Spatial Data Model Research Articles

A SPATIAL EVIDENCE ON THE DETERMINANTS OF HIGH-TECH EXPORTS IN THE EUROPE

The aim of this study is to analyze the determinants of high-tech exports in the 40 European countries sample from 2011-2021, while considering spatial spillover. To achieve this, a spatial panel data model was employed. The results indicate a positive impact of per capita GDP, per capita patent applications, and trade openness on high-tech exports. However, there is no significant impact of R&D expenditures, foreign direct investment, and economic freedom on high-tech exports. The results showed a differentiation when considering spatial effects. The analysis model revealed the presence of both spatial lag and spatial error. Additionally, the results suggest that high-tech exports from surrounding countries have a positive impact on the high-tech exports of the country. Based on these findings, it is recommended that policymakers work towards eliminating trade barriers between countries, ameliorating income and promoting the commercialization of knowledge.

Synthetic data for reef modelling

Synthetic data mimics the statistical properties of real-world datasets while removing reference to sensitive or confidential information in the original dataset (Quintana, 2020). Synthetic data is also useful for general model testing and development, with many methods available for generating data from machine learning models (Raghunathan, 2021). Although not widely used in the context of ecological and environmental modelling, synthetic data can support and accelerate model testing and analyses where rightsholders are sensitive to data disclosure for study areas, or data collection is expensive.In the context of reef modelling, synthetic data can be used to support model analyses that can be published without referring to specific sites, reefs, or study areas. This is desirable in the context of decision support for restoration of the Great Barrier Reef. The Reef has many stakeholders and release of early modelling results for intervention scenarios for specific areas would be premature until management or intervention strategy options have been discussed with stakeholders and/or rightsholders. Synthetic data allows a path to publish model and method demonstrations to share knowledge with the reef decision support community without prematurely suggesting policy recommendations for reefs which are sensitive to rightsholders or stakeholders.We showcase a synthetic data pipeline developed for the reef decision-support system ADRIA (Adaptive Dynamic Reef Intervention Algorithms), using methods from the Python package Synthetic Data Vault (Patki et al., 2016) and others. The synthetic data models are developed to emulate the statistics of case-study reefs for publishing decision-support tool demonstrations, testing and method validation without revealing sensitive reef site information. This pipeline includes developing models for tabular (benthic/compositional reef data), spatial-temporal (wave and heat stress data) and spatial network data (coral larval connectivity). Conditional sampling methods which connect spatial relationships across datasets are used to develop synthetic reef data packages which mimic the statistical properties of the original dataset. The utility of the synthetic data is demonstrated on a sample reef data package, and methods used for anonymizing the data are detailed. The results are discussed in the context of formalizing synthetic data for reef modelling. All synthetic data code is available at ADRIA-synthetic-data/README.md at v0.1.0 · open-AIMS/ADRIA-synthetic-data (github.com), DOI: https://doi.org/10.5281/zenodo.10158323.

Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation

Multiple-point geostatistics (MPS) is an established tool for the uncertainty quantification of Earth systems modeling, particularly when dealing with the complexity and heterogeneity of geological data. This study presents a novel pixel-based MPS method for modeling spatial data using advanced machine-learning algorithms. Pixel-based multiple-point simulation implies the sequential modeling of individual points on the simulation grid, one at a time, by borrowing spatial information from the training image and honoring the conditioning data points. The developed methodology is based on the mapping of the training image patterns database using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm for dimensionality reduction, and the clustering of patterns by applying the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, as an efficient unsupervised classification technique. For the automation, optimization, and input parameter tuning, multiple stages are implemented, including entropy-based determination of the template size and a k-nearest neighbors search for clustering parameter selection, to ensure the proposed method does not require the user’s interference. The proposed model is validated using synthetic two- and three-dimensional datasets, both for conditional and unconditional simulations, and runtime information is provided. Finally, the method is applied to a case study gold mine for stochastic orebody modeling. To demonstrate the computational efficiency and accuracy of the proposed method, a two-dimensional training image with 101 by 101 pixels is simulated for 100 conditional realizations in 453 s (~4.5 s per realization) using only 361 hard data points (~3.5% of the simulation grid), and the resulting average simulation has a good visual match and only an 11.8% pixel-wise mismatch with the training image.

Max-convolution processes with random shape indicator kernels

In this paper, we introduce a new class of models for spatial data obtained from max-convolution processes based on indicator kernels with random shape. We show that these models have appealing dependence properties including tail dependence at short distances and independence at long distances. We further consider max-convolutions between such processes and processes with tail independence, in order to separately control the bulk and tail dependence behaviors, and to increase flexibility of the model at longer distances, in particular, to capture intermediate tail dependence. We show how parameters can be estimated using a weighted pairwise likelihood approach, and we conduct an extensive simulation study to show that the proposed inference approach is feasible in relatively high dimensions and it yields accurate parameter estimates in most cases. We apply the proposed methodology to analyze daily temperature maxima measured at 100 monitoring stations in the state of Oklahoma, US. Our results indicate that our proposed model provides a good fit to the data, and that it captures both the bulk and the tail dependence structures accurately.

Research on the Validity of Bootstrap LM-Error Test in Spatial Random Effect Models

Under the condition of non-classical distributed errors, the test for spatial dependence in spatial panel data models is still a problem waiting to be solved. In this paper, we apply the FDB (Fast Double Bootstrap) method to spatial panel data models to test spatial dependence. In order to research the validity of the Bootstrap LM-Error test in spatial random effect models under the condition that the error term obeys a normal distribution, heteroscedasticity, or time-series correlation, we construct Bootstrap LM-Error statistics and make use of Monte Carlo simulation from size distortion and power aspects to carry out our research. The Monte Carlo simulation results show that the asymptotic LM-Error test in the spatial random effects model has a large size of distortion when the error term disobeys classical distribution. However, the FDB LM-Error test can effectively correct the size distortion of the asymptotic test with the precondition that there is nearly no loss of power in the FDB test. Obviously, compared to the asymptotic LM-Error test, the FDB LM-Error test is a more valid method to test spatial dependence in a spatial random effects model.

Spatial-Temporal Patterns and Driving Factors of Logistics Carbon Emissions: Case Study of Yangtze River Delta in China

With the growing dependence of human beings on the logistics industry, the trend of logistics carbon emissions (LCEs) growth has become much more serious. To investigate the spatial-temporal pattern of LCEs as well as their driving factors, city-scale LCEs were calculated by combining them with nighttime light remote sensing data. In addition, a spatial panel data model was applied in the case of China’s Yangtze River Delta. The results showed that the overall LCEs performed as a rising trend during 2010–2019. The LCEs of the eastern cities and provincial capitals were significantly higher than those of other cities with obvious spatial agglomerations. For driving factors, the gross domestic product, population size, and proportion of tertiary industry all had significant positive influences on the LCEs. Overall, this study is of great practical significance to accurately obtain information on the spatial-temporal dynamics of LCEs at the city-level scale, so as to facilitate the differentiated implementation of carbon reduction measures.

Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014–2021

BackgroundTuberculosis (TB) remains a pressing public health issue, posing a significant threat to individuals' well-being and lives. This study delves into the TB incidence in Chinese mainland during 2014–2021, aiming to gain deeper insights into their epidemiological characteristics and explore macro-level factors to enhance control and prevention.MethodsTB incidence data in Chinese mainland from 2014 to 2021 were sourced from the National Notifiable Disease Reporting System (NNDRS). A two-stage distributed lag nonlinear model (DLNM) was constructed to evaluate the lag and non-linearity of daily average temperature (℃, Atemp), average relative humidity (%, ARH), average wind speed (m/s, AWS), sunshine duration (h, SD) and precipitation (mm, PRE) on the TB incidence. A spatial panel data model was used to assess the impact of demographic, medical and health resource, and economic factors on TB incidence.ResultsA total of 6,587,439 TB cases were reported in Chinese mainland during 2014–2021, with an average annual incidence rate of 59.17/100,000. The TB incidence decreased from 67.05/100,000 in 2014 to 46.40/100,000 in 2021, notably declining from 2018 to 2021 (APC = -8.87%, 95% CI: -11.97, -6.85%). TB incidence rates were higher among males, farmers, and individuals aged 65 years and older. Spatiotemporal analysis revealed a significant cluster in Xinjiang, Qinghai, and Xizang from March 2017 to June 2019 (RR = 3.94, P < 0.001). From 2014 to 2021, the proportion of etiologically confirmed cases increased from 31.31% to 56.98%, and the time interval from TB onset to diagnosis shortened from 26 days (IQR: 10–56 days) to 19 days (IQR: 7–44 days). Specific meteorological conditions, including low temperature (< 16.69℃), high relative humidity (> 71.73%), low sunshine duration (< 6.18 h) increased the risk of TB incidence, while extreme low wind speed (< 2.79 m/s) decreased the risk. The spatial Durbin model showed positive associations between TB incidence rates and sex ratio (β = 1.98), number of beds in medical and health institutions per 10,000 population (β = 0.90), and total health expenses (β = 0.55). There were negative associations between TB incidence rates and population (β = -1.14), population density (β = -0.19), urbanization rate (β = -0.62), number of medical and health institutions (β = -0.23), and number of health technicians per 10,000 population (β = -0.70).ConclusionsSignificant progress has been made in TB control and prevention in China, but challenges persist among some populations and areas. Varied relationships were observed between TB incidence and factors from meteorological, demographic, medical and health resource, and economic aspects. These findings underscore the importance of ongoing efforts to strengthen TB control and implement digital/intelligent surveillance for early risk detection and comprehensive interventions.Graphical

Application of 3D laser scanning technology for mapping and accuracy assessment of the point cloud model for the Great Achievement Palace heritage building

Heritage buildings represent history and act as vessels of human civilisation. The significance of these architectural phenomena increases as society increases, which renders their preservation and ethical use increasingly crucial. Hence, preserving heritage buildings is necessary for societal benefit. The accuracy of three-dimensional (3D) point cloud models of heritage buildings is important to their digital conservation. Traditional mapping methods typically require more time and human resources. Furthermore, the outcome is subject to measurement omissions, errors, and other issues, which are contrary to the protection of the object measured. The complex elements of ancient Chinese architecture render it challenging for traditional measuring techniques to accurately capture spatial structural information. The 3D laser scanning technology is a novel technology to obtain 3D data rapidly. In this paper, the Great Achievement Palace of the Confucian Temple in Yuci District, Jinzhong City, Shanxi Province, was used as an example to examine the application of 3D laser scanning technology to acquire point cloud models of heritage buildings. Field data collection for architectural heritage using 3D laser scanning technology requires measuring station setting analysis to ensure effect accuracy while considering the elements of target location and quantity. For the 3D point cloud model of the Great Achievement Palace, error analysis and accuracy assessment were conducted on the quality elements of alignment accuracy between scanning stations, point cloud model reliability, point cloud data noise condition, and whether the point cloud data were stratified. The findings demonstrated that the improved method quickly and accurately acquired 3D point cloud model data and obtained realistic spatial data models of the heritage building.

Trending Time-Varying Coefficient Spatial Panel Data Models

This article investigates the estimation and inference of spatial panel data models in which the regression coefficient vector is a trending function. We use time differences to eliminate the individual effects and employ various GMM estimations for regression coefficients with both linear and quadratic moments. Time trend estimator based on these GMM estimations is also proposed. Monte Carlo experiments show that the finite sample performance of the estimators is satisfactory. As an empirical illustration, we investigate the trending pattern of the spillover effect of air pollution among Chinese cities from 2015 to 2021.

The impact of agricultural credit on the cattle inventory and deforestation in Colombia: a spatial analysis

The relationship between agricultural credit and agricultural production as well as the impacts on sustainable development (i.e., poverty alleviation, reduction of inequalities, food and nutrition security, and stimulation of economic growth) have been widely documented. The objective of this study is to analyze the impact of credit on cattle production and deforestation in Colombia through spatial panel data models. For this purpose, a departmental data panel for the period 2011–2020 was built, based on available information from public entities. The results suggest that in Colombia, the relationship between access to credit and cattle production is significant and can be either negative or positive. In addition, there is evidence of spatial dependence, meaning that cattle production in one department is being affected by cattle production in a neighboring department or by all the departments that make up the national territory. Regarding deforestation, results show that, although the number of cattle present in a department does affect its annual deforestation rate due to a poor coverage of sustainably intensified cattle ranching systems, there is no relationship between deforestation and the access to credit nor any spatial correlations.

Inference of Dynamic Spatial Panel Data Model and Its Application in Carbon Emission Analysis

The Spatial Dynamic Panel Data (SDPD) model is a widely used statistical model in the fields of economics and social sciences and has been the subject of extensive research by many scholars in recent years. Existing methods for parameter estimation primarily focus on improvements to the Generalized Method of Moments (GMM) and Quasi-Maximum Likelihood (QML). In this paper, we employ the method of Empirical Likelihood (EL) for statistical inference of the dynamic spatial panel data model and obtain confidence regions for the parameters. Through numerical simulations, we present the performance of the confidence regions obtained using the Empirical Likelihood (EL) and the Asymptotically Normal (NA) methods under finite samples and compare the two approaches. Finally, we analyze carbon using the suggested model and techniques.

A class of models for large zero-inflated spatial data

AbstractSpatially correlated data with an excess of zeros, usually referred to as zero-inflated spatial data, arise in many disciplines. Examples include count data, for instance, abundance (or lack thereof) of animal species and disease counts, as well as semi-continuous data like observed precipitation. Spatial two-part models are a flexible class of models for such data. Fitting two-part models can be computationally expensive for large data due to high-dimensional dependent latent variables, costly matrix operations, and slow mixing Markov chains. We describe a flexible, computationally efficient approach for modeling large zero-inflated spatial data using the projection-based intrinsic conditional autoregression (PICAR) framework. We study our approach, which we call PICAR-Z, through extensive simulation studies and two environmental data sets. Our results suggest that PICAR-Z provides accurate predictions while remaining computationally efficient. An important goal of our work is to allow researchers who are not experts in computation to easily build computationally efficient extensions to zero-inflated spatial models; this also allows for a more thorough exploration of modeling choices in two-part models than was previously possible. We show that PICAR-Z is easy to implement and extend in popular probabilistic programming languages such as and .

MONITORING AND ASSESSMENT OF TENURE INSTRUMENTS FOR LAND ADMINISTRATION AND MANAGEMENT

Abstract. The Unified Map and Data Sharing or UMD-Sharing Project are tools and protocols for the preparation of a unified map for the various tenure instrument. It aims to provide NGAs and LGUs with guidelines on how to effectively utilize several tenure data from various NGA and LGU sources. With varying scales of data sources, in both spatial and textual dimensions, the project aims to explore these dimensions to create the Unified Map’s spatial data model. In general, map is regarded as a basic tool in land administration and management (LAM) and a unified map’s solution of harmonizing these disparate tenure instruments could mitigate conflicts. To implement the Unified Map and to show how data sharing is handled among the different organizations, including the LGUs, the project developed a QGIS plugin interface that can utilize a remote database repository.

Mapping Residential Land Suitability Using a WEB-GIS-Based Multi-Criteria Spatial Analysis Approach: Integration of AHP and WPM Methods

Along with the rise in population and the acceleration of economic expansion, there has been a concomitant rise in the urgent requirement for additional property that can serve as a venue for a wide variety of community activities. It is not uncommon for large cities that are the epicenter of urbanization, such as the city of Surabaya, to experience a sharp uptick in the demand for land. One of the regions that has excellent accessibility is the Sidoarjo Regency, which is comparable to the City of Surabaya in this regard. The goal of this research is to use Web-GIS to conduct an analysis of spatial data in order to identify the land functions that are most suitable for use in residential areas. The Analytic Hierarchy Process (AHP) and the Weighted Product Model (WPM) are two of the methodologies that are included in the spatial data modeling method that uses multi-criteria decision making (MCDM). The characteristics parameters that are employed are derived from data such as the distance to the city center, the distance to the market, the distance to the hospital, the distance to public transit, the slope, the kind of soil, and the amount of rainfall. The outcomes of spatial data modeling categorize the suitability of new residential land into categories of land that is not suitable for residential use and land that is acceptable for residential use. A K value of 0.27 is the outcome of the comparison test that was run between the two MCDM approaches using Cohen's Kappa coefficients.

Spatiotemporal evolution and influencing factors of urban industrial carbon emission efficiency in the Mid-Yangtze River urban agglomeration of China

Carbon emissions from energy consumption caused by industrial production have become an important factor in global warming, and China now has the highest global carbon emissions, with enormous pressure to reduce emissions. The Mid-Yangtze River urban agglomeration (MYRUA), which is an important region for China, also faces the contradiction of balancing environmental protection and economic production. Currently, there is little discussion on low-carbon pollution research in the MYRUA in China. Therefore, this study takes the MYRUA as the research object, uses a slack-based model (SBM) and exploratory spatial data analysis (ESDA) to explore the spatiotemporal evolution of the industrial carbon emission efficiency (ICEE) of the MYRUA in China from 2006 to 2019, and empirically analyses the factors influencing the ICEE based on a spatial econometric model. The results show that (1) in the period 2006–2019, the ICEE in the MYRUA region generally exhibited a “jagged” trend of decreasing, then increasing until finally decreasing again. Spatially, ICEE generally exhibited a dynamic trend of being low throughout, with high-efficiency areas tending to move to the center and low-efficiency areas being scattered. (2) The global Moran's I coefficient for ICEE ranged between 0.002 and 0.213, with the highest Moran's I value and significant spatial correlation occurring in 2006, after which the value gradually decreased. On the local autocorrelation, the ICEE in 2006 exhibited an agglomeration effect and weakened after 2010. (3) In 2019, the level of industrialization (LI) and the level of external openness (LEO) in the MYRUA changed from nonsignificant to significant positive correlations, and the LEO had the largest impact. The energy consumption structure (ECS) was always significantly negatively correlated. The levels of social development (LSD) and environmental regulation (ER) changed from showing a significant positive correlation to showing a nonsignificant correlation. In conclusion, ICEE is affected by interactions of multiple factors. In the future, the ECS of the MYRUA should be continuously optimized and replaced by clean energy. In addition, foreign investment compensates for the lack of production and technology research funds, which can increase investment and promote the digitalization of manufacturing enterprises.

SGWR: similarity and geographically weighted regression

Geographically weighted regression (GWR) offers a local approach to modeling spatial data, considering geographical location and spatial relationships between observations. A salient feature of GWR is the emphasis on geographical proximity, in accordance with Tobler’s First Law of Geography, which assumes that closer entities have a greater influence on the target location. Traditional GWR models have been augmented to consider various forms of physical distances aimed at enhancing model performance, and they often disregarded the potential influence of other data attributes, a shortcoming that extends to most GWR extensions. In this study, we introduce a novel weight matrix construction, which integrates data attribute similarity alongside the conventional geographically weighted matrix. The two weights are integrated in a manner that results in improved model performance. The proposed model, called Similarity and Geographically Weighted Regression or SGWR, was applied to five distinct datasets: housing prices, crime rates, and three health outcomes including mental health, depression, and HIV. Results show that SGWR significantly improved model performance based on several statistical measures, outperforming the global regression model and the traditional GWR.

Varying coefficient panel data models and methods under correlated error components: Application to disparities in mental health services in England

The contribution of this paper is twofold. Firstly, it introduces novel regression models that combine two important areas of the methodological development in panel data analysis, namely a varying coefficient specification and spatial error dependence. The former allows relatively flexible nonlinear interactions; the latter enables spatial correlations of the disturbance and thus differ significantly from the other random effect models in the literature. To estimate the model, a new estimation procedure is established that can be viewed as a generalization of the quasi-maximum likelihood method for a spatial panel data model to the well-known conditional local likelihood procedure. Novel inference methods, particularly variable selection and hypothesis testing of the parameter constancy, are introduced and are shown to be effective under the complex spatial error dependence. Equally importantly, this paper makes a substantial contribution to the understanding of financing and expenditure for health and social care. In particular, we empirically analyze and explain the effects of political ideologies on the local fiscal policy in England, especially the expenditure on mental health services.

Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data

This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n , T → ∞ , and the strength of the dominant units is equal to 1 when T → ∞ . For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.

Partition refinement of WorldPop population spatial distribution data method: A case study of Zhuhai, China.

Currently, the core idea of the refined method of population spatial distribution is to establish a correlation between the population and auxiliary data at the administrative-unit level and, then, refine it to the grid unit. However, this method ignores the advantages of public population spatial distribution data. Given these problems, this study proposed a partition strategy using the natural break method at the grid-unit level, which adopts the population density to constrain the land class weight and redistributes the population under the dual constraints of land class and area weights. Accordingly, we used the dasymetric method to refine the population distribution data. The study established a partition model for public population spatial distribution data and auxiliary data at the grid-unit level and, then, refined it to smaller grid units. This method effectively utilizes the public population spatial distribution data and solves the problem of the dataset being not sufficiently accurate to describe small-scale regions and low resolutions. Taking the public WorldPop population spatial distribution dataset as an example, the results indicate that the proposed method has higher accuracy than other public datasets and can also describe the actual spatial distribution characteristics of the population accurately and intuitively. Simultaneously, this provides a new concept for research on population spatial distribution refinement methods.

Testing Homoskedasticity in Spatial Panel Data Models

A robust test statistic for testing homoskedasticity in spatial panel data models that have entity and time fixed effects is introduced in a quasi maximum likelihood estimation setting. A size-correction approach is introduced to ensure that the score functions have an asymptotic distribution centered around zero in the local presence of certain nuisance parameters. The outer-product-of-martingale-difference (OPMD) method is used to formulate an estimator for the asymptotic variance of the score functions. The OPMD estimator and the adjusted score functions are used to formulate a computationally simple robust test statistic. The suggested test statistic does not require knowing the presence of spatial dependence in the outcome variable and/or the disturbance terms. The asymptotic distribution of the test statistic is established under the null and local alternative hypotheses. Through Monte Carlo simulations, the finite sample size and power properties of the proposed test statistic are investigated. Finally, two empirical applications are provided to illustrate the practical use of the proposed test statistic.