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We present a novel method for estimating species abundance using presence–absence maps. Our approach takes the spatial context into consideration, distinguishing it from conventional methods. The proposed method is built upon a well-known kernel estimation for point pattern intensity, with the addition of a new parameter representing the mean abundance in each occupied cell. The parameter estimate is obtained through maximum likelihood estimation. The expected abundance corresponds to the integral of the intensity over the study area, which can be estimated by taking the Riemann sum of the intensity. The implementation of our method is straightforward, using existing packages in the R software. We compared various bandwidth selection methods within our approach and assessed the estimation performance against some approaches based on the random placement model or negative binomial model through the simulation study and an empirical forestry data in Barro Colorado Island (BCI), Panama. The results of the simulation and the application demonstrate that our method, with a carefully chosen bandwidth, outperforms the alternatives for highly aggregated data and improves the issue of underestimation. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Aim: Addressing how woody plant species are distributed in space can reveal inconspicuous drivers that structure plant communities. The spatial structure of conspecifics varies not only at local scales across co‐existing plant species but also at larger biogeographical scales with climatic parameters and habitat properties. The possibility that biogeographical drivers shape the spatial structure of plants, however, has not received sufficient attention. Location: Global synthesis. Time Period: 1997–2022. Major Taxa Studied: Woody angiosperms and conifers. Methods: We carried out a quantitative synthesis to capture the interplay between local scale and larger scale drivers. We modelled conspecific spatial aggregation as a binary response through logistic models and Ripley's L statistics and the distance at which the point process was least random with mixed effects linear models. Our predictors covered a range of plant traits, climatic predictors and descriptors of the habitat. Results: We hypothesized that plant traits, when summarized by local scale predictors, exceed in importance biogeographical drivers in determining the spatial structure of conspecifics across woody systems. This was only the case in relation to the frequency with which we observed aggregated distributions. The probability of observing spatial aggregation and the intensity of it was higher for plant species with large leaves but further depended on climatic parameters and mycorrhiza. Main Conclusions: Compared to climate variables, plant traits perform poorly in explaining the spatial structure of woody plant species, even though leaf area is a decisive plant trait that is related to whether we observe homogenous spatial aggregation and its intensity. Despite the limited variance explained by our models, we found that the spatial structure of woody plants is subject to consistent biogeographical constraints and that these exceed beyond descriptors of individual species, which we captured here through leaf area. [ABSTRACT FROM AUTHOR]

Objective To understand the changes in spatial aggregation of HIV cases among men who have sex with men (MSM) in Chengdu from 2010 to 2021, providing a scientific basis for the regional prevention and control of MSM cases. Methods Data were collected from the Chinese Disease Prevention and Control Information System. Spatial-temporal auto-correlation and aggregation analyses were conducted using ArcGIS 10.6 software, while StatsCan software was utilized for spatial-temporal scanning analysis. Results The average annual growth rate of homosexual transmission cases in Chengdu was 27.41% from 2010 to 2015 and -5.03% from 2015 to 2021. The HIV epidemic affected 370 towns/street areas, demonstrating spatial aggregation, with the epidemic concentrated in the central urban area. Over time, the northeastern region spread southeast, the southwestern area expanded southwest and south, and the northwestern region spread northward. The hotspot areas exhibited similar aggregation patterns. Spatial-temporal scanning analysis indicated that from 2013 to 2018, the primary aggregation areas comprised 40 streets in the central urban area. Conclusion During the period from 2010 to 2021, the trend of the HIV epidemic among MSM in Chengdu showed a rapid increase followed by a slow decline. The spatial distribution exhibited significant imbalances, with the epidemic spreading from the central urban area to the suburban areas. Increased efforts are needed to enhance HIV awareness in schools and areas with concentrated mobile populations. [ABSTRACT FROM AUTHOR]

Taylor's power law (TPL) describes the expected range of parameters of the mean–variance scaling relationship and has been extensively used in studies examining temporal variations in abundance. Few studies though have focused on biological and ecological covariates of TPL, while its statistical inherences have been extensively debated. In the present study, we focused on species‐specific features (i.e. functional traits) that could be influential to temporal TPL.We combined field surveys of 180 fish species from 972 sites varying from small streams to large rivers with data on 31 ecological traits describing species‐specific characteristics related to three main niche dimensions (trophic ecology, life history, and habitat use). For each species, the parameters of temporal TPL (intercept and slope) were estimated from the log–log mean–variance relationships while controlling for spatial dependencies and biological covariates (species richness and evenness). Then, we investigated whether functional traits explained variations in TPL parameters.Differences in TPL parameters among species were explained mostly by life history and environmental determinants, especially TPL slope. Life history was the main determinant of differences in TPL parameters and thereby aggregation patterns, with traits related to body size being the most influential, thus showing a high contrast between small‐sized species with short lifespans and large‐bodied migratory fishes, even after controlling for phylogenetic resemblances.We found that life history traits, especially those related to body size, mostly affect TPL and, as such, can be determinants of temporal variability of fish populations. We also found that statistical effects and phylogenetic resemblances are embedded in mean–variance relationships for fish, and that environmental drivers can interact with ecological characteristics of species in determining temporal fluctuations in abundance. [ABSTRACT FROM AUTHOR]

Objective To analyze the incidence trends and spatiotemporal distribution characteristics of dengue fever in China based on the data from the China Disease Control Information System and the "National Statutory Infectious Disease Epidemic Overview" published by the National Health Commission of the People's Republic of China, and to provide a scientific basis for the prevention and control of dengue fever epidemics. Methods The incidence rates and monthly case numbers of dengue fever in China from 2015 to 2023 were collected. Provinces were classified according to the severity of the epidemic and the distribution status of the Aedes vector. The incidence trends, seasonal distribution, and spatial aggregation of dengue fever in China were statistically analyzed. Results The top five provinces with the highest average annual incidence rates from 2015 to 2023 were Yunnan (6.16/100 000), Guangdong (1.70/100 000), Hainan (1.13/100 000), Fujian (1.13/100 000), and Chongqing (0.74/100 000). The average annual incidence rate of dengue fever in China increased by 70.79% from 2015 to 2019 (AAPC=70.79, P=0.045). In Class I regions, the incidence of Guangxi and Hainan showed a linear regression trend, with average annual increases of 844.73% (AAPC=844.73, P=0.021) and 516.51% (AAPC=516.51, P=0.013), respectively. In Class II regions, except for Shanghai, the incidence of other provinces showed a linear regression trend, with Jiangxi having the highest average annual increase of 610.16% (AAPC=610.16, P=0.021), followed by Chongqing at 345.12% (AAPC=345.12, P=0.038). In Class III regions, the incidence of Hebei, Shanxi, and Liaoning had a linear regression trend, with average annual increases of 80.67% (AAPC=80.67, P=0.028), 202.31% (AAPC=202.31, P=0.001), and 70.19% (AAPC=70.19, P=0.031), respectively. The epidemic of dengue fever showed no obvious seasonality in 2021, strict seasonality in 2022, and strong seasonality in the rest of the years. The epidemic peak days were mostly concentrated in the middle and late September to early October of each year, and the epidemic peak periods were mainly concentrated in August-early November. From 2015 to 2018, the incidence rate had no spatial aggregation nationwide, and the incidence rate in 2019 had spatial aggregation nationwide. In 2015-2019 and 2023, there were no high-low gathering areas in the country, and high-high gathering areas appeared in 2018 (Fujian, Hainan) and 2019 (Guangxi). Conclusions The situation of dengue fever prevention and control in China is grim. High-risk epidemic areas of dengue fever will no longer be limited to Class I provinces, and the risk of expansion to Class II provinces is increasing. [ABSTRACT FROM AUTHOR]

The role of soil organic carbon (SOC) is crucial not only for numerous soil functions and processes but also for addressing various environmental crises and challenges we face. Consequently, the demand for information on the spatiotemporal variability of SOC is increasing, posing new methodological challenges, such as the need for information on SOC and SOC changes with quantified uncertainty across a wide variety of spatial scales and temporal periods. Our objective was to present a methodology based on a combination of machine learning and space–time geostatistics to predict the spatiotemporal variability of SOC stock with quantified uncertainty at various spatial supports (i.e., point support, 1 × 1 km, 5 × 5 km, 10 × 10 km, 25 × 25 km, counties, and the entire country) for Hungary, between 1992 and 2016. The role of geostatistics is pivotal, as it accounts for the spatiotemporal correlation of the interpolation errors, which is essential for reliably quantifying the uncertainty associated with spatially aggregated SOC stock and SOC stock change predictions. Five times repeated 10-fold leave-location-out cross-validation was used to evaluate the point support predictions and uncertainty quantifications, yielding acceptable results for both SOC stock (ME = −0.897, RMSE = 19.358, MEC = 0.321, and G = 0.912) and SOC stock change (ME = 0.414, RMSE = 16.626, MEC = 0.160, and G = 0.952). We compiled a series of maps of SOC stock predictions between 1992 and 2016 for each support, along with the quantified uncertainty, which is unprecedented in Hungary. It was demonstrated that the larger the support, the smaller the prediction uncertainty, which facilitates the identification and delineation of larger areas with statistically significant SOC stock changes. Moreover, the methodology can overcome the limitations of recent approaches in the spatiotemporal modelling of SOC, allowing the prediction of SOC and SOC changes, with quantified uncertainty, for any year, time period, and spatial scale. This methodology is capable of meeting the current and anticipated demands for dynamic information on SOC at both national and international levels.

To ensure soil preservation, it is essential to incorporate the soil's ability to provide ecosystem services into the spatial planning process. For well‐informed planning decisions, stakeholders need spatially explicit information on the state of the soils and the functions they fulfil, with sufficient spatial resolution and quantified uncertainty. It has been shown that Digital Soil Mapping (DSM) products can provide such information. However, in some cases, fine spatial resolution coupled with high levels of uncertainty may lead stakeholders to overlook the inherent uncertainties in the information. Spatial aggregation of DSM products opens up a promising avenue for obtaining maps that are more tailored to the users' scales of decision making while facilitating uncertainty communication. In this perspective, we propose a new spatial aggregation approach relying on spatially constrained agglomerative clustering (AC). The spatial aggregation approach is applied to a 25‐m‐resolution soil potential multifunctionality index (SPMI) map developed for the coastal plain of the Occitanie Region. This DSM product was increasingly aggregated to obtain SPMI maps of different resolutions displaying two distinct areal metrics: proportions of area above a given threshold of SPMI, and mean SPMI. Each map was evaluated through a set of indicators selected for their potential impact on user decision making: mean spatial resolution, overall predicted uncertainty, quantity of information and mean within‐unit variability. The maps were compared with respect to these indicators to other maps obtained with alternative aggregation methods employed in DSM literature (maps aggregated according to some administrative units and QuadMaps). We show that all the tested aggregation methods produced a substantial decrease of the map uncertainty with moderate loss of spatial resolution. However, only AC preserved the fine spatial pattern of the initial DSM product while enabling fine tuning of the uncertainty displayed to end‐users. We show that AC can simplify the identification of extensive regions characterized by low uncertainty without losing information regarding soil multifunctionality, thereby facilitating and enhancing the efficiency of planning decisions. [ABSTRACT FROM AUTHOR]

Objective To analyze the epidemiological characteristics, changing trends, and spatial -temporal aggregation of hand-foot-mouth disease (HFMD) in Wenzhou from 2013 to 2022, so as to scientifically guide the decision-making of prevention and controL Methods The infectious disease report data for HFMD in Wenzhou city were collected from the China Disease Prevention and Control Information System for the period 2013--2022. Spatial -temporal scanning analysis was conducted using StatsCan software, and spatial autocorrelation analysis was performed using ArcMap 10.2 software to identify spatial -temporal aggregation patterns. Results A total of 260 401 cases of HFMD were reported in Wenzhou from 2013 to 2022, with an average annual incidence of 276.93 per 100 000 and a severe disease rate of 13,82 per 100 000, The incidence exhibited a decreasing trend (X2t«nd=26 402.606, P < 0.05). The incidence rate among individuals over 6 years old increased (X2tnd=l 814.455, P < 0.05), as did the rates among nursery children, students, and teachers (%2=5 493.085, P < 0.05; x2t«nd=l 752.584, P < 0.05;忙剧二9 424, P < 0,05). Spatial analysis revealed aggregation of HFMD cases in 2017, with a Moran I value of 0.433 514 (Z=2.42 775 and P=0.015 193), indicating positive spatial correlation and hot spots concentrated in Wencheng county and Pingyang county. Spatial-temporal scanning identified a primary aggregation covering RuiJ an city, Cangnan county, Pingyang county, and Wencheng county, from January 1, 2014, to December 31, 2018, with a radiation radius of 44.67km (LLR=12 534.45, RR=L9& P < 0,01). The proportion of enterovirus type 71 (EV-A71) in group A showed a downward trend (%2=171-245, P < 0.05), while the proportion of other enteroviruses, including coxsackievirus group A 16 (CV-A16), increased (%2tMnd=93.481!1 P < 0.05). Conclusion The incidence of HFMD in Wenzhou remains high, although both the incidence and severity rates have decreased. Other enteroviruses, including coxsackievirus group A 6 (CV-A6), have become the dominant pathogens, and there has been an increase in incidence among teachers and individuals over 6 years old. The main aggregation areas were identified as RuiJ an city, Cangnan county, Pingyang county, and Wencheng county. [ABSTRACT FROM AUTHOR]

PM2.5 is a main air pollutant in China. Considering the unevenly distributed PM2.5 and population in China, an accurate assessment of PM2.5 exposure is needed. In this study, the population-weighted exposure (PWE) is used to measure the overall PM2.5 exposure based on 2766 counties across mainland China. The population exposure risk (PER) is used to better assess the partial PM2.5 exposure risk level for residents at the county level. The PM2.5 PWE and PER are calculated with the latest 2020 census data and the predicted concentrations estimated by spatial models considering both the geographic similarities and aggregation. The PWE differed from the concentrations across China, especially in four heavily polluted regions and three detected high-concentration clusters. In China, the average PM2.5 PWE in 2019 was 39.46 μg/m3, 2.41 μg/m3 higher than the mean concentration (37.05 μg/m3). The exposure in three detected clusters was much higher than in the Sichuan Basin (SCB), the Pearl River Delta (PRD), and the Yangtze River Delta (YRD), suggesting the focus of environmental governance should not only be the traditional heavily polluted areas according to administrative divisions. Regions with high concentrations also differed from regions with high PM2.5 exposure risk. The counties with higher PM2.5 PER were located in east-central and eastern coastal China, different from the distribution of concentrations. This study clarified the necessity of considering spatial aggregation of PM2.5 in LUR models and also emphasized the importance of calculating PM2.5 PWE as exposures in further health effect assessments. [ABSTRACT FROM AUTHOR]

Plant reproductive failure is a critical concern for conserving rare and endangered species that typically have low‐density and sparse populations. One important factor contributing to reproductive failure is the spatial arrangement of plants within a population, which can lead to isolation and negatively affect seed production, particularly in obligate outcrossers. Additionally, plant size can compound this effect, influencing seed production via multiple processes. Here, we investigate how spatial distribution and size influence the reproductive success of Vasconcellea chilensis, an endemic‐threatened papaya species in Chile. We first examined whether V. chilensis can produce seeds via apomixis using pollinator exclusion experiments. We then used Spatial Point Pattern Analysis (SPPA) in three populations to explore the spatial arrangement of plants. Finally, we assessed whether plant size and neighbor distance influence the reproductive success V. chilensis is a dioecious shrub unable to produce fruits through apomixis. The SPPA revealed significant clustering of female and male plants at different spatial scales, indicating a non‐random distribution. Moreover, a significant spatial association between the sexes was observed. In two populations, closer proximity to male plants was linked to higher seed production. Our study revealed that the reproductive system of V. chilensis is susceptible to distance‐dependent reproductive failure due to pollen limitation. While the species' spatial structure may partially mitigate this risk, female plants isolated from male counterparts will likely experience reduced seed set. [ABSTRACT FROM AUTHOR]

The development of standardised data acquisition strategies and analytical workflows is crucial to quantify gully changes. In this study, we explore synergies between unoccupied aircraft vehicles (UAV) and satellite remote sensing in order to classify gully morphodynamics. Using Time Series Forest (TSF) and the Sentinel‐1 radar backscatter coefficient (σo), gully scenarios can be classified into four categories: gully topographical change, no change outside gully, no change inside gully, and non‐topographical change. In addition, a Random Forest (RF) classification is performed employing individual features obtained from elevation models and temporally aggregated datasets. Training data are generated from multitemporal UAV‐borne photogrammetric point clouds through a manual segmentation of different gullies in Namibia. This information is transferred from point clouds (sub‐m) to satellite imagery (10 m) generating training data at Sentinel‐1 pixel level. Results indicate that the TSF (on the σo Vertical‐Vertical polarisation) and RF (on temporally aggregated features) perform best when training and testing areas are located within the same geographical extent. Both approaches yield similar Total Accuracy (TA ≈ 79%–80%) and Cohen's Kappa value (Kappa ≈ 0.7), but TSF achieves superior Producer Accuracy (PA = 78.5%) and User Accuracy (UA = 84.6%) for the gully topographical change class. Additionally, the utilisation of TSF in Vertical‐Vertical polarisation is the most effective method if the testing and training areas are in different geographical locations, allowing gully identification with TA > 80% and Kappa = 0.49. However, this method presents limitations to precisely delineate the change types, as dynamics are rain‐driven and therefore are geographically related. In summary, by combining the complementary benefits of UAV‐based and satellite‐based solutions, this study opens a line of research for the study and classification of surface land dynamics and geomorphological feature extraction in regional extents. [ABSTRACT FROM AUTHOR]

ObjectiveThis study aimed to provide suggestions for the prevention and control of bacterial food poisoning. The pattern of occurrence and epidemiological characteristics of bacterial food poisoning in China were determined， and its spatial aggregation was explored.MethodsDescriptive epidemiology was conducted to analyze and compare national bacterial food poisoning events from 2010 to 2019， and ArcGIS 10.6 was used to analyze the data spatially and draw relevant maps.ResultsA total of 1 110 cases of bacterial food poisoning were reported in China from 2010 to 2019， involving 49 457 poisonings and 87 deaths. Most bacterial food poisoning incidents occurred in the third quarter and in September. The highest number of incidents with identified causes was attributed to the contamination of meat and meat products， accounting for 19.10% of the total incidents. Non-typhoid Salmonella， Vibrio parahaemolyticus， and Bacillus cereus were the main causative agents of bacterial food poisoning， accounting for 56.04% and 61.17% of the total number of incidents and poisonings， respectively. The number of bacterial food poisoning incidents （39.37%） and morbidity incidents （34.76%） caused by collective canteens was the highest. The highest number of deaths （83.91%） from bacterial food poisoning occurred in households. Guangdong， Sichuan， Yunnan， Guangxi， and Shandong reported the highest number of incidents with the highest number of people poisoned in the country. The national bacterial food poisoning incidents exhibited a clustered spatial distribution， with “hot spots” mainly concentrated in parts of Yunnan， western and northern Guangxi， southern Sichuan， Chongqing， western and northern Hubei， western and southern Hu’nan， and central and northern Guangdong.ConclusionThe number of bacterial food poisoning incidents ranks first among all kinds of food poisoning incidents in China， suggesting the need for relevant departments to strengthen supervision and training， particularly in crucial settings such as collective canteens and catering service units. Various food safety standards and requirements should be strictly implemented， and publicity and education should be strengthened for key populations. It is recommended that hotspot regions of bacterial food poisoning explore the underlying causes and mechanisms of their spatial aggregation and implement corresponding measures to reduce the occurrence of such incidents.

Abstract Plant reproductive failure is a critical concern for conserving rare and endangered species that typically have low‐density and sparse populations. One important factor contributing to reproductive failure is the spatial arrangement of plants within a population, which can lead to isolation and negatively affect seed production, particularly in obligate outcrossers. Additionally, plant size can compound this effect, influencing seed production via multiple processes. Here, we investigate how spatial distribution and size influence the reproductive success of Vasconcellea chilensis, an endemic‐threatened papaya species in Chile. We first examined whether V. chilensis can produce seeds via apomixis using pollinator exclusion experiments. We then used Spatial Point Pattern Analysis (SPPA) in three populations to explore the spatial arrangement of plants. Finally, we assessed whether plant size and neighbor distance influence the reproductive success V. chilensis is a dioecious shrub unable to produce fruits through apomixis. The SPPA revealed significant clustering of female and male plants at different spatial scales, indicating a non‐random distribution. Moreover, a significant spatial association between the sexes was observed. In two populations, closer proximity to male plants was linked to higher seed production. Our study revealed that the reproductive system of V. chilensis is susceptible to distance‐dependent reproductive failure due to pollen limitation. While the species' spatial structure may partially mitigate this risk, female plants isolated from male counterparts will likely experience reduced seed set.

In contrast to much of the literature that focuses on the issue of spatial dependence in housing price research, this study addresses the spatial aggregation of housing transactions and analyzes the effects of transaction hotspots and short-term flipping hotspots on housing prices by using real housing transaction data in Taipei City, Taiwan. The empirical results show that after controlling for the effects of spatial dependence and individual housing attributes, the impact of transaction hotspot areas on housing prices is significantly negative, while the impact of flipping hotspot areas on housing prices is significantly positive. The results verify that the key to driving up housing prices lies in flipping activities. Furthermore, the results of the spatial quantile regression model show that low-priced residential properties are more sensitive to the spatial concentration of housing transactions and flipping transactions in the housing market. Our results have implications for the government’s policy intending to control hot trade volumes to cool skyrocketing housing prices in a booming housing market. It is suggested that the government should pay attention to restraining short-term flipping activities in the housing market rather than setting constrains on housing transactions.

Channel attention has been shown to improve the performance of deep convolutional neural networks efficiently. Channel attention adaptively recalibrates the importance of each channel, determining what to attend to. However, channel attention only encodes inter-channel information but neglects the importance of positional information. Positional information is crucial in determining where to attend to. To address this issue, we propose a novel channel-spatial attention method named Spatial-Decomposition-Aggregation Attention (SDAA) method. First, a high-axis spatial direction is decomposed into multiple low-axis spatial directions. Then, a shared transformation sub-unit establishes attention in each low-axis space direction. Next, all the low-axis attention masks are aggregated into a high-axis attention mask. Finally, the generated high-axis attention mask is fused into the input features, thus enhancing the input features. Essentially, our method is a divide-and-conquer process. Experimental results demonstrate that our SDAA method outperforms the existing channel-spatial attention methods. [ABSTRACT FROM AUTHOR]

Dominance of neotropical tree communities by a few species is widely documented, but dominant trees show a variety of distributional patterns still poorly understood. Here, we used 503 forest inventory plots (93,719 individuals ≥2.5 cm diameter, 2609 species) to explore the relationships between local abundance, regional frequency and spatial aggregation of dominant species in four main habitat types in western Amazonia. Although the abundance‐occupancy relationship is positive for the full dataset, we found that among dominant Amazonian tree species, there is a strong negative relationship between local abundance and regional frequency and/or spatial aggregation across habitat types. Our findings suggest an ecological trade‐off whereby dominant species can be locally abundant (local dominants) or regionally widespread (widespread dominants), but rarely both (oligarchs). Given the importance of dominant species as drivers of diversity and ecosystem functioning, unravelling different dominance patterns is a research priority to direct conservation efforts in Amazonian forests. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of ecosystem service values (ESVs) and ecological risk are critical indicators to represent the regional ecological protection level and potential of sustainable development, which largely depend on land-use patterns. Aiming to contribute to global climate mitigation, China has proposed dual-carbon goals that would remarkably influence the land-use/cover change (LUCC) distribution. Based on the Landsat land cover data of 2000, 2010 and 2020 and multisource satellite products, several driving factors are integrated into the patch-generating land use simulation (PLUS) model to simulate future LUCC patterns for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) under rapid urbanization, cropland protection and carbon neutral (CN) scenarios from 2020 to 2050. Spatial–temporal ecosystem service and ESVs are allocated using INVEST and the equivalent factor method and thus ecological risks are evaluated using the entropy method. Results indicate that forest growth is the largest under the CN scenario, especially in the northwestern and northeastern GBA, exceeding 25,800 km2 in 2050, which results in both the highest habitat quality and carbon storage. The largest ESVs, reaching higher than 5210 yuan/pixel, are found in the CN scenario, particularly expanding toward the suburban area, leading to the lowest ecological risks. From 2020 to 2050, habitat quality, carbon storage and ESVs improve, while ecological risks decline in the CN scenario. This research provides implications for economic and ecological balanced development and gives references to the carbon-neutral pathway for the GBA. [ABSTRACT FROM AUTHOR]

The conservation of traditional village clusters, especially those within traditional village aggregation areas, is an important part of historical cultural landscape preservation. With the hilly regions of Zhejiang province as the study area, this study innovatively proposed a traditional village aggregation area identification method from the perspective of historical layering. Because the influence of social economics on the spatial development of villages would gradually lead to spatially geographical features in the village evolution process, this study evaluated eight spatial characteristic indicators from natural geography, socioeconomics, and historical culture to identify ordinary traditional villages based on the threshold value extracted from protective traditional villages. In order to avoid the spatial identification deviation caused by only taking protective traditional villages as samples, we chose them together with ordinary traditional villages to identify 14 traditional village aggregation areas, which were categorized into four types based on regional protection conditions and development potential. This study expands the perspective for understanding the formation of the traditional villages' spatial characteristics and provides development guidance for traditional village aggregation areas in hilly regions of Zhejiang province. [ABSTRACT FROM AUTHOR]

The potentially large spatial footprint of earthquake disasters and the increased concentration of population and values in dense urban areas call for an explicit consideration of seismic risk at a regional, building portfolio level. The relation between the building‐level seismic risk and the portfolio‐level seismic risk is helpful if one wants to meet a specific regional seismic risk tolerance level by specifying seismic risk targets for individual buildings. We examine four types of common risk measures and point to the importance of subadditivity, a risk measure mathematical property, for deriving a conservative upper‐bound relation between the building‐specific and the building portfolio seismic risks. Subadditive risk measures, such as the Expected Shortfall, allow estimates of conservative upper bounds of the portfolio‐level seismic risk. In a case study, we show that nonsubadditive risk measures commonly used in earthquake engineering can lead to counter‐intuitive and nonconservative perceptions of the regional seismic risk, especially when one extrapolates from individual buildings to the entire building portfolio. We also illustrate the advantages of subadditive risk measures for regional seismic risk assessment. [ABSTRACT FROM AUTHOR]

The corn earworm, Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae), is a cosmopolitan pest in the field crop landscape in the southeastern United States. Field corn (Zea mays L.) is the most important midseason host for H. zea where intensive selection pressure occurs for resistance to insecticidal toxins from Bacillus thuringiensis (Bt). Because spatial patterns of H. zea in field corn have not been extensively studied, field corn was sampled for H. zea larvae and injury in 2021 and 2022. Patterns of spatial aggregation were identified in a number of fields in both larval populations and injury. Aggregation of H. zea larvae was less common at R5 than at R2. Associations between the spatial patterns of H. zea and the variability in crop phenology were identified in some fields, with positive associations between plant height and H. zea larvae, indicating that ovipositing H. zea moths avoid areas with reduced plant height and delayed reproductive maturity. Additionally, negative spatial associations between stink bug ear injury and H. zea larvae and their injury were found in a small number of cases, indicating some spatial interactions between the two pest complexes and their injury. Results from these studies provide valuable insight into the spatial patterns of H. zea in field corn. An understanding of the local dispersal and population dynamics of H. zea can be used to help further improve integrated pest management and insect resistance management programs for this major polyphagous pest. [ABSTRACT FROM AUTHOR]

Land degradation under the influence of global warming and ecological environmental destruction due to poor land management is the main challenge facing the Mongolian Plateau, and its future ecological risk change trends and drivers are also unclear. Therefore, to address the context relevant to this challenge, planning based on measured information from land use patterns is required. Based on land use and land cover (LULC), this study evaluates the landscape ecological risk (LER) of the Selenga River Basin by calculating the landscape pattern index. The spatiotemporal pattern and influencing factors of landscape ecological risk in the Selenga River Basin from 1990 to 2040 were analyzed. According to the results of LULC analysis, forest and grassland were the primary land use types in the Selenga River Basin. The built area, forest, and cropland showed an increasing trend, while the grassland area showed a fluctuating decreasing trend. From 1990 to 2010, the comprehensive land use dynamic degree showed a trend of rising first and then falling, specifically from 0.13% in 1990 to 0.29% in 2010, and will drop to 0.06% by 2040, indicating that the range of land use change is becoming more and more stable. The landscape ecological risk assessment shows a distribution pattern of "low at the edge and high in the middle". The landscape ecological risk index (LER) first increases and then decreases, with the peak value in 2010 (0.085). By calculating the spatial aggregation of LER and the partial correlation with climate, we found that the Moran's I index showed an "anti-V"-shaped change trend from 1990 to 2040, and the average landscape ecological risk presents positive spatial correlation, primarily with high-value aggregation, and peaked in 2010. Precipitation had a negative correlation with landscape ecological risk controlling for temperature, while there was a positive relationship between temperature and landscape ecological risk under the influence of controlling precipitation. This study provides a scientific basis for LULC planning in the Selenga River Basin, and is of great significance for maintaining the ecological security of the Mongolian Plateau. [ABSTRACT FROM AUTHOR]

Recently, Transformer-based models are taken much focus on solving the task of image super-resolution (SR) due to their ability to achieve better performance. However, these models combined huge computational cost during the computing self-attention mechanism. To solve this problem, we proposed a multi-order gated aggregation super-resolution network (MogaSRN) for low-level vision based on the concept of the MogaNet that is developed for high-level vision. The concept of the MogaSRN model is based on spatial multi-order context aggregation and adaptive channel-wise reallocation with the aid of the multi-layer perceptron (MLP). In contrast to the MogaNet model, in which the resolution of each stage decreased by a factor of 2, the resolution of the MogaSRN is stayed fixed during the deep features extraction. Moreover, the structure of the MogaSRN model is built based on balancing the performance and the model complexity. We evaluated our model based on five benchmark datasets concluding that the MogaSRN model can achieve significant improvements compared to the state-of-the-art. Moreover, our model shows the good visual quality and accuracy of the reconstruction. Finally, our model has 3.7 × faster runtime at the scale of × 4 compared to LWSwinIR with better performance. • Image super-resolution based on a deep learning model is proposed. • A MLP multi-order gated aggregation block (MMogaB) is designed. • A MogaSRN model based on utilizing the MMogaB is developed. [ABSTRACT FROM AUTHOR]

In recent years, the export trade of Beijing, Tianjin, and Hebei has demonstrated stability and improved quality, contributing to the advancement of the dual domestic and international cycle and presenting both opportunities and challenges for the region. However, the spatial spillover effects of export trade on environmental impacts in the Beijing-Tianjin-Hebei city cluster remain understudied. This research paper addresses this gap by enhancing the STIRPAT model and utilizing panel data from the Beijing-Tianjin-Hebei city cluster spanning 2009 to 2020. The study examines the influence of foreign trade on carbon emissions and air pollution from the perspective of export trade. The results reveal that carbon dioxide emissions in the Beijing-Tianjin-Hebei city cluster are not significantly spatially correlated; whereas PM2.5 demonstrates significant spatial correlation and forms a distinct cluster in terms of geographical distribution. Based on this regression model from panel data, it is concluded that a 1% increase in export trade results in a 4.794% increase in CO2 emissions; spatial econometric analysis concludes that a 1% increase in export trade leads to an 11.54% increase in PM2.5 concentration values. Additionally, GDP per capita and urban green space has a certain mitigation effect on environmental pollution. Regional synergy is an important guide for the development of large urban agglomerations. In the new era, high-level economic development must be pursued alongside ecological and environmental protection. Accelerating the green transformation of industries and promoting synergistic emission reduction in the Beijing-Tianjin-Hebei urban agglomeration is the key to development. [ABSTRACT FROM AUTHOR]

The high-quality development of new agricultural business entities (NABEs) is an important driving force for realizing rural revitalization and accelerating the modernization of agriculture and rural areas. The main purpose of the study is to investigate the spatial distribution pattern, aggregation scales, development mechanism, and internal differences of various types of NABEs in different regions. It provides targeted ideas for alleviating regional differences in the development of NABEs in different agricultural regions. Kernel density estimation, nearest neighbor distance analysis, Tyson's polygon coefficient of variation, and Ripley's K function are used to study the spatial and temporal evolution, spatial aggregation, and scale divergence of various types of NABEs, and Pearson correlation analysis is incorporated to explore the specific factors affecting the development of various types of NABEs. The study results: First, family farms are the most widely distributed, and agricultural enterprises are the most sparsely distributed, being distributed "more in the southeast and less in the northwest" in all three categories. Second, the strongest aggregation scales of different NABEs are increasing, and the strongest aggregation scales of agricultural enterprises are larger than those of family farms and cooperatives in all agricultural areas. Third, the development of specialized farmers' cooperatives (abbreviated as 'cooperatives') is more constrained by traditional agricultural inputs and is a kind of agricultural input-oriented development. Family farms are more constrained by the living standards of rural residents in the region and are a kind of rural economy-oriented development. Agricultural enterprises are more subject to the economic level of the region, which is a kind of market economy-oriented development. Finally, in the process of developing NABEs, regional differences should be emphasized, and a small number of agriculturally leading enterprises and model cooperatives should drive a large number of small-scale family farms and smallholder farmers in order to become a characteristic path for China's agricultural development. [ABSTRACT FROM AUTHOR]

The identification, evaluation, and spatial distribution of "Production–Living–Ecological" space (PLEs) have been widely studied, but there is still little attention paid to whether their distribution characteristics will vary with scale changes. This article takes the organic whole of the PLEs composed of production space, living space, and ecological space on Hainan Island as the research object. Starting from the perspective of spatial heterogeneity, it quantitatively evaluates its spatial functions and explores the regularities of its aggregation and coordination characteristics with changes in scale, revealing the evolution of its distribution pattern with changes in scale. The results show that: (1) The distribution of PLEs in Hainan Island has obvious heterogeneity. The functional values of production and living space are distributed in a pattern of high in the south and north, low in the middle; The ecological space shows a high distribution pattern in the middle and low around it. (2) The PLEs in Hainan Island is significantly affected by scale effects. The degree of aggregation decreases as the scale increases, with the ecological space showing the most obvious downward trend, the living space showing a weaker downward trend and the production space being moderate. (3) The overall level of coupling-coordination of PLEs in Hainan Island is low, which decreases as the scale increases, with 500 m × 500 m being the peak value. The research results of this article indicate that there are scale effects in the functional distribution characteristics of PLEs, which can provide decision support for the national spatial planning at different scales. [ABSTRACT FROM AUTHOR]

Abstract Background Cardiovascular disease (CVD) is the leading cause of mortality worldwide and in Colombia. The analysis of CVD mortality has been mainly relied on individual factors and rates, but occurrence is also related to contextual conditions. Understanding the distribution of CVD in a region will contribute to implement more focused-preventive and care interventions. Methods Using the national mortality register established by the Department of National Statistics, standardized rates and spatial distribution of CVD mortality were estimated for Cali, Colombia, between 2010–2017. Global and local spatial aggregation was assessed using the Geary’s C test and for each district standardized mortality ratios smoothed by the Bayesian empirical were estimated. Results Over the period, CVD was the main cause of mortality with 28,804 deaths accounting for 23,8% of total deaths. The global CVD mortality rate varied from 235.9 to 257.4 per 100.000 habitants, with an average increase of 9.1% in the percentage change. The main cause of mortality were hypertensive diseases following by ischemic heart diseases and stroke. The standardized mortality ratios smoothed by the Bayesian empirical method showed that the districts 7, 13, 14, 15 and 16 located at the eastern area with the lowest socio-economic strata and the district 22 at the south of the city with the highest socio-economic strata had the high risks of CVD mortality. All these areas were at the boundary of the city. The the lowest risk was observed in districts 1 and 2 at the northwest area with the upper socio-economic strata. Over the study period, a spatial autocorrelation was found in the districts 1,9 10, 11, 12, 13, 14, 15, 19, and 21 by using the Geary’s C test. The highest significant spatial association was found in the districts 1 and 21. Conclusion Of 22 districts in Cali, the highest risk of CVD mortality was found in three at the lowest and one in the upper socio-economic strata between 2013 and 2017. Over the period a global spatial aggregation was identified due mainly to districts peripherical located suggesting that there could be contextual conditions influencing the risk. Therefore, there is a need for considering local conditions to prevent CVD mortality.

Improving land use efficiency through rational use of land resources is an important way to ensure agricultural production and promote social and economic development. Based on the survey data of 635 farmers in Qinba mountain area of southern Shaanxi, the land use efficiencies for different types of farmers were calculated by data envelopment analysis(DEA), the spatial distribution characteristics of land use efficiency were analyzed by Moran index, and its influencing factors were explored by Tobit model. The results show that ① the overall land use efficiency of farmers is relatively low, and the efficiency of single household is the highest, while that of non-farmer is the lowest; ② the agglomeration characteristics of land use efficiency of farmers are remarkable; the overall spatial distribution pattern is high in the west and low in the east, and the cities are Hanzhong, Ankang and Shangluo in the descending order of land use efficiency of farmers; ③ the health status of labor force, total arable land area, degree of land fragmentation, and agricultural income all significantly affect the land use efficiency of farmers, and different factors have difference for types and regions of farmers. In general, it is suggested that the local government improves land use efficiency of farmers from agricultural protection system, land circulation system and land use patterns, and promotes coordinated regional development on this basis. [ABSTRACT FROM AUTHOR]

The relative influence of both conspecific and heterospecific plant density on the fecundity of generalist rewardless plants is unclear. To test whether distance from both conspecific and heterospecific plants influence pollen removal, deposition, and fruit set in the nectarless orchid, Dactylorhiza sambucina that co-flowers with the rewarding Ranunculus apenninus. I experimentally translocated D.sambucina inflorescences and measured pollen removal and deposition rates. I measured plant height and distance to D.sambucina and R.apenninus plants in 30 D.sambucina patches of varying density. I quantified the proportion of pollen removed, pollinia deposited, and fruit set and tested whether these vary according to plant height and distance to both D.sambucina and R.apenninus. Translocated inflorescences outside the population had a higher proportion of pollen removal than control plants within the population, while pollinia deposition rates did not differ. Increased D. sambucina height increased pollen removal rates, while increasing distance from R. apenninus resulted in both increased pollen deposition and fruit set. Pollination and fecundity of a nectarless orchid may increase with increasing distance from rewarding heterospecifics. This study improves our understanding of how the fecundity of rewardless plants is influenced by co-flowering plants. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide and in Colombia. The analysis of CVD mortality has been mainly relied on individual factors and rates, but occurrence is also related to contextual conditions. Understanding the distribution of CVD in a region will contribute to implement more focused-preventive and care interventions. Methods: Using the national mortality register established by the Department of National Statistics, standardized rates and spatial distribution of CVD mortality were estimated for Cali, Colombia, between 2010–2017. Global and local spatial aggregation was assessed using the Geary’s C test and for each district standardized mortality ratios smoothed by the Bayesian empirical were estimated. Results: Over the period, CVD was the main cause of mortality with 28,804 deaths accounting for 23,8% of total deaths. The global CVD mortality rate varied from 235.9 to 257.4 per 100.000 habitants, with an average increase of 9.1% in the percentage change. The main cause of mortality were hypertensive diseases following by ischemic heart diseases and stroke. The standardized mortality ratios smoothed by the Bayesian empirical method showed that the districts 7, 13, 14, 15 and 16 located at the eastern area with the lowest socio-economic strata and the district 22 at the south of the city with the highest socio-economic strata had the high risks of CVD mortality. All these areas were at the boundary of the city. The the lowest risk was observed in districts 1 and 2 at the northwest area with the upper socio-economic strata. Over the study period, a spatial autocorrelation was found in the districts 1,9 10, 11, 12, 13, 14, 15, 19, and 21 by using the Geary’s C test. The highest significant spatial association was found in the districts 1 and 21. Conclusion: Of 22 districts in Cali, the highest risk of CVD mortality was found in three at the lowest and one in the upper socio-economic strata between 2013 and 2017. Over the period a global spatial aggregation was identified due mainly to districts peripherical located suggesting that there could be contextual conditions influencing the risk. Therefore, there is a need for considering local conditions to prevent CVD mortality. [ABSTRACT FROM AUTHOR]

• SOC stock was predicted between 1992 and 2016 at various aggregation levels. • Both static and dynamic environmental covariates were extensively used. • Crucial to consider spatiotemporal correlation in upscaling with quantified uncertainty. • Larger support reduces uncertainty, aids in identifying significant SOC stock changes. • Exhaustive SOC stock predictions support environmental and agricultural challenges. The role of soil organic carbon (SOC) is crucial not only for numerous soil functions and processes but also for addressing various environmental crises and challenges we face. Consequently, the demand for information on the spatiotemporal variability of SOC is increasing, posing new methodological challenges, such as the need for information on SOC and SOC changes with quantified uncertainty across a wide variety of spatial scales and temporal periods. Our objective was to present a methodology based on a combination of machine learning and space–time geostatistics to predict the spatiotemporal variability of SOC stock with quantified uncertainty at various spatial supports (i.e., point support, 1 × 1 km, 5 × 5 km, 10 × 10 km, 25 × 25 km, counties, and the entire country) for Hungary, between 1992 and 2016. The role of geostatistics is pivotal, as it accounts for the spatiotemporal correlation of the interpolation errors, which is essential for reliably quantifying the uncertainty associated with spatially aggregated SOC stock and SOC stock change predictions. Five times repeated 10-fold leave-location-out cross-validation was used to evaluate the point support predictions and uncertainty quantifications, yielding acceptable results for both SOC stock (ME = −0.897, RMSE = 19.358, MEC = 0.321, and G = 0.912) and SOC stock change (ME = 0.414, RMSE = 16.626, MEC = 0.160, and G = 0.952). We compiled a series of maps of SOC stock predictions between 1992 and 2016 for each support, along with the quantified uncertainty, which is unprecedented in Hungary. It was demonstrated that the larger the support, the smaller the prediction uncertainty, which facilitates the identification and delineation of larger areas with statistically significant SOC stock changes. Moreover, the methodology can overcome the limitations of recent approaches in the spatiotemporal modelling of SOC, allowing the prediction of SOC and SOC changes, with quantified uncertainty, for any year, time period, and spatial scale. This methodology is capable of meeting the current and anticipated demands for dynamic information on SOC at both national and international levels. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of ecosystem service values (ESVs) and ecological risk are critical indicators to represent the regional ecological protection level and potential of sustainable development, which largely depend on land-use patterns. Aiming to contribute to global climate mitigation, China has proposed dual-carbon goals that would remarkably influence the land-use/cover change (LUCC) distribution. Based on the Landsat land cover data of 2000, 2010 and 2020 and multisource satellite products, several driving factors are integrated into the patch-generating land use simulation (PLUS) model to simulate future LUCC patterns for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) under rapid urbanization, cropland protection and carbon neutral (CN) scenarios from 2020 to 2050. Spatial–temporal ecosystem service and ESVs are allocated using INVEST and the equivalent factor method and thus ecological risks are evaluated using the entropy method. Results indicate that forest growth is the largest under the CN scenario, especially in the northwestern and northeastern GBA, exceeding 25,800 km2 in 2050, which results in both the highest habitat quality and carbon storage. The largest ESVs, reaching higher than 5210 yuan/pixel, are found in the CN scenario, particularly expanding toward the suburban area, leading to the lowest ecological risks. From 2020 to 2050, habitat quality, carbon storage and ESVs improve, while ecological risks decline in the CN scenario. This research provides implications for economic and ecological balanced development and gives references to the carbon-neutral pathway for the GBA.

The conservation of traditional village clusters, especially those within traditional village aggregation areas, is an important part of historical cultural landscape preservation. With the hilly regions of Zhejiang province as the study area, this study innovatively proposed a traditional village aggregation area identification method from the perspective of historical layering. Because the influence of social economics on the spatial development of villages would gradually lead to spatially geographical features in the village evolution process, this study evaluated eight spatial characteristic indicators from natural geography, socioeconomics, and historical culture to identify ordinary traditional villages based on the threshold value extracted from protective traditional villages. In order to avoid the spatial identification deviation caused by only taking protective traditional villages as samples, we chose them together with ordinary traditional villages to identify 14 traditional village aggregation areas, which were categorized into four types based on regional protection conditions and development potential. This study expands the perspective for understanding the formation of the traditional villages’ spatial characteristics and provides development guidance for traditional village aggregation areas in hilly regions of Zhejiang province.

Energy system models that consider variable renewable energy sources (VRESs) are computationally complex. The greater spatial scope and level of detail entailed in the models exacerbates complexity. As a complexity-reduction approach, this paper considers the simultaneous spatial and technological aggregation of energy system models. To that end, a novel two-step aggregation scheme is introduced. First, model regions are spatially aggregated to obtain a reduced region set. The aggregation is based on model parameters such as VRES time series, capacities, etc. In addition, spatial contiguity of regions is considered. Next, technological aggregation is performed on each VRES, in each region, based on their time series. The aggregations' impact on accuracy and complexity of a cost-optimal, European energy system model is analyzed. The model is aggregated to obtain different combinations of numbers of regions and VRES types. Results are benchmarked against an initial resolution of 96 regions, with 68 VRES types in each. System cost deviates significantly when lower numbers of regions and/or VRES types are considered. As spatial and technological resolutions increase, the cost fluctuates initially and stabilizes eventually, approaching the benchmark. Optimal combination is determined based on an acceptable cost deviation of <5% and the point of stabilization. A total of 33 regions with 38 VRES types in each is deemed optimal. Here, the cost is underestimated by 4.42 % , but the run time is reduced by 92.95 % . [ABSTRACT FROM AUTHOR]

Insect–host relationships can generate spatial patterns. The effect of plant species has been widely investigated, whereas the effect of plant size was ignored.The authors characterized the spatial pattern of the swift moth Endoclita excrescens Butler by combining spatial analysis by distance indices (SADIE) and spatial point pattern analysis (SPPA). Two contiguous plots of monospecific plantations of Manchurian ash Fraxinus mandshurica with different diameters at breast height (DBH) were sampled, and the position, DBH, and count of attack holes of each ash tree were recorded.The aggregation at the landscape scale is far stronger than those at the stand scale. At the landscape scale, trees with small number of bored holes were close to each other at distances of r < 4 m. None of the significant patterns were found at the stand scale.SADIE results showed a strong association between count of attack holes and DBH at the landscape scale, whereas weak association at the stand scale. SPPA results showed that, either at the landscape or stand scale, the locations of ash trees were randomly distributed at small distance, but aggregated at large distance. The counts of holes are only dependent on the point (tree) at small distances (r < 2.5 m) at the landscape scale. These results provide basic information for sampling and precision‐targeted controlling programs. [ABSTRACT FROM AUTHOR]

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Land degradation under the influence of global warming and ecological environmental destruction due to poor land management is the main challenge facing the Mongolian Plateau, and its future ecological risk change trends and drivers are also unclear. Therefore, to address the context relevant to this challenge, planning based on measured information from land use patterns is required. Based on land use and land cover (LULC), this study evaluates the landscape ecological risk (LER) of the Selenga River Basin by calculating the landscape pattern index. The spatiotemporal pattern and influencing factors of landscape ecological risk in the Selenga River Basin from 1990 to 2040 were analyzed. According to the results of LULC analysis, forest and grassland were the primary land use types in the Selenga River Basin. The built area, forest, and cropland showed an increasing trend, while the grassland area showed a fluctuating decreasing trend. From 1990 to 2010, the comprehensive land use dynamic degree showed a trend of rising first and then falling, specifically from 0.13% in 1990 to 0.29% in 2010, and will drop to 0.06% by 2040, indicating that the range of land use change is becoming more and more stable. The landscape ecological risk assessment shows a distribution pattern of “low at the edge and high in the middle”. The landscape ecological risk index (LER) first increases and then decreases, with the peak value in 2010 (0.085). By calculating the spatial aggregation of LER and the partial correlation with climate, we found that the Moran’s I index showed an “anti-V”-shaped change trend from 1990 to 2040, and the average landscape ecological risk presents positive spatial correlation, primarily with high-value aggregation, and peaked in 2010. Precipitation had a negative correlation with landscape ecological risk controlling for temperature, while there was a positive relationship between temperature and landscape ecological risk under the influence of controlling precipitation. This study provides a scientific basis for LULC planning in the Selenga River Basin, and is of great significance for maintaining the ecological security of the Mongolian Plateau.

The identification, evaluation, and spatial distribution of “Production–Living–Ecological” space (PLEs) have been widely studied, but there is still little attention paid to whether their distribution characteristics will vary with scale changes. This article takes the organic whole of the PLEs composed of production space, living space, and ecological space on Hainan Island as the research object. Starting from the perspective of spatial heterogeneity, it quantitatively evaluates its spatial functions and explores the regularities of its aggregation and coordination characteristics with changes in scale, revealing the evolution of its distribution pattern with changes in scale. The results show that: (1) The distribution of PLEs in Hainan Island has obvious heterogeneity. The functional values of production and living space are distributed in a pattern of high in the south and north, low in the middle; The ecological space shows a high distribution pattern in the middle and low around it. (2) The PLEs in Hainan Island is significantly affected by scale effects. The degree of aggregation decreases as the scale increases, with the ecological space showing the most obvious downward trend, the living space showing a weaker downward trend and the production space being moderate. (3) The overall level of coupling-coordination of PLEs in Hainan Island is low, which decreases as the scale increases, with 500 m × 500 m being the peak value. The research results of this article indicate that there are scale effects in the functional distribution characteristics of PLEs, which can provide decision support for the national spatial planning at different scales.

Aggregating data to other spatial resolutions can result in spatial aggregation errors, which can affect the data and subsequent analyses. This paper investigates the nature of spatial aggregation errors in the calculation of agglomeration benefits. The finding shows that inappropriate aggregation can result in significant errors in the monetised benefits used in transport appraisals. The error is analysed using both a stylised model and four case studies. Using arithmetic employment-weighted averages for transport costs when calculating the access to economic mass (ATEM) of the aggregated zones resulted in an error of +/-70%. An arithmetic trip-weighted average reduces errors. The error is further reduced using a harmonic employment-weighted average in the ATEM calculation, but it is not eliminated. The remaining error stems from the altered structure of the data stemming from the aggregation process, which is called the modifiable area unit problem (MAUP). Our findings, which concur with the broader MAUP literature, indicate that this error is unavoidable. Our policy recommendation is to calculate the agglomeration benefits as disaggregated as possible, but if aggregation is necessary, use the harmonic employment-weighted average for transport costs. [ABSTRACT FROM AUTHOR]

Human-environment relationship, as the interaction between human society and natural environment, is the inevitable way for sustainable development. The coordinated relationship between human and environment determines the quality of regional economic development and ecological environment. To coordinate human-environment relationship for natural resources can support human society development, we investigated the dynamic evolutionary characteristics of the human-environment coordination of the Yellow River Delta over the past 30 years using the coupled coordination model, artificial classification and spatial autocorrelation analysis. From the analytical results, the findings are as follows: (1) Human activity intensity in the Yellow River Delta increased to 35.24 % from 1990 to 2018, which spatial distribution pattern showed low in the coastal and high inland. Ecological carrying capacity was decreased by 1.61 × 104 hm2. The spatial distribution pattern showed the same characteristics as human activity intensity. (2) The area of human-environment coordination showed increasing trend from 1990 to 2018. Among them, “Severe Uncoordinated” and “High-quality Corrdianted” area changed the most obviously, the area of this two coordinated levels were increased by 731.15 km2 and 674.33 km2 respectively. “Good Coordinated” area dramatically decreased 1 479.61 km2, “Relatively Uncoordinated” and “Low Coordianted” were stable in the study area; (3) The coordination of human-environment indicates clear spatial agglomeration characteristics that, high aggregation in the coastal but low inland. Moreover, the research provides references for coordinating human-environment relationship.

It is imperative to rapidly and precisely acquire crop lodging area and severity for disaster prevention and yield prediction. However, estimation of crop lodging area at a large scale remains challenging due to the relatively low sensitivity of remote sensing signal to the lodging variation, limited availability of remote sensing images, and lodging statistical data. This study proposes a new method for lodging area estimation based on the optimal grid cell of Sentinel-2 and crop lodging percentage, overcoming the limitation of traditional pixel-based mapping approaches that fail to obtain quantitative lodging information. Basing the spatial aggregation method, we analyzed the optimal grid size of Sentinel-2 data for lodging percentage estimation. Then we investigated the spectral response for different lodging percentage levels and analyzed the potential of lodging percentage estimation for Sentinel-2 metrics (including selected spectral bands and their derived vegetation indexes (VIs)). A quantitative model was established between the training set and the Sentinel-2 metrics using the random forest (RF) algorithm. Finally, around 1462.62 ha fields from six counties or districts in Heilongjiang province in China were estimated for lodging percentage. Results indicate that the proposed method can estimate the crop lodging percentage on the testing set with an R2 and RMSE of 0.64 and 25.24, respectively, which can explain around 95 % spatial variation of lodging crop. Moreover, the overall magnitude of reflectance increased with the increase in lodging percentage. Among all Sentinel-2 optimal metrics, the Green, SWIR1, and Red edge 1 bands are the most crucial indicators for lodging percentage estimation. Our results on lodging percentage estimation in the study area indicate that there is more lodging maize in the Meilisidawoerzu district than in other areas. Although typhoons passed over Fuyu and Lindian counties, the lodging percentage in these areas is relatively low. The lodging percentage map has great value in agriculture management and insurance claim.

This study proposes a human body motion prediction model that can adapt to the disorders in various human motion patterns and represent the kinematic constraints. In human motion prediction, the acquisition of features that capture inter-motion and inter-joint linkages is considered effective. To generate links that are adaptive to the reference time of dominant features and their crossing-over joints, we construct an attention-based network that aggregates motion sequences temporally and spatially. We evaluated the motion prediction results using the Human3.6M dataset with the indices of mean angle error and mean per joint position error and showed that our method outperforms other state-of-the-art methods.

Based on the epidemic data of COVID-19 in 50 states of the United States (the US) from December 2021 to January 2022, the spatial and temporal clustering characteristics of COVID-19 in the US are explored and analyzed. First, the retrospective spatiotemporal analysis is performed by using SaTScan 9.5, and 17 incidence areas are obtained. Second, the reliability of the results is tested by the circular distribution method in the time latitude and the clustering method in the spatial latitude, and it is confirmed that the retrospective spatiotemporal analysis accurately measures in time and reasonably divides regions according to the characteristics in space. Empirical results show that the first-level clustering area of the epidemic has six states with an average relative risk of 1.28 and the second-level clustering area includes 18 states with an average relative risk of 0.86. At present, the epidemic situation in the US continues to expand. It is necessary to do constructive work in epidemic prevention, reduce the impact of epidemic, and effectively control the spread of the epidemic.