Unsampled Locations Research Articles

Advanced Kernel DM+V/W+ method for precise mapping of pollutant dispersion in indoor environments

This study introduces an advanced method for reconstructing pollutant concentration fields in indoor ventilated environments, aimed at enhancing air quality and ensuring occupant health. Our newly developed Kernel DM + V/W+ method significantly refines existing models by accurately capturing variations in pollutant dispersion in downwind areas while minimizing overestimations in upwind zones. This innovative approach overcomes the limitations of the traditional Kernel DM + V method, which neglects the impact of airflow, and the Kernel DM + V/W method, which inadequately differentiates between upwind and downwind dispersion. The effectiveness of this method is validated through a dataset derived from previously confirmed computational fluid dynamics (CFD) simulations across two standard indoor ventilation scenarios. A subset of this dataset was randomly selected for evaluation using the three methods, enabling a detailed performance analysis. The results demonstrate that the Kernel DM + V/W+ method markedly enhances accuracy in estimating concentrations at unsampled locations and significantly improves the identification of pollution sources, providing substantial benefits for environmental engineering applications.

Spatial distribution of soil rare earth elements in Sicily (Italy)

Rare earth elements (REEs) are becoming increasingly interesting as indicators of soil processes, and modelling their spatial distribution has become a fundamental requirement for this purpose. This study aims to model and quantify the spatial distribution of soil REEs taking into account their compositional nature in their mapping, and to assess the associated spatial uncertainty. In particular, the cerium anomaly (Ce⁎) and the ratio of the sum (Σ) of light to heavy REEs (ΣLREEs/ΣHREEs) were calculated and analysed. The study was carried out in Sicily (Italy), one of the largest (25,832 km2) and most populous island in the Mediterranean Sea. Soil REE data were obtained from the soil database of Sicily region. Soil REEs were transformed into a vector of isometric log-ratio (ilr) coordinates and analysed using a geostatistical approach for predicting their values at unsampled locations and generating 100 REEs realizations using turning bands simulation. Each REE element and Ce⁎ were mapped as well as the ΣLREEs/ΣHREEs ratio. The joint variability of the investigated REEs and the ΣLREEs/ΣHREEs ratio provided insights into their abundance and distribution patterns. From the simulated realizations, standard deviation maps were calculated for the Ce⁎ and the ΣLREEs/ΣHREEs ratio, providing a measure of their spatial uncertainty. Although with some limitations, the study established a first baseline of the Ce⁎ and the ΣLREEs/ΣHREEs ratio. The maps of spatial uncertainty may be a useful tool for planning future soil sampling and optimise the choice of new sampling locations.

Reconstruction of Baltic Gridded Sea Levels from Tide Gauge and Altimetry Observations Using Spatiotemporal Statistics from Reanalysis

A method for reconstructing weekly Baltic gridded sea levels was developed and tested. This method uses input data from tide gauge and altimetry observations. The reconstruction is based on sea level empirical orthogonal function (EOF) modes, calculated as spatiotemporal statistics from daily model reanalysis results available from the Copernicus Marine Service for the 1993–2021 period. In the semi-enclosed, tideless Baltic Sea, the four leading EOF modes cover 99% of the sea level variance. Several experiments with different combinations of input data were carried out. This method was validated against coastal tide gauges and altimetry data. The best reconstruction was obtained when both the tide gauges and altimetry data were used as inputs. An assessment of the centered root-mean-square difference (cRMSD) of the reconstruction versus the tide gauges revealed a value of 0.05 m, and a result of 0.10 m was revealed versus altimetry. The average coefficient of determination (R2) was determined to be 0.93 for the tide gauges and 0.82 for the altimetry data. In the cases where only one type of input data was used, the reconstruction worsened with respect to other data sources. The reconstruction method demonstrated its usefulness for the reconstruction of coastal sea levels in unsampled locations and the calculation of changes in sea volume.

PEMETAAN JUMLAH KRIMINALITAS DI KALIMANTAN SELATAN MENGGUNAKAN METODE CO-KRIGING DAN INVERSE DISTANCE WEIGHTING

AbstractCriminality is any form of action that harms economically and psychologically and violates the law, social norms and religion. South Kalimantan is in 21st position with an average of 3,282 criminal cases per year in the period 2018 to 2020. One method to interpolate data in unsampled locations is the co-kriging and inverse distance weighting (IDW) method. This study aims to estimate the number of crimes in South Kalimantan with the co-kriging and IDW methods, compare the results to determine the best method, and present a thematic map of the distribution of crime in South Kalimantan. In the co-kriging method, the number of crimes is the primary variable and population density is the secondary variable. The result of this research is that between the two methods, IDW is better for interpolation in estimating the crime value than the co-kriging method. The IDW method provides good interpolation results whose interpolation value is close to the value of the real data. Although co-kriging has the potential to provide accurate results if the cross-variogram model can be formed properly, the constraints faced in this study indicate that IDW remains a more stable and reliable choice in estimating crime in South Kalimantan. Keywords: Crime, Co-kriging, Inverse Distance Weighting

Geographic principles applied to population dynamics: A spatially interpolated integrated population model

Abstract A major impediment to wildlife conservation and management, from a quantitative perspective, is dealing with high degrees of uncertainty associated with population estimates. Integrated population models (IPMs) can help alleviate that challenge, but they are often limited to narrow spatial or temporal windows owing to the financial and logistical burdens of acquiring requisite datasets. To expand the spatiotemporal scope of practical IPM implementation, we developed a novel method that expresses demographic relatedness among sampled and unsampled locations using geographic principles of spatial autocorrelation. We interpolated demographic parameters at unsampled locations using parameter estimates from data‐informed locations. Errors attributable to the interpolative process were corrected using a joint likelihood and locally recorded count data (‘cheaper’ and broadly distributed). We evaluated the spatially interpolated IPM (SIIPM) for precision and accuracy under variable levels of spatial autocorrelation using simulated data and a Leave‐One‐Out Cross‐Validation (LOOCV) technique. Conventional IPMs and state‐space models (SSM) were fit to the same simulated datasets to provide a comparative assessment of the novel method. In a final, empirical demonstration we fit the SIIPM to data collected from Greater Sage‐Grouse (Centrocercus urophasianus; sage‐grouse) populations located in Nevada, U.S.A. during 2013–2021. SIIPMs outperformed conventional IPMs when fit to data possessing moderate‐to‐high levels of spatial autocorrelation. Under moderate levels of autocorrelation, the average improvement in parameter estimation was 13.6% for survival, 65.3% for recruitment and 23.7% for rate of population change (). When spatial autocorrelation was low, the SIIPM still outperformed contemporary approaches in areas that were geographically close (<67 km) to sampling locations. Under low autocorrelation‐near distance scenarios, we observed SIIPM parameters that were 30.8% (recruitment), 32.5% (; IPM comparison) and 54.0% (; SSM comparison) more precise than contemporary models. Spatial autocorrelation is often assumed but rarely tested when comparing population dynamics across regions of large geographic extent. We demonstrated that SIIPMs can improve precision of species' vital rate estimation when extrapolating model inference beyond populations for which long‐term monitoring data exists. Specific to sage‐grouse, these results support previous conclusions of broad‐scale spatial autocorrelation in population dynamics and a reproductive‐survival trade‐off previously documented at smaller scales.

Hybrid Machine Learning and Geostatistical Methods for Gap Filling and Predicting Solar-Induced Fluorescence Values

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.

Prediction of hourly wind speed time series at unsampled locations using machine learning

Various models for wind speed mapping have been developed, with increasing attention on models focusing on mapping wind speed distribution. This study extends these models to predict hourly wind speed time series at unsampled locations. A model based on the quantile mapping (QM) procedure was compared to a traditional and machine-learning model to interpolate wind speed spatially. These proposed models were also used with inputs from the ERA5 reanalysis dataset, enabling them to consider local variation in orography and large-scale wind fields. A widely used procedure for mean bias correction of reanalysis based on the Global Wind Atlas (GWA) was implemented and compared to the proposed models. It was found that the QM and machine learning model, both using input from ERA5, significantly outperformed GWA bias correction in terms of time series correlation and probability distribution. Despite being more computationally intensive than GWA bias correction, both models are recommended due to their significantly (in a statistical sense) superior performance.

Geostatistics and GIS as a Technique for Chemical Soil Properties Mapping

Soil properties play a fundamental role in agriculture, and understanding their spatial distribution is essential for effective land management and increasing agricultural productivity. This study focuses on the application of geostatistics and Geographic Information Systems (GIS) as techniques for mapping soil properties, in the case of the Mozambique Sugar cane company (ADM) in Mafambisse, in the province of Sofala, Mozambique. The central objective is to demonstrate how geostatistics and GIS can be used to characterize and map soil properties. The research was materialized using field surveys that estimated the pH in the field of several sampling points at different depths in the irrigated perimeter of the ADM. Geostatistics is applied, using methods such as kriging and variogram analysis, allowing the creation of continuous maps that represent soil properties in unsampled locations. GIS is an essential tool for integrating geostatistical data and creating accessible and informative maps. The results of this study indicate that the combination of geostatistics and GIS offers an effective approach for mapping soil properties. This knowledge is crucial for Mozambique sugar cane company, an important company in the sugar industry, to optimize its agricultural practices, increase productivity and minimize environmental impacts. Thus, offering solid bases for making informed and sustainable agricultural decisions, contributing to efficient agricultural development in Mozambique and other regions with similar challenges.

Linear Geostatistical Estimating Methods Compared Using Annual Average Daily Traffic Data from Low-Volume Roads in Minnesota

Geostatistical methods are utilized to analyze and predict values associated with spatial or spatiotemporal phenomena. These techniques incorporate both the spatial and, in some cases, temporal coordinates of the data. The mining industry prefers these methods for ore reserve estimation, and they have also gained popularity in other specialized fields. Notably, the transportation industry is gradually adopting them to estimate annual average daily traffic (AADT). Although researchers use various predictive techniques to estimate AADT, many of them are experiential or based on simple subjective assessments, especially for low-volume roadways. The shortcomings of these assessment techniques include high margins of error, non-integrated spatial and temporal variability in the datasets, and inadequate consideration of total coverage. Geostatistical techniques are superior predictive tools in many fields because their algorithms account for these limitations. Moreover, geostatistical methods can predict data at both sampled and unsampled locations with minimal available data. Therefore, the crucial question arises as to which method best estimates AADT. This paper focuses on investigating linear univariate geostatistical methods, namely empirical Bayesian Kriging (EBK), ordinary Kriging (OK), simple Kriging (SK), and universal Kriging (UK), along with proportional valuations, to estimate AADT on low-volume roadways and provides a recommendation based on the findings. The study utilizes AADT data from 1,486 and 2,731 locations on low-volume roads throughout Minnesota in 2009 and 2016, respectively. The results obtained from this investigation hold significant systematic and practical implications for estimating AADT on low-volume roads and can be adapted for high-volume roadways as well.

Spatial analysis of modern contraceptive use among women who need it in Ethiopia: Using geo-referenced data from performance monitoring for action.

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. 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. 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. 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.

Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification

Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar petrophysical and flow characteristics. Flow Zone Indicator (FZI), a crucial measure derived from pore throat size, permeability, and porosity, serves as a key parameter, but its determination is time-consuming and expensive. The objective is to employ supervised and unsupervised machine learning to predict FZI and classify the reservoir into distinct HFUs. Unsupervised learning using K-means clustering and supervised algorithms including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were employed. FZI values from RCAL data formed the basis for model training and testing, then the developed models were used to predict FZI in unsampled locations. A methodical approach involves 3 k-fold cross-validation and hyper-parameter tuning, utilizing the random search cross-validation technique over 50 iterations was applied to optimize each model. The four applied algorithms indicate high performance with coefficients determination (R2) of 0.89 and 0.91 in training and testing datasets, respectively. RF showed the heist performance with training and testing R2 values of 0.957 and 0.908, respectively. Elbow analysis guided the successful clustering of 212 data points into 10 HFUs using k-means clustering and Gaussian mixture techniques. The high-quality reservoir zone was successfully unlocked using the unsupervised technique. It has been discovered that the areas between 2370–2380 feet and 2463–2466 feet are predicted to be high-quality reservoir potential areas, with average FZI values of 500 and 800, consecutively. The application of machine learning in reservoir characterization is deemed highly valuable, offering rapid, cost-effective, and precise results, revolutionizing decision-making in field development compared to conventional methods.

Protocol for spatial prediction of soil transmitted helminth prevalence in the Western Pacific region using a meta-analytical approach

BackgroundSoil transmitted helminth (STH) infections are estimated to impact 24% of the world’s population and are responsible for chronic and debilitating morbidity. Disadvantaged communities are among the worst affected and are further marginalized as infection prevalence fuels the poverty cycle. Ambitious targets have been set to eliminate STH infections, but accurate epidemiological data will be required to inform appropriate interventions. This paper details the protocol for an analysis that aims to produce spatial prediction mapping of STH prevalence in the Western Pacific Region (WPR).MethodsThe protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) guidelines. The study design will combine the principles of systematic review, meta-analysis, and geospatial analysis. Systematic searches will be undertaken in PubMed, Scopus, ProQuest, Embase, and Web of Science for studies undertaken post 2000, to identify surveys that enable the prevalence of human STH infection within the WPR to be calculated. Covariate data for multivariable analysis will be obtained from publicly accessible sources. Survey data will be geolocated, and STH prevalence and covariates will be linked to produce a spatially referenced dataset for analysis. Bayesian model-based geostatistics will be used to generate spatially continuous estimates of STH prevalence mapped to a resolution of 1 km2. A separate geospatial model will be constructed for each STH species. Predictions of prevalence will be made for unsampled locations and maps will be overlaid for each STH species to obtain co-endemicity maps.DiscussionThis protocol facilitates study replication and may be applied to other infectious diseases or alternate geographies. Results of the subsequent analysis will identify geographies with high STH prevalence’s and can be used to inform resource allocation in combating this neglected tropical disease.Trial registrationOpen Science Framework: osf.io/qmxcj.

A new interpolation method to resolve under-sampling of UAV-lidar snow depth observations in coniferous forests

Obtaining accurate snow depth estimates under dense canopies using airborne lidar (light detection and ranging) techniques is challenging due to the under-sampling of ground and snow surfaces. Existing interpolation techniques do not adequately address this problem and they often result in an overestimation of under-canopy snow depths. To address this issue, we introduce and evaluate a new interpolation method that incorporates intra-canopy snow depth variability to provide more accurate estimations at unsampled locations. Four interpolation methods were tested, considering systematic trends (landscape trend, canopy vs. gap trend, and intra-canopy trend) along with spatial interpolation of the residuals. Our results show that spatial interpolation methods without consideration of trends are sufficient to capture and reconstruct the small-scale variability of snow depths below a separation distance of 1 m between sampled and unsampled locations, (i.e., ground surface point density > 1 pt. m−2). However, beyond a separation distance of 2.5–3 m (point density < 0.33–0.40 pt. m−2), spatial interpolation based on proximity alone becomes unreliable because point separation becomes larger than the snow depth spatial correlation scale. Within these limiting distances, the method that incorporates trends along with spatial interpolation techniques can resolve the small-scale variability and thereby reduce the likely overestimation of snow depths under the canopy.

Spatial classification in the presence of measurement error

In recent decades, spatial classification has received considerable attention in a wide array of disciplines. In practice, binary response variable is often subject to measurement error, misclassification. To account for the misclassified response in spatial classification, we proposed validation data-based adjustment methods that use interval validation data to rectify misclassified responses. Regression calibration and multiple imputation methods are utilized to correct the misclassified outcomes at the locations where the gold-standard device is not available. Generalized linear mixed model and indicator Kriging are applied for spatial classification at unsampled locations. Simulation studies are performed to compare the proposed methods with naive methods that ignore the misclassification. It was found that the proposed models significantly improve prediction accuracy. Additionally, the proposed models are applied for precipitation detection in South Korea.

Directional Differences in Thematic Maps of Soil Chemical Attributes with Geometric Anisotropy

In the study of the spatial variability of soil chemical attributes, the process is considered anisotropic when the spatial dependence structure differs in relation to the direction. Anisotropy is a characteristic that influences the accuracy of the thematic maps that represent the spatial variability of the phenomenon. Therefore, the linear anisotropic Gaussian spatial model is important for spatial data that present anisotropy, and incorporating this as an intrinsic characteristic of the process that describes the spatial dependence structure improves the accuracy of the spatial estimation of the values of a georeferenced variable in unsampled locations. This work aimed at quantifying the directional differences existing in the thematic map of georeferenced variables when incorporating or not incorporating anisotropy into the spatial dependence structure through directional spatial autocorrelation. For simulated data and soil chemical properties (carbon, calcium and potassium), the Moran directional index was calculated, considering the predicted values at unsampled locations, and taking into account estimated isotropic and anisotropic geostatistical models. The directional spatial autocorrelation was effective in evidencing the directional difference between thematic maps elaborated with estimated isotropic and anisotropic geostatistical models. This measure evidenced the existence of an elliptical format of the subregions presented by thematic maps in the direction of anisotropy that indicated a greater spatial continuity for greater distances between pairs of points.

Deep Filtered Back Projection for CT Reconstruction.

Filtered back projection (FBP) is a classic analytical algorithm for computed tomography (CT) reconstruction, with high computational efficiency. However, images reconstructed by FBP often suffer from excessive noise and artifacts. The original FBP algorithm uses a window function to smooth signals and a linear interpolation to estimate projection values at un-sampled locations. In this study, we propose a novel framework named DeepFBP in which an optimized filter and an optimized nonlinear interpolation operator are learned with neural networks. Specifically, the learned filter can be considered as the product of an optimized window function and the ramp filter, and the learned interpolation can be considered as an optimized way to utilize projection information of nearby locations through nonlinear combination. The proposed method remains the high computational efficiency of the original FBP and achieves much better reconstruction quality at different noise levels. It also outperforms the TV-based statistical iterative algorithm, with computational time being reduced in an order of two, and state-of-the-art post-processing deep learning methods that have deeper and more complicated network structures.

Comparative Study of Geospatial Techniques for Interpolating Groundwater Quality Data in Agricultural Areas of Punjab, Pakistan

Groundwater Arsenic (As) data are often sparse and location-specific, making them insufficient to represent the heterogeneity in groundwater quality status at unsampled locations. Interpolation techniques have been used to map groundwater As data at unsampled locations. However, the results obtained from these techniques are affected by various inherent and external factors, which lead to uncertainties in the interpolated data. This study was designed to determine the best technique to interpolate groundwater As data. We selected ten interpolation techniques to predict the As concentration in the groundwater resources of Punjab, Pakistan. Two external factors, the spatial extent of the study area and data density, were considered to assess their impact on the performance of interpolation techniques. Our results show that the Inverse Distance Weighting (IDW) and Spline interpolation techniques demonstrate the highest accuracy with the lowest RMSE (13.5 ppb and 16.7 ppb) and MAE (87.8 ppb and 89.5 ppb), respectively, while the Natural Neighbor technique shows the lowest accuracy with the highest RMSE (2508.7 ppb) and MAE (712.1 ppb) to interpolate groundwater As data. When the study area’s extent was modified, IDW showed the best performance, with errors within ±1.5 ppb for 95% of the wells across the study area. While data density has a positive correlation with interpolation accuracy among all techniques, the IDW remained the best method for interpolation. It is therefore concluded that IDW should be used to interpolate groundwater quality data when observed data are sparse and randomly distributed. The utilization of IDW can be useful for As monitoring and management in groundwater resources.

A criterion and incremental design construction for simultaneous kriging predictions

In this paper, we further investigate the problem of selecting a set of design points for universal kriging, which is a widely used technique for spatial data analysis. Our goal is to select the design points in order to make simultaneous predictions of the random variable of interest at a finite number of unsampled locations with maximum precision. Specifically, we consider as response a correlated random field given by a linear model with an unknown parameter vector and a spatial error correlation structure. We propose a new design criterion that aims at simultaneously minimizing the variation of the prediction errors at various points. We also present various efficient techniques for incrementally building designs for that criterion scaling well for high dimensions. Thus the method is particularly suitable for big data applications in areas of spatial data analysis such as mining, hydrogeology, natural resource monitoring, and environmental sciences or equivalently for any computer simulation experiments. We have demonstrated the effectiveness of the proposed designs through two illustrative examples: one by simulation and another based on real data from Upper Austria.

Integrating disparate survey data in species distribution models demonstrate the need for robust model evaluation

Marine spatial planning and conservation initiatives benefit from an understanding of species distributions across larger geographic areas than are often sampled by any one survey. Here, we test whether the integration of disparate survey data can improve habitat predictions across a region not well sampled by a single survey using Dungeness crab ( Metacarcinus magister) from British Columbia as a case study. We assemble data from dive, trawl, and baited-trap surveys to generate six candidate generalized linear mixed-effect models with spatial random fields. To compare single-survey and integrated models, we evaluate predictive performance with spatially buffered leave-one-out cross-validation and independently with two novel approaches using fisheries catch data. We find improved predictive performance and reduced uncertainty when integrating data from surveys that suffer from small sample size, low detectability, or limited spatial coverage. We demonstrate the importance of robust model evaluation when integrating data and predicting to unsampled locations. In addition, we highlight the need for careful consideration of sampling biases and model assumptions when integrating data to reduce the risk of prediction errors.

CoYangCZ: a new spatial interpolation method for nonstationary multivariate spatial processes

In multivariate spatial interpolation, the accuracy of a variable of interest can be improved using ancillary variables. Although geostatistical methods are widely used for multivariate spatial interpolation, these methods usually require second-order stationary assumption of spatial processes, which is difficult to satisfy in practice. We developed a new multivariate spatial interpolation method based on Yang-Chizhong filtering (CoYangCZ) to overcome this limitation. CoYangCZ does not solve the multivariate spatial interpolation problem from a purely statistical point of view but integrates geometry and statistics-based strategies. First, we used a weighted moving average method based on binomial coefficients (i.e. Yang-Chizhong filtering) to fit the spatial autocorrelation structure of each spatial variable from a geometric perspective. We then quantified the spatial autocorrelation of each spatial variable and the correlations between different spatial variables by analyzing the variances of different spatial variables. Finally, we obtain the best linear unbiased estimators at the unsampled locations. Experiments on air pollution and meteorological datasets show that CoYangCZ has a higher interpolation accuracy than cokriging, regression kriging, gradient plus-inverse distance squared, sequential Gaussian co-simulation, and the kriging convolutional network. CoYangCZ can adapt to second-order non-stationary spatial processes; therefore, it has a wider scope of application than purely statistical methods.