Spatial Autoregressive Research Articles

Changes in the European Union households' consumption structure and the sustainable development

The subject of this study is the relationship between the households' consumption structure changes and sustainable development. This research was conducted for the European Union (EU) countries in the years 2008-2020. Additionally, in the investigation, the considered area is divided into two separate regimes: countries that joined the EU before 2000 (referred to as Old EU Member States) and countries that joined the EU later (called New EU Member States). It is widely known that the territorial units in the first regime are relatively wealthier than those in the second group. As a result of differences in wealth between the defined regimes, consumer behaviours can vary. Sustainable consumption (SC) is one of the major aspects of sustainable development. SC indicates consumer choices that are both intentional and effective, and these choices demonstrate a positive impact on environmental and/or social conditions. In this research, the influence of households' consumption changes on the environmental pillar of sustainable development is evaluated. Caring for the natural environment is one of the main challenges today. In this research, another approach to measure environmental development is proposed. So far, different types of footprints have been used. This analysis considers most of the environmental indicators defined in the Sustainable Development Goals (SDG's). The environmental development of EU countries is evaluated based on the synthetic measure of development proposed by Hellwig. Moreover, the spatio-temporal sensitivity models are estimated and verified. The sensitivity parameter of these models shows the impact of consumption structure changes on environmental development, wherein the consumption structure is categorized into four groups of goods: services, non-durable, semi-durable, and durable. The spatial autoregressive (SAR) models are also estimated and verified. The investigation results point out the differences in the influence of consumption structure changes on sustainable development depending on the considered group of countries.

Changes in hydraulic and physico-water parameters of surface waters under the influence of anthropogenic activities in the Western Carpathians

To evaluate the quality of watercourses in the Western part of Carpathians from a hydro-chemical perspective, a systematic approach is required. This involves gradually excluding factors that contribute to the washing, mixing, and transportation of contaminants in the watercourse pathway. The model that considers spatial dependencies by autoregression was implemented in this study to determine the correlation between hydrodynamic and physico-chemical characteristics of waters at surface in different groups and forms of catchment use. The surface water at forested areas had the maximum average shear stress of 0.178 N∙m−2. The watercourse at sustained grassland had the maximum average Reynolds number (Re) of 23,654 and the minimum number of 0.426 at arable lands. Spatial autoregression analysis revealed space-time relations in various measurement points. When constructing the space-physical model, it is important to consider the influence of hydraulic characteristic parameters on the generation of physicochemical indicators in the flysch basin. Specifically, it may be beneficial to take into account the turbulent diffusion coefficient. The autoregression analysis demonstrated that for the ions P-PO43− and K+ in surface water on cropland and for total iron and the cation K+ ion grassland (p < 0.05), the turbulent diffusion coefficient proved to be of great importance. The study did not identify any physicochemical dependency for woodland surface waters. The findings can be utilised to create an erosion model that considers the contribution of material supply in a catchment area, specifically from weathered Carpathian flysch or surface runoff, to the alimentation of alluvial deposits.

Towards socially equitable public transport systems: The effect of COVID-19 on taxi trip behavior

The COVID-19 pandemic emerged as a very influential occurrence with a profound impact on a global scale. The onset of the pandemic abruptly disrupted the regular course of everyday activities, primarily impacting urban regions. Hence, it is imperative to understand the effects of the COVID-19 pandemic on modern urban areas. This study seeks to analyze the effect of the pandemic on travel behavior by utilizing GPS data obtained from taxis, with a specific focus on spatial socioeconomic features. The M2 metro line in Istanbul has been selected for evaluation. In this analysis, four distinct periods are considered: total, off-peak, morning, and evening peaks. The stations are categorized using K-means clustering. The estimation models are constructed using ordinary least squares (OLS), spatial autoregression (SAR), and geographically weighted regression (GWR) techniques, which are applied to the variation in daily average cab trips and the characteristics of stations. The GWR models provide superior performance in comparison to the other two models, with notable distinctions observed in peak times, particularly morning peak when compared to total and off-peak counts. The findings indicate that factors such as population, population density, socioeconomic status, and the quantity of shopping malls are influential variables in elucidating and forecasting the fluctuations in taxi trip counts.

Comparison between the Bayesian method and the Particle Swarm Algorithm for Estimating a Spatial Autoregressive Model

The spatial autoregressive model is one of the models that is very similar to ordinary time series models, as the spatial mixed model is formed when the spatially lagged dependent variable is included as one of the explanatory variables. To find out whether or not there is spatial dependence between these variables, a spatial autoregressive model is used Moran Test. If this reliability is ignored or not taken into consideration, this leads to the loss of important information. Accordingly, in the research, the spatial autoregressive model was estimated when the model error is distributed with a mixed distribution (represented by the t-distribution) by the Bayesian method when the initial information is available from one side of the artificial intelligence algorithm, represented by the Particle Swarm Optimization (PSO) algorithm from On the other hand, the application to real data related to the poverty gap sample in developing countries for the years (2005-2010-2015-2020) and through the Moran test and the Lagrange multiplier showed that there is a spatial autocorrelation between the observations and the researchers concluded that the subsequent probability distribution of the vector of parameters (▁θ) follows a multivariate t distribution, and the posterior probability distribution of the variance parameter was an uncommon but appropriate distribution (Proper), in addition to the superiority of the PSO algorithm for estimating the spatial autoregressive model with heavy tails in the Bayesian manner through the criteria of mean square error and mean relative error.

Evaluating the Impact of Long-Term Demographic Changes on Local Participation in Italian Rural Policies (2014–2020): A Spatial Autoregressive Econometric Model

This study elaborates on a typology of demographic change and tests this definition at the lowest granular level (LAU2, municipality) with official data. This typology distinguishes between fragile and resilient municipalities based on population dynamics (in terms of duration and intensity) over 1991–2021. This study’s second aim is to elaborate a spatial autoregressive econometric model to evaluate to what extent and in which direction the rate of participation of potential beneficiaries of the Rural Development Programmes (RDPs) of 2014–2020 is affected by demographic change and other explanatory variables. Regression models compare the results of the OLS (aspatial) and spatial autoregressive models (SAR) of four types of participation rates (all RDP schemes; all LEADER schemes; sectoral schemes of RDP and LEADER; non-sectoral schemes of RDPs and LEADER). This comparison makes it possible to understand the differences between centralised and decentralised management and between sectoral and broader rural-targeted schemes. The results of the models appear attractive in interpreting the role of RDP instruments in different regions and local areas. First, the rate of participation is strongly dependent on macro-regional differences. Regarding the demographic factors at the local level, this study highlights that demographic fragility does not necessarily hamper the use of RDP measures. Conversely, the participation rate in RDP policy schemes seems particularly significant in very fragile areas, whereas significance has yet to be proved in other demographic typologies. This result holds particularly true for the policy uptake of non-sectoral schemes. Furthermore, LEADER decentralised interventions fit the fragile areas more than resilient and vital ones due to the territorially targeted approach followed by the Local Action Groups.

Spatiotemporal trends in the burden of colorectal cancer incidence and risk factors at country level from 1990 to 2019.

Worldwide, the incidence of colorectal cancer (CRC) continues to rise and remains a major public health concern. This study aimed to analyze the temporal and spatial trends in CRC incidence and related risk factors at the country level. Data on CRC and related risk factors were obtained from the Global Burden of Disease Study (GBD) 2019 study. Temporal trends were evaluated using estimated annual percentage change while spatial trends were analyzed using spatial autocorrelation and autoregression. Additionally, linear mixed-effects models were employed to identify risk factors linked to CRC incidence. Globally, from 1990 to 2019, the incidence cases of CRC increased by 157.23%. At the national level, the incidence of CRC increased in most countries, with the highest increases of age-standardized incidence rate (ASIR) in Equatorial Guinea, Vietnam, and China. In both 1990 and 2019, global spatial clustering of CRC ASIR highlighted hotspots in Europe, characterized by elevated CRC ASIR levels. A comparative analysis of risk factors between hotspot countries and others indicated that gender and alcohol use exerted greater influence in hotspots than elsewhere. Although from 1990 to 2019, the highest growth in ASIR of CRC has been observed in African, Asian, and Latin American countries, the hotspots are still concentrated in Europe. In the identified hotspots, gender and alcohol use exert a more significant impact on CRC incidence compared with other countries. Thus, we should pay attention to countries where the CRC incidence is increasing and these risk factors.

How Does Digital Finance Contribute to Sustainable Wealth Growth: Perspective from Residents’ Income

Sustainable growth relies on common prosperity, which is reflected in increasing total income and equitable income distribution. This study first proposes the theoretical mechanisms by which digital financial development affects residents’ total income and income distribution. After that, a two-stage generalized method of moments estimation model with endogeneity treatment is constructed to investigate the impact of digital finance on residents’ total income in 31 Chinese provinces. Moreover, Moran’s I and a spatial autoregression model are used to explore the impact of digital finance on residents’ income distribution. The results demonstrate that digital financial development can significantly contribute to the increase in residents’ total income in both urban and rural areas, thus contributing to regional sustainable wealth growth. In addition, digital finance has a spatial direct effect and a spatial spillover effect on the optimization of residents’ income distribution. This indicates that a region’s digital financial development benefits regional sustainable wealth growth, as it not only can improve residents’ income distribution within the same region but also can promote the income distribution of neighboring regions.

Research on the coupling coordination and spatial spillover effect of industrial development and environmental protection

ABSTRACT Analysing the coordination and spatial spillover effects between industrial development and environmental protection is crucial for regional sustainable development. This paper examines the spatial and temporal evolution characteristics of the coupling coordination level between industrial development and environmental protection, as well as the evolution of the decoupling type within this system. under the evolution of the coupling coordination mode and decoupling model. Additionally, it analyzes the spatial spillover effects using the spatial panel vector autoregression model. The main conclusions are as follows: First, the coordination level between industrial development and environmental protection is increasing over time in each province. Second, the decoupling status of industrial development and environmental protection shows distinct stages, progressing towards strong decoupling over time. Third, regional industrial development has a positive spillover effect on both industrial development and environmental protection in neighbouring regions, whereas regional environmental protection has a negative spillover effect on industrial development but a positive spillover effect on environmental protection in neighbouring regions. This paper suggests that provinces should focus on the synchronisation industrial development and environmental protection and implement differentiated strategies based on their specific conditions. Additionally, joint management and governance should be pursued between neighbouring provinces.

Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models

Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. The statistical properties of parameter and forecast estimates strongly depend on the structure of such matrices. The least squares (LS) method is the most flexible and can estimate systems of large dimensions; however, it is biased in the presence of multilateral (sparse) matrices. Instead, the unilateral specification of SAR models provides triangular weight matrices that allow consistent LS estimates and sequential prediction functions. These two properties are strictly related and depend on the linear and recursive nature of the system. In this paper, we show the better performance in out-of-sample forecasting of unilateral SAR (estimated with LS), compared to multilateral SAR (estimated with maximum likelihood, ML). This conclusion is supported by numerical simulations and applications to real geological data, both on regular lattices and irregularly distributed points.

Does a more compact urban center layout matter in reducing household carbon emissions? Evidence from Chinese cities

In recent years, compact urban development and carbon emissions reduction have been considered essential approaches for achieving sustainable development goals worldwide. Existing research has focused on the correlation between urban spatial structure and carbon emissions with inconsistent results. This study explores the correlation between urban compactness and household CO₂ emissions. Two indices of urban compactness and four categories of household CO₂ emissions are constructed. We utilize Spatial Durbin Models (SDM) and Spatial Autoregressive Models (SAC) on panel data from over 284 cities at the prefecture level and above in China, spanning 2008–2018. The results indicate that, except for heating consumption, household CO₂ emissions exhibit positive associations with two urban compactness indices. These findings suggest that a city with evenly developed urban cores within a relatively smaller urban area may have better household CO₂ emissions efficiency both locally and regionally. Our study contributes to the existing literature on the sustainability of compact city development with new evidence emphasizing a condensed but balanced urban structure.

Investigating the spatiotemporal dynamics and interplay mechanisms between population urbanization and carbon dioxide emissions in China

Population urbanization significantly influences urban carbon dioxide (CO2) emissions. As urban areas expand and populations grow, the demand for energy and resources rises, contributing to higher CO2 emissions. Understanding the objective laws and mechanisms linking these factors is crucial for promoting low-carbon and environmentally sustainable urban practices. This study utilizes the Spatial Autoregressive (SAR) model to explore the impact of population urbanization on CO2 emissions and innovatively investigates the roles of policies and demographic factors in this process. By examining 30 representative provincial administrative regions in China, the results indicate: (1) From 2000 to 2020, the global Moran's I index for CO2 emissions generally shows a declining trend, with high-emission areas clustering increasingly in the northern regions; (2) CO2 emissions exhibit an accelerating upward trend with the progression of population urbanization, with significant regional disparities across eastern, central, and western China; (3) Energy transition policies and the establishment of a carbon emissions trading system are effective in reducing CO2 emissions; (4) During the process of population urbanization, it is essential to consider urban environmental carrying capacity and employment guidance to ensure a fair energy transition.

ANALISIS SPATIAL AUTO REGRESSIVE (SAR) TERHADAP FAKTOR YANG MEMPENGARUHI KASUS DIARE DI KOTA MEDAN

Diarrhea disease is categorized as the top 10 most common diseases in North Sumatra, especially Medan City. The number of diarrhea cases in each sub-district in Medan City varies, indicating a relationship between regions. This is the basis for the use of spatial models to find out how much influence variables have on the incidence of diarrhea in Medan City. The Spatial Auto regressive (SAR) model is used to adjust the relationships that exist between a set of observations or locations. In this study, the results of SAR modeling of diarrhea cases in 21 sub-districts in Medan City compared to the OLS model produced the best model. The SAR model yielded 35.82%. The significant variable for diarrhea cases in Medan City is the number of people who do not have access to PAM water ( ).

MODELING CRIME IN EAST JAVA USING SPATIAL DURBIN MODEL REGRESSION

The high crime rate will create unrest and losses for the community. One of the provinces with high crime rates is East Java. This study aims to analyze the factors that influence criminality in East Java to ensure appropriate crime prevention and control measures can be taken. The factors that potentially influence crime in East Java studied include population density, the number of poor people, unemployment, Human Development Index (HDI), Gross Regional Domestic Product (GRDP), and per Capita Expenditure, which are associated with geographical conditions in each region (regency/city) collected from BPS East Java in 2022. Meanwhile, the number of crimes is collected from the East Java Regional Police. This research uses a statistical method, namely the Spatial Durbin Model (SDM), which is a particular form of the Spatial Autoregressive Model (SAR) method with Queen Contiguity weighting by analyzing geographically (spatial processes). Based on the results of the analysis, it was found that the influential factors were unemployment, HDI, GRDP, and per Capita Expenditure, and the R-square result was obtained at 85.18%. This shows a relationship between spatial accessibility and crime, where unemployment, HDI, GRDP, and per Capita Expenditure in an area can affect regional vulnerability to crime

Impact of Zoning Regulations on Transportation Equity of Job Accessibility: A Spatial Autoregressive Approach for the Dallas–Fort Worth and Houston-Galveston Area Council Areas

Impact of Zoning Regulations on Transportation Equity of Job Accessibility: A Spatial Autoregressive Approach for the Dallas–Fort Worth and Houston-Galveston Area Council Areas

Unraveling the spatial dynamics of regional economic disparities in East Java

The objective of this research was to analyze the determinants of regional disparities in East Java Province. This study utilized panel data, incorporating research locations across 38 regencies in East Java, with a data series spanning from 2016 to 2020. Spatial panel data models were employed to address the research objective, specifically examining the impact of spatial and non-spatial factors on disparities in East Java. The study's findings indicated that the Spatial Autoregressive (SAR) model was the most effective for estimating disparities in the region. The effects of each variable are as follows: the average duration of schooling and investment had positive and significant effects on disparities, while the variable of open unemployment rate did not exhibit any significant effect. Additionally, there is a spatial effect in the form of an endogenous interaction effect, signifying that the extent of disparities is influenced by the disparities in neighboring areas.

Implementation of Spatial Autoregressive with Autoregressive Disturbance (SARAR) using GMM to Identify Factors Caused Poverty in West Java

Implementation of Spatial Autoregressive with Autoregressive Disturbance (SARAR) using GMM to Identify Factors Caused Poverty in West Java

Local Walsh-average regression for spatial autoregression single index varying coefficient models

. In real life, a large number of variables are spatially correlated in adjacent regions in various fields, such as finance, sociology, ecology, and geographic information systems. And the factors affecting these variables often have both linear and non linear relationships with them, how to accurately depict the relationship between them with a spatial regression model is a significant work. In order to further accurately describe the relationship between explanatory variables and response variables, we establish a spatial autoregressive single index varying coefficient model. The least squares method is the main method of model estimation but it is not robust enough when the parameter distribution is not normal. We present a new estimation method based on local Walsh average-regression. Under some general assumptions, we establish the asymptotic properties of the parametric and non parametric estimators. We further verify the accuracy and efficiency of the estimators under the condition of non normal error distribution through numerical experiments. In addition, the robustness of the proposed method is verified when the spatial weight matrix is interfered and when the bandwidth is changed within a certain range. Finally, we demonstrate the accuracy of forecasting by our proposed model compared with traditional spatial autoregressive model through real data analysis.

Spatial Autoregressive Model with Mixture of Gaussian Distribution for the Random Effect: Formulation, Estimation and Application

Spatial Autoregressive Model with Mixture of Gaussian Distribution for the Random Effect: Formulation, Estimation and Application

Spatial patterns of European innovation: a spatial econometric approach with technological proximity weights

The paper deals with a spatial econometric analysis of 220 European regions. The analysis follows the Mixed Geographically Weighted Regression – Spatial Autoregressive approach. Patent applications were a proxy for innovation output. Instead of traditionally applied geographical proximity, the technological similarity was considered. The results supported the assumption of spatial differentiation of model parameters and indicated that regional innovation activities do not have only a local character in almost half of the regions. The region's technological similarity appears to be a significant factor stimulating innovation for regions that are not among the top innovators.

Using a spatial autoregressive model with spatial autoregressive disturbances to investigate origin-destination trip flows.

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance.