Spatial Aggregation Research Articles

Spatiotemporal Changes and Influencing Factors of Hand, Foot, and Mouth Disease in Guangzhou, China, From 2013 to 2022: Retrospective Analysis.

In the past 10 years, the number of hand, foot, and mouth disease (HFMD) cases reported in Guangzhou, China, has averaged about 60,000 per year. It is necessary to conduct an in-depth analysis to understand the epidemiological pattern and related influencing factors of HFMD in this region. This study aims to describe the epidemiological characteristics and spatiotemporal distribution of HFMD cases in Guangzhou from 2013 to 2022 and explore the relationship between sociodemographic factors and HFMD incidence. The data of HFMD cases in Guangzhou come from the Infectious Disease Information Management System of the Guangzhou Center for Disease Control and Prevention. Spatial analysis and space-time scan statistics were used to visualize the spatiotemporal distribution of HFMD cases. Multifactor ordinary minimum regression model, geographically weighted regression, and geographically and temporally weighted regression were used to analyze the influencing factors, including population, economy, education, and medical care. From 2013 to 2022, a total of 599,353 HFMD cases were reported in Guangzhou, with an average annual incidence rate of 403.62/100,000. Children aged 5 years and younger accounted for 93.64% (561,218/599,353) of all cases. HFMD cases showed obvious bimodal distribution characteristics, with the peak period from May to July and the secondary peak period from August to October. HFMDs in Guangzhou exhibited a spatial aggregation trend, with the central urban area showing a pattern of low-low aggregation and the peripheral urban area demonstrating high-high aggregation. High-risk areas showed a dynamic trend of shifting from the west to the east of peripheral urban areas, with coverage first increasing and then decreasing. The geographically and temporally weighted regression model results indicated that population density (β=-0.016) and average annual income of employees (β=-0.007) were protective factors for HFMD incidence, while the average number of students in each primary school (β=1.416) and kindergarten (β=0.412) was a risk factor. HFMD cases in Guangzhou were mainly infants and young children, and there were obvious differences in time and space. HFMD is highly prevalent in summer and autumn, and peripheral urban areas were identified as high-risk areas. Improving the economic level of peripheral urban areas and reducing the number of students in preschool education institutions are key strategies to controlling HFMD.

Spatial conditional convergence analysis of total factor energy efficiency based on industry transfer and technology diffusion

Measuring energy efficiency is important to achieve the goal of sustainable economic development. This paper adopts the Malmquist index to measure the trend of regional total factor energy efficiency. This paper analyses the spatial aggregation characteristics of TFEE and the impact mechanism of industrial transfer and technology diffusion on TFEE convergence from a dynamic perspective. The results of the study show that there is a large gap in TFEE between regions in China, with a spatial characteristic of “high in the south and low in the north”. In 2006–2021, the convergence rate of TFEE shows a slowing down trend. Industrial transfer has a negative effect on TFEE. The impact of technology diffusion on TFEE is different in different periods. The impact of researchers, technology market, technology introduction, and import-export on TFEE have significant differences. The convergence rate of the south is higher than that of the north. The impact of industrial transfer and technology diffusion on TFEE in the two parts is different. Policymakers should implement differentiated energy policies based on the characteristics of the industrial structure and technological development of each region. They should actively guide technological innovation, promote industrial diversification, and build a clean and efficient energy system.

Crop mapping and quantitative evaluation of cultivated land use intensity in Shandong Province, 2018–2022

AbstractIndustrialization and urbanization have intensified land‐use pressures on agroecosystems. Monitoring cultivated land use intensity (CLUI) is crucial for implementing sustainable agriculture. However, current agroecosystem management in Shandong Province lacks high‐resolution CLUI information. To address this gap, this study measured and analyzed CLUI at a 1‐km scale in Shandong Province from 2018 to 2022, using self‐produced crop maps and the human appropriation of net primary production (HANPP) framework. The spatial autocorrelation model was used to analyze the spatiotemporal pattern and aggregation characteristics of cultivated land use intensity. The influencing factors of CLUI were analyzed using the propensity score matching method, which helps reduce the interference of confounding factors. The results are as follows: (1) The wheat‐maize planting pattern in Shandong Province has remained relatively stable, with a notable trend toward intensified cultivation in the western region. (2) CLUI exhibited notable spatial and temporal heterogeneity, with low and medium values predominantly located in the western region. CLUI increased from 1.13 to 1.24, exceeding the global average of 0.84. (3) CLUI showed significant spatial aggregation characteristics. In 2018, 2020, and 2022, the western region was mainly characterized by high‐high and high‐low types. In 2019 and 2021, it was mainly characterized by the low‐low type, with less prevalence of low‐high type. The center of gravity of high‐high and low‐high types shifted southwest, whereas that of high‐low and low‐low types shifted northeast. (4) Chemical fertilizers, pesticides, and plastic mulch exhibited significant positive correlations with CLUI, whereas temperature and precipitation showed significant negative correlations. Favorable natural conditions can mitigate human interference, leading to lower CLUI.

Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data

Effective monitoring and management of inland waterbodies depend on reliable assessments of water quality through remote sensing technologies. Match-up analysis plays a significant role in investigating the comparability between in situ and remote sensing data of physical and biogeochemical variables. By exploring different spatial aggregations and temporal windows, we aimed to identify which configurations are most effective and which are less effective for the assessment of remotely sensed water quality data within the context of governmental monitoring programs. Therefore, in this study, remote sensing data products, including the variables of Secchi depth, chlorophyll-a, and turbidity, derived from the Copernicus satellites Sentinel-2 and Sentinel-3, were compared with in situ laboratory data from >100 waterbodies (lakes and reservoirs) in Germany, covering a period of 5 years (2016–2020). Processing was carried out using two different processing schemes, CyanoAlert from Brockmann Consult GmbH and eoapp AQUA from EOMAP GmbH & Co. KG, in order to analyze the influence of different processors on the results. To investigate appropriate spatial aggregations and time windows for validation (the match-up approach), we performed a statistical comparison of different spatial aggregations (1 pixel; 3 × 3, 5 × 5, and 15 × 15 macropixels; and averaging over the whole waterbody) and time windows (same day, ±1 day, and ±5 days). The results show that waterbody-wide values achieved similar accuracies and biases compared with the macropixel variants, despite the large differences in spatial aggregation and spatial variability. An expansion of the temporal window to up to ±5 days did not impair the agreement between the in situ and remote sensing data for most target variables and sensor–processor combinations, while resulting in a marked rise in the number of matches.

Mutual-guided scale-aggregation denoising network for seismic noise attenuation

The background noise contained in seismic records contaminate the effective reflection waves and impact the subsequent processes, such as inversion and migration. The properties of seismic noises, such as non-Gaussianity and non-linearity, will be even more complex in challenging exploration environments. Deep-learning techniques are effective in suppressing complex seismic noises and outperform conventional denoising algorithms. Nonetheless, most deep learning networks are designed to extract the features of input data in single-scale only, which leads to inadequate performance when dealing with complicated seismic data. To enhance the denoising capability for seismic noises of deep learning, a novel mutual-guided scale-aggregation denoising network (MSD-Net) is designed to suppress seismic noises by utilizing the multi-scale features of input data. Specifically, the MSD-Net achieves functions including multi-scale feature extraction, fusion, and guidance through information interaction between different scales. Spatial aggregation attention is used in MSD-Net to enhance relevant features, which improves the separation of effective reflection waves and noises further. Additionally, a model-based training data generation strategy is devised to ensure the efficiency of learning and the denoising capability of MSD-Net. Compared to conventional denoising algorithms and typical deep learning networks, MSD-Net shows powerful result in suppressing complex seismic noises and generalization.

The Impact of Foreign Aid on Local Development: A Grid Cell Analysis

We examine whether geocoded World Bank foreign aid projects affect economic activity at the level of equally sized grid cells with a spatial resolution of 0.5 decimal degrees latitude and longitude. We use geospatial data for 58,676 grid cells during the period 1997-2013 with about one million grid-by-year observations. Using fixed effects estimating techniques, we evaluate the impact of World Bank foreign aid projects on grid cell night-time light growth. The estimates reveal that World Bank foreign aid projects contribute significantly and positively to grid cell economic activity measured by night-time light growth. We complement the fast-growing literature using geocoded data to assess aid effectiveness at the subnational level. We provide evidence that the lack of robust empirical findings at the country and subnational (e.g., ADM1 and ADM2) level can be explained due to spatial aggregation and the choice of spatial units. Additional estimates and robustness tests further corroborate our main findings.

Multimodal spatiotemporal aggregation for point cloud accumulation

Point cloud accumulation is a crucial technique in point cloud analysis, facilitating various downstream tasks like surface reconstruction. Current methods merely rely on raw LiDAR points, yielding unsatisfactory performance due to the limited geometric information, particularly in complex scenarios characterized by intricate motions, diverse objects, and an increased number of frames. In this paper, we introduce camera modality data, which is usually acquired alongside LiDAR data at minimal expense. To this end, we present the Multimodal Spatiotemporal Aggregation solution (termed MSA) to thoroughly explore and aggregate these two distinct modalities (sparse 3D points and multi-view 2D images). Concretely, we propose a multimodal spatial aggregation module to bridge the data gap between different modalities in the Bird’s-Eye-View (BEV) space and further fuse them by learnable adaptive channel-wise weights. By assembling their respective strengths, this module generates a reliable and consistent scene representation. Subsequently, we design a temporal aggregation module to capture continuous motion information across consecutive sequences, which is beneficial for identifying the motion state of the foreground scene and enabling the model to extend robustly to longer sequences. Experiments demonstrate MSA outperforms state-of-the-art (SoTA) point cloud accumulation methods across all evaluation metrics in the public benchmark, especially with more frames.

Testing hypotheses for male aggregations in lek and the implications for sexual selection in the White-throated manakin, Corapipo gutturalis (Aves: Pipridae)

Abstract To understand male spatial aggregation in lek systems, questions have focused on whether males aggregate in areas with higher female abundance, around males that attract more females, or because of a female preference for certain male. For this, we investigated how social organization and male–male interactions are associated with female visitation in leks of the white-throated manakin (Corapipo gutturalis). Specifically, we evaluated whether females prefer higher-ranked males (based on the fidelity to court, display, and male age) and high-activity courts, as predicted by the hotshot hypothesis. In addition, but not mutually exclusive, we evaluated whether multi-male visitation at court and/or shorter spatial aggregation of courts attracted more females. We found that the highest-ranked males received significantly more female and male visits. However, the total activity at courts did not predict female visitation to males, but did predict male visitation to males. Furthermore, female visitation at courts was not predicted by the proportion of multi-male visitation at courts but by spatial aggregation of courts. These results suggest that the hotshot hypothesis illustrated by the male rank at court explains female visitation and male aggregation in leks of this species, generating a system in which males tend to compete for the highest rank in the court.

Topographic and Climatic Factors Effect Spatiotemporal Coupling Relationship of Soil Water Conservation Function with Vegetation in Source of the Yellow River

The Gannan Water Conservation area is an indispensable part of the ecological barrier on the Tibetan Plateau and is a key ecological area for the water supply. Exploring the coupled coordination relationship between vegetation and soil contributes to the conservation and planning of the natural environment. In this study, soil water conservation function (SWCF) was investigated with Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Gannan Water Conservation Area at the source of the Yellow River, and the spatiotemporal coupling relationship between the SWCF and vegetation was explored. Meanwhile, their responses to topographic and climatic factors were investigated with structural equation models. The main results indicated that the coupling coordination degree (DVS) in the soil depth was in a barely coordinated state, with 0–10 cm > 20–30 cm > 10–20 cm, showing that the area proportion of ‘Basic balanced–Synchronous development of VEG and SWCF’ was the highest, and the spatial aggregation feature was obvious. As the gradient of topographic factors varied, the coupling coordination also varied at various soil depths. Meanwhile, the absolute values of the correlation coefficients of the temperature and precipitation with the coupling coordination were the highest at 20–30 cm compared to the other soil depths, demonstrating that the effect was more significant in deeper soils than in shallower ones. Furthermore, the path coefficients of the topographic factors were larger than those of the climatic factors in the 10–20 cm and 20–30 cm layers, while the opposite was true in the 0–10 cm layer. In general, the vegetation conditions and water conservation function of soil in the source area of the Yellow River are basically developing synchronously, and the topographic factor is the key factor for the geographical difference in the coupling relationship between the two factors.

Spatial scale effects on the value of ecosystem services in China's terrestrial area

Identifying the response characteristics of ecosystem service value (ESV) to changes in spatial scales, known as spatial scale effects, is crucial in guiding the development of corresponding management strategies. This paper examines ESV in China's terrestrial area during the year 2020, revealing the spatial aggregation characteristics of ESV and the trade-off and synergistic relationships of ecosystem services at different spatial scales, ranging from 1 km × 1 km–10 km × 10 km, with a gradient of 1 km. The results indicate: 1) The distribution pattern of ESV in China's terrestrial area is "high in the southeast and low in the northwest." 2) The spatial characteristics of ESV in China's terrestrial area undergo a distinct transition at the 3 km × 3 km scale. In detail, the spatial clustering features show a trend of first rising and then falling with the increase in spatial scale, while the synergistic relationships between different ecosystem services strengthen and the trade-off relationships weaken with the increase of the spatial scale. These findings can inform the formulation of differentiated ecological protection compensation policies and enable cross-area trading of ecological values in China.

Evaluation of Ecological Environment Quality and Analysis of Influencing Factors in Wuhan City Based on RSEI

It is crucial to assess the quality of ecological environments in urban areas and investigate the driving forces that would affect urban ecological environments. Utilizing the GEE platform, RSEI was computed by us for Wuhan from 1990 to 2020. Employing geodetector tools and the PLS-SEM approach, driving factors for ecological environment quality in Wuhan were discussed. The overall trend of ecological environment quality in Wuhan was to decline at first and then rise from 1990 to 2020 spatial aggregation characteristics of RSEI were significant; moreover, land use, location, population density, and GDP were included as the main influence factors causing spatial differentiation of RSEI; each influence factor’s effect was also different. Over the past three decades, a fluctuating decline has been exhibited by ecological environment quality in Wuhan. Central urban areas have poor ecological environment quality, while southern and northern distant urban zones have superior ecological environment quality. Clustering is shown to be significant spatially by both. The main influencers of ecological quality in Wuhan are human geographic factors, while natural geographic factors have comparatively minor impacts.

Spatial Prediction and Influencing Factors Analysis of Soil Salinization in Coastal Area Based on MGWR

Quantitative analysis of the spatial non-stationary characteristics of soil salinization influencing factors and the prediction of its spatial distribution are of great significance for the rational use of coastal saline soil resources and the formulation of local prevention and control measures. In this study, the Hekou District of Dongying City, Shandong Province, was used as the study area, and the descriptive statistics of soil salinization status were conducted using classical statistical methods. Spatial autocorrelation theory was used to explore the characteristics of global and local spatial structure of soil salinization in the study area. Influential factors related to soil salinity were selected, and multivariate linear regression （MLR）, geographically weighted regression （GWR）, and multi-scale geographically weighted regression （MGWR） methods were used to model and predict the spatial distribution of soil salinity in the study area and to analyze the spatial heterogeneity of the effects of different influencing factors on soil salinity. The results showed that： ① The mean value of soil salinity in the study area was 5.84 g·kg-1, indicating severe salinization, with a global Moran's I index of 0.19 （P<0.00） and obvious spatial aggregation characteristics. ② Among the three models, the MGWR model had the highest modeling accuracy. Compared with that of the MLR model, the Radj2 of GWR and MGWR improved by 0.05 and 0.07, respectively, and the RSS decreased by 210.13 and 179.95, respectively. ③ The results of MGWR regression showed that the spatial distribution of soil salinity appeared to be mainly affected by the middle soil salinity, soil clay content, and vegetation cover from the mean values of standardized regression coefficients of different influencing factors. Different influencing factors had significant spatial non-stationary characteristics on soil salinization. ④ The results of the spatial distribution prediction of soil salinity in MGWR showed that the areas of high soil salinity （≥6 g·kg-1） were mainly distributed in the northern part of the study area, with an overall spatial trend of decreasing from the coast to the interior. The results of the study can be used as a reference for the analysis and predictive mapping of factors affecting soil salinization in the county and on a larger scale using MGWR.

Spatiotemporal Evolution of Habitat Quality and Scenario Modeling Prediction in the Tuha Region

In recent years, increasing urbanization has profoundly impacted the quality of regional habitats, presenting a severe risk to the ability of a region to develop in a high-quality manner. Therefore, the scientific assessment of the features of habitat quality (HQ) evolution over time and space and the prediction of future trends in changes in the HQ are of great significance for the formulation of effective ecological protection policies. Based on five periods of land use and land cover (LULC) data from 2000 to 2020, InVEST model was used to estimate both geographical and chronological trends in the HQ in the Tuha region, China. Spatial autocorrelation analysis methods were used to assess HQ and spatial aggregation of habitat degradation, and ecological zoning was delineated in conjunction with the Human Footprint Index (HFI). Based on the results of ecological zoning, the study predicted changes in habitat quality (HQ) in 2040 under three scenarios: natural development (ND), ecological preservation (EP), and urban development (UD) by applying the Patch-Generating Land Use Simulation (PLUS) model. The results demonstrated that (1) from 2000 to 2020, the habitat quality in the Tuha region exhibited a downward trend, with the proportion of low HQ increasing from 83.63% to 84.24%. Spatially, high habitat quality (HQ) is mainly concentrated in the Tianshan Mountains. From 2000 to 2020, the Moran index for habitat quality (HQ) decreased from 0.967 to 0.959, while the Moran index for habitat degradation declined from 0.805 to 0.780. The habitat quality (HQ) and degradation exhibit significant spatial aggregation, and the degree of degradation has increased incrementally. (2) From 2000 to 2020, human activities in the Tuha area increased continuously and were mainly concentrated in Tuha district and counties. The proportion of high Human Footprint Index (HFI) increased from 0.66% to 1.32%, while the proportion of medium HFI increased from 3.13% to 7.46%. (3) The expansion of urbanized land has exacerbated habitat degradation. The proportion of high HQ in the EP scenario is higher than that in ND and UD scenario. The results show that the ecological protection scenario is more conducive to the sustainable development of habitat quality in the Tuha region. The results can provide a scientific basis for ecological management and protection in the Tuha area.

Slow recovery rates and spatial aggregation of Triatoma infestans populations in an area with high pyrethroid resistance in the Argentine Chaco

BackgroundThe emergence of pyrethroid resistance has threatened the elimination of Triatoma infestans from the Gran Chaco ecoregion. We investigated the status and spatial distribution of house infestation with T. infestans and its main determinants in Castelli, a municipality of the Argentine Chaco with record levels of triatomine pyrethroid resistance, persistent infestation over 2005–2014, and limited or no control actions over 2015–2020.MethodsWe conducted a 2-year longitudinal survey to assess triatomine infestation by timed manual searches in a well-defined rural section of Castelli including 14 villages and 234 inhabited houses in 2018 (baseline) and 2020, collected housing and sociodemographic data by on-site inspection and a tailored questionnaire, and synthetized these data into three indices generated by multiple correspondence analysis.ResultsThe overall prevalence of house infestation in 2018 (33.8%) and 2020 (31.6%) virtually matched the historical estimates for the period 2005–2014 (33.7%) under recurrent pyrethroid sprays. While mean peridomestic infestation remained the same (26.4–26.7%) between 2018 and 2020, domestic infestation slightly decreased from 12.2 to 8.3%. Key triatomine habitats were storerooms, domiciles, kitchens, and structures occupied by chickens. Local spatial analysis showed significant aggregation of infestation and bug abundance in five villages, four of which had very high pyrethroid resistance approximately over 2010–2013, suggesting persistent infestations over space-time. House bug abundance within the hotspots consistently exceeded the estimates recorded in other villages. Multiple regression analysis revealed that the presence and relative abundance of T. infestans in domiciles were strongly and negatively associated with indices for household preventive practices (pesticide use) and housing quality. Questionnaire-derived information showed extensive use of pyrethroids associated with livestock raising and concomitant spillover treatment of dogs and (peri) domestic premises.ConclusionsTriatoma infestans populations in an area with high pyrethroid resistance showed slow recovery and propagation rates despite limited or marginal control actions over a 5-year period. Consistent with these patterns, independent experiments confirmed the lower fitness of pyrethroid-resistant triatomines in Castelli compared with susceptible conspecifics. Targeting hotspots and pyrethroid-resistant foci with appropriate house modification measures and judicious application of alternative insecticides with adequate toxicity profiles are needed to suppress resistant triatomine populations and prevent their eventual regional spread.Graphical

Using spatial aggregation of soil multifunctionality maps to support uncertainty‐aware planning decisions

AbstractTo 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.

Refining urban morphology: An explainable machine learning method for estimating footprint-level building height

Fine-grained 3D buildings with diverse morphology are a cornerstone of urban physical structures and have profound implications for sustainable city development. However, accurately estimating building height at the footprint-level is a challenge. This study bridged this gap by using random forest models to integrate the elevation, geometry and shape attributes of individual buildings, further refining those with spatial aggregation. It considered over one million buildings across 10 large Chinese cities and trained two-types models that demonstrated commendable performance in city-specific (the mean absolute error (MAE) ranged from 3.43 m to 5.06 m) and combined (MAE = 4.68 m) models. Results revealed that the current dataset had a finer urban morphology compared with existing datasets and showed outstanding generalisability in method transfer and feature ablation tests. By incorporating Shapley values, we explored the features' global and local impacts. The explainable results demonstrated that building area was the most impactful feature, and the elevation-dimension features were particularly beneficial in estimating high-rise buildings. Using the fine-grained 3D buildings, we explored the connections between explicit morphology differences and implicit contexts in cities. Overall, our work is an endeavour to estimate footprint-level building height as fuel for refining urban morphology and enabling sustainable city studies.

Leaf area predicts conspecific spatial aggregation of woody species

AbstractAimAddressing 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.LocationGlobal synthesis.Time Period1997–2022.Major Taxa StudiedWoody angiosperms and conifers.MethodsWe 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.ResultsWe 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 ConclusionsCompared 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.

Statistical and density-based clustering of geographical flows for crowd movement patterns recognition

With the rapid development of sensors and communication technologies, it has become easy to collect large-scale and long-term crowd movement positioning data, which brings new opportunities for studying crowd movement patterns. This paper proposes a novel Statistical and Density-Based Clustering algorithm (SDBC) to identify implicit significant spatial aggregation patterns in geographical flow data. Unlike existing flow clustering algorithms, this method identifies the hot spots of origin-destination (OD) flows based on local spatial statistics and density-growing clustering. It also evaluates the significance of the identified geographic flow clusters, effectively reducing the identification of spurious clusters generated by chance in data. In our method, the spatial neighborhood of each flow is first obtained based on spatial proximity, temporal similarity, and directional similarity. Then, the number of flows in the spatial neighborhood of each flow is calculated and used as the density measure. Based on this, high-density flows are automatically detected using local spatial aggregation statistics, and a hierarchical density-based clustering strategy is developed to merge adjacent high-density flows to generate candidate flow clusters. Finally, we perform permutation tests to infer the statistical significance of each flow cluster and eliminate the candidate clusters generated by chance. Experiments on synthetic data and real-world taxi trajectory data were conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method can accurately identify the statistically significant flow clusters of different shapes and densities and performs better than the available state-of-the-art flow clustering algorithms.

Spatial-temporal characteristics and driving factors of carbon emissions from the construction industry in the Belt and Road region of China

To promote sustainable economic development in the Belt and Road region of China, reducing carbon emissions is essential. The construction industry is a major contributor to carbon emissions in China. Therefore, studying the dynamic evolution of carbon emissions from the construction industry in this region and its driving factors is of great significance for effectively controlling emissions and achieving China’s carbon peak and carbon neutrality targets. This paper first employs the Slope model, Moran’s I index, and standard deviation ellipse to reveal the spatial-temporal characteristics of carbon emissions from the construction industry, and then applies the geographical detector model to identify the main driving factors of carbon emissions. The results indicate that: (1) From 2006 to 2021, the total carbon emissions showed a fluctuating growth trend, and there were significant differences in emissions among different regions. (2) Carbon emissions in most provinces exhibited a moderate growth trend, and there was significant spatial correlation and aggregation of inter-provincial emissions. Regional carbon emissions from 2006 to 2021 showed a spatial distribution pattern from northeast to southwest, with a weakening trend, and the center of gravity mainly distributed in the east of the region. (3) Labor input, urbanization rate, total output value of the construction industry, degree of opening up, and energy intensity are the main factors influencing the spatial heterogeneity of carbon emissions from the construction industry, and the majority of the interaction types between factors were bivariate enhancement. This study aims to provide theoretical support for policymakers to formulate appropriate policies for building energy conservation and emission reduction.

Comprehensive assessment of refined greenhouse gas emissions from China's livestock sector

Livestock and poultry products are an essential human food source. However, the rapid development of the livestock sector (LS) has caused it to become a significant source of greenhouse gas (GHG) emissions. Consequently, investigating the spatio-temporal characteristics and evolution of GHG emissions is crucial to facilitate the green development of the LS and achieve “peak carbon and carbon neutrality”. This study combined life cycle assessment (LCA) with the IPCC Tier II method to construct a novel GHG emissions inventory. The GHG emissions of 31 provinces in China from 2000 to 2021 were calculated, and their spatio-temporal characteristics were revealed. Then, the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model was used to identify the main driving factors of GHG emissions in six regions of China and explore the emission reduction potential. The results showed that GHG emissions increased and then decreased from 2000 to 2021, following a gradual and steady trend. The peak of 628.55 Mt CO2-eq was reached in 2006. The main GHG-producing segments were enteric fermentation, slaughtering and processing, and manure management, accounting for 45.39 %, 26.34 %, and 23.08 % of total GHG emissions, respectively. Overall, the center of gravity of GHG emissions in China migrated northward, with spatial aggregation observed since 2016. The high emission intensity regions were mainly located west of the “Hu Huanyong line”. Economic efficiency and emissions intensity were the main drivers of GHG emissions. Under the baseline scenario, GHG emissions are not projected to peak until 2050. Therefore, urgent action is needed to promote the low-carbon green development of the LS in China. The results can serve as scientific references for the macro-prevention and control of GHG emissions, aiding strategic decision-making. Additionally, they can provide new ideas for GHG accounting in China and other countries around the world.