Spatial Data Model Research Articles

The Effects of Roads on Trade and Migration: Evidence from a Planned Capital City

A large body of literature studies how infrastructure facilitates trade. We ask whether infrastructure also facilitates migration. Using a general equilibrium trade model and rich spatial data, we study the impact of a large, plausibly exogenous shock to highways in Brazil on both goods and labor markets. We find the highway system increased welfare by 2.8 percent, of which 76 percent was due to reduced trade costs and 24 percent to reduced migration costs. An implication of costly migration is spatial heterogeneity in benefits: the range of welfare improvement is 1 to 15 percent, as opposed to uniform gains with perfect mobility. (JEL H54, H76, O18, R23, R42, R53)

Dynamic evaluation of China's atmospheric environmental pressure from 2008 to 2017: Trends and drivers

Evaluating the pressure of atmospheric pollutant emissions on the atmospheric environment is crucial for effective pollution control and emission reduction policies. This study introduces a novel Atmospheric Environmental Pressure Index (AEPI) and employs a dynamic comprehensive method to evaluate China's Atmospheric Environmental Pressure (AEP) across 31 provinces from 2008 to 2017. The drivers of AEP were analyzed using a spatial panel data model, uncovering the integral role of pollution reduction policies, particularly the Air Pollution Prevention and Control Action Plan, which led to a 25% reduction in AEP during its enforcement. Our findings reveal significant spatial disparities in AEP, with higher levels in the Beijing-Tianjin-Hebei and Yangtze River Delta regions. The regression analysis identifies economic development, industrial structure, energy efficiency, environmental regulations, and urbanization as key influencing factors, though their impacts vary across different regions, suggesting the need for region-specific pollution control policies. Furthermore, the shift in the AEP gravity center from 2008 to 2017 indicated a southeastward movement, suggesting the necessity to focus air pollution control efforts on the southeast provinces. In conclusion, the AEPI developed in this study enables comparative analysis of AEP across different regions and facilitates the monitoring of long-term trends, which is valuable in guiding regional air pollution control in China.

Spatial spillover effect of green finance on urban-rural income gap

ABSTRACT This paper constructs a spatial panel data model and establishes the Green Finance Index of China’s provinces with the entropy value method to empirically test the impact of green finance on China’s urban-rural income gap. The results of the study show that green finance not only reduces the local urban-rural income gap but also has an obvious inhibitory effect on the urban-rural income gap in the surrounding areas. In terms of regional analysis, green finance has limited inhibitory effects in developed regions and significant inhibitory effects in developing regions.

Spatial Autoregressive Quantile Regression Modeling of the Distribution of Drug Users in the District Karo

BNN data shows that an average of 50 people die from drugs every day, and Karo Regency is in second place for the distribution of drug abuse in North Sumatra after Medan City. Variables that have this risk include drug factors, namely availability and ease of obtaining drugs, individual factors, environmental factors, family factors, and social factors. Based on this, a model is needed to determine the development of the case. The SARQR model combines SAR modeling with quantile regression (QR). Combining the SAR model with quantile regression produces a model that is good for overcoming the problems of dependency and heterogeneity in modeling spatial data and is resistant to outlier data. This research aims to determine the Spatial Autoregressive Quantile Regression Model for the distribution of drug users in the Karo Regency. The type of research used is quantitative research. The data type used is secondary data, namely, the kind of data already existing, and the data source used in this research is drug users in the Karo district. The research results show that the Spatial Autoregressive Quantile Regression model for the distribution of drug users in Karo Regency obtained estimation results for the distribution parameters of drug users using a significance test. This model explains that the factors that significantly influence drug abuse are age, gender, occupation, and other underlying factors.

Mechanisms for realizing the ecological products value: Green finance intervention and support

The essence of ecological product value is the rational development and utilization of natural ecological and environmental resources, which are converted into social attributes or economic value. An accurate assessment of the value of ecological products is a prerequisite for realizing their economic value. However, few studies have measured the interaction mechanism between green finance and ecological products value. This study measured the ecological products value at the city level in China from 2010 to 2021, based on the dynamic equivalent moderator model. A spatial panel data model was used to analyze the spatial pattern and dynamic evolution of ecological products value and identify their driving factors. The ecological products value in China showed an increasing trend from 2010 to 2021, and there were obvious spatial correlations between regions. Green finance significantly contributes to the realization of ecological products value, and robust estimation results can still be obtained after controlling for endogeneity. Meanwhile, the role of green finance has a spatial spillover effect. This study provides a new research perspective for correctly assessing ecological products value and studying the role of green finance in realizing ecological products value.

Empirical likelihood for spatial cross-sectional data models with matrix exponential spatial specification

Empirical likelihood for spatial cross-sectional data models with matrix exponential spatial specification

Testing Spatial Dynamic Panel Data Models with Heterogeneous Spatial and Regression Coefficients

Spatio‐temporal data are often analysed by means of spatial dynamic panel data (SDPD) models. In the last decade, several versions of these models have been proposed, generally based on specific assumptions and estimator properties. We focus on an SDPD model with heterogeneous coefficients both in the spatial and exogeneous regression components. We propose a strategy to identify the specific structure of the SDPD model through a multiple testing procedure that allows to choose between a general version of the model and a nested version derived from the general one by imposing restrictions on the parameters. Our proposal can be used to test the homogeneity of the model parameters as well as the absence of specific components, such as spatial effects, dynamic effects or exogenous regressors. It is also possible to use the proposed testing procedure for the identification of relevant locations. The theoretical results highlight the consistency of the testing procedure in the high‐dimensional setup, when the number of spatial units goes to infinity and exceeds the number of time‐observations per spatial unit. Further, we also conduct a Monte Carlo simulation study, which gives empirical evidence of the good performance of the testing procedure in finite samples.

Alleviating environmental pressure from livestock production: A “livestock-resource environmental carrying capacity” perspective in China

Scientific assessment of the relationships and influencing factors between livestock and resource-environmental carrying capacity (LS-RECC) is essential for alleviating environmental pressure. The crop-livestock balance method was used to measure China’s livestock production potential from 2005 to 2020. The improved coupling coordination degree model (I-CCDM) and exploratory spatial data analysis (ESDA) were used to analyze the spatial-temporal coordinated development characteristics of the LS-RECC. Furthermore, geographically and temporally weighted regression (GTWR) was used to identify the factors influencing the coordination development of the LS-RECC. The findings showed that (1) From 2005 to 2020, the nutrient demand for crop cultivation in China increased, and, the crop system nutrient demand exceeded the livestock nutrient supply in 2007 to 190 ×104 tons, reaching a maximum of 925 ×104 tons in 2019. However, the production potential of livestock in Ningxia, Tibet, Guizhou, Gansu, Yunnan, and Qinghai was severely overloaded. (2) The coupling coordination degree (CCD) of the LS-RECC showed a progressively increasing trend, which indicates that the sustainable development ability of China's livestock industry is constantly increasing. However, compared with that in eastern and central China, the coordinated development level of the LS-RECC in western China was lower, further indicating excessive livestock industry development in western China. (3) Environmental regulation had the strongest effect on the regional coordinated development of the LS-RECC, with obvious spatial differences. The economic development level had a positive effect, but urbanization and Engel's coefficients had the opposite effect. These results provide not only references for reducing environmental pressure on the livestock industry in China but also guidance for the sustainable development of the livestock industry in other large livestock production countries.

A Semantic-Sensitive Approach to Indoor and Outdoor 3D Data Organization

Building model data organization is often programmed to solve a specific problem, resulting in the inability to organize indoor and outdoor 3D scenes in an integrated manner. In this paper, existing building spatial data models are studied, and the characteristics of building information modeling standards (IFC), city geographic modeling language (CityGML), indoor modeling language (IndoorGML), and other models are compared and analyzed. CityGML and IndoorGML models face challenges in satisfying diverse application scenarios and requirements due to limitations in their expression capabilities. It is proposed to combine the semantic information of the model objects to effectively partition and organize the indoor and outdoor spatial 3D model data and to construct the indoor and outdoor data organization mechanism of “chunk-layer-subobject-entrances-area-detail object.” This method is verified by proposing a 3D data organization method for indoor and outdoor space and constructing a 3D visualization system based on it.

Vector-Based Realization of Area-Weight Proportional Multiplicatively Weighted Voronoi Diagrams With the ArcGIS Engine

In the geographical field, the studies have been on service areas using the Voronoi diagram and its derived models is extensive, but there is a lack of effective methods to achieve a good area-weight proportionality between generators and their exclusive regions. As a famous visualizing method, adaptive multiplicatively weighted Voronoi diagrams are able to achieve it, but are limited to displaying non-spatial data. The approach of the area-weight proportional multiplicatively weighted Voronoi diagram is proposed to solve these problems by allowing for spatial division with a point-fixed iteration approach and a vector-based multiplicatively weighted Voronoi diagram construction method from point features with spatial coordinates and references in GIS environments. It enables one to create a set of regions that is proportional to the weights of the generators. The method is successfully tested on a series of cases. The approach aims to establish a kind of spatial data model to represent demand and supply situations in real life.

Analysis of the coordinated evolution and driving mechanism of the coupling of tourism economy and urbanization in Southwest China

ABSTRACT The coordinated development of the tourism economy and urbanization is one of the hot issues in the practice and research of regional development. Exploring the complex dynamic relationship between the two can promote new urbanization construction and sustainable regional economic development. This paper uses the coupled coordination degree (CCD) model and exploratory spatial data analysis (ESDA) model to study the spatiotemporal evolution characteristics and driving mechanisms of the coordinated development of Southwest China’s tourism industry and urbanization. The results show that from 2010 to 2020, the comprehensive level of tourism economic development is in an inverted ‘V’ shape, while the complete level of urbanization is on a continuous upward trend. The spatial difference for CCD between the tourism economy and urbanization is remarkable, offering a spatial pattern of the highest in the middle, the second in the east, and the lowest in the west. In addition, the urbanization rate, population density, per capita fiscal revenue, and per capita park green land have significant negative spatial effects on CCD. Per capita budgetary revenue has a positive spatial effect on CCD with a time lag. This study provides a decision-making reference for promoting tourism development and urbanization in southwest regions.

Co-movements of crude oil prices

ABSTRACT This paper investigates whether the world crude oil market is globalised in a ‘one great pool’ or is regionalised by examining the prices of 21 different types of crude oil from various regions worldwide. We employ monthly data from October 2011 to November 2020 to construct spatial panel data models. Using three weight matrices, we account for the physical properties affecting crude oil quality and geographical factors. The resulting empirical evidence reveals that although we cannot refute the globalisation hypothesis, there are distinct price mechanisms specific to each type of crude oil. The pricing of crude oil appears to be influenced by a combination of similar and dissimilar qualities, including American Petroleum Institute gravity (API) gravity, sulfur content and geographical location.

Incorporating Subsampling into Bayesian Models for High-Dimensional Spatial Data

Incorporating Subsampling into Bayesian Models for High-Dimensional Spatial Data

Construction Strategy of Regional Investment and Financial Management System under Spatial Effect Modeling

Abstract In recent years, China’s regional economic differences have become more and more apparent, and the problem of unbalanced allocation of financial resources has become more and more prominent. In this context, it is of great practical significance to study the construction strategy of regional investment and financial management systems using the spatial effect model. This study adopts the spatial econometric method to establish the spatial effect model. Considering the limitations of the spatial panel data model, this paper also uses the general dynamic spatial panel model with fixed effects to analyze the relationship between regional investment and the financial management system. Finally, through the Granger causality test on the spatial effect analysis of regional investment and financial management, this paper verifies the study of the interaction between the financial management system and regional investment. The research data show that in the spatial panel model, the regression coefficient of the explanatory variable F financial management system is 0.2431, which is significant at the 1% level, indicating that with a little increase in the degree of financial management system, the regional economy grows by 0.2431. The fixed effect general dynamic spatial panel model’s superiority is confirmed by the higher goodness of fit of the general dynamic spatial panel model compared to the spatial panel model.

AI-enhanced digital inspection of bridges

Civil infrastructure inspection -and consequently maintenance- is carried out primarily through visual inspections. AI-enhanced (Artificial Intelligence) digital inspection methods, integrated with risk-based probabilistic approaches, have been promoted to keep existing structures, especially infrastructures, safe and predictable. Drones are used to obtain a significant number of images to cover the surface of a bridge, which are further integrated into a digital 3D (three-dimensional) model. According to the IFC standards (Industry Foundation Class), this 3D model is GPS-positioned (Global Positioning System) and connected to BIM (Building Information Modelling). Post-processing the accumulated data volume of all digital images is very time-consuming. For this reason, appropriate AI-based algorithms streamline this process significantly, enabling partially automated damage detection and assessment. To this end, images of various types of damage on different bridges are used to train and test the AI-enhanced models. In addition, damage identification and classification are developed. Six visually detectable defects can be identified, and theoretical models estimate the associated structural diseases. Finally, a probability-based risk assessment presents the basis for defining the criticality of the structure. With the help of digital images, it is possible to create a high-fidelity digital model and quantitative surface and spatial data records of the structural health condition of bridges and other infrastructures.

Strong consistency rate in functional single index expectile model for spatial data

<abstract><p>Analyzing the real impact of spatial dependency in financial time series data is crucial to financial risk management. It has been a challenging issue in the last decade. This is because most financial transactions are performed via the internet and the spatial dependency between different international stock markets is not standard. The present paper investigates functional expectile regression as a spatial financial risk model. Specifically, we construct a nonparametric estimator of this functional model for the functional single index regression (FSIR) structure. The asymptotic properties of this estimator are elaborated over general spatial settings. More precisely, we establish Borel-Cantelli consistency (BCC) of the constructed estimator. The latter is obtained with the precision of the convergence rate. A simulation investigation is performed to show the easy applicability of the constructed estimator in practice. Finally, real data analysis about the financial data (Euro Stoxx-50 index data) is used to illustrate the effectiveness of our methodology.</p></abstract>

Spatial patterns of mortality among the able-bodied population in the Republic of Bashkortostan

BACKGROUND: Regional differentiation adds complexity to the issue of high mortality among the working-age population. The identified regional patterns of mortality causes are typically not confined to specific areas, but rather influenced by spatial factors.
 AIM: To study temporal trends in spatial patterns of mortality of the working-age population mortality from the main causes of death in the Republic of Bashkortostan. In addition, we identified territorial factors associated with the mortality patterns and trends.
 METHODS: Mortality data on 6 main causes of death from 54 municipal districts, 20 urban districts and urban settlements were collected for the period from 2002 through 2020. The analysis included estimation of the Moran's index and the spatial panel data modeling.
 RESULTS: Spatial mortality patterns were evident for mortality from diseases of the circulatory system, infectious and parasitic diseases, and external causes. The impact of crime levels in a specific region (male: p=0.046; female: p=0.019) negatively affected mortality rates from circulatory diseases among working-age men and women. Additionally, for men, the volume of gross municipal product (GMP) had an additional effect (p=0.046). Factors that contribute to a decrease in mortality from external causes among able-bodied men and women included the growth of GMP (male: p=0.010; female: p=0.003 — for women), an increase in the number of doctors per 10,000 people in the population (male: p=0.001; female: p=0.037), and the availability of nursing staff (male: p=0.026; female: p=0.005). Furthermore, crime rates had a significant impact on mortality from external causes for men (p=0.028), while the availability of hospital beds played a crucial role for women (p=0.030).
 CONCLUSION: The study provides the evidence on the presence of spatial autocorrelation in the context of causes of death, as well as common and differing factors influencing male and female mortality in Bashkortostan. The findings provide a solid foundation for further analysis of spatially determined factors contributing to mortality among working-age population.

Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data

Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses. In simulated and real datasets, Smoother enables accurate data imputation, cell-type deconvolution, and dimensionality reduction with remarkable efficiency. In colorectal cancer, Smoother-guided deconvolution reveals plasma cell and fibroblast subtype localizations linked to tumor microenvironment restructuring. Additionally, joint modeling of spatial and single-cell human prostate data with Smoother allows for spatial mapping of reference populations with significantly reduced ambiguity.

Housing Choices of Young Adults in Sweden

This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policymakers is crucial to implementing effective policies to support young adults and promote financial independence and well-being. Using a data set from 1998 to 2021 at the municipal level in Sweden, we used a spatial autoregressive panel data model to examine the proportion of young adults living at home and the regional disparities. The study uncovered intraregional variations that illustrate how different municipalities in Sweden exhibit different patterns of young adults living at home. Our findings reveal that economic factors such as unemployment significantly impact this pattern. Housing market dynamics, demographic factors, cultural differences, and location-specific characteristics also play an essential role in explaining this pattern. These findings suggest that the key drivers are the lack of rental housing, high unemployment rates, a high degree of urbanisation, interregional migration, and social capital (such as social cohesion and inclusion).

Recognition and variable selection in sparse spatial panel data models with fixed effects

Recognition and variable selection in sparse spatial panel data models with fixed effects