Application Of Spatial Models Research Articles

Application of spatial metrology models in cell molecular localization and functional prediction

Understanding a protein’s exact cellular location is often essential to understanding its function. Even with the advancements in computer approaches, protein localization prediction indeed faces major obstacles such as interpretability and handling numerous localization sites. In this research, a novel approach, Squirrel Search Optimized Dynamic Visual Geometry Group Network (SSO-DVGG), is proposed to improve protein sub-cellular localization predictions by utilizing spatial metrology models to tackle these problems. With its simplified architecture, SSO-DVGG can explain whether a protein is directed to particular cellular sites, as well as identify important sequence components like sorting motifs or localization signals. This model allows users to select acceptable error levels by providing a confidence estimate for each prediction and highlighting sequence properties that are responsible for localization. This makes the model interpretable. Furthermore, SSO-DVGG uses a probabilistic methodology and integrates a large amount of data from dual-targeted proteins, which enables it to predict multiple localization locations per protein accurately. SSO-DVGG outperforms the best predictors and shows superior capacity to predict multiple localizations when tested on several independent datasets. By providing a clear and accurate understanding of protein distribution and function, this method promotes the application of spatial metrology models in cell molecular localization and functional prediction.

Beyond Parametric Bounds: Exploring Regional Unemployment Patterns Using Semiparametric Spatial Autoregression

Abstract Background It is a well-known phenomenon that nonlinearities that are inherent in the relationship among economic variables negatively affect the commonly used estimators in the econometric models. The nonlinearities cause an instability of the estimated parameters that, in particular, are unable to capture a local relationship between the response and the covariate. Objectives The main aim of the paper is the simultaneous consideration of spatial effects as well as nonlinearities through an advanced semiparametric spatial autoregressive econometric model. The paper seeks to contribute to empirical studies of regional science focused on the application of semiparametric spatial autoregressive econometric models. Methods/Approach We outline an approach that can be used to correct nonlinearities by incorporating a semiparametric idea within the framework of econometric models. We use an expansion by penalised basis splines that are highly flexible and are able to capture local nonlinearities between variables. Results In the empirical study, we fit different econometric models that attempt to explain the dynamics of the European Union's regional unemployment. Conclusions The results show that regional unemployment exhibits significant spatial dependence, indicating interconnectedness among neighbouring regions and suggesting the adoption of a semiparametric spatial autoregressive model for improved modelling flexibility, surpassing traditional parametric approaches.

Towards zero-carbon cities: how does environmental information disclosure impact the risk of carbon locking?

Utilizing panel data from 119 cities spanning from 2013 to 2019, the study rigorously investigates the influence of environmental information disclosure (EID) on mitigating the risk of urban carbon lock-in through the application of both two-way fixed effects and spatial econometric models. The findings robustly indicate that EID plays a significant role in facilitating carbon unlocking within cities, a conclusion that remains steadfast even after addressing potential endogeneity issues. Mechanistically, it is revealed that EID can effectively decrease the risk of carbon lock-in by enhancing urbanization levels and reducing energy intensity. Furthermore, the study unveils that the efficacy of EID in curbing urban carbon lock-in risk is also contingent upon factors such as carbon intensity, population size, and environmental quality, highlighting the multifaceted nature of EID's impact on urban carbon management. Specifically, the increase in carbon intensity, the expansion of population size, and the deterioration of environmental quality will reduce the carbon unlocking effect, which may not be conducive to the achievement of the goal of "carbon neutrality" in cities. The spatial effect test found that EID and carbon locking risk are both spatially significant positive autocorrelation, the deepening of local EID can significantly cut the risk of carbon locking in neighboring cities, and the urbanization pathway and energy saving pathway exist both locally and in neighboring places.

Application of factor analytic and spatial mixed models for the analysis of multi-environment trials in common bean (Phaseolus vulgaris L.) in Ethiopia.

Common bean (Phaseolus vulgaris L.) is one of the most important grain legumes consumed globally, especially in Ethiopia, for its edible seeds, cash crops, and supply of protein for farmers. Efficient statistical methods must be employed for the evaluation of common bean varieties to accurately select superior varieties that contribute to agricultural productivity. The objective of this study was to identify promising large mottled bean varieties through analysis of multi-environment trials (MET) data using multiplicative spatial mixed models. In this study, 16-18 large mottled common bean varieties, including one check, were sown across nine growing environments in Ethiopia using lattice and alpha lattice designs, with three replications laid out in a square or rectangular (row by column) array of plots, respectively during the main cropping season from 2015 to 2018. We present a linear mixed model analysis that integrates spatial and factor analytic (FA) models, and the heritability measure was used to evaluate the efficiency of these models with the conventional analysis. The analysis of the spatial model, and more significantly, the spatial+FA model, revealed a notable enhancement in heritability. With the exception of a trial conducted at Kobo, a genotype DAP 292, found to be good performing for days to flowering and maturity, but for yield only across four clusters of trials, C2, C3, C5 and C7, formed with trials of relatively high genetic variance. Across these clusters, the yield advantage of this variety over the check ranged from 10-32%. This genotype also has a yield that is somewhat comparable to the check across the remaining clusters. Overall, both the spatial and factor analytic models proved to be effective approaches for analyzing the data in this study. The analysis of multi-environment trial data through the use of more efficient statistical models can provide a more robust platform for evaluating common bean varieties with greater confidence in selecting superior varieties across a range of environments. Hence, scaling up the use of this efficient analysis method is indispensable for enhancing the selection of superior varieties.

Nonlinear effects of socio-economic factors on urban haze in China: Evidence from spatial econometric smooth transition autoregressive regression approach

In recent years, China has achieved numerous economic miracles but it has also been plagued by severe air pollution. The frequent hazy weather has severely restricted China's sustainable development. To investigate the nonlinear threshold effect of socio-economic factors on urban haze in China, this study constructs a spatial econometric Smooth Transition Autoregressive Regression (STAR) model based on the STIRPAT theory by using the remote sensing inversion PM2.5 data of 223 prefecture-level and above cities in China mainland during 2004-2016. In this study, the ARAR-STAR model is estimated by quasi-maximum likelihood estimation, and the accuracy of parameter estimation is verified by Monte Carlo simulation, which proves that the ARAR-STAR model constructed in this study is robust. It is concluded that: there is a complex spatial nonlinear relationship between socio-economic factors such as economic development level, population density, advanced industrial structure, energy consumption, opening-up, and haze pollution. The effect of socio-economic factors on haze emission reduction under the spatial influence has complex heterogeneity with the smooth transition between high and low regimes with economic development. The ARAR-STAR model constructed in this paper, which has both individual fixed effects and time fixed effects, expands the form of existing spatial panel nonlinear models and enriches and implements the application of spatial panel smooth transfer threshold models in the environmental field. Not only can it provide policy recommendations for China to achieve "coordinated efficiency in pollution reduction and carbon reduction" as soon as possible, but it also contributes to China's plan to address global climate change and promote global sustainable development.

Correlating Access to Primary Medical Care and Veterinary Care Providers: A Novel Application of Spatial Gravity Modelling.

Increasingly, health professionals are recognizing the need for a more holistic, or One Health, approach to wellness. Companion animals share the lives and homes of many Americans, and yet little research exists on the intersection of disparities of access to veterinary care and access to human health care. This research aims to fill that gap by exploring the use of a spatial model that identifies the relationship of supply and demand while also considering reductions as a function of travel time to a care facility. Four counties in Alabama were analyzed at the census tract level to determine the supply of primary care physicians and veterinary care providers. This research provides a unique application of the Enhanced Two-Step Floating Catchment Area model by independently examining access to each type of care and then comparing both access supply values at the census level. Results indicated a statistically significant positive relationship between access to both types of care, implying areas with lower access to primary care have concomitantly low access to veterinary care. Implications for practice include the introduction of this methodological approach, identification of future parameter specification research to improve the approach, and identification of an area of significant concern in the One Health framework. Further, the results can inform solution strategies such as offering integrated care interventions for both humans and their companion animal household members with direct use for policymakers aimed at increasing equitable access to health care across the One Health spectrum.

Can CATPCA be utilized for spatial modeling? a case of the generation susceptibility of gully head in a watershed

Can CATPCA be utilized for spatial modeling? a case of the generation susceptibility of gully head in a watershed

Surface and subsurface oceanographic features drive forage fish distributions and aggregations: Implications for prey availability to top predators in the US Northeast Shelf ecosystem.

Forage fishes are a critical food web link in marine ecosystems, aggregating in a hierarchical patch structure over multiple spatial and temporal scales. Surface-level forage fish aggregations (FFAs) represent a concentrated source of prey available to surface- and shallow-foraging marine predators. Existing survey and analysis methods are often imperfect for studying forage fishes at scales appropriate to foraging predators, making it difficult to quantify predator-prey interactions. In many cases, general distributions of forage fish species are known; however, these may not represent surface-level prey availability to predators. Likewise, we lack an understanding of the oceanographic drivers of spatial patterns of prey aggregation and availability or forage fish community patterns. Specifically, we applied Bayesian joint species distribution models to bottom trawl survey data to assess species- and community-level forage fish distribution patterns across the US Northeast Continental Shelf (NES) ecosystem. Aerial digital surveys gathered data on surface FFAs at two project sites within the NES, which we used in a spatially explicit hierarchical Bayesian model to estimate the abundance and size of surface FFAs. We used these models to examine the oceanographic drivers of forage fish distributions and aggregations. Our results suggest that, in the NES, regions of high community species richness are spatially consistent with regions of high surface FFA abundance. Bathymetric depth drove both patterns, while subsurface features, such as mixed layer depth, primarily influenced aggregation behavior and surface features, such as sea surface temperature, sub-mesoscale eddies, and fronts influenced forage fish diversity. In combination, these models help quantify the availability of forage fishes to marine predators and represent a novel application of spatial models to aerial digital survey data.

The vast relevance of applications of spatial regression models has recently captured the interest of Economics and Agriculture, in the sense of better understanding the spatial behavior of the region under study, in the different forms of approaches. It is interesting to understand why some regions show greater variability than others, and why some forms of regional development are better

There is a steady increase in research on livestock monitoring systems that offer new ways to remotely track the health of the livestock, early predict the diseases that may affect them and intervene in the early stages to save the situation by monitoring the various vital biodata of the livestock, as well as monitoring their feeding and tracking their location to prevent any damage or rustling. In this context, this paper comes in order to highlight and discuss the most recently published articles that study the topic of cattle health monitoring and location tracking systems using advanced IoT sensors. In addition, the research provides a review of the most important software and dashboards available in the market that can be used for this purpose. The research constitutes a reference for researchers in this field and for those who wish to develop similar monitoring systems.

Spatial mismatch between wild bee diversity hotspots and protected areas.

Wild bees are critical for multiple ecosystem functions but are currently threatened. Understanding the determinants of the spatial distribution of wild bee diversity is a major research gap for their conservation. We modeled wild bee α and β taxonomic and functional diversity in Switzerland to uncover countrywide diversity patterns and determine the extent to which they provide complementary information, assess the importance of the different drivers structuring wild bee diversity, identify hotspots of wild bee diversity, and determine the overlap between diversity hotspots and the network of protected areas. We used site-level occurrence and trait data from 547 wild bee species across 3343 plots and calculated community attributes, including taxonomic diversity metrics, community mean trait values, and functional diversity metrics. We modeled their distribution with predictors describing gradients of climate, resource availability (vegetation), and anthropogenic influence (i.e., land-use types and beekeeping intensity). Wild bee diversity changed along gradients of climate and resource availability; high-elevation areas had lower functional and taxonomic α diversity, and xeric areas harbored more diverse bee communities. Functional and taxonomic β diversities diverged from this pattern, with high elevations hosting unique species and trait combinations. The proportion of diversity hotspots included in protected areas depended on the biodiversity facet, but most diversity hotspots occurred in unprotected land. Climate and resource availability gradients drove spatial patterns of wild bee diversity, resulting in lower overall diversity at higher elevations, but simultaneously greater taxonomic and functional uniqueness. This spatial mismatch among distinct biodiversity facets and the degree of overlap with protected areas is a challenge to wild bee conservation, especially in the face of global change, and calls for better integrating unprotected land. The application of spatial predictive models represents a valuable tool to aid the future development of protected areas and achieve wild bee conservation goals.

Os efeitos espaciais são fatores determinantes dos preços das terras agrícolas no Brasil

Abstract This study aims to determine whether spatial effects are determinants of agricultural land prices in Brazil. For this purpose, data on the value of the bare land in municipalities in Brazil for 2020, provided by the Federal Revenue Service, were used. Although this database has national coverage, the contiguous data necessary for the application of spatial econometric models allowed us to cover the central-south region of the country. An exploratory spatial data analysis was performed, and the spatial Durbin econometric model was applied. Based on our results, there is a strong spatial dependence on land price, manifested both in the dependent variable, spatially lagged, and through the spillover effects of the independent variables. In addition, it was found that the degree of urbanization of a municipality, the municipal gross domestic product per capita, the average size of properties, the agricultural productivity, and the area dedicated to soybean planting in a rural property are also important determining factors in the pricing of agricultural land. Accordingly, it is possible to conclude that spatial effects influence the determination of agricultural land prices in Brazil.

Modelling Outflow Remittances in the Digital Era: A Subnational Analysis of Russia

Relevance. The outflow of funds from the Russian economy, including remittances, can have a significant impact on the country's economic development. The influence of digital technologies and the heterogeneity of Russian regions further complicate the study of factors affecting remittance outflows. This study aims to address this research gap by examining the spatial effects of remittance outflows from Russia at the regional level within the context of digital transformation. Research Objective. The primary objective of this paper is to investigate whether spatial effects exist in the outflow of remittances from Russia at the regional level and how digitalization affects this phenomenon. By exploring spatial autocorrelation and fitting spatial models, we aim to understand the mutual influence and interdependence of regions in cross-border remittances. Data and Methods. The study utilizes open annual data from the Bank of Russia on cross-border remittances and data from the Federal State Statistics Service of the Russian Federation. The research methodology includes spatial autocorrelation estimation and the application of spatial models. Results. Our findings indicate that the digitalization of business transactions leads to a decline in cross-border remittances that do not involve payment systems. This finding primarily relates to the relatively low share of non-resident remittances within formal remittances. The results underscore the importance of considering spatial differentiation among regions in terms of remittance volume when formulating regional policies. Furthermore, the study reveals a shifting trend towards the emergence of major centers for outgoing transfers in the Far Eastern part of Russia and an increase in remittance volumes from border regions in the European part of Russia. Conclusions. The policy implications of this study highlight the need to encourage the participation of the population's savings in the domestic economy. This can be achieved through measures aimed at facilitating internal investment and financial inclusion.

Inequity in Environmental Pollution of China’s Livestock and Poultry Industry: A Frontier Applications of Spatial Models

The study’s main aim is to find out the environmental livestock and poultry pollution. The study used data from 31 provinces in China from 2007 to 2019. This paper used two steps for empirical findings. In the 1st step, it conducted an initial analysis of the farmland pollution burden and water pollution that arises from the livestock and poultry industry. After this, through the fair distribution index researcher, the study analyzed the inequity of the environmental pollution burden on the livestock and poultry industry. Finally, by constructing a frontier spatial matrix and spatial econometric model, the study has analyzed the impact of economic development on the inequity of the environmental pollution burden. The econometrical analysis has provided the following conclusions: (1) China’s arable land is facing a serious pollution burden due to livestock and poultry manure. The results show that the livestock and poultry manure burden value is greater than 1. This value explained the serious environmental threat in 13 areas of China. Water pollution showed a fluctuating trend in four areas of China, while the threat of farmland pollution showed a downward trend. (2) The environmental equity index of the livestock and poultry industry in many regions of China is less than 1. This means one region is producing more pollution than its capacity. So, the pollution has crowded out the environmental capacity of other regions, resulting in an unfair environmental burden. This unfair environmental burden is especially prominent in the western region of China. (3) The phenomenon of environmental pollution-burden inequality has a spatial correlation. The environmental pollution burden inequality of a region has a significant spatial crowding out effect on the surrounding areas at the level of 1%, and the coefficient of spatial effect is −0.909. (4) The per capita GDP of the surrounding areas has a significant “inverted U-shaped” indirect impact on the environmental distribution equity index of the region, with an inflection point of 33,500 yuan/person. The research points out that clear property rights, guidance to regulate emissions trading, control blind pollution transfer, moderate industrial structure adjustment, improving rural residents’ education level, etc., are beneficial to the improvement of the environmental livestock and poultry pollution.

Spatial-Temporal Epidemiology of COVID-19 Using a Geographically and Temporally Weighted Regression Model

This article describes the application of spatial statistical epidemiological modeling and its inference and applies it to COVID-19 case data, looking at it from a spatial perspective, and considering time-series data. COVID-19 cases in Indonesia are increasing and spreading in all provinces, including Kalimantan. This study uses applied mathematics and spatiotemporal analysis to determine the factors affecting the constant rise of COVID-19 cases in Kalimantan. The spatiotemporal analysis uses the Geographically Temporally Weighted Regression (GTWR) model by developing a spatial and temporal interaction distance function. The GTWR model was applied to data on positive COVID-19 cases at a scale of 56 districts/cities in Kalimantan between the period of January 2020 and August 2021. The purpose of the study was to determine the factors affecting the cumulative increase in COVID-19 cases in Kalimantan and map the spatial distribution for 56 districts/cities based on the significant predictor variables. The results of the study show that the GTWR model with the development of a spatial and temporal interaction distance function using the kernel Gaussian fixed bandwidth function is a better model compared to the Ordinary Least Squares (OLS) model. According to the significant variables, there are various factors affecting the rise in cases of COVID-19 in the region of Kalimantan, including the number of doctors, the number of TB cases, the percentage of elderly population, GRDP, and the number of hospitals. The highest factors that affect COVID-19 cases are the high number of TB cases, population density, and the lack of health services. Furthermore, an area map was produced on the basis of the significant variables affected by the rise in COVID-19 cases. The results of the study provide local governments with decision-making recommendations to overcome COVID-19-related issues in their respective regions.

Land use efficiency of functional urban areas: Global pattern and evolution of development trajectories

The application of last-generation spatial data modelling, integrating Earth Observation, population, economic and other spatially explicit data, enables insights into the sustainability of the global urbanisation processes with unprecedented detail, consistency, and international comparability. In this study, the land use efficiency indicator, as developed in the Sustainable Development Goals, is assessed globally for the first time at the level of Functional Urban Areas (FUAs). Each FUA includes the city and its commuting zone as inferred from statistical modelling of available spatial data. FUAs represent the economic area of influence of each urban centre. Hence, the analysis of land consumption within their boundary has significance in the fields of spatial planning and policy analyses as well as many other research areas. We utilize the boundaries of more than 9,000 FUAs to estimate the land use efficiency between 1990 and 2015, by using population and built-up area data extracted from the Global Human Settlement Layer. This analysis shows how, in the observed period, FUAs in low-income countries of the Global South evolved with rates of population growth surpassing the ones of land consumption. However, in almost all regions of the globe, more than half of the FUAs improved their land use efficiency in recent years (2000–2015) with respect to the previous decade (1990–2000). Our study concludes that the spatial expansion of urban areas within FUA boundaries is reducing compactness of settlements, and that settlements located within FUAs do not display higher land use efficiency than those outside FUAs.

Spatial sampling, data models, spatial scale and ontologies: Interpreting spatial statistics and machine learning applied to satellite optical remote sensing

This paper summarizes the development and application of spatial statistical models in satellite optical remote sensing. The paper focuses on the development of a conceptual model that includes the measurement and sampling processes inherent in remote sensing. We organized this paper into five main sections: introducing the basis of remote sensing, including measurement and sampling; spatial variation, including variation through the object-based data model; advances in spatial statistical modelling; machine learning and explainable AI; a hierarchical ontological model of the nature of remotely sensed scenes. The paper finishes with a summary. We conclude that optical remote sensing provides an important source of data and information for the development of spatial statistical techniques that, in turn, serve as powerful tools to obtain important information from the images.

Exploring the Impact of “Double Cycle” and Industrial Upgrading on Sustainable High-Quality Economic Development: Application of Spatial and Mediation Models

In the context of the integration into the world economy, the domestic and international cycles of the economy constitute the basic pattern of economic operation. This pattern is closely related to the industrial structure, and obviously it can affect quality of the economic development. The Chinese government has put forward an international and domestic dual cycle strategy for reinforcing an environmentally sustainable high-quality economic development. Similarly, it seems critically important to explore what impact the “dual cycle strategy” and industrial upgrading will have on the environmentally sustainable high-quality economic development. To do so, this paper adopted a dataset ranging from the year 2004 to 2019 from a regional perspective in China, and an empirical research is carried out based on the spatial Dubin model-mediation effect model to analyze the impact of “dual cycle strategy” and industrial upgrading on environmentally sustainable high-quality economic development. The results of the study show that the rising industrial structure effect has emerged prominently. Both the “dual cycle” and industrial structure upgrading have a positive spillover effect on environmentally sustainable high-quality economic development. Further, the direct effect of domestic circulation is significant, but the indirect effect is not obvious. In addition, we found that the direct and indirect effects of international circulation are both significantly positive; industrial upgrading has a partial mediating effect in the “double cycle”. The study holds promising implications for a policy development.

A Comparative Analysis of the Application of Spatial Statistical Network Models for Water Quality within Two Distinct River Basins of Turkey: The Case of Alkalinity

A Comparative Analysis of the Application of Spatial Statistical Network Models for Water Quality within Two Distinct River Basins of Turkey: The Case of Alkalinity

Spatial epidemiological characteristics and exponential smoothing model application of tuberculosis in Qinghai Plateau, China.

The epidemic of tuberculosis has posed a serious burden in Qinghai province, it is necessary to clarify the epidemiological characteristics and spatial-temporal distribution of TB for future prevention and control measures. We used descriptive epidemiological methods and spatial statistical analysis including spatial correlation and spatial-temporal analysis in this study. Furthermore, we applied an exponential smoothing model for TB epidemiological trend forecasting. Of 43 859 TB cases, the sex ratio was 1.27:1 (M:F), and the average annual TB registered incidence was 70.00/100 000 of 2009-2019. More cases were reported in March and April, and the worst TB stricken regions were the prefectures of Golog and Yushu. High TB registered incidences were seen in males, farmers and herdsmen, Tibetans, or elderly people. 7132 cases were intractable, which were recurrent, drug resistant, or co-infected with other infections. Three likely cases clusters with significant high risk were found by spatial-temporal scan on data of 2009-2019. The exponential smoothing winters' additive model was selected as the best-fitting model to forecast monthly TB cases in the future. This research indicated that TB in Qinghai is still a serious threaten to the local residents' health. Multi-departmental collaboration and funds special for TB treatments and control are still needed, and the exponential smoothing model is promising which could be applied for forecasting of TB epidemic trend in this high-altitude province.

Application of spatial auto-beta models in statistical classification

In this paper, spatial data specified by auto-beta models is analysed by considering a supervised classification problem of classifying feature observation into one of two populations. Two classification rules based on conditional Bayes discriminant function (BDF) and linear discriminant function (LDF) are proposed. These classification rules are critically compared by the values of the actual error rates through the simulation study.