Spatial Estimation Research Articles

Spatial modeling of the water table and its historical variations in Northeastern Italy via a geostatistical approach

Groundwater resources are the most important freshwater source for any human use and its preservation in the context of climatic variability is of crucial importance. So far, no systematic spatial estimation of the water table in Northeastern Italy has ever been made available, whereas the Veneto Region (North-Eastern Italy) provides a free database with recorded water table measurements between 1999 and 2022. These starting data have been cleaned, leading to a final database of 53 yearly water table data. The lack of knowledge about the spatial estimation of the water table within the historical period is overcome through the well-known geostatistical technique of cokriging. The latter is the most suitable approach for reproducing water table elevations upon local terrain elevation as an auxiliary variable. Results underline modest decrements between 1999 and 2022, which are more pronounced in the Prealps area and in general in sedimentary soils, and less in the Fontanili belt, a crucial freshwater source area. Moreover, the estimated water table change is highly non-linear in time from year to year and highly varying in space. The results seem particularly interesting in the plain areas and within the Fontanili belt, where the management of groundwater resources is becoming crucial. However, the spatial estimation in the Prealps area needs to be improved for a better assessment and future reliable predictions.

Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization

To facilitate a prospective estimation of the effective dose of an CT scan prior to the actual scanning in order to use sophisticated patient risk minimizing methods, a prospective spatial dose estimation and the known anatomical structures are required. To this end, a CT reconstruction method is required to reconstruct CT volumes from as few projections as possible, i.e. by using the topograms, with anatomical structures as correct as possible. In this work, an optimized CT reconstruction model based on a generative adversarial network (GAN) is proposed. The GAN is trained to reconstruct 3D volumes from an anterior-posterior and a lateral CT projection. To enhance anatomical structures, a pre-trained organ segmentation network and the 3D perceptual loss are applied during the training phase, so that the model can then generate both organ-enhanced CT volume and organ segmentation masks. The proposed method can reconstruct CT volumes with PSNR of 26.49, RMSE of 196.17, and SSIM of 0.64, compared to 26.21, 201.55 and 0.63 using the baseline method. In terms of the anatomical structure, the proposed method effectively enhances the organ shapes and boundaries and allows for a straight-forward identification of the relevant anatomical structures. We note that conventional reconstruction metrics fail to indicate the enhancement of anatomical structures. In addition to such metrics, the evaluation is expanded with assessing the organ segmentation performance. The average organ dice of the proposed method is 0.71 compared with 0.63 for the baseline model, indicating the enhancement of anatomical structures.

Modeling health and well-being measures using ZIP code spatial neighborhood patterns

Individual-level assessment of health and well-being permits analysis of community well-being and health risk evaluations across several dimensions of health. It also enables comparison and rankings of reported health and well-being for large geographical areas such as states, metropolitan areas, and counties. However, there is large variation in reported well-being within such large spatial units underscoring the importance of analyzing well-being at more granular levels, such as ZIP codes. In this paper, we address this problem by modeling well-being data to generate ZIP code tabulation area (ZCTA)-level rankings through spatially informed statistical modeling. We build regression models for individual-level overall well-being index and scores from five subscales (Physical, Financial, Social, Community, Purpose) using individual-level demographic characteristics as predictors while including a ZCTA-level spatial effect. The ZCTA neighborhood information is incorporated by using a graph Laplacian matrix; this enables estimation of the effect of a ZCTA on well-being using individual-level data from that ZCTA as well as by borrowing information from neighboring ZCTAs. We deploy our model on well-being data for the U.S. states of Massachusetts and Georgia. We find that our model can capture the effects of demographic features while also offering spatial effect estimates for all ZCTAs, including ones with no observations, under certain conditions. These spatial effect estimates provide community health and well-being rankings of ZCTAs, and our method can be deployed more generally to model other outcomes that are spatially dependent as well as data from other states or groups of states.

Spatial Decay Estimates and Continuous Dependence for the Oldroyd Fluid

This article investigates the Oldroyd fluid, which is widely used in industrial and engineering environments. When the Oldroyd fluid passes through a three-dimensional semi-infinite cylinder, the asymptotic properties of the solutions are established. On this basis, we also studied the continuous dependence of the viscosity coefficient.

Examining inequalities in spatial access to national health insurance fund contracted facilities in Kenya.

Kenya aims to achieve universal health coverage (UHC) by 2030 and has selected the National Health Insurance Fund (NHIF) as the 'vehicle' to drive the UHC agenda. While there is some progress in moving the country towards UHC, the availability and accessibility to NHIF-contracted facilities may be a barrier to equitable access to care. We estimated the spatial access to NHIF-contracted facilities in Kenya to provide information to advance the UHC agenda in Kenya. We merged NHIF-contracted facility data to the geocoded inventory of health facilities in Kenya to assign facility geospatial locations. We combined this database with covariates data including road network, elevation, land use, and travel barriers. We estimated the proportion of the population living within 60- and 120-minute travel time to an NHIF-contracted facility at a 1-x1-kilometer spatial resolution nationally and at county levels using the WHO AccessMod tool. We included a total of 3,858 NHIF-contracted facilities. Nationally, 81.4% and 89.6% of the population lived within 60- and 120-minute travel time to an NHIF-contracted facility respectively. At the county level, the proportion of the population living within 1-hour of travel time to an NHIF-contracted facility ranged from as low as 28.1% in Wajir county to 100% in Nyamira and Kisii counties. Overall, only four counties (Kiambu, Kisii, Nairobi and Nyamira) had met the target of having 100% of their population living within 1-hour (60min) travel time to an NHIF-contracted facility. On average, it takes 209, 210 and 216min to travel to an NHIF-contracted facility, outpatient and inpatient facilities respectively. At the county level, travel time to an NHIF-contracted facility ranged from 10min in Vihiga County to 333min in Garissa. Our study offers evidence of the spatial access estimates to NHIF-contracted facilities in Kenya that can inform contracting decisions by the social health insurer, especially focussing on marginalised counties where more facilities need to be contracted. Besides, this evidence will be crucial as the country gears towards accelerating progress towards achieving UHC using social health insurance as the strategy to drive the UHC agenda in Kenya.

Scaling Landscape Fire History: Wildfires Not Historically Frequent in the Main Population of Threatened Gunnison Sage-Grouse

The main population of ~5000 threatened Gunnison sage-grouse (GUSG; Centrocercus minimus) in Colorado depends on sagebrush plants that are killed by wildfires, with recovery taking decades, so frequent fire is a threat, but did it occur historically? Early land surveys showed that the historical (preindustrial) fire rotation (FR), the expected period to burn area equal to a focal land area, was 90–143 years in GUSG ranges, which is not classed as frequent fire (≤25 years). However, recent research, based on fire scars on trees at ten sites near sagebrush, suggested some frequent fire historically in the main population. That study was not spatial, essential to estimate FR, so spatial data were created in GIS with land-survey reconstructions, survey dates, fire-scar sites, mapped sagebrush, and Thiessen polygons around sites. The previous study assumed fires that burned 2+ sites likely burned across sagebrush. Historical FRs were calculated several ways over a common period. A recovery estimate of FR was 90–135 years, a land-survey estimate was 82–131 years, and three spatial scar-based estimates were 93–107 years, showing agreement. However, the comparison found that only 8.8% of the land-survey fire area was detected at fire-scar sites. Detailed analysis showed that 10 fire-scar sites were insufficient to detect historical fire sizes and distributions across the large 168,753 ha sagebrush area. Adequate fire reconstruction could require ~45–60 fire-scar sites, making it feasible to study only ~30,000 ha of sagebrush. Using the two remaining methods, which cross-validate, showed frequent fire did not occur historically in the study area, as historical FRs were 82–135 years.

THE IMPACT OF MINING EXCAVATIONS (VOIDS) ON THE WATER QUALITY MANAGEMENT IN THE NORTH KALIMANTAN PROVINCE

The presence of a coal mining business permit not only has an economic impact but can also cause environmental problems. The objectives of this research are (1) to analyze the impact of PT XXX's overflowing voids on water management and water quality, and (2) to carry out spatial analysis, mapping and estimation of coal reserves that cannot be extracted by PT YYY. The research method is a case study selected by purposive sampling at two companies in North Kalimantan Province using water quality test analysis with grab sampling and estimating the potential loss of coal reserves using the rule of gradual changes and rule of nearest point methods as well as aerial photography. The research results show that in general the water use meets Class 3, namely: Water that can be used for cultivating freshwater fish, animal husbandry, water for irrigating crops, and/or other uses that require the same water quality as that use. Apart from that, PT XXX has built an overflow water channel into the PT YYY area without management approval, thus having a negative impact on PT YYY's mining plans which are dominated by peat land. Map of PT YYY's lost coal potential of 714,688 MT with an affected area of 15.16 hectares with details of 6.4 ha being the void area included in PT YYY's IUP area, 1.86 ha being the road and buffer zone area and 6.9 ha is a potential lost area of PT YYY.

Spatial Structure Matching-Based DoA Estimation and Tracking for Integrated Sensing and Communication Massive MIMO OFDM System

Spatial Structure Matching-Based DoA Estimation and Tracking for Integrated Sensing and Communication Massive MIMO OFDM System

Graph-Based Spatial Segmentation of Areal Data

Smoothing is often used to improve the readability and interpretability of noisy areal data. However, there are many instances where the underlying quantity is discontinuous. For such cases, specific methods are needed to estimate the piecewise constant spatial process. A well-known approach in this setting is to perform segmentation of the signal using the adjacency graph, such as the graph-based fused lasso. However, this method does not scale well to large graphs. A new method is introduced for piecewise constant spatial estimation that (i) is faster to compute on large graphs and (ii) yields sparser models than the fused lasso (for the same amount of regularization), resulting in estimates that are easier to interpret. The method is illustrated on simulated data and applied to real data on overweight prevalence in the Netherlands. Healthy and unhealthy zones are identified, which cannot be explained by demographic or socio-economic characteristics alone. The method is found capable of identifying such zones and can assist policymakers with their health improving strategies.

Training flexible spatial-cognitive estimation strategies using augmented reality

We investigated the potential for augmented reality (AR) as a training aid for spatial estimation skills. Though there are many tools to support spatial judgments, from measuring cups to rulers, not much is known about training spatial skills for retention and transfer. Display of AR was manipulated to train the spatial skill of portion estimation. In Experiment 1, an AR-aided strategy of creating smaller portions out of a larger example amount was compared to a no-AR control condition. This manipulation was based on previous non-AR experiments where amorphous foods were better estimated when divided into smaller portions. There was a significant benefit of estimating using a solid AR shape. In Experiment 2, cognitive anchoring was manipulated. Using meaningful AR anchors resulted in the best performance and most learning. We conclude that spatial estimation skills can be combined with mental strategies and trained via AR.

Recommendation system using bio-inspired algorithms for urban orchards

According to the Food and Agriculture Organization of the United Nations (FAO), climate change is exponentially affecting agricultural production worldwide, with food prices expected to increase by up to 90 percent by 2030 and hunger and malnutrition rates to rise by 2050. This paper presents the development of a platform based on the Internet of Things (IoT) for monitoring urban gardens as a strategy to mitigate hunger, promote food sovereignty and circular economy in areas of food shortage. To this end, an Internet of Things (IoT) architecture is proposed and implemented that involves a social design layer that allows an effective transfer of knowledge to communities and a recommendation system based on evolutionary computation to optimize and maximize the productivity of urban orchards, and thus contribute to the 2030 agenda of the Sustainable Development Goals (SDGs). Finally, three experiments in urban gardens are shown to validate evolutionary computation and artificial intelligence models, such as multiple linear regression, genetic algorithms, ant colony algorithms and spatial estimation and inference algorithms such as the Kriging algorithm. The productivity of urban lettuce orchards is increased between 25 and 45%.

Spatial Analysis of High-Tech Export Performance of the Eastern European Member States of the EU

Some Central and East European countries have integrated into the European Union after 2004 and followed free market economy principles. This paper studies the spatial spillovers effects of high-tech export, Gross Domestic Product per capita, research and development expenditure, number of research and development full time equivalent, and technology accumulation among European Union member Central and East European countries over the period 2007-2020 by using spatial panel estimation models. The main purpose of the paper is to investigate whether high-tech export of European Union member Central and East European countries has a dependency and interaction with each other after EU membership. The estimation results of spatial panel model results reveal that an increment in high-tech exports of adjacent countries increases the high-tech exports of other regional countries. Our results also provide evidence on Gross Domestic Product per capita, and research and development expenditure of European Union member transition economies have positive and significant impacts on high-tech exports of those countries.

SPATIAL ESTIMATION OF SOIL EROSION USING GEOSPATIAL TECHNIQUE – A CASE STUDY OF JANJEVA RIVER CATCHMENT (KOSOVO)

The aim of the study is to analyse the spatial estimation of soil erosion in a small size river catchment of Janjeva (Kosovo). Soil erosion is becoming a serious threat and its spatial estimation is crucial for soil resource protection and management. The Revised Universal Soil Loss Equation (RUSLE) methodology integrated with GIS techniques was applied to estimate the annual erosion rate. Different datasets including open-source data were used to find main components of soil erosion. The results show different soil erosion classes depending on lithological settings, landforms, climate conditions, soil properties, land cover and soil conservation practices. By calculating RUSLE parameters for Janjeva River catchment (84 km²), we found out that mean annual erosion rate is 1 t·ha-¹·yr-¹. Erosion rate class <5 t·ha-¹·yr-¹ dominates the catchment’s area with 92.5%, while other classes have 7.5% of catchments total area. Highest rates of soil erosion are found in volcanic rocks, steep slopes and soils without conservation practices. Soils located in western part of the catchment with adequate conservation practice have minimum erosion rate.

The national distribution of lymphatic filariasis cases in Malawi using patient mapping and geostatistical modelling.

In 2020 the World Health Organization (WHO) declared that Malawi had successfully eliminated lymphatic filariasis (LF) as a public health problem. Understanding clinical case distributions at a national and sub-national level is important, so essential care packages can be provided to individuals living with LF symptoms. This study aimed to develop a national database and map of LF clinical cases across Malawi using geostatistical modelling approaches, programme-identified clinical cases, antigenaemia prevalence and climate information. LF clinical cases identified through programme house-to-house surveys across 90 sub-district administrative boundaries (Traditional Authority (TA)) and antigenaemia prevalence from 57 sampled villages in Malawi were used in a two-step geostatistical modelling process to predict LF clinical cases across all TAs of the country. First, we modelled antigenaemia prevalence in relation to climate covariates to predict nationwide antigenaemia prevalence. Second, we modelled clinical cases for unmapped TAs based on our antigenaemia prevalence spatial estimates. The models estimated 20,938 (95% CrI 18,091 to 24,071) clinical cases in unmapped TAs (70.3%) in addition to the 8,856 (29.7%), programme-identified cases in mapped TAs. In total, the overall national number of LF clinical cases was estimated to be 29,794 (95% CrI 26,957 to 32,927). The antigenaemia prevalence and clinical case mapping and modelling found the highest burden of disease in Chikwawa and Nsanje districts in the Southern Region and Karonga district in the Northern Region of the country. The models presented in this study have facilitated the development of the first national LF clinical case database and map in Malawi, the first endemic country in sub-Saharan Africa. It highlights the value of using existing LF antigenaemia prevalence and clinical case data together with modelling approaches to produce estimates that may be used for the WHO dossier requirements, to help target limited resources and implement long-term health strategies.

Measuring The Spatio-Temporal Distribution of Sulfur Dioxide (SO2) with Copernicus Sentinel-5P Near Real Time in Medan City

This study aims to monitor the impact of sulfur dioxide (SO2), a significant contributor to atmospheric corrosion and associated with air pollution, in urban and industrial areas through empirical models of spatial distribution and temporal estimation in Medan. Datasets of Sentinel 5P NRTI are worth using to monitor the formation of sulfate aerosols or SO2 emissions from natural sources and anthropogenic origin in near real-time from 2019 until 2023 on the local scale, Medan. The source dataset covers 1,11 km of spatial image resolution on orbital in daily revisit time with a spectral bound between the ultraviolet and the shortwave infrared. The measurement of atmospheric SO2 concentrations is generated by enumerating the SO2 vertical column density technique at the soil surface with the cloud-based platform The Earth Engine (EE) Code Editor to handle large data sizes and produce interactive mapping. Based on the availability of auxiliary results, the number of spreading SO2 concentrations fell from 0,92 mmol/m2 in 2019 to 0,41 mmol/m2 in 2023, a reduction of 49.98 percent. The average of SO2 concentrations has decreased substantially over the years. Consequently, these pollutants have recorded long-term health and meteorological parameters impact that substances can rise or reduce acid rain.Keywords: Air pollution, Meteorological parameters, Sentinel 5P NRTI, SO2

Local economic resilience and economic specialization in Greece during the crisis

This paper scrutinizes the issue of economic resilience, aiming to detect the existence of a systematic link with economic specialization. To this end, the paper conducts an empirical analysis at the local (i.e., municipal) level of Greece during the economic crisis period (2009–2015), providing cartographic visualizations and spatial econometrics estimations. Greece is the country that was hit the hardest by the economic crisis. Thus, the need to stabilize the national economy in Greece has seemingly outweighed policy issues of spatial interest, which are also of extreme importance. The findings of the paper reveal that, inter alia, both the level and the nature of economic specialization exert an impact on local economic resilience, providing insight into both theory and policy making.

Compositional kriging analysis: A spatial interpolation method for distributions

Effective development of subsurface petroleum resources relies on the estimation of spatially distributed parameters at undrilled locations. Established geostatistical algorithms based on kriging exist for the estimation of scalar spatial variables such as porosity and permeability. This may not be suitable for the estimation of distributions of properties, such as grain size, whose whole distribution varies spatially. The current conventional approach is to fit a normal or log-normal distribution to the available data, and then to estimate the parameters of the distribution, the mean and standard deviation, spatially using kriging, taking care to consider any dependence between the mean and standard deviation. The assumption that all the grain size distributions can be approximated by a single distribution type is unsatisfactory, since datasets have very different-looking distributions, with variations in skewness, kurtosis, and modality. This paper presents an alternative approach that can handle significant variability in the distribution shape by separating the distribution into bins, like a histogram, and treating these bins as percentages in a composition. Compositional data needs to be mapped onto a simplex to overcome spurious correlations between those components, in addition, spatial estimation methods for compositional data have already been developed. However, the contribution to this field is the mapping of continuous data from distributions into a composition that enables the compositional kriging method to predict distributions at new locations. Moreover, the results showed the prediction distributions in the presence of varying modality, skewness, and kurtosis. Therefore, the grain size datasets in this paper have been working with the confidentiality restrictions so it explains the technique with a dataset of population ages from the US census 2010 for the state of Texas, which shows similar variability in distribution.

Taking advantage of sampling designs in spatial small-area survey studies

Spatial small area estimation models have become very popular in some contexts, such as disease mapping. Data in disease mapping studies are exhaustive, that is, the available data are supposed to be a complete register of all the observable events. In contrast, some other small area studies do not use exhaustive data, such as survey based studies, where a particular sampling design is typically followed and inferences are later extrapolated to the entire population. In this article we propose a spatial model for small area survey studies, taking advantage of spatial dependence between units, which is the key assumption used for yielding reliable estimates in exhaustive data based studies. In addition, and in contrast to most survey-based spatial studies, we also take into account information on the sampling design and additional supplementary variables to obtain estimates in small areas. This makes it possible to merge spatial and sampling models into a common proposal.

Development of a 2D spatial displacement estimation method for turbulence velocimetry of the gas puff imaging system on EAST

A gas puff imaging (GPI) diagnostic has been developed and operated on EAST since 2012, and the time-delay estimation (TDE) method is used to derive the propagation velocity of fluctuations from the two-dimensional GPI data. However, with the TDE method it is difficult to analyze the data with fast transient events, such as edge-localized mode (ELM). Consequently, a method called the spatial displacement estimation (SDE) algorithm is developed to estimate the turbulence velocity with high temporal resolution. Based on the SDE algorithm, we make some improvements, including an adaptive median filter and super-resolution technology. After the development of the algorithm, a straight-line movement and a curved-line movement are used to test the accuracy of the algorithm, and the calculated speed agrees well with preset speed. This SDE algorithm is applied to the EAST GPI data analysis, and the derived propagation velocity of turbulence is consistent with that from the TDE method, but with much higher temporal resolution.

Separable Representations for Duration and Distance in Virtual Movements.

To navigate through the environment, humans must be able to measure both the distance traveled in space, and the interval elapsed in time. Yet, how the brain holds both of these metrics simultaneously is less well known. One possibility is that participants measure how far and how long they have traveled relative to a known reference point. To measure this, we had human participants (n = 24) perform a distance estimation task in a virtual environment in which they were cued to attend to either the spatial or temporal interval traveled while responses were measured with multiband fMRI. We observed that both dimensions evoked similar frontoparietal networks, yet with a striking rostrocaudal dissociation between temporal and spatial estimation. Multivariate classifiers trained on each dimension were further able to predict the temporal or spatial interval traveled, with centers of activation within the SMA and retrosplenial cortex for time and space, respectively. Furthermore, a cross-classification approach revealed the right supramarginal gyrus and occipital place area as regions capable of decoding the general magnitude of the traveled distance. Altogether, our findings suggest the brain uses separate systems for tracking spatial and temporal distances, which are combined together along with dimension-nonspecific estimates.