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In countries where population census data are limited, generating accurate subnational estimates of health and demographic indicators is challenging. Existing model‐based geostatistical methods leverage covariate information and spatial smoothing to reduce the variability of estimates but often ignore the survey design, while traditional small area estimation approaches may not incorporate both unit‐level covariate information and spatial smoothing in a design consistent way. We propose a smoothed model‐assisted estimator that accounts for survey design and leverages both unit‐level covariates and spatial smoothing. Under certain regularity assumptions, this estimator is both design consistent and model consistent. We compare it with existing design‐based and model‐based estimators using real and simulated data. [ABSTRACT FROM AUTHOR]

In this research, we propose a novel technique for visualizing nonstationarity in geostatistics, particularly when confronted with a single realization of data at irregularly spaced locations. Our method hinges on formulating a statistic that tracks a stable microergodic parameter of the exponential covariance function, allowing us to address the intricate challenges of nonstationary processes that lack repeated measurements. We implement the fused lasso technique to elucidate nonstationary patterns at various resolutions. For prediction purposes, we segment the spatial domain into stationary sub-regions via Voronoi tessellations. Additionally, we devise a robust test for stationarity based on contrasting the sample means of our proposed statistics between two selected Voronoi subregions. The effectiveness of our method is demonstrated through simulation studies and its application to a precipitation dataset in Colorado. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Various big geo-data provide a social sensing approach to measure spatial interactions. Existing studies often aggregate individual-level movement trajectories or social ties to obtain the interaction intensity between places, neglecting the detailed meanings (i.e. the semantics) behind spatial interactions. However, such meanings help to understand the relationship between two places, and consequently, the characteristics of both places. We argue that semantics can be extracted from spatial interactions through features of space, time, symmetry, and individual-based statistics. Whereafter the calculation and applications of the features are given. Furthermore, we discuss the construction of spatial interaction networks with semantics, as well as approaches to representing places according to spatial interactions. Finally, we illustrate the potential value of spatial interaction semantics in facilitating decision-making through an example in the context of tourism planning. [ABSTRACT FROM AUTHOR]

This research focuses on exploring and mapping Ankara's urban points of interest and their values on different urban infrastructure types using geolocated data from Flickr. All publicly available data dated between 2005 and 2021 from Flickr were obtained through application programming interface and the infrastructure types were reclassified as green, blue, grey, and yellow, using the land use and land cover data based on Corine 2018. Spatial patterns for cultural service flow of landscape features and/or landscapes on different urban infrastructure types were mapped using Kernel density estimation in QGIS 3.21. The hotspots were assessed in terms of the cultural services they offered based on the Millennium Ecosystem Assessment (MA) framework, site visits, and observations. The findings showed that the city core mostly provided cultural heritage, identity, and recreational values. Results for the rural and peri-urban areas indicated that natural landscapes and protected areas stood out for their provision of cultural services, particularly for their recreational values. Our research identified user-intensive geographical clusters and hotspots that could benefit from interventions and enhancement for a more sustainable urban environment. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often limited by spatial and temporal sparsity, as well as discontinuities. These limitations primarily arise from incomplete satellite trajectories. Additionally, variability in cloud cover and periodic issues specific to the instruments can compromise data quality. Two families of methods have been developed to address data discontinuity: (1) machine learning-based gap-filling techniques and (2) geostatistical techniques (various forms of kriging). The former techniques utilize the relationships between ancillary data and SIF, while the latter usually rely on the available SIF data recordings and their covariance structure to provide estimates at unsampled locations. In this study, we create a synthetic approach for SIF gap filling by hybridizing the two approaches under the umbrella of kriging with external drift. We performed leave-one-out cross-validation of the OCO-2 SIF retrieval aggregates for the entire year of 2019, comparing three methods: ordinary kriging, ML-based estimation using ancillary data, and kriging with external drift. The Mean Absolute Error (MAE) for ML, ordinary kriging, and the hybrid approach was found to be 0.1399, 0.1318, and 0.1183 mW m2 sr−1 nm−1, respectively. We demonstrate that the performance of the hybrid approach exceeds both parent techniques due to the incorporation of information from multiple resources. This use of multiple datasets enriches the hybrid model, making it more robust and accurate in handling the spatio-temporal variability and discontinuity of SIF data. The developed framework is portable and can be applied to SIF retrievals at various resolutions and from various sources (satellites), as well as extended to other satellite-measured variables. [ABSTRACT FROM AUTHOR]

Air pollution remains a significant issue, particularly in urban areas. This study explored the prediction of hourly point-based PM10 concentrations using the XGBoost algorithm to assimilate them into a geostatistical land use regression model for spatially and temporally high-resolution prediction maps. The model configuration and training incorporated meteorological data, station metadata, and time variables based on statistical values and expert knowledge. Hourly measurements from approximately 400 stations from 2009 to 2017 were used for training. The selected model performed with a mean absolute error (MAE) of 6.88 μg m−3, root mean squared error (RMSE) of 9.95 μg m−3, and an R² of 0.65, with variations depending on the siting type and surrounding area. The model achieved a high accuracy of 98.54% and a precision of 73.96% in predicting exceedances of the current EU-limit value for the daily mean of 50 μg m−3. Despite identified limitations, the model can effectively predict hourly values for assimilation into a geostatistical land use regression model. [ABSTRACT FROM AUTHOR]

A precise soil organic carbon (SOC) content estimate is crucial soil quality parameter for agricultural produce and ecological safety. Moreover, geospatial modeling of SOC is critical when there are limited laboratory equipment and chemical reagents for soil analysis. This study used geostatistics—ordinary kriging (OK) and inverse distance weighting (IDW)—to map SOC in Libokemkem area, Northwest Ethiopia, for improved SOC management. About 107 soil samples were obtained from the plow layer at a 20‐cm depth and SOC was determined. Statistical Package for Social Sciences version 24.0 was used to generate descriptive statistics, and geostatistical analysis was also performed on the data using ArcGIS platform. The coefficient of determination (R2) and root mean square error (RMSE) derived from the validation of the predicted maps were used to assess the models. The results revealed homogeneity (coefficient of variation < 10%), low (0.12%–1.74%), and optimal (1.74%–4.06%) mean levels of SOC in study area. The OK showed an R2 of 0.74 and an RMSE of 13%, and the IDW revealed an R2 of 0.69 and an RMSE of 14%. The semivariogram results indicate a moderate dependence for SOC with stable, circular, spherical, exponential, and Gaussian models. We conclude that the sustainable monitoring of SOC is significant in enhancing soil quality. However, further study considering all drivers of spatial variability for SOC in the study and other soil sampling approaches improving performance of the prediction models is needed. Core Ideas: Ordinary kriging and inverse distance weighting viably predict the geographical variability of soil organic carbon (SOC).Stable, circular, spherical, exponential, and Gaussian models showed a moderate dependence for SOC.The spatial homogeneity of SOC was proven by the ordinary kriging and inverse distance weighting models.The geostatistical‐based SOC maps could benefit nutrient management strategies. [ABSTRACT FROM AUTHOR]

Introduction: The challenge of achieving maternal and neonatal health-related goals in developing countries is significantly impacted by high fertility rates, which are partly attributed to limited access to family planning and access to the healthcare systems. The most widely used indicator to monitor family planning coverage is the proportion of women in reproductive age using contraception (CPR). However, this metric does not accurately reflect the true family planning coverage, as it fails to account for the diverse needs of women in reproductive age. Not all women in this category require contraception, including those who are pregnant, wish to become pregnant, sexually inactive, or infertile. To effectively address the contraceptive needs of those who require it, this study aims to estimate family planning coverage among this specific group. Further, we aimed to explore the geographical variation and factors influencing contraceptive uptake of contraceptive use among those who need. Method: We used data from the Performance Monitoring for Action Ethiopia (PMA Ethiopia) survey of women of reproductive age and the service delivery point (SDP) survey conducted in 2019. A total of 4,390 women who need contraception were considered as the analytical sample. To account for the study design, sampling weights were considered to compute the coverage of modern contraceptive use disaggregated by socio-demographic factors. Bayesian geostatistical modeling was employed to identify potential factors associated with the uptake of modern contraception and produce spatial prediction to unsampled locations. Result: The overall weighted prevalence of modern contraception use among women who need it was 44.2% (with 95% CI: 42.4%—45.9%). Across regions of Ethiopia, contraceptive use coverage varies from nearly 0% in Somali region to 52.3% in Addis Ababa. The average nearest distance from a woman's home to the nearest SDP was high in the Afar and Somali regions. The spatial mapping shows that contraceptive coverage was lower in the eastern part of the country. At zonal administrative level, relatively high (above 55%) proportion of modern contraception use coverage were observed in Adama Liyu Zone, Ilu Ababor, Misrak Shewa, and Kefa zone and the coverage were null in majority of Afar and Somali region zones. Among modern contraceptive users, use of the injectable dominated the method-mix. The modeling result reveals that, living closer to a SDP, having discussions about family planning with the partner, following a Christian religion, no pregnancy intention, being ever pregnant and being young increases the likelihood of using modern contraceptive methods. Conclusion: Areas with low contraceptive coverage and lower access to contraception because of distance should be prioritized by the government and other supporting agencies. Women who discussed family planning with their partner were more likely to use modern contraceptives unlike those without such discussion. Thus, to improve the coverage of contraceptive use, it is very important to encourage/advocate women to have discussions with their partner and establish movable health systems for the nomadic community. [ABSTRACT FROM AUTHOR]

The upsurges of location-based social media and geo-location based online social networks have accelerated the spread of rumors, making rumor detection an increasingly important challenge. To tackle this challenge, a variety of conventional and deep learning based approaches are developed to detect rumors based on geo-textual data. This data includes textual, spatial, and temporal information generated from a variety of location-aware social media services. However, the effectiveness of early-stage detection methods based on geo-textual data is limited due to additional information deficiencies, such as the lack of comments and retweets. To address this limitation, we propose a model named Dual-Level Network for Rumor Detection (DLRD). The DLRD model extracts both post-level features and topic-level features. A topic-level network is applied to capture the features of geo-textual data with similar expressions and meanings, such as those that share the same geographic location and time point, indicating that they have a high probability of sharing the same truth value. Specifically, we first leverage the topic modeling method to analyze text content including spatio-temporal information, divide source posts of all events into disjoint topic groups, and then construct a topic-post graph for each group. In the DLRD model, two graph convolutional layers and two full connection layers are employed to learn topic-level and post-level features, respectively. We conduct extensive experiments to compare our model against existing baselines on two public real-world datasets. The experimental results demonstrate that the DLRD model achieves state-of-the-art performance over existing baselines. [ABSTRACT FROM AUTHOR]

The quality and quantity of groundwater resources are often assessed by sampling a limited number of wells drilled sparsely across a plain. So far, various methods have been developed for assessing and modeling groundwater quality, each of which has its capabilities and limitations. In the present study, the copula functions were applied to multivariate analysis of groundwater quality variables (including SAR, K, Mg, Na, Ca, Cl, EC, pH, TDS, SO4, TH and HCO3) in Shahrekord plain, Iran. For this purpose, the quality data from 24 wells distributed across the Shahrekord plain during the period of 1990–2020 were used. For bivariate analyses of groundwater quality, first the fitness of some common distributions in hydrology were examined to the quality variables and the appropriate marginal distribution was determined by Kolmogorov–Smirnov test (K–S). The results revealed that the Generalized Extreme Value distribution has an acceptable fitness on groundwater quality variables of Shahrekord plain. The results of correlation analysis indicated that the highest correlation based on Spearman Rho, Kendall Tau and Pearson correlation coefficient is related to the paired variables of SAR-Na and EC-TDS with correlation coefficient greater than 0.9. Therefore, these variables were selected for further analysis. In the next step, bivariate distribution of two different quality variables in a well (point analysis) and also bivariate distribution of a quality variable in two different wells (inter-well analysis) were created using copula functions. To do this, the fitness of different copulas (including Plackett, Joe, Clayton, Frank, Farlie–Gumbel–Morgenstern, Ali–Mikhail–Haq, Gumbel, Gumbel–Hougaard, Gumbel–Barnett, Philip–Gumbel and Galambos) was tested to construct a bivariate distribution of quality variables. For choosing the best fitted copula on studied paired variables, the theoretical copula values was compared with the corresponding values of empirical copula based on the goodness of fit criteria. Based on the results of point analysis, the Joe copula function was chosen as the best fitted copula function for multivariate analysis of two quality variables in a well. For inter-well analysis (IWA), the Clayton copula function was selected for creating bivariate distribution of a quality variable in two different wells. Comparing the performance of the proposed IWA method with geostatistical methods showed that in addition to the IWA method having acceptable accuracy, it has a higher efficiency than geostatistical methods, especially in areas where the number of sampling wells is less. [ABSTRACT FROM AUTHOR]

The health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including variography, ordinary kriging, and G clustering, was used to generate national distribution maps for various factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers. [ABSTRACT FROM AUTHOR]

Predator–prey interactions in time and space determine stock productivity, making them an important consideration when managing marine resources, rebuilding stocks or considering reopening a fishery.We analysed fine‐scale diet data from surveys conducted in 2009–2010 and 2018–2019 in three fjords in northern Norway with geostatistical models investigating how predation varied in space, time and between predator species. Our focus prey species was northern shrimp (Pandalus borealis), valuable both as a commercial resource and a major food source for other important species like Atlantic cod (Gadus morhua).Diet composition of fish predators differed clearly between fjords. While predator species and size were good predictors of shrimp predation, the relationships with bathymetry, prey density and geospatial variables were complex.Our study indicates that predation of forage species, such as shrimp, varies spatially in heterogenous fjord ecosystems. Shrimp consumption was not highest in the fjord with highest predator density, indicating a higher dependency of cod on shrimp in specific areas.Realized predation is a complex combination of predator and prey densities and predator ecology that differed in each of the three fjords.Synthesis and applications. Ignoring spatial variations in predator–prey interactions may lead to an inaccurate perception of stock productivity, suboptimal management and possibly unsustainable management targets. We recommend spatially explicit assessment and management for fish stocks where predator–prey interactions vary substantially in space, such as fjords and reefs. [ABSTRACT FROM AUTHOR]

Geostatistical methods are powerful tools for understanding the spatial structure of the metallicity distribution of galaxies, and enable construction of accurate predictive models of the 2D metallicity distribution. However, so far these methods have only been applied to very high spatial resolution metallicity maps, leaving it uncertain if they will work on lower quality data. In this study, we apply geostatistical techniques to high-resolution spectroscopic maps of three local galaxies convolved to eight different spatial resolutions ranging from ∼40 pc to ∼1 kpc per pixel. We fit a geostatistical model to the data at all resolutions, and find that for metallicity maps where small-scale structure is visible by eye (with ≳10 resolution elements per R e), all parameters, including the metallicity correlation scale, can be recovered accurately. At all resolutions tested, we find that point metallicity predictions from such a geostatistical model outperform a circularly symmetric metallicity gradient model. We also explore dependence on the number of data points, and find that N ≳ 100 spatially resolved metallicity values are sufficient to train a geostatistical model that yields more accurate metallicity predictions than a radial gradient model. Finally, we investigate the potential detrimental effects of having spaxels smaller than an individual H  ii region by repeating our analysis with metallicities integrated over H  ii regions. We see that spaxel-based measurements have more noise, as expected, but the underlying spatial metallicity distribution can be recovered regardless of whether spaxels or integrated regions are used. [ABSTRACT FROM AUTHOR]

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions Ca 2 + , Cl − , and Na + . We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition. [ABSTRACT FROM AUTHOR]

In the Algerian-Tunisian border region lies the Tebessa plain, which belongs to the Saharan Atlas domain and is characterized by a semi-arid climate and high salinity, often rendering the groundwater unusable. The objective of this research is to highlight groundwater salinization using statistical and geostatistical methods to model this spatial heterogeneity. A total of 30 data points were used, and the statistical analysis revealed a strong association between chlorides, sulfates, electrical conductivity, and values significantly exceeding the standards. Surface and directional variograms for EC (electrical conductivity), SO4 (sulfates), and Cl (chlorides) exhibit anisotropy, with the major axis showing a hole effect and a periodicity of 5 km. Geostatistical mapping through ordinary kriging allowed us to identify areas of high salinity and deduce the underlying cause; which is the dissolution of gypsiferous formations. These results were confirmed by thermodynamics and Sr2+/Ca2+ ratio. [ABSTRACT FROM AUTHOR]

In the present study, three different depth conversion methods have been used with varying equations at Mishrif Formation to determine the best method to produce better, more realistic and accurate interpretation results and to reduce the risk of future petroleum production wells drilling. Three horizons were picked (Tanuma, Mishrif and Rumaila) and converted into a two-way time grid surface. In addition, the velocity parameters (instantaneous velocity and rate of velocity change with depth) were computed and converted to the form grid surface from the well data, showing lateral velocity variation. Among the three models carried out, the Model 1 is considered the most accurate depth conversion method in this region according to the results of the geostatistical analysis. This method gave fewer errors and mistakes between actual and predicted depth with the best intra-extrapolation technique at all directions away from the well area with a standard deviation value of 13.2, and the standard deviation values of 13.6 and 13.9 for models 2 and 3, respectively. [ABSTRACT FROM AUTHOR]

The simulation and characterisation of randomly rough surfaces is an important topic in surface science, tribology, geo- and planetary sciences, image analysis and optics. Extensions to general random processes with two continuous variables are straightforward. Several surface generation algorithms are available, and preference for one or another method often depends on the specific scientific field. The same holds for the methods to estimate the fractal dimension D. This work analyses six algorithms for the determination of D as a function of the size of the domain, variance, and the input value for D, using surfaces generated by Fourier filtering techniques and the random midpoint displacement algorithm. Several of the methods to determine fractal dimension are needlessly complex and severely biased, whereas simple and computationally efficient methods produce better results. A fine-tuned analysis of the power spectral density is very precise and shows how the different surface generation algorithms deviate from ideal fractal behaviour. For large datasets defined on equidistant two-dimensional grids, it is clearly the most sensitive and precise method to determine fractal dimension. [ABSTRACT FROM AUTHOR]

This article investigates the role of different approaches and interpolation methods in gridding terrestrial gravity anomalies. In this regard, first of all, simple and complete Bouguer anomalies are considered in gravity data gridding. In the comparison results of gridding these two Bouguer anomaly datasets, the effect of the high-frequency contribution of topographic gravitation (by means of the terrain correction) is clarified. After that, the role of the used interpolation algorithm on the resulting grid of mean gravity anomalies and hence on the geoid modeling accuracy is inspected. For this purpose, four different interpolation methods including geostatistical Kriging, nearest neighbor, inverse distance to a power (IDP), and artificial neural networks (ANNs) are applied. Here, the IDP and nearest neighbor methods represent simple-structured algorithms among the interpolation methods tested in this study. The ANN method, on the other hand, is preferred as a complex, optimization-based soft computing method that has been applied in recent years. In addition, the geostatistical Kriging method is one of the conventional methods that is mostly applied for gridding gravity data in geodesy and geophysics. The calculated gravity anomalies in grids are employed in high-resolution geoid model computations using the least squares modifications of Stokes formula with additive corrections (LSMSA) technique. The investigations are carried out using the test datasets of Auvergne, France that are provided by the International Service for the Geoid for scientific research. It is concluded that the interpolation algorithms affect the gravity gridding results and hence the geoid model determination. The ANN method does not provide superior results compared to the conventional algorithms in gravity gridding. The geoid model with 4.1 cm accuracy is computed in the test area. [ABSTRACT FROM AUTHOR]

The spatial analysis of health data usually raises geoprivacy issues. Due to the virulence of COVID-19, scientists and crisis managers do need to analyze the distribution and spread of the disease with spatially precise data. In particular, it is useful to locate each case on a map to identify clusters of cases. To allow such analyses without breach of geoprivacy, geomasking techniques are necessary. This paper experiments with the geomasking techniques from the literature to solve this problem: masking the real address of positive cases while preserving the local spatial cluster patterns. In particular, two different approaches based on aggregation and perturbation are adapted to the geomasking of addresses in areas with different densities of population. A new simulated cluster crowding method is also proposed to preserve clusters as much as possible. The results show that geomasking techniques can spatially anonymize addresses while preserving clusters, and the best geomasking method depends on the use of the anonymized data. [ABSTRACT FROM AUTHOR]

Precision agriculture for coffee production requires spatial knowledge of crop yield. However, difficulties in implementation lie in low-sampled areas. In addition, the asynchronicity of this crop adds complexity to the modeling. It results in a diversity of phenological stages within a field and also continuous production of coffee over time. Big Data retrieved from remote sensing can be tested to improve spatial modeling. This research proposes to apply the Sentinel-2 vegetation index (NDVI) and the Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) dataset as auxiliary variables in the multivariate geostatistical modeling of coffee yield characterized by the presence of outliers and assess improvement. A total of 66 coffee yield points were sampled from a 4 ha area in a quasi-regular grid located in southeastern Brazil. Ordinary kriging (OK) and block cokriging (BCOK) were applied. Overall, coupling coffee yield with the NDVI and/or SAR in BCOK interpolation improved the accuracy of spatial interpolation of coffee yield even in the presence of outliers. Incorporating Big Data for improving the modeling for low-sampled fields requires taking into account the difference in supports between different datasets since this difference can increase uncontrolled uncertainty. In this manner, we will consider, for future research, new tests with other covariates. This research has the potential to support precision agriculture applications as site-specific plant nutrient management. [ABSTRACT FROM AUTHOR]

Electrical resistivity tomography (ERT) and topographic profiles obtained by differential global positioning system were used to study depth continuity of structures showing neotectonic activity in the southwestern sector of the San Guillermo Elevated Block (SGEB), a morphostructural unit located in the Pampas region of Argentina. Data obtained from the surface and near-surface allowed us to identify the features of a fold caused by the propagation of high-angle reverse blind faults. Distribution of resistivities found in the near-surface seems to indicate westward propagation of the deformation front. One of the geoelectric levels interpreted in the ERT sections was used as a stratigraphic marker; the top of this level was compared with the depth found in wells drilled in different localities surrounding the study area, identifying lateral discontinuities that allow us to infer at least one stage of tectonic activity during the Pliocene–Early Pleistocene. The top of these deposits is shallower within the SGEB. On the other hand, the analysis of additional exploratory drilling allowed us to verify that the crystalline basement is fractured and is deeper than the western area outside the morphostructural unit. We propose that there are ancient pre-existing faults, created under an extensional regime, that were later reactivated involving multiple deformation events under a compressional regime. The normal displacement of crystalline basement blocks due to extensional tectonics would have been only partially inverted later during reactivation. Schematic block diagram showing the electrical resistivity tomography (ERT) sections and the General Roca well projected at the latitude of the ERT, representing the interpreted folding (dashed line) and the difference in level of the stratigraphic marker of the Miocene Paraná/Puelches sands. The wider and deeper block diagram (down right) shows the normally-faulted crystalline basement at depth. Note that the ERT could be a tool to map the location of the fault tips, recently reactivated with reverse displacement. SGEB: San Guillermo Elevated Block. [ABSTRACT FROM AUTHOR]

We propose a twofold adjustment for Event Studies considering spatiotemporal data in a multivariate time series framework where the data are characterized by spatial and temporal dependence. The first adjustment consists of modeling the spatiotemporal dynamics of the data by implementing several geostatistical models capable of handling both spatial and temporal components, as well as estimating the relationship between the response variable and a set of exogenous factors. With the second adjustment, we propose to use cross-sectional-adjusted test statistics directly accounting for spatial cross-correlation. The proposed methods are applied to the case of NO 2 concentrations observed in Northern Italy during the first wave of the COVID-19 pandemic. The key findings are as follows. First, all the considered geostatistical models estimate larger reductions in the major metropolitan and congested areas, while smaller reductions are estimated in rural plains and in the mountains. Second, the models are nearly equivalent in terms of fitting and are capable of identifying the true event window. Third, by using spatiotemporal models we ensure the residuals are uncorrelated across space and time, thus allowing Event Studies test statistics to provide reliable and realistic estimates. Fourth, as expected, all test statistics show significant reductions in NO 2 concentrations starting from the first few days of lockdown. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Preferential sampling models have garnered significant attention in recent years. Although the original model was developed for geostatistics, it founds applications in other types of data, such as point processes in the form of presence-only data. While this has been recognized in the Statistics literature, there is value in incorporating ideas from both presence-only and preferential sampling literature. In this paper, we propose a novel model that extends existing ideas to handle a continuous variable collected through opportunistic sampling. To demonstrate the potential of our approach, we apply it to sardine biomass data collected during commercial fishing trips. While the data is intuitively understood, it poses challenges due to two types of preferential sampling: fishing events (presence data) are non-random samples of the region, and fishermen tend to set their nets in areas with a high quality and value of catch (i.e., bigger schools of the target species). We discuss theoretical and practical aspects of the problem, and propose a well-defined probabilistic approach. Our approach employs a data augmentation scheme that predicts the number of unobserved fishing locations and corresponding biomass (in kg). This allows for evaluation of the Poisson Process likelihood without the need for numerical approximations. The results of our case study may serve as an incentive to use data collected during commercial fishing trips for decision-making aimed at benefiting both ecological and economic aspects. The proposed methodology has potential applications in a variety of fields, including ecology and epidemiology, where marked point process model are commonly used. [ABSTRACT FROM AUTHOR]

Modern height systems are based on the combination of satellite positioning and gravity field models of high resolution. However, in many regions, especially developing or newly industrializing countries, there is no (reliable) regional gravity model at all, due to challenges such as limited data availability, unknown/low data quality, and missing metadata. This paper addresses this issue in a case study of Colombia, where eight decades of historical terrestrial and airborne gravity measurements are available but widely contain systematic errors, outliers, and biases. Correspondingly, processing strategies and structures are proposed and applied to validate and improve the quality of old gravity datasets. A novel method is developed based on spherical radial basis functions (SRBFs) for estimating biases, which are found in different airborne surveys with values exceeding 40 mGal. The validity of this bias estimation method is demonstrated both by a simulation test and by the evaluation of the airborne data in comparison to the SATOP (SAtellite-TOPography) model, which merges the satellite-only global gravity model GOCO06s with the Earth2014 topography model. The terrestrial and airborne data are then combined with a global gravity model (GGM), ultra-high-resolution topography models, as well as altimetry-derived gravity anomalies from DTU21GRA for the offshore areas. The results are presented in terms of height anomalies (QGeoidCOL2023), and they are thoroughly validated using GPS/leveling data both in the absolute and relative manner. The standard deviation in comparison to the GPS/leveling data after applying a correction surface to account for the datum inconsistencies amounts to 15.76 cm, which is 27% smaller compared to the mean standard deviation value given by five recent high-resolution GGMs, and 36% smaller than the one delivered by the latest South American quasi-geoid model QGEOID2021. The relative validation results show that QGeoidCOL2023 performs better, i.e., delivers lower RMS errors than the GGMs and QGEOID2021 in all the baseline length groups. These results indicate the validity and benefits of the developed methods and procedures, which can be used for other data-challenging areas to facilitate the realization of geopotential-based height systems. [ABSTRACT FROM AUTHOR]

Background: The latest national survey on the distribution of human parasites in China demonstrated that Guangdong was among the endemic provinces with the highest Clonorchis sinensis infection rates. High-resolution, age- and gender-specific risk maps of the temporal and spatial distributions are essential for the targeted control work. Methods: Disease data on the prevalence of C.sinensis infection from 1990 onwards, either age- and gender-specific or aggregated across age and gender, were collected through systematic review and four large-scale surveys in Guangdong Province. Environmental and socioeconomic variables were obtained from open-access databases and employed as potential predictors. A Bayesian geostatistical model was developed to estimate the C.sinensis infection risk at high spatial resolution. Results: The final dataset included 606 surveys at 463 unique locations for C.sinensis infection. Our findings suggested that following an initial increase and stabilization, the overall population-adjusted prevalence had declined to 2.2% (95% Bayesian credible interval: 1.7–3.0%) in the period of 2015 onwards. From 2015 onwards, moderate and high infection risks were found in the northern regions (e.g. Heyuan and Shaoguan cities) and the southern Pearl River Delta (e.g. Foshan, Zhongshan, Zhuhai and Jiangmen cities), respectively. Age- and gender-specific risk maps revealed that males had a higher infection risk than females, and the infection risk was higher in adults compared to children. Conclusions: Our high-resolution risk maps of C. sinensis infection in Guangdong Province identified the spatial, temporal, age and gender heterogeneities, which can provide useful information assisting tailored control strategies. [ABSTRACT FROM AUTHOR]

Nowadays, shopping centers not only provides commercial function but also serve as a public space. In this article, we take Nanshan district of Shenzhen as an example, based on the characteristics information of people activities provided by Mobile-phone Signaling Data, using the standard deviation ellipse method to classify the direction of people in shopping centers, and then applying the entropy weighting method to analyze the vitality factors of shopping centers from three perspectives: visitors' density, revisit rate, and the average length of stay. Finally, we analyzed the influence factors of the surrounding built environment based on correlation analysis to discuss the results with field survey data. The results show that (1) shopping centers in Nanshan District are classified into wide-area type and geo-regional type according to the gathering of visitors. The shopping centers with high comprehensive vitality are basically wide-area type. (2) The factors influencing the vitality of shopping centers are different between wide-area type and geo-regional type. The vitality of wide-area type is mainly influenced by the traffic accessibility and whether they are located in adjacent to large public spaces such as squares and green public; the vitality of geo-regional type shopping centers is mainly influenced by the number of people within a 15-minute walking circle, and the high-vitality of geo-regional shopping centers are generally located in densely populated areas. [ABSTRACT FROM AUTHOR]

In this expository review paper, we show that co-kriging, a widely used geostatistical multivariate optimal linear estimator, has a diverse range of extensions that we have collected and illustrated to show the potential of this spatial interpolator. In the context of spatial stochastic processes, this paper covers scenarios including increasing the spatial resolution of a spatial variable (downscaling), solving inverse problems, estimating directional derivatives, and spatial interpolation taking boundary conditions into account. All these spatial interpolators are optimal linear estimators in the sense of being unbiased and minimising the variance of the estimation error. [ABSTRACT FROM AUTHOR]

To enable proper remediation of accidental groundwater contamination, the contaminant plume evolution needs to be accurately estimated. In the estimation, uncertainties in both the contaminant source and hydrogeological structure should be considered, especially the temporal release history and hydraulic transmissivity. Although the release history can be estimated using geostatistical approaches, previous studies use the deterministic hydraulic property field. Geostatistical approaches can also effectively estimate an unknown heterogeneous transmissivity field via the use of joint data, such as a combination of hydraulic head and tracer data. However, tracer tests implemented over a contaminated area necessarily disturb the in situ condition of the contamination. Conversely, measurements of the transient concentration data over an area are possible and can preserve the conditions. Accordingly, this study develops a geostatistical method for the joint clarification of contaminant plume and transmissivity distributions using both head and contaminant concentration data. The applicability and effectiveness of the proposed method are demonstrated through two numerical experiments assuming a two-dimensional heterogeneous confined aquifer. The use of contaminant concentration data is key to accurate estimation of the transmissivity. The accuracy of the proposed method using both head and concentration data was verified achieving a high linear correlation coefficient of 0.97 between the true and estimated concentrations for both experiments, which was 0.67 or more than the results using only the head data. Furthermore, the uncertainty of the contaminant plume evolution was successfully evaluated by considering the uncertainties of both the initial plume and the transmissivity distributions, based on their conditional realizations. [ABSTRACT FROM AUTHOR]

Research on spatio-temporal geostatistical modeling remains a critical challenge in numerous scientific and engineering disciplines. This paper introduces a novel extension of dual kriging, called spatio-temporal dual kriging (ST-DK), in which drift functions with fixed and adaptive coefficients are established. The approach appears to be effective in modeling complex spatio-temporal dynamics, particularly when relevant auxiliary variables exert substantial influence on the target variable. To illustrate its performance, we compare the ST-DK model with the classical spatio-temporal regression kriging (ST-RK) and geographically and temporally weighted regression (GTWR) models for estimating temperature and air pressure data from Thailand in 2018. Our findings demonstrate that both the ST-DK and ST-RK models when utilizing adaptive coefficients outperform their fixed coefficient counterparts. Furthermore, the ST-DK method consistently exhibits superior performance compared to the ST-RK and GTWR methods. [ABSTRACT FROM AUTHOR]

Spatial precipitation analysis is essential for effectively managing hydrological modeling, construction of water structures, and irrigation planning. In this study, the ordinary kriging (OK), simple kriging (SK), global polynomial interpolation (GPI), local polynomial interpolation (LPI), inverse distance weighted (IDW), radial basis functions (RBF), and artificial neural network (ANN)-based hybrid techniques were compared to determine the spatial variation of annual precipitation. Statistical indicators derived from Willmott's index of agreement, root mean square error, mean absolute percentage error, and the violin plot and boxplot graphical approaches were used to determine the most effective technique for precipitation interpolation. According to the analysis results, it has been observed that the ANN model significantly improves the prediction performance of single interpolation methods. The OK-ANN hybrid model was determined to be the most accurate representation of precipitation distribution, with the GPI-ANN model coming in second. The most precise results were obtained using the deterministic method, RBF with inverse multiquadric kernel function, LPI with Epanechnikov kernel function, and GPI with 3rd-order polynomial interpolations. In addition, it was determined that deterministic approaches produce more successful results than geostatistical approaches in the basin due to the presence of homogeneous and densely distributed meteorological observation networks. [ABSTRACT FROM AUTHOR]

For complex and multi-layered clastic oil reservoir formations, modeling lithofacies and petrophysical parameters is essential for reservoir characterization, history matching, and uncertainty quantification. This study introduces a real oilfield case study that conducted high-resolution geostatistical modeling of 3D lithofacies and petrophysical properties for rapid and reliable history matching of the Luhais oil reservoir in southern Iraq. For capturing the reservoir's tidal depositional setting using data collected from 47 wells, the lithofacies distribution (sand, shaly sand, and shale) of a 3D geomodel was constructed using sequential indicator simulation (SISIM). Based on the lithofacies modeling results, 50 sets of porosity and permeability distributions were generated using sequential Gaussian simulation (SGSIM) to provide insight into the spatial geological uncertainty and stochastic history matching. For each rock type, distinct variograms were created in the 0° azimuth direction, representing the shoreface line. The standard deviation between every pair of spatial realizations justified the number of variograms employed. An upscaled version of the geomodel, incorporating the lithofacies, permeability, and porosity, was used to construct a reservoir-flow model capable of providing rapid, accurate, and reliable production history matching, including well and field production rates. [ABSTRACT FROM AUTHOR]

In recent years, numerous multitemporal global land use and land cover products have been published acting as valuable source for training spatially explicit geosimulation models forecasting urban growth. However, there is a notable gap in research that specifically addresses the sensitivity of models traing with those data sets when it comes to regional modeling purposes. Accordingly, the objectives of this study were to calibrate, validate, and employ global urban input datasets for the regional simulation of urban growth by the year 2030. The SLEUTH urban growth model, focused on the metropolitan area of the Ruhr, Germany, was calibrated using the Global Human Settlement Layer, World Settlement Footprint Evolution, historical OpenStreetMap data, and a Digital Land Cover Model for Germany. The goal was to compare the results in terms of accuracy, certainty, quantity, and allocation, particularly in urban areas susceptible to floods and heat. While all models achieved high accuracy levels concerning quantity and allocation, the extent of new settlements varied from 40.77 km² to 477.91 km². The models based on World Settlement Footprint and OpenStreetMap exhibited higher certainty and lower stochasticity. As the simulated urban growth increased, there was a corresponding rise in the likelihood of allocating new settlements in areas affected by natural hazards. While all models presented a similar relative portion of new settlement areas impacted by floods, variations emerged in terms of areas affected by unfavorable thermal conditions. This study underscored the potential use of historical OpenStreetMap data in training cellular automation for geosimulating future settlement growth. Furthermore, it highlighted the applicability of global Earth observation-based urban datasets for regional geosimulation and explored the impacts of diverse input data on the accuracy, certainty, quantity, and allocation performances in simulating future conditions. [ABSTRACT FROM AUTHOR]

The choice of the prior distribution is a key aspect of Bayesian analysis. The spatial Student-t regression model poses some challenges when eliciting priors. It is well known that the propriety of the posterior distribution over objective priors is not always guaranteed, whereas the use of proper prior distributions may dominate and bias the posterior analysis. In this paper, we show the conditions under which our proposed reference prior and the two introduced Jeffreys priors yield to a proper posterior distribution. Simulation studies and a real data application are used in order to evaluate the performance of the reference prior. [ABSTRACT FROM AUTHOR]

Modern and fossil pollen data are widely used in paleoenvironmental research to characterize past environmental changes in a given location. However, their discrete and discontinuous nature can limit the inferences that can be made from them. Deriving continuous spatial maps of the pollen presence from point-based datasets would enable more robust regional characterization of such past changes. To address this problem, we propose a comprehensive collection of European pollen presence maps including 194 pollen taxa derived from the interpolation of pollen data from the Eurasian Modern Pollen Database (EMPD v2) restricted to the Euro-Mediterranean Basin. To do so, we developed an automatic Kriging-based interpolation workflow to select an optimal geostatistical model describing the spatial variability for each taxon. The output of the interpolation model consists of a series of multivariate predictive maps of Europe at 25 km scale, showing the occurrence probability of pollen taxa, the predicted presence based on diverse probability thresholds, and the interpolation uncertainty for each taxon. Combined visual inspections of the maps and systematic cross-validation tests demonstrated that the ensemble of predictions is reliable even in data-scarce regions, with a relatively low uncertainty, and robust to complex and non-stationary pollen distributions. The maps, freely distributed as GeoTIFF files (10.5281/zenodo.10015695,), are proposed as a ready-to-use tool for spatial paleoenvironmental characterization. Since the interpolation model only uses the coordinates of the observation to spatialize the data, the model can also be employed with fossil pollen records (or other presence/absence indicators), thus enabling the spatial characterization of past changes, and possibly their subsequent use for quantitative paleoclimate reconstructions. [ABSTRACT FROM AUTHOR]

Geological modeling is essential for the characterization of natural phenomena and can be done in two steps: (1) clustering the data into consistent groups and (2) modeling the extent of these groups in space to define domains, honoring the labels defined in the previous step. The clustering step can be based on the information of continuous multivariate data in space instead of relying on the geological logging provided. However, extracting coherent spatial multivariate information is challenging when the variables show complex relationships, such as nonlinear correlation, heteroscedastic behavior, or spatial trends. In this work, we propose a method for clustering data, valid for domaining when multiple continuous variables are available and robust enough to deal with cases where complex relationships are found. The method looks at the local correlation matrix between variables at sample locations inferred in a local neighborhood. Changes in the local correlation between these attributes in space can be used to characterize the domains. By endowing the space of correlation matrices with a manifold structure, matrices are then clustered by adapting the K-means algorithm to this manifold context, using Riemannian geometry tools. A real case study illustrates the methodology. This example demonstrates how the clustering methodology proposed honors the spatial configuration of data delivering spatially connected clusters even when complex nonlinear relationships in the attribute space are shown. [ABSTRACT FROM AUTHOR]

In this paper, a multi-point geostatistical (MPS) method based on variational function partition simulation is proposed to solve the key problem of MPS 3D modeling using 2D training images. The new method uses the FILTERSIM algorithm framework, and the variational function is used to construct simulation partitions and training image sequences, and only a small number of training images close to the unknown nodes are used in the partition simulation to participate in the MPS simulation. To enhance the reliability, a new covariance filter is also designed to capture the diverse features of the training patterns and allow the filter to downsize the training patterns from any direction; in addition, an information entropy method is used to reconstruct the whole 3D space by selecting the global optimal solution from several locally similar training patterns. The stability and applicability of the new method in complex geological modeling are demonstrated by analyzing the parameter sensitivity and algorithm performance. A geological model of a uranium deposit is simulated to test the pumping of five reserved drill holes, and the results show that the accuracy of the simulation results of the new method is improved by 11.36% compared with the traditional MPS method. [ABSTRACT FROM AUTHOR]

This article continues a series of publications by the authors with the results of studying the variations in electrical resistivity in four layers of the geoelectric section. Precision soundings using the VES method have been carried out on a stationary multielectrode array with a maximum spacing of the supply electrodes of 3 km in the central part of the Garm geophysical test site in Tajikistan daily for 12 years. As a result of observations, a profile containing about 4500 pickets was obtained. Unlike conventional soundings, each picket of this profile corresponds not to a geographic point, but to a certain point in time. For the inversion of the time profile, specially developed precision algorithms with additional regularization of the inverse problem are used. The solution error is controlled by numerical simulation methods, during which the direct and then the inverse VES problem is solved first for profiles simulating the experimental profile, and the actual solution errors are estimated. Analysis has shown that the error in calculating the seasonal component of resistivity variations in layers 1–4 is 1–2%, and the error for flicker noise is from 1.3 to 3%. In this case, the total amplitude of seasonal variations in resistivity in the upper layer is more than 50%, and in the second layer it is 5.5%; the amplitude of variations in flicker-noise components for the same layers is estimated as 54 and 24%, respectively. In this paper we analyze the effect of external (exogenous) factors on resistivity variations at different depths. The fact of a significant effect of the groundwater level and soil temperature on the resistivity of the upper layer of the section with a thickness of 1.5 m has been established. For temperature, the coefficient of proportionality averaged over the entire layer is –0.58 ± 0.12%/deg, for the groundwater level, it is –0.8%/cm. For the second layer of the section (depth 1.5–10.2 m), an exact coincidence of the form of the seasonal variation of resistivity and atmospheric pressure is found. This coincidence is not accidental, since both atmospheric pressure and resistivity in layer 2 are characterized by an anomalous form of seasonal variation with two maxima and two minima during the year, which is completely atypical for seasonal changes in resistivity. At the same time, for relatively higher frequency (HF) variations (periods from several days to several weeks), there is no correlation effect. The authors attribute the possible reason for the observed effect to pressure regulation of competitive sources of groundwater inflow into the aquifer confined to the second layer of the section. It is assumed that the salinity and conductivity of water in competing sources differ sharply, which is quite plausible from geological considerations. Changes in atmospheric pressure change the inflow of water from these two sources, which leads to a change in the conductivity of the layer. The inertia of water exchange processes determines the absence of dependence of resistivity on atmospheric pressure at relatively high frequencies and the presence of such a relationship for variations with a characteristic duration of more than a month. [ABSTRACT FROM AUTHOR]

Interplanetary scintillation (IPS) is a useful tool for detecting coronal mass ejections (CMEs) throughout interplanetary space. Global magnetohydrodynamic (MHD) simulations of the heliosphere, which are usually used to predict the arrival and geo-effectiveness of CMEs, can be improved using IPS data. In this study, we demonstrate an MHD simulation that includes IPS data from multiple stations to improve CME modelling. The CMEs, which occurred on 09–10 September 2017, were observed over the period 10–12 September 2017 using the Low-Frequency Array (LOFAR) and IPS array of the Institute for Space-Earth Environmental Research (ISEE), Nagoya University, as they tracked through the inner heliosphere. We simulated CME propagation using a global MHD simulation, SUSANOO-CME, in which CMEs were modeled as spheromaks, and the IPS data were synthesised from the simulation results. The MHD simulation suggests that the CMEs merged in interplanetary space, forming complicated IPS g-level distributions in the sky map. We found that the MHD simulation that best fits both LOFAR and ISEE data provided a better reconstruction of the CMEs and a better forecast of their arrival at Earth than from measurements when these simulations were fit from the ISEE site alone. More IPS data observed from multiple stations at different local times in this study can help reconstruct the global structure of the CME, thus improving and evaluating the CME modelling. [ABSTRACT FROM AUTHOR]

To improve the detection precision of the transient electromagnetic method, dense observation points and windows are necessary. However, to keep the number of samples in every acquisition window unchanged, the sampling rate of principal transient electromagnetic instruments decreases with transmitter frequency, and the increased windows reduce the original signal-to-noise ratio at the cost of decreasing the number of samples participating in the statistical average. Taking the V8 receiver from Phoenix Geophysics as an example, this study analyzes the main steps in the signal detection of a transient electromagnetic instrument. Following the Wiener-Khinchin law of large numbers, herein, a fixed, high-sampling-rate receiver scheme, which does not decrease the number of samples participating in the statistical average while increasing the windows, is proposed. Starting from the Nyquist sampling theorem, we provide the definition of detection precision with the dimension of length. Then, according to the necessary conditions for reconstructing a geoelectric structure, we determine the relationship between a survey grid space and a detection depth space corresponding to window interval as well as the minimum period in a geoelectric signal spectrum. The exploration of the multilayer, water-filled goaf of the Dazigou Coal Mine in Datong shows the aliasing caused by undersampling and the fine geoelectric section obtained using additional conditions under a geophysical premise. The necessary conditions for the reconstruction of the geoelectric structure indicate that satisfying the Nyquist sampling theorem may be insufficient in reconstructing the geoelectric structure, but the structure must not be reconstructed completely without not satisfying this theorem. [ABSTRACT FROM AUTHOR]

Accurate spatial distribution of gridded gross domestic product (GDP) data is crucial for revealing regional disparities within administrative units, thus facilitating a deeper understanding of regional economic dynamics, industrial distribution, and urbanization trends. The existing GDP spatial models often rely on prediction residuals for model evaluation or utilize residual distribution to improve the final accuracy, frequently overlooking the modifiable areal unit problem within residual distribution. This paper introduces a hybrid downscaling model that combines random forest and area-to-area kriging to map gridded GDP. Employing Thailand as a case study, GDP distribution maps were generated at a 1 km spatial resolution for the year 2015 and compared with five alternative downscaling methods and an existing GDP product. The results demonstrate that the proposed approach yields higher accuracy and greater precision in detailing GDP distribution, as evidenced by the smallest mean absolute error and root mean squared error values, which stand at USD 256.458 and 699.348 ten million, respectively. Among the four different sets of auxiliary variables considered, one consistently exhibited a higher prediction accuracy. This particular set of auxiliary variables integrated classification-based variables, illustrating the advantages of incorporating such integrated variables into modeling while accounting for classification characteristics. [ABSTRACT FROM AUTHOR]

The Ångström–Prescott formula is commonly used in climatological calculation methods of solar radiation simulation. Aiming at the characteristics of a vast area, few meteorological stations, and uneven distribution in the tropical regions of China, in order to obtain the optimal parameters of the global solar radiation calculation model, this study proposes a suitable monthly global solar radiation model based on the single-station approach and the between-groups linkage of the A–P model, which utilizes monthly measured meteorological data from 80 meteorological stations spanning the period from 1996 to 2016 in the tropical zone of China, considering the similarity in changes of monthly sunshine percentage between stations. The applicability and accuracy of the correction parameters (a and b coefficients) were tested and evaluated, and then the modified parameters were extended to conventional meteorological stations through Thiessen polygons. Finally, the spatial distribution of solar radiation in the tropical region of China was simulated by kriging, IDW, and spline interpolation techniques. The results show the following: (1) The single-station model exhibited the highest accuracy in simulating the average annual global solar radiation, followed by the model based on the between-groups linkage. After optimizing the a and b coefficients, the simulation accuracy of the average annual global solar radiation increased by 5.3%, 8.1%, and 4.4% for the whole year, dry season, and wet season, respectively. (2) Through cross-validation, the most suitable spatial interpolation methods for the whole year, dry season, and wet season in the tropical zone of China were IDW, Kriging, and Spline, respectively. This research has positive implications for improving the accuracy of solar radiation prediction and guiding regional agricultural production. [ABSTRACT FROM AUTHOR]

Humanity benefits immensely from nature, including through cultural ecosystem services. Geotagged crowdsourced data provide an opportunity to characterize these services at large scales. Flickr data, for example, have been widely used as an indicator of recreational value, while Wikipedia data are increasingly being used as a measure of public interest, potentially capturing often overlooked and less-tangible aspects of socio-cultural values (such as educational, inspirational, and spiritual values). So far, few studies have explored how various geotagged crowdsourced data complement each other, or how correlated these may be, particularly at national scales. To address this knowledge gap, we compare Flickr and Wikipedia datasets in their ability to help characterize the sociocultural value of designated areas in England and assess how this value relates to species richness. Our results show that there was at least one Flickr photo in 35% of all designated areas in England, and at least one Wikipedia page in 60% of them. The Wikipedia and Flickr data were shown not to be independent of each other and were significantly correlated. Species richness was positively and significantly associated with the presence of at least one geotagged Wikipedia page; more biodiverse designated areas, however, were not any more likely to have at least one Flickr photo within them. Our results highlight the potential for new, emerging datasets to capture and communicate the socio-cultural value of nature, building on the strengths of more established crowdsourced data. [ABSTRACT FROM AUTHOR]

Reservoir characterization is one of the main engineering components to ensure the optimum exploration and development of oil and gas. Deep-learning-based approaches are effective and widely used in reservoir modeling in recent years. In this work, a novel reservoir characterization method based on variational auto-encoder and selective attention mechanism (SA-VAE) is proposed. We construct a new network architecture and improved loss functions to achieve conditional reservoir modeling. To improve the ability of extracting connectivity characteristics of variational auto-encoder, the selective attention mechanism is also applied in the proposed SA-VAE model. Besides, to accurate describe the spatial characteristics of connected structures, an innovative strategy by reconstructing the whole structures step by step according to the corresponding structure connectivity is used in SA-VAE. We validate the performance of SA-VAE by using both 2D and 3D datasets with different reservoir structures. The experimental results indicate that our method can reproduce geological structures accurately under the constraints of conditioning data both in morphology and geostatistics. These prove that SA-VAE is able to characterize heterogeneous reservoir structures effectively. [ABSTRACT FROM AUTHOR]

In the last decades, geostatistics has been widely used for precision agriculture (PA) producing quite exciting results. Research on this topic is important for sustainable agriculture growth in Brazil. The objective of the review is an attempt to outline the current state of using geostatistical tools for PA applications in Brazil in the last 20 years (2002–2022), but not to provide an exhaustive review of models. We analyzed the scientific literature on this field in Brazil to identify their merits and weaknesses in the present, and to conjecture on future developments. We analyzed 151 proceeding papers and 144 peer-reviewed journal articles regarding applications of geostatistics in PA in Brazil from 2002 to 2022 using bibliometric techniques to reveal current research trends and hotspots. We detected using geostatistics for PA has been limited, mostly for univariate interpolation purposes. The co-citation analysis reveals four broad research clusters in the literature: (i) spatial variability, semivariogram, soil management, (ii) soil fertility, ordinary kriging, spatial dependence, (iii) coffee plant, coffee, Coffea arabica, and (iv) glycine max, zea mays, management zones. The presented review is a springboard to future modeling developments useful for geostatistics applications to PA in Brazil. We suggest expanding the use of geostatistics for smart agricultural technology by adding new potential approaches in new research. Combined with other approaches, such as machine learning, uncertainty modeling, efforts for more geostatistical training, and data fusion from multi-sensor and multi-source are a new frontier to be explored more often by the Brazilian PA community. [ABSTRACT FROM AUTHOR]

The significance of velocity–resistivity relationships has been utilized in various geologic terrains and under different conditions. The approach is yet to be exploited in tropical granitic terrains, with no definitive empirical relationships being developed. The empirical relationships are critical for rapidly delineating subsurface petrophysical, geomechanical, hydrogeological, and soil–rock features. As a result, a novel approach has been used in this study to develop velocity–resistivity relationships for tropical granitic environments, combining complex collocated velocity (Vp) and resistivity (ρ) models with simple linear regression analysis. The granitic terrain of Penang Island, Malaysia, was chosen as the study area. The geotomographic results delineated three layers, which include the residual soils (topsoil and completely weathered granite), highly to relatively weathered granitic unit (including fractures), and integral/fresh granitic bedrock. Due to the complexity, ruggedness, and varying weathering and fracturing conditions of the subsurface lithologic units in tropical regions, the supervised regression modeling successfully developed a unified and other three specific velocity–resistivity empirical relations for the lithologic units. The derived velocity–resistivity empirical relations have high practical prediction accuracies to predict Vp data. The predicted Vp data and models from the velocity–resistivity relations had good lithological and structural correlations with their observed models. The overall performance of the results indicated that the velocity–resistivity empirical relations could delineate the subsurface geologic variabilities distinctively because they are resistivity-dependent. Hence, the developed comprehensive methodological and SLR workflows and the velocity–resistivity empirical relations were posited for use in granitic terrains with similar geology to the study area, especially in areas with shallow overburden. [ABSTRACT FROM AUTHOR]

Globally, war is the major cause of displacement from the usual place of the biological environment. The war of Tigray exposed thousands of people to internal displacement and migration. Evidence has shown that displaced people and migrants shoulder the health and economic burden to ensure survival. However, evidence of the impact of the war on health and the economy related to the displaced people and their hosting communities is not documented. Thus, this study aimed to investigate the health and economic impact of the war on displaced people and the hosting community. A community-based survey was conducted among randomly selected 3572 households of 48 woredas/districts from August 06 to 30/2021 in Tigray. Each district had 4 enumeration sites and there were 20 households (HHs) to be sampled per each enumeration site. Data were collected using a pretested structured questionnaire using face-to-face interviews of displaced and hosting household heads. The entered data is exported to SPSS version 26 statistical packages for data analysis. Summary statistics and geo-spatial analysis was computed. The war had a significant impact on the health and economy of the community of Internally Displaced People (cIDPs) and hosting households. There were 12,691 cIDPs and 3572 hosting HHs. About 12.3% had chronic illness12.3% of (cIDP) who had chronic diseases and follow-up medication was forced to stop their medication. 536 (15%) civilian family members of cIDPs were killed at their homes. During the war, 244 (6.83%) of civilian family members faced physical disability. Consequentially, 43.8% and 58.8% of respondents of cIDPs suffered from severe depression and post-traumatic stress disorder. The war had a significant amount of personal resources such as domestic animals, cereals, cars, machinery, and HH furniture was looted and vandalized by the perpetrator forces from the cIDPs and hosting HHs. The range of family size in the hosting households was 3 to 22. The war had a significant health and economic impact on both cIDPs and hosting HHs. cIDPs suffered from various illnesses and disabilities related to the war with no medical access and follow-up care leading them to stressful situations such as depression and PTSD. There was also a huge economic damage and distraction which threatens the survival of the survivors. [ABSTRACT FROM AUTHOR]

Accessing and utilizing geospatial data from various sources is essential for developing scientific research to address complex scientific and societal challenges that require interdisciplinary knowledge. The traditional keyword-based geosearch approach is insufficient due to the uncertainty inherent within spatial information and how it is presented in the data-sharing platform. For instance, the Gulf of Mexico Coastal Ocean Observing System (GCOOS) data search platform stores geoinformation and metadata in a complex tabular. Users can search for data by entering keywords or selecting data from a drop-down manual from the user interface. However, the search results provide limited information about the data product, where detailed descriptions, potential use, and relationship with other data products are still missing. Language models (LMs) have demonstrated great potential in tasks like question answering, sentiment analysis, text classification, and machine translation. However, they struggle when dealing with metadata represented in tabular format. To overcome these challenges, we developed Meta Question Answering System (MetaQA), a novel spatial data search model. MetaQA integrates end-to-end AI models with a generative pre-trained transformer (GPT) to enhance geosearch services. Using GCOOS metadata as a case study, we tested the effectiveness of MetaQA. The results revealed that MetaQA outperforms state-of-the-art question-answering models in handling tabular metadata, underlining its potential for user-inspired geosearch services. [ABSTRACT FROM AUTHOR]

This study attempts to design a novel direction–oriented approach for estimating shear wave velocity (VS) through geostatistical methods (GM) using density employing geophysical log data. The research area involves three hydrocarbon wells drilled in carbonate reservoirs that are comprised of oil and water. Firstly, VS was estimated using the four selected empirical rock physics relationships (ERR) in well A (target well), and then all results were evaluated by ten statistical benchmarks. All results show that the best ERR is Greenberg and Castagna, with R2 = 0.8104 and Correlation = 0.90, while Gardner's equation obtained the poorest results with R2 = 0.6766 and correlation = 0.82. Next, Gardner's method was improved through GM by employing Ordinary Kriging (OKr) in two directions in well A, and then Cross-Validation and Jack-knife methods (JKm and CVm, respectively) were used to assess OKr's performance and efficiency. Initially, CVm and JKm were employed to estimate Vs using the available density and its relationship with shear wave velocity, where the performance of CVm was better with R2 = 0.8865 and correlation = 0.94. In this step, some points from the original VS were used to train the data. Finally, Vs was estimated through JKm and using the relationship between the shear wave velocity of two wells near the target well, including wells B and C; however, in this step, the original shear wave velocity of the target well was completely ignored. Reading the results, JKm could show excellent performance with R2 = 0.8503 and Corr = 0.922. In contrast to previous studies that used only Correlation and R-squared (R2), this study further provides accurate results by employing a wide range of statistical benchmarks to investigate all results. In contrast to traditional empirical rock physics relationships, the developed direction-oriented technique demonstrated improved predicted accuracy and robustness in the investigated carbonate field. This work demonstrates that GM can effectively estimate Vs and has a significant potential to enhance VS estimation using density. [ABSTRACT FROM AUTHOR]

[Objective] The present paper aimed to comprehensively and systematically study organic matter in arable soil as well as pH spatial distribution in the entire region of Guizhou, rationally plan and exploit cultivated land, and provide support for food security. [Method] Statistical analysis and ArcGIS space interpolation method were combined to analyze the spatial distribution of organic matter and pH in cultivated soil under the traditional cultivation pattern in Guizhou, and to study the influencing factors. [Result] The spatial distribution of soil organic matter content in cultivated land was uneven, with average content of 31. 13 g/kg, but the organic matter in cultivated land of Guizhou stayed abundant in general, and the content in each region showed as Liupanshui ( 52. 39 g/kg) > Guiyang ( 36. 01 g/kg) > Qianxinan (34. 02 g/kg) > Bijie(33. 89 g/kg) > Qiannan(32. 97 g/kg) > Anshun(31. 28 g/kg) > Qiandongnan(29. 92 g/kg) > Zunyi(25. 59 g/ kg) > Tongren(23. 01 g/kg). The pH of arable soil in Guizhou were 3. 99 - 8. 66, with the average value of 6. 06, and 90. 55% of the arable soils were dominantly acidic, weak acidic and neutral, accounting for 29. 48%, 38. 11 % and 22. 96% respectively. In the study area, the soil types display yellow-brown soil had the highest organic matter content and red soil had the lowest organic matter content The organic matter content with different soil utilization types was paddy field > irrigated land > dry land, while the pH showed as irrigated land > paddy field > dry land. [ Conclusion] The spatial distribution of organic matter content in arable soil of Guizhou province is uneven because of terrain, elevation and other influence factors, but the overall content stayed abundant. The spatial distribution of organic matter demonstrated as high in the west and south, low in the east and north. The pH of the provincial arable soil is mainly acidic or weak acidic, and its spatial distribution appeared uneven, showing a macro pattern of high in the west and north, low in the east and south. Soil type has a significant influence on pH of the soil, while it has insignificant impact on soil organic matter. Land utilizing type has extremely significant effect on both soil organic matter and pH. [ABSTRACT FROM AUTHOR]