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The distribution of surface particulate matter (PM2.5) exhibits significant spatiotemporal patterns, especially with a spatial clustering effect. Therefore, resolving spatial characteristics is essential in the modeling process. This study proposes a space–time varying coefficients (STVC-LG) model by considering spatial effects from the perspective of eigenvector spatial filtering and incorporating spatial effects and temporal features into the LightGBM model to estimate ground PM2.5. A site-based cross-validation result of the estimated daily 1 km PM2.5 concentration in China from 2016 to 2020 shows that the proposed model offers high accuracy, an R2 of 0.88, and an RMSE of 12.21 μg/m3 (17% and 32% enhanced compared to the original LightGBM model). It uses a series of spatial eigenvectors to describe different spatial patterns. It then constructs spatial interaction terms by combining the eigenvectors and influencing variables, allowing the values of the variable to vary spatially with the eigenvectors. The obtained PM2.5, with a decreasing trend, had a clear distributional consistency with NO2 and SO2 according to the co-occurrence distribution maps, particularly in winter. In areas where high concentrations co-occur, both NO2 and SO2 concentrations are Granger-causing PM2.5 concentrations from a statistical perspective.

Accurate and continuous PM2.5 data is essential for effective prevention of PM2.5 pollution. Despite the achievements of deep learning methods in estimating PM2.5 concentrations, existing neural network models have relied too much on the self-learning capability and have ignored geographic patterns of PM2.5. Few have taken a geographic perspective when modeling PM2.5, resulting in lower model interpretability. In this paper, rather than inputting spatiotemporal information directly into the networks, we propose an improved geographic pattern based residual neural network (IGeop-ResNet) for estimating PM2.5 concentrations in the Beijing-Tianjin-Hebei region (BTH) of China considering spatial heterogeneity and spatial autocorrelation by introducing spatial eigenvector and attention mechanism, as well as the encoding and embedding methods for temporal categorical variables. A DEM-weighted loss function was introduced to enhance the spatial predictive ability, particularly in high-altitude regions. The results show that the IGeop-ResNet model achieves excellent spatial predictive abilities (R2 of 0.925 in terms of station-based cross-validation) and offers a certain level of interpretability compared to the Ori-STResNet (ordinary directly inputs temporal and spatial information in the ResNet model) and the Geop-ResNet model (without the DEM-weighted loss function). Continuous maps derived from the IGeop-ResNet model suggest the PM2.5 concentrations in the BTH region exhibited a downward trend from 2015 to 2018 and experienced a sharp drop in 2017. The results indicate that NO2 is the Granger cause of PM2.5, while the relationship between SO2 and PM2.5 is insignificant.

Forest canopy height (FCH) is crucial for monitoring forest structure and aboveground biomass. Light detecting and ranging (LiDAR), as a promising remote sensing technology, provides various forms of data for measuring and mapping FCH. Airborne laser scanning (ALS) could accurately measure FCH at the plot-level. Spaceborne lidar system (SLS) allows for global sampling of FCH at the footprint-level. However, ALS data has limited spatial coverage, while SLS data has relatively lower estimation accuracy. To this end, we proposed a two-step FCH mapping framework by combining ALS, SLS and auxiliary data. Firstly, using the ALS-derived FCH as reference, the SLS-derived relative height metrics were calibrated at the footprint-level using a regression method. Secondly, to further address the spatial discontinuities in SLS-derived FCH maps, a site-level FCH model was built using a weighted ensemble multi-machine learning model incorporating spatial effects (WEML_SE). The calibrated footprint-level calibration FCH model was used as a reference, and multiple remote sensing data metrics were selected and subjected to important variable selection. Specifically, a spatial adjacency matrix was established based on the spatial locations of SLS footprints, and spatial feature vectors were extracted. The result indicated that the correlation coefficient between the SLS-derived FCH and the ALS-derived FCH (r = 0.39–0.73, MRE=10.6–25.9 %, and RMSE=2.58–9.37 m) improved at footprint-level (r = 0.71–0.84, MRE=7.7–18.7 %, RMSE=1.96–7.68 m). Moreover, the WEML_SE exhibited better performance (r = 0.59–0.75, MRE=8.8–14.8 %, RMSE=2.12–5.4 m) compared to the model without incorporating spatial effects (r = 0.45–0.71, MRE=9.4–15.8 %, RMSE=2.28–5.89 m). This study emphasizes the advantages of integrating spaceborne and airborne LiDAR data to construct footprint-level estimation of FCH. The proposed WEML_SE model provides new possibilities for accurately generating wall-to-wall estimates of forest biomass.

Abstract Global climate patterns fundamentally shape the distribution of species and ecosystems. For example, Bergmann’s rule predicts that homeothermic animals, including birds and mammals, inhabiting cooler climates are generally larger than close relatives from warmer climates. The modern world, however, lacks the comparative data needed to evaluate such macroecological rules rigorously. Here, we test for Bergmann’s rule in Mesozoic dinosaurs and mammaliaforms that radiated within relatively temperate global climate regimes. We develop a phylogenetic model that accounts for biases in the fossil record and allows for variable evolutionary dispersal rates. Our analysis also includes new fossil data from the extreme high-latitude Late Cretaceous Arctic Prince Creek Formation. We find no evidence for Bergmann’s rule in Mesozoic dinosaurs or mammaliaforms, the ancestors of extant homeothermic birds and mammals. When our model is applied to thousands of extant dinosaur (bird) and mammal species, we find that body size evolution remains independent of latitude. A modest temperature effect is found in extant, but not in Mesozoic, birds, suggesting that body size evolution in modern birds was influenced by Bergmann’s rule during Cenozoic climatic change. Our study provides a general approach for studying macroecological rules, highlighting the fossil record’s power to address longstanding ecological principles.

Agricultural land use and temperature change can profoundly affect soil organic carbon (SOC), but their combined effects on regulating cropland SOC fractions over broad geographic scales remain poorly understood. Based on 871 topsoil (0–20 cm) samples collected from the Sichuan Basin of China, we used random forest model and partial correlations analysis to explore how the functionally distinct SOC fractions, including particulate and mineral-associated organic C (POC and MAOC), are affected by both agricultural land use and temperature over a large scale. The results show that POC accounts for 32.54 % of the total cropland SOC and soils with high SOC contents have larger proportions of POC. Cropland type is the dominant control of the two SOC fractions and regulates the impacts of other factors. The contents of soil POC and MAOC in dryland are much lower than that in paddy field and dryland-paddy rotation land. Significant correlations between mean annual temperature and the two SOC fractions are only found in dryland and dryland-paddy rotation land, while mean annual temperature has no impact on both POC and MAOC in paddy field. Meanwhile, the correlation coefficients between mean annual temperature and POC and MAOC for dryland decreased by 61.90 % and 69.23 %, respectively, and for dryland-paddy rotation land decreased by 28.00 % and 14.29 %, respectively, after removing correlations with net primary productivity. Additionally, correlation between mean annual temperature and POC is slightly higher than that between mean annual temperature and MAOC in dryland, while the reverse in dryland-paddy rotation land. These results suggest that the temperature effects on POC and MAOC are cropland type-dependent. To accurately predicting future cropland SOC change and reliably evaluate the potential contribution of cropland soil C sequestration, fraction-specific control mechanisms related to cropland type should be sufficiently considered in carbon models.

Abstract The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people’s homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals’ obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people’s use of dynamic food environments and links to diet and health.

Intraoperative neurophysiological monitoring (IONM) provides data on the state of neurological functionality. However, the current state of technology impedes the reliable and timely extraction and communication of relevant information. Advanced signal processing and machine learning (ML) technologies can develop a robust surveillance system that can reliably monitor the current state of a patient's nervous system and promptly alert the surgeons of any imminent risk. Various ML and signal processing tools can be utilized to develop a real-time, objective, multi-modal IONM based-alert system for spine surgery. Next generation systems should be able to obtain inputs from anesthesiologists on vital sign disturbances and pharmacological changes, as well as being capable of adapting patient baseline and model parameters for patient variability in age, gender, and health. It is anticipated that the application of automated decision guiding of checklist strategies in response to warning criteria can reduce human work-burden, improve accuracy, and minimize errors.

Abstract This article begins by briefly examining the multitude of ways in which climate and climate change affect human health and wellbeing. It then proceeds to present a quick overview of how geospatial data, methods and tools are playing key roles in the measurement, analysis and modelling of climate change and its effects on human health. Geospatial techniques are proving indispensable for making more accurate assessments and estimates, predicting future trends more reliably, and devising more optimised climate change adaptation and mitigation plans.

Scale conversion between DEMs is an important issue in geomorphometry. There are many mature studies on the generation of low-resolution(LR) DEMs from high-resolution(HR) DEMs. However, as an important and convenient means of obtaining HR DEMs, traditional super resolution (SR) methods have shown insufficient consideration of the terrain features embedded in DEMs. Therefore, this article investigates the combination of terrain features and the use of convolutional neural networks (CNN) to reconstruct HR DEMs, and proposes a multi-terrain feature-based deep CNN for super-resolution(SR) DEMs (MTF-SR). In the experiments, from the perspective of vector and raster terrain features, we fuse raster terrain features in the input and loss functions, and fuse vector terrain features in the optimization of the output of the model. The results show that the MTF-SR model has a 30–50 % reduction in mean absolute error (MAE) compared with interpolation methods, has the lowest slope and aspect error and has a 10 to 30 % improvement in streamline matching rate (SMR). These results point to the advantages of the method in overall elevation accuracy and the preservation of terrain features.

Abstract Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM2.5) for 10 patients with asthma from 7 households near Salt Lake City, UT; the patients were followed for up to 373 days each. Compared to previous SOM algorithms using timestamp alignment on time series data, the DTW-SOM algorithm produced fewer quantization errors and more detailed diurnal patterns. DTW-SOM identified the expected typical diurnal patterns in outdoor temperature which varied by season, as well diurnal patterns in PM2.5 which may be related to daily asthma outcomes. In summary, DTW-SOM is an innovative feature engineering method that can be applied to highly time-resolved environmental exposures assessed by sensors to identify typical diurnal (or hourly or monthly) patterns and provide new insights into the health effects of environmental exposures.

The eucentrosauran centrosaurines Einiosaurus procurvicornis and Achelousaurus horneri are the two most commonly recovered ceratopsids from the Campanian Two Medicine Formation of northwestern Montana, USA. Einiosaurus procurvicornis is known from at least 15 individuals recovered from two monospecific bonebeds, while Achelousaurus horneri is primarily known from one articulated adult cranium as well as two isolated subadult individuals previously referred to the taxon. Previous assessments of ontogeny in these taxa, alongside closely related centrosaurines, focused primarily on crania of mature individuals and disarticulated elements of immature individuals. Here we describe an articulated subadult Einiosaurus procurvicornis skull (MOR 456 8-8-87-1) from the Einiosaurus procurvicornis type locality bonebed and compare its cranial ornamental development with the only identically sized articulated subadult eucentrosauran skull from the Two Medicine Formation, MOR 591. These individuals represent the only known articulated subadult skulls from the hypothesized eucentrosauran lineage in the Two Medicine Formation, thereby enabling comparison of early ontogenetic developmental sequence and timing of all three primary cranial ornaments (nasal, supraorbital, and parietosquamosal frill). Comparison indicates that parietosquamosal frill and supraorbital ornamentation development may have preceded nasal horncore development in these taxa. MOR 456 8-8-87-1 fills a gap between the plesiomorphic morphology of juvenile Einiosaurus procurvicornis supraorbital horncores and the rounded, spheroid mass of bone which characterizes adults. The complete left squamosal of MOR 456 8-8-87-1 is of adult size, in contrast to its shorter face and immature facial ornamentation, which suggests that in Einiosaurus procurvicornis, the face and facial ornamentation development occurred after the parietosquamosal frill had reached adult size.

Accurate snow water equivalent (SWE) products are vital for monitoring hydrological processes and managing water resources effectively. However, the coarse spatial resolution (typically at 25 km from passive microwave remote sensing images) of the existing SWE products cannot meet the needs of explicit hydrological modeling. Linear regression ignores the spatial autocorrelation (SA) in the variables, and the measure of SA in the data assimilation algorithm is not explicit. This study develops a Resolution-enhanced Multifactor Eigenvector Spatial Filtering (RM-ESF) method to estimate daily SWE in the western United States based on a 6.25 km enhanced-resolution passive microwave record. The RM-ESF method is based on a brightness temperature gradience algorithm, incorporating not only factors including geolocation, environmental, topographical, and snow features but also eigenvectors generated from a spatial weights matrix to take SA into account. The results indicate that the SWE estimation from the RM-ESF method obviously outperforms other SWE products given its overall highest correlation coefficient (0.72) and lowest RMSE (56.70 mm) and MAE (43.88 mm), compared with the AMSR2 (0.33, 131.38 mm, and 115.45 mm), GlobSnow3 (0.50, 100.03 mm, and 83.58 mm), NCA-LDAS (0.48, 98.80 mm, and 81.94 mm), and ERA5 (0.65, 67.33 mm, and 51.82 mm), respectively. The RM-ESF model considers SA effectively and estimates SWE at a resolution of 6.25 km, which provides a feasible and efficient approach for SWE estimation with higher precision and finer spatial resolution.

Abstract While the spatial resolution of exposure surfaces has greatly improved, our ability to locate people in space remains a limiting factor in accurate exposure assessment. In this case‐control study, two approaches to geocoding participant locations were used to study the impact of geocoding uncertainty on the estimation of ambient pesticide exposure and breast cancer risk among women living in California's Central Valley. Residential and occupational histories were collected and geocoded using a traditional point‐based method along with a novel area‐based method. The standard approach to geocoding uses centroid points to represent all geocoded locations, and is unable to adapt exposure areas based on geocode quality, except through the exclusion of low‐certainty locations. In contrast, area‐based geocoding retains the complete area to which an address matched (the same area from which the centroid is returned), and therefore maintains the appropriate level of precision when it comes to assessing exposure by geography. Incorporating the total potential exposure area for each geocoded location resulted in different exposure classifications and resulting odds ratio estimates than estimates derived from the centroids of those same areas (using a traditional point‐based geocoder). The direction and magnitude of these differences varied by pesticide, but in all cases odds ratios differed by at least 6% and up to 35%. These findings demonstrate the importance of geocoding in exposure estimation and suggest it is important to consider geocode certainty and quality throughout exposure assessment, rather than simply using the best available point geocodes.

Superblocks can help to transform urban areas into pedestrian-centric neighborhoods. First launched in Barcelona, Spain, they are expected to reduce harmful environmental exposures, increase green space access and thereby provide substantial health benefits. However, few studies have examined the practicality and likely benefits of implementing Superblocks in other metropolitan areas. We developed a methodological framework to build a generalizable City of Los Angeles (LA) Superblocks Model and evaluate the predicted health benefits that would follow such an intervention. We derived and used five rules to guide the choice of arterial streets and candidate blocks and the choice of major bounding streets that could facilitate mobility across the metropolitan area. We next used the BenMap-CE model to perform a quantitative assessment of the health and economic benefits that would accompany five scenarios that would transform 5–50% of the residential areas in the City of LA to Superblocks. We found that the creation of superblocks resulted in significant reductions in hospital admissions and significant economic savings. The benefits were strongest when 5–10% of residential areas were transformed, but rapidly decreased as the threshold reached 30%. These results will help stakeholders determine the optimal balance between reduced car traffic and improved health outcomes. Moreover, we illustrated how to develop a Superblocks model for a highly versatile and populated metropolitan area like the City of LA and how the model can be used to assess the potential health benefits and benchmark the relationship between the scale of the Superblock implementation and the accompanying health benefits moving forward.

Human mobility influenced the spread of the COVID-19 virus, as revealed by the high spatiotemporal granularity location service data gathered from smart devices. We conducted time series clustering analysis to delineate the relationships between human mobility patterns (HMPs) and their social determinants in California (CA) using aggregated smart device tracking data from SafeGraph. We first identified four types of temporal patterns for five human mobility indicator changes by applying dynamic-time-warping self-organizing map clustering methods. We then performed an analysis of variance and linear discriminant analysis on the HMPs with 17 social, economic, and demographic variables. Asians, children under five, adults over 65, and individuals living below the poverty line were found to be among the top contributors to the HMPs, including the HMP with a significant increase in the median home dwelling time and the HMP with emerging weekly patterns in full-time and part-time work devices. Our findings show that the CA shelter-in-place policy had varying impacts on HMPs, with socially disadvantaged places showing less compliance. The HMPs may help practitioners to anticipate the efficacy of non-pharmaceutical interventions on cases and deaths in pandemics.

The scale of digital elevation models (DEMs) is vital for terrain analysis, surface simulation, and other geographic applications. Compared to traditional super-resolution (SR) methods, deep convolutional neural networks (CNNs) have shown great success in DEM SR. However, in terms of these CNN-based SR methods, the features extracted by the stackable residual modules cannot be fully utilized as the depth of the network increases. Therefore, our study proposes an enhanced residual feature fusion network (ERFFN) for DEM SR. The designed residual fusion module groups four residual modules to make better use of the local residual features. Meanwhile, the residual structure is refined by inserting a lightweight enhanced spatial residual attention module into each basic residual block to further strengthen the efficiency of the network. Considering the continuity of terrain features, terrain weight modules are integrated into the loss module. Based on two large-scale datasets, our ERFFN shows a 10–20% reduction in the mean absolute error and the lowest error in terrain features, such as slope, demonstrating the superiority of an ERFFN-based DEM SR over state-of-the-art methods. Finally, to demonstrate potential value in real-world applications, we deploy the ERFFN to reconstruct a large geographic area covering 44,000 km2 which contains missing parts.

Satellite observations show that the rapid urbanization and emergence of megacities with 10 million or more residents have raised PM2.5 concentrations across the globe during the past few decades. This study examines PM2.5 dynamics for the 33 cities included on the UN list of megacities published in 2018. These megacities were classified into densely (>1500 residents per km2), moderately (300–1500 residents per km2) and sparsely (35 μg/m3). In 2016, the least polluted megacity was New York and most polluted megacity was Delhi whose average annual PM2.5 concentration of 110 μg/m3 is nearly three times the WHO’s non-attainment threshold.

Ceratopsids are among the most ubiquitous dinosaur taxa from the Late Cretaceous terrestrial formations of the Western Interior of North America, comprising two subfamilies, Chasmosaurinae and Centrosaurinae. The Two Medicine Formation of northwestern Montana has produced numerous remains of centrosaurine dinosaurs, which represent three taxa previously considered valid: Rubeosaurus ovatus, Einiosaurus procurvicornis and Achelousaurus horneri. Here, we reassess the previous referral of specimens to Rubeousaurus ovatus and demonstrate that this taxon is represented solely by its holotype specimen, which was first diagnosed as Styracosaurus ovatus. One of the specimens previously referred to ‘Rubeosaurus’ ovatus instead represents a new eucentrosauran centrosaurine taxon diagnosed here, Stellasaurus ancellae gen. et sp. nov. Stellasaurus expresses a unique combination of eucentrosauran centrosaurine characters, including an elongate nasal horncore, diminutive supraorbital horncores, and a parietal bearing straight, elongate P3 processes, semi-elongate P4 processes and non-elongate P5, P6 and P7 processes. Within the stratigraphic succession of Eucentrosaura, Stellasaurus occurs intermediate to Styracosaurus albertensis and Einiosaurus, and likewise reflects intermediate morphology. Assessed within the stratigraphic, geographical, taphonomic, ontogenetic and phylogenetic framework of Unified Frames of Reference, we fail to reject the hypothesis that Stellasaurus ancellae represents a transitional taxon within an anagenetic lineage of eucentrosauran centrosaurines.

Whilst increasing mental workload has been shown to have a detrimental effect on cycling performance and more generally to increase the risk of harm, no studies have measured how mental workload changes as a function of ultra-distance cycling, indoors or outdoors. Our objective was to measure the difference in mental workload, as indicated by changes in EEG theta power, components of HRV and psychomotor vigilance and as reported using the ‘NASA Task Load Index questionnaire’, before and after a 5 h indoor ride and outdoor ride completed at 65% of functional threshold power. Results of the NASA-TLX indicated the mental demand of outdoor cycling to be significantly less than that of indoor cycling. There were significant differences in the PVT results between the pre and the post outdoor ride average and median response times. The slowest 10% PVT responses were significantly slower pre than post the indoor ride. There were significant differences in HRV between pre and post outdoor and indoor rides, specifically, in the average RR intervals, RMSSD (ms2), LFPower (ms2), NN50. There were modest changes in indicators of mental workload during an ultra-distance cycle ride. As such, mental workload during ultra-distance cycling is unlikely to be a contributory factor to decreases in performance or to an increased likelihood of accident and injury.

Particulate matter (PM2.5) is a significant public health concern in China, and the Chinese government has implemented a series of laws, policies, regulations, and standards to improve air quality. This study documents the changes in PM2.5 and evaluates the effects of industrial transformation and clean air policies on PM2.5 levels in urban and suburban areas of China’s three largest urban agglomerations, Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) based on a new degree of urbanization classification method. We used high-resolution PM2.5 concentration and population datasets to quantify the differences in PM2.5 concentrations in urban and suburban areas of these three urban agglomerations. From 2000 to 2020, the urban areas have expanded while the suburban areas have shrunk. PM2.5 concentrations in urban areas were approximately 32, 10, and 7 μg/m3 higher than those in suburban areas from 2000 to 2020 in BTH, YRD, and PRD, respectively. Since 2013, the PM2.5 concentrations in the urban regions of BTH, YRD, and PRD have declined at average annual rates of 7.30, 5.50, and 5.03 μg/m3/year, respectively, while PM2.5 concentrations in suburban areas have declined at average annual rates of 3.11, 4.23 and 4.69 μg/m3/year, respectively. By 2018, all of the urban and suburban areas of BTH, YRD, and PRD satisfied their specific targets in the Air Pollution and Control Action Plan. By 2020, the PM2.5 declines of BTH, YRD, and PRD exceeded the targets by two, three, and four times, respectively. However, the PM2.5 exposure risks in urban areas are 10–20 times higher than those in suburban areas. China will need to implement more robust air pollution mitigation policies to achieve the World Health Organization’s Air Quality Guideline (WHO-AQG) and reduce long-term PM2.5 exposure health risks.

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

Drucker’s saying that “What gets measured gets managed” is examined in the context of corporate social responsibility. The United Nations’ Sustainable Development Goals have encouraged sustainability reporting, and a reporting tool, the Social and Human Capital Protocol, has been developed to assist measurement and provide information to support the achievement of sustainability. This information should be valid and reliable; however, it is not easy to measure social and human capital factors. Additionally, companies use a large number of methodologies and indicators that are difficult to compare, and they may sometimes only present positive outcomes as a form of greenwashing. This lack of full transparency and comparability with other companies has the potential to discredit their reports, thereby supporting the claims of climate change deniers, free-market idealogues and conspiracy theorists who often use social media to spread their perspectives. This paper will describe the development of environmental reporting and CSR, discuss the natural capital protocol, and assess the extent to which the Social and Human Capital Protocol is able to fulfil its purpose of providing SMART objective measurements. It is the first academic article to provide a detailed examination of the Social and Human Capital Protocol.

High-resolution DEMs are important spatial data, and are used in a wide range of analyses and applications. However, the high cost to obtain high-resolution DEM data over a large area through sensors with higher precision poses a challenge for many geographic analysis applications. Inspired by the convolution neural network (CNN) excellent performance in super-resolution (SR) image analysis, this paper investigates the use of deep residual neural networks and low-resolution DEMs to generate high-resolution DEMs. An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic high-resolution DEM. The results were compared with those generated with the bicubic, bilinear, and EDSR methods. The numerical accuracy and terrain feature preserving effects of the EDEM-SR method can generate reconstructed DEMs that better match the original DEMs, show lower MAE and RMSE, and improve the accuracy of the terrain parameters. MAE is reduced by about 30 to 50% compared with traditional interpolation methods. The results show how the EDEM-SR method can generate high-resolution DEMs using low-resolution DEMs.

Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R2 = 0.98). The ordinary least squares (OLS) (mean R2 = 0.86) and spatial error model (SEM) (mean pseudo R2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.

Research of the main university stakeholders has only been of a cross-sectional or short-term nature thereby limiting our understanding of how universities have evolved as a result of stakeholder influence. Indeed, neglect of stakeholders in strategic planning may result in both companies and universities becoming less successful and less competitive. For this reason, a temporal perspective was adopted to enable a consideration of events, their antecedents and subsequent effects thereby identifying emerging evolutionary trends and responding to them so that there can be appropriate decision making and accountability. This paper uses historical organisational studies to provide a longitudinal overview of internal and external stakeholder influence on university evolution and change from their foundations in the early Medieval period. Five university generations are described: Medieval, Humboldtian, Civic/Land Grant, Mass, and Stakeholder. This investigation reveals a number of strategic shifts in stakeholders as their voices have become increasingly prominent or have declined. Over time, the number of stakeholders have grown as their salience has been acknowledged through concepts such as the third mission; corporate social responsibility and helix structure; and, although some of the main stakeholders have remained constant such as learners and faculty, their influence has fluctuated.

The TanSat carbon satellite is to be launched at the end of 2016. In order to verify the performance of its instruments, a flight test of TanSat instruments was conducted in Jilin Province in September, 2015. The flight test area covered a total area of about 11,000 km2 and the underlying surface cover included several lakes, forest land, grassland, wetland, farmland, a thermal power plant and numerous cities and villages. We modeled the column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) surface based on flight test data which measured the near- and short-wave infrared (NIR) reflected solar radiation in the absorption bands at around 760 and 1610 nm. However, it is difficult to directly analyze the spatial distribution of XCO2 in the flight area using the limited flight test data and the approximate surface of XCO2, which was obtained by regression modeling, which is not very accurate either. We therefore used the high accuracy surface modeling (HASM) platform to fill the gaps where there is no information on XCO2 in the flight test area, which takes the approximate surface of XCO2 as its driving field and the XCO2 observations retrieved from the flight test as its optimum control constraints. High accuracy surfaces of XCO2 were constructed with HASM based on the flight’s observations. The results showed that the mean XCO2 in the flight test area is about 400 ppm and that XCO2 over urban areas is much higher than in other places. Compared with OCO-2’s XCO2, the mean difference is 0.7 ppm and the standard deviation is 0.95 ppm. Therefore, the modelling of the XCO2 surface based on the flight test of the TanSat instruments fell within an expected and acceptable range.