaluated by the degradation of rhodamine B (RhB) and ciprofloxacin (CIP) under simulated sunlight and compared to the activities of BiOIO 3 and Bi 2 O 4 operating alone. The results showed that Bi 2 O 4 was closely attached to the surface of BiOIO 3 , and the composite had a good degradation effect on RhB and CIP, with the best performing material being a 0.9-BiOIO 3 /Bi 2 O 4 composite, which achieved 97.6% degradation of RhB within 60 min and 85.6% degradation of CIP within 120 min. The observed degradation performance was considerably higher than that of other composite samples and monomers due to the good energy level matching of the BiOIO 3 /Bi 2 O 4 composite, which broadened the light response interval, improved the electron transfer rate and enhanced the photocatalytic activity. Free radical capture experiments showed that the ·O 2 - and h+ free radicals significantly contributed to the degradation of pollutants. Cyclic tests showed that the composite also had good stability. The results of this study showed that bismuth-based photocatalysts might be used for environmental remediation. • BiOIO 3 /Bi 2 O 4 heterojunctions were prepared by ultrasonic impregnation method. • BiOIO 3 /Bi 2 O 4 heterojunctions could degrade 97.6% of rhodamine B within 60 min. • The prepared composite has a good response to visible light. • BiOIO 3 /Bi 2 O 4 showed excellent stability for five consecutive cycles. [ABSTRACT FROM AUTHOR]

Background: Overweight is a known risk factor for various chronic diseases and poses a significant threat to middle-aged and elderly adults. Previous studies have reported a strong association between overweight and air pollution. However, the spatial relationship between the two remains unclear due to the confounding effects of spatial heterogeneity. Methods: We gathered height and weight data from the 2015 China Health and Retirement Long-term Survey (CHARLS), comprising 16,171 middle-aged and elderly individuals. We also collected regional air pollution data. We then analyzed the spatial pattern of overweight prevalence using Moran's I and Getis-Ord Gi* statistics. To quantify the explanatory power of distinct air pollutants for spatial differences in overweight prevalence across Southern and Northern China, as well as across different age groups, we utilized Geodetector's q-statistic. Results: The average prevalence of overweight among middle-aged and elderly individuals in each city was 67.27% and 57.39%, respectively. In general, the q-statistic in southern China was higher than that in northern China. In the north, the prevalence was significantly higher at 54.86% compared to the prevalence of 38.75% in the south. SO2 exhibited a relatively higher q-statistic in middle-aged individuals in both the north and south, while for the elderly in the south, NO2 was the most crucial factor (q = 0.24, p < 0.01). Moreover, fine particulate matter (PM2.5 and PM10) also demonstrated an important effect on overweight. Furthermore, we found that the pairwise interaction between various risk factors improved the explanatory power of the prevalence of overweight, with different effects for different age groups and regions. In northern China, the strongest interaction was found between NO2 and SO2 (q = 0.55) for middle-aged individuals and PM2.5 and SO2 (q = 0.27) for the elderly. Conversely, in southern China, middle-aged individuals demonstrated the strongest interaction between SO2 and PM10 (q = 0.60), while the elderly showed the highest interaction between NO2 and O3 (q = 0.42). Conclusion: Significant spatial heterogeneity was observed in the effects of air pollution on overweight. Specifically, air pollution in southern China was found to have a greater impact on overweight than that in northern China. And, the impact of air pollution on middle-aged individuals was more pronounced than on the elderly, with distinct pollutants demonstrating significant variation in their impact. Moreover, we found that SO2 had a greater impact on overweight prevalence among middle-aged individuals, while NO2 had a greater impact on the elderly. Additionally, we identified significant statistically interactions between O3 and other pollutants. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new HPLC–MS/MS method based on paired ion electrospray ionization (PIESI) has been developed to determine acidic pesticides at ultratrace levels. [•] This method provides orders of magnitude greater sensitivity than in the usual negative mode. [•] Sub-picogram level LODs were obtained using optimal ion-pairing reagent. [•] Trace amounts of pesticides in water samples were determined by using this method. [ABSTRACT FROM AUTHOR]

Children's exposure to lead is a global health problem, especially in low- and middle-income countries. However, research on the relationship between children's blood lead levels (BLLs) and the development of the lead industry is still limited. This study examined whether children's BLLs were associated with the development of lead industry in different regions. Using survey data on the BLLs of children living in 250 prefectures in China with corresponding data on their economic factors and lead industries, we explored the regional variation of children's BLLs using statistical methods. The results show that the level of economic development in leaded areas was associated with inequity in children's BLLs and met the environmental Kuznets hypothesis. In areas without lead industries, there was little correlation between the level of economic development and the BLLs of children and thus the environmental Kuznets hypothesis was not supported. Lead mines, lead smelting and chemical companies are major sources of blood lead in children living in leaded areas. This study demonstrated the success of control policies for lead-acid battery manufacturers in promoting the prevention and control of childhood lead poisoning in China. China should consciously support the improvement of children's BLLs in undeveloped areas with lead industries through national financing and policies to avoid the continuous effects of the regional inequality problem of high children's BLLs. [Display omitted] • Children's BLLs associated with development of regional lead industry in China. • The leaded areas met the environmental Kuznets hypothesis. • The areas without lead did not meet the environmental Kuznets hypothesis. [ABSTRACT FROM AUTHOR]

While one has yet to be built in China, a rice supply flow network would provide essential information for stabilizing and optimizing food distribution and ensuring food security. Moreover, it is unclear how socioeconomic and natural environmental factors affect the rice supply flow. In this study, 35,768 random samples from the rice sales market were collected to build the rice flow network of China. The rice supply patterns at a regional and provincial level were revealed by the rice flow network. At the regional level, Northeast, Central, and East China are the main rice source areas, supplying almost all of China. At the provincial level, economically developed megacities, such as Beijing and Shanghai, and provinces with low temperatures like Tibet and Qinghai, rely heavily on imported rice. Determinant analysis shows that the rice supply patterns are mainly attributable to the cultivated area, primary industry employees, the permanent population, and tertiary industry proportion. These factors explain 75%, 59%, 48%, and 31%, respectively, of the rice supply spatial patterns. Furthermore, the interactions of cultivated area and temperature, cultivated area and primary industry proportion, and cultivated area and precipitation can explain 94%, 91%, and 89%, respectively, of the rice supply patterns. These findings reveal China's rice supply patterns and their determinants in recent years, providing essential information for optimizing the layout of rice production and ensuring food security in the future scenarios of climate change and socioeconomic development. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Previous research pointed to a close relationship between the incidence of tuberculosis (TB) in aging populations and socio-economic conditions, however there has been lack of studies focused on a region of unbalanced socio-economic development. The aim of this paper is to explore the spatio-temporal variation in TB incidence and examine risk determinants of the disease among aging populations in a typical region.Methods: Data on TB-registered cases between 2009 and 2014, in addition to social-economic factors, were collected for each district/county in Beijing, Tianjin and Hebei, a region characterized by an aging population and disparities in social-economic development. A Bayesian space-time hierarchy model (BSTHM) was used to reveal spatio-temporal variation in the incidence of TB among the elderly in this region between 2009 to 2014. GeoDetector was applied to measure the determinant power (q statistic) of risk factors for TB among the elderly.Results: The incidence of TB among the elderly exhibited geographical spatial heterogeneity, with a higher incidence in underdeveloped rural areas compared with that in urban areas. Hotspots of TB incidence risk among the elderly were mostly located in north-eastern and southern areas in the study region, far from metropolitan areas. Areas with low risk were distributed mainly in the Beijing-Tianjin metropolitan areas. Social-economic factors had a non-linear influence on elderly TB incidence, with the dominant factors among rural populations being income (q = 0.20) and medical conditions (q = 0.17). These factors had a non-linear interactive effect on the incidence of TB among the elderly, with medical conditions and the level of economic development having the strongest effect (q = 0.54).Conclusions: The findings explain spatio-temporal variation in TB incidence and risk determinants of elderly TB in the presence of disparities in social-economic development. High-risk zones were located mainly in rural areas, far from metropolitan centres. Medical conditions and the economic development level were significantly associated with elderly TB incidence, and these factors had a non-linear interactive effect on elderly TB incidence. The findings can help to optimize the allocation of health resources and to control TB transmission in the aging population in this region. [ABSTRACT FROM AUTHOR]

Background: Previous studies have indicated that the risk of infectious disease spread is greatest in locations where a population has massive and convenient access to the epicenter of an outbreak. However, the spatiotemporal variations and risk determinants of COVID-19 in typical labor export regions of China remain unclear. Understanding the geographical distribution of the disease and the socio-economic factors affecting its transmission is critical for disease prevention and control.Methods: A total of 2152 COVID-19 cases were reported from January 21 to February 24, 2020 across the 34 cities in Henan and Anhui. A Bayesian spatiotemporal hierarchy model was used to detect the spatiotemporal variations of the risk posed by COVID-19, and the GeoDetector q statistic was used to evaluate the determinant power of the potential influence factors.Results: The risk posed by COVID-19 showed geographical spatiotemporal heterogeneity. Temporally, there was an outbreak period and control period. Spatially, there were high-risk regions and low-risk regions. The high-risk regions were mainly in the southwest areas adjacent to Hubei and cities that served as economic and traffic hubs, while the low-risk regions were mainly in western Henan and eastern Anhui, far away from the epicenter. The accessibility, local economic conditions, and medical infrastructure of Wuhan in Hubei province all played an important role in the spatiotemporal heterogeneity of COVID-19 transmission. The results indicated that the q statistics of the per capita GDP and the proportion of primary industry GDP were 0.47 and 0.47, respectively. The q statistic of the population flow from Wuhan was 0.33. In particular, the results showed that the q statistics for the interaction effects between population density and urbanization, population flow from Wuhan, per capita GDP, and the number of doctors were all greater than 0.8.Conclusions: COVID-19 showed significant spatiotemporal heterogeneity in the labor export regions of China. The high-risk regions were mainly located in areas adjacent to the epicenter as well as in big cities that served as traffic hubs. Population access to the epicenter, as well as local economic and medical conditions, played an important role in the interactive effects of the disease transmission. [ABSTRACT FROM AUTHOR]

Interest in assessing the effects of temperature on hand, foot, and mouth disease (HFMD) has increased. However, little evidence is available on spatial heterogeneity in relationship to temperature and HFMD in metropolitan (capital city and municipal districts) and other areas where economic levels are significantly different. In this study, the Bayesian space-time hierarchy model was applied to identify the spatiotemporal heterogeneity of HFMD. GeoDetector was then used to quantify the determinant power of temperature to the disease in regions where the economic level has significant spatial heterogeneity. There was significant spatial heterogeneity in the influence of temperature on the incidence of HFMD in metropolitan and other areas. In metropolitan areas, where the disease risk is higher (hot spots), the HFMD incidence was higher alongside an increase in average temperature. However, in non-metropolitan areas, where the disease risk is lower (cold spots), there was an approximately S-shaped relationship between the temperature and the HFMD risk. More specifically, when the temperature was >25 °C, the HFMD incidence no longer increased monotonically with the increasing temperature. There was significant spatial heterogeneity in the effects of temperature on the HFMD incidence in metropolitan and non-metropolitan areas. This finding may serve as a suggestion and basis for the surveillance and control of this disease and it is conducive to the rational allocation of medical resources in different areas. Unlabelled Image • Spatial heterogeneity of HFMD • The hot/cold spots were detected using Bayesian space-time hierarchy model. • The effect of average temperature on HFMD were different in metropolitan and other areas. [ABSTRACT FROM AUTHOR]

Background: Hand, foot and mouth disease (HFMD) is a common infectious disease whose mechanism of transmission continues to remain a puzzle for researchers. The measurement and prediction of the HFMD incidence can be combined to improve the estimation accuracy, and provide a novel perspective to explore the spatiotemporal patterns and determinant factors of an HFMD epidemic.Methods: In this study, we collected weekly HFMD incidence reports for a total of 138 districts in Shandong province, China, from May 2008 to March 2009. A Kalman filter was integrated with geographically weighted regression (GWR) to estimate the HFMD incidence. Spatiotemporal variation characteristics were explored and potential risk regions were identified, along with quantitatively evaluating the influence of meteorological and socioeconomic factors on the HFMD incidence.Results: The results showed that the average error covariance of the estimated HFMD incidence by district was reduced from 0.3841 to 0.1846 compared to the measured incidence, indicating an overall improvement of over 50% in error reduction. Furthermore, three specific categories of potential risk regions of HFMD epidemics in Shandong were identified by the filter processing, with manifest filtering oscillations in the initial, local and long-term periods, respectively. Amongst meteorological and socioeconomic factors, the temperature and number of hospital beds per capita, respectively, were recognized as the dominant determinants that influence HFMD incidence variation.Conclusions: The estimation accuracy of the HFMD incidence can be significantly improved by integrating a Kalman filter with GWR and the integration is effective for exploring spatiotemporal patterns and determinants of an HFMD epidemic. Our findings could help establish more accurate HFMD prevention and control strategies in Shandong. The present study demonstrates a novel approach to exploring spatiotemporal patterns and determinant factors of HFMD epidemics, and it can be easily extended to other regions and other infectious diseases similar to HFMD. [ABSTRACT FROM AUTHOR]

Background: As a common infectious disease, hand, foot and mouth disease (HFMD) is affected by multiple environmental and socioeconomic factors, and its pathogenesis is complex. Furthermore, the transmission of HFMD is characterized by strong spatial clustering and autocorrelation, and the classical statistical approach may be biased without consideration of spatial autocorrelation. In this paper, we propose to embed spatial characteristics into a spatiotemporal additive model to improve HFMD incidence assessment.Methods: Using incidence data (6439 samples from 137 monitoring district) for Shandong Province, China, along with meteorological, environmental and socioeconomic spatial and spatiotemporal covariate data, we proposed a spatiotemporal mixed model to estimate HFMD incidence. Geo-additive regression was used to model the non-linear effects of the covariates on the incidence risk of HFMD in univariate and multivariate models. Furthermore, the spatial effect was constructed to capture spatial autocorrelation at the sub-regional scale, and clusters (hotspots of high risk) were generated using spatiotemporal scanning statistics as a predictor. Linear and non-linear effects were compared to illustrate the usefulness of non-linear associations. Patterns of spatial effects and clusters were explored to illustrate the variation of the HFMD incidence across geographical sub-regions. To validate our approach, 10-fold cross-validation was conducted.Results: The results showed that there were significant non-linear associations of the temporal index, spatiotemporal meteorological factors and spatial environmental and socioeconomic factors with HFMD incidence. Furthermore, there were strong spatial autocorrelation and clusters for the HFMD incidence. Spatiotemporal meteorological parameters, the normalized difference vegetation index (NDVI), the temporal index, spatiotemporal clustering and spatial effects played important roles as predictors in the multivariate models. Efron's cross-validation R2 of 0.83 was acquired using our approach. The spatial effect accounted for 23% of the R2, and notable patterns of the posterior spatial effect were captured.Conclusions: We developed a geo-additive mixed spatiotemporal model to assess the influence of meteorological, environmental and socioeconomic factors on HFMD incidence and explored spatiotemporal patterns of such incidence. Our approach achieved a competitive performance in cross-validation and revealed strong spatial patterns for the HFMD incidence rate, illustrating important implications for the epidemiology of HFMD. [ABSTRACT FROM AUTHOR]

Assessing the association between maps is a basic task in GIS. It not only helps to discover the consistency between mapped distributions and to reveal the relationship between various spatial patterns, but also implies attribution. Although literatures are rich in the map association measures, their applications are still tricky because of the complexity in spatial data. In this study, we investigate nine representative measures of map association, where each map could be in three scales of measurement: nominal, ordinal and quantitative (interval or ratio). The strengths and limitations of individual methods are addressed and compared, which include their applicability to different measurement scales, interpretability, and potential applications in complex scenarios. The theoretical comparisons are then demonstrated by two empirical examples. These works provide a rule of thumb for the selection of map association measures for practical purposes. [ABSTRACT FROM AUTHOR]

Leaf-blight disease caused by the Fusarium oxysporum is an emerging problem in Dendrobium chrysotoxum production in China. Symptoms of leaf blight were observed on seedlings of D. chrysotoxum cultivated in a nursery in Ruili City, Yunnan Province, China. In this study, we isolated the Fusarium sp. associated with leaf-blight disease of D. chrysotoxum from the diseased seedlings. A pathogenicity test was performed to fulfill Koch's postulates to confirm the pathogenicity of isolated strains and identified using morphological and molecular techniques. The results revealed that all four isolated Fusarium sp. isolates (DHRL-01~04) produced typical blight symptoms followed by marginal necrosis of leaves on the D. chrysotoxum plants. On the PDA medium, the fungal colony appeared as a white to purplish color with cottony mycelium growth. Microconidia are oval-shaped, whereas macroconidia are sickle-shaped, tapering at both ends with 2–4 septations. The phylogenetic trees were construed based on internal transcribed spacer (ITS), translation elongation factor (EF-1α), and RNA polymerase subunit genes RPB1 and RPB2 genes, respectively, and blasted against the NCBI database for species confirmation. Based on the NCBI database's blast results, the isolates showed that more than 99% identify with Fusarium oxysporum. To our knowledge, this is the first comprehensive report on the identification of Fusarium oxysporum as the causal agent of Dendrobium chrysotoxum leaf blight in Yunnan Province, China, based on morphological and molecular characteristics. [ABSTRACT FROM AUTHOR]

Spatial statistics is an important methodology for geospatial data analysis. It has evolved to handle spatially autocorrelated data and spatially (locally) heterogeneous data, which aim to capture the first and second laws of geography, respectively. Examples of spatially stratified heterogeneity (SSH) include climatic zones and land-use types. Methods for such data are relatively underdeveloped compared to the first two properties. The presence of SSH is evidence that nature is lawful and structured rather than purely random. This induces another "layer" of causality underlying variations observed in geographical data. In this article, we go beyond traditional cluster-based approaches and propose a unified approach for SSH in which we provide an equation for SSH, display how SSH is a source of bias in spatial sampling and confounding in spatial modeling, detect nonlinear stochastic causality inherited in SSH distribution, quantify general interaction identified by overlaying two SSH distributions, perform spatial prediction based on SSH, develop a new measure for spatial goodness of fit, and enhance global modeling by integrating them with an SSH q statistic. The research advances statistical theory and methods for dealing with SSH data, thereby offering a new toolbox for spatial data analysis. [ABSTRACT FROM AUTHOR]

Deepening the understanding of diurnal characteristics and driving mechanisms of surface urban heat islands (SUHIs) across different local climate zones (LCZs) and time scales is of great significance for guiding urban surface heat mitigation. However, a comprehensive investigation of SUHIs from the diurnal, local, multi-seasonal, and interactive perspectives remains a large gap. Here, we generalized major diurnal patterns of LCZ-based SUHI intensities (SUHIIs) throughout 2020 over the urban area of Beijing, China, based on diurnal temperature cycle modeling, block-level LCZ mapping, and hierarchical clustering. A geographical detector was then employed to explore the individual and interactive impacts of 10 morphological, socioeconomic, and meteorological factors on the multi-temporal spatial differentiations of SUHIIs. Results indicate six prevalent diurnal SUHII patterns with distinct features among built LCZ types. LCZs 4 and 5 (open high- and mid-rise buildings) predominantly display patterns one, two, and five, characterized by an afternoon increase and persistently higher values during the night. Conversely, LCZs 6, 8, and 9 (open, large, and sparsely built low-rise buildings) mainly exhibit patterns three, four, and six, with a decrease in SUHII during the afternoon and lower intensities at night. The maximum/minimum SUHIIs occur in the afternoon–evening/morning for patterns 1–3 but in the morning/afternoon for patterns 5–6. In all four seasons, the enhanced vegetation index (EVI) and gross domestic product (GDP) have the top two individual effects for daytime spatial differentiations of SUHIIs, while the air temperature (TEM) has the largest explanatory power for nighttime differentiations of SUHIIs. All factor interactions are categorized as two-factor or nonlinear enhancements, where nighttime interactions exhibit notably greater explanatory powers than daytime ones. The strongest interactions are EVI ∩ GDP (q = 0.80) during the day and TEM ∩ EVI (q = 0.86) at night. The findings of this study contribute to an improved interpretation of the diurnal continuous dynamics of local SUHIIs in response to various environmental conditions. [ABSTRACT FROM AUTHOR]