Spatial Autoregressive Research Articles

Pemodelan Regresi Global (Glm) dan Regresi Spasial (Sar Dan Sdm) Pada Kasus Indeks Pembangunan Manusia Di Provinsi Kalimantan Selatan

The Human Development Index (HDI) is a parameter that functions to assess the success of the quality of human life. The factors used in the research are the severity of poverty, population density and net participation rate. The research carried out aims to see what factors influence the HDI in South Kalimantan Province in 2022, the HDI value for South Kalimantan Province is below the Indonesian HDI value and quite a few regencies/cities in South Kalimantan Province have HDI values below the HDI value. Indonesia. The statistical analysis used is a spatial approach, where the SAR and HR spatial regression models will be searched. The Global Regression Model (GLM) obtained in this study is , while the Spatial Autoregressive (SAR) model is and Spatial Durbin Model is The best model that can be obtained is the Spatial Durbin Model (SDM) with an AIC value of 52,82654 and an value of 95,86%.

Assessing the impact of rural E-commerce on regional poverty alleviation: A spatial spillover analysis

China's innovative and sustainable e-commerce poverty alleviation model has not only generated significant social and economic value but also serves as an effective practical case for global poverty reduction. This study employed 611 counties in contiguous poverty-affected areas in China from 2011 to 2022 as valid research samples. It adopted the Spatial Autoregression Model, Spatial Error Model, and Spatial Durbin Model to examine the impact of rural e-commerce on the quality of poverty alleviation, spatial spillover effects, and regional heterogeneity in contiguous poverty-affected areas. The results indicate that rural e-commerce improves the quality of poverty alleviation in local regions and has a spatial spillover effect on the quality of poverty alleviation in adjacent regions. Moreover, significant regional heterogeneity exists in the direct promotion and spatial spillover effects of rural e-commerce on the quality of poverty alleviation in contiguously poor areas. Specifically, in the impoverished areas of eastern, central, and northeastern China, rural e-commerce not only enhances the quality of poverty alleviation in the local region but also has a positive spillover effect on the poverty alleviation quality of neighboring regions. The findings of this study provide valuable insights for rural e-commerce to consolidate the achievements of poverty alleviation and align them with rural revitalization efforts.

Effect of Social Vulnerability on Cocaine-Related Mortality Rates in U.S. Counties

ABSTRACT Cocaine-related mortality rates have risen sharply since 2013 and social vulnerability is a crucial indicator for drug-related mortality rates. Therefore, the purpose of this study was to investigate the relationship between social vulnerability and cocaine-related mortality rates in U.S. counties. The Data were collected from the CDC WONDER, CDC’s Social Vulnerability Index (CDC’s SVI), and American Community Survey (ACS). The Data were analyzed by spatial autoregression models. According to present results, first, counties with social vulnerability (socioeconomic) were positively related to higher rates of cocaine overdose death (spatial lag: B = 0.323, p < .05; spatial error: B = 0.513, p < .01). Second, counties with social vulnerability (minority status & language) were negatively related to higher rates of cocaine overdose death (spatial lag: B = -0.233, p < .05). Third, counties with social vulnerability (housing type & transportation) were positively related to higher rates of cocaine overdose death (spatial lag: B = 0.413, p < .001; spatial error: B = 0.378, p < .001). In conclusion, the spread of cocaine overdose on U.S. counties with social vulnerabilities demonstrated a disproportionate burden of cocaine-related mortality.

A Semiparametric Bayesian Approach to Heterogeneous Spatial Autoregressive Models.

Many semiparametric spatial autoregressive (SSAR) models have been used to analyze spatial data in a variety of applications; however, it is a common phenomenon that heteroscedasticity often occurs in spatial data analysis. Therefore, when considering SSAR models in this paper, it is allowed that the variance parameters of the models can depend on the explanatory variable, and these are called heterogeneous semiparametric spatial autoregressive models. In order to estimate the model parameters, a Bayesian estimation method is proposed for heterogeneous SSAR models based on B-spline approximations of the nonparametric function. Then, we develop an efficient Markov chain Monte Carlo sampling algorithm on the basis of the Gibbs sampler and Metropolis-Hastings algorithm that can be used to generate posterior samples from posterior distributions and perform posterior inference. Finally, some simulation studies and real data analysis of Boston housing data have demonstrated the excellent performance of the proposed Bayesian method.

Household-level effects of seasonal malaria chemoprevention in the Gambia.

In 2022 the WHO recommended the discretionary expansion of the eligible age range for seasonal malaria chemoprevention (SMC) to children older than 4 years. Older children are at lower risk of clinical disease and severe malaria so there has been uncertainty about the cost-benefit for national control programmes. However, emerging evidence from laboratory studies suggests protecting school-age children reduces the infectious reservoir for malaria and may significantly impact on transmission. This study aimed to assess whether these effects were detectable in the context of a routinely delivered SMC programme. In 2021 the Gambia extended the maximum eligible age for SMC from 4 to 9 years. We conducted a prospective population cohort study over the 2021 malaria transmission season covering 2210 inhabitants of 10 communities in the Upper River Region, and used a household-level mixed modelling approach to quantify impacts of SMC on malaria transmission. We demonstrate that the hazard of clinical malaria in older participants aged 10+ years ineligible for SMC decreases by 20% for each additional SMC round per child 0-9 years in the same household. Older inhabitants also benefit from reduced risk of asymptomatic infections in high SMC coverage households. Spatial autoregression tests show impacts are highly localised, with no detectable spillover from nearby households. Evidence for the transmission-reducing effects of extended-age SMC from routine programmes implemented at scale has been previously limited. Here we demonstrate benefits to the entire household, indicating such programmes may be more cost-effective than previously estimated.

HETEROSKEDASTICITY ROBUST SPECIFICATION TESTING IN SPATIAL AUTOREGRESSION

Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial and network interactions. SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. Also, the choice of a SAR model over other alternatives, such as spatial Durbin (SD) or spatial lagged X (SLX) models, is often arbitrary, raising issues of potential specification error. To address such issues, this paper develops a new specification test within the SAR framework that can detect general forms of misspecification including that of the spatial weight matrix, the functional form and the model itself. The test is robust to the presence of heteroskedasticity of unknown form in the disturbances and the approach relates to the conditional moment test framework of Bierens ([1982, Journal of Econometrics 20, 105–134], [1990, Econometrica 58, 1443–1458]). The Bierens test is shown to be inconsistent in general against spatial alternatives and the new test introduces modifications to achieve test consistency in the spatial setting. A central element is the infinite-dimensional endogeneity induced by spatial linkages. This complexity is addressed by introducing a new component to the omnibus test that captures the effects of potential spatial matrix misspecification. With this modification, the approach leads to a simple pivotal test procedure with standard critical values that is the first test in the literature to have power against misspecifications in the spatial linkages. We derive the asymptotic distribution of the test under the null hypothesis of correct SAR specification and prove consistency. A Monte Carlo study is conducted to study its finite sample performance. An empirical illustration on the performance of the test in modeling tax competition in Finland is included.

Spatial Weight Matrix Comparison of SAR-X Model using Casetti Approach

The Spatial Autoregressive Exogenous (SAR-X) model with the Casetti approach is used to describe the influence of location and exogenous variables in the description and prediction of spatial observations, namely, people's habits and behavior towards culture in Java Island. The SAR-X model with the Casetti approach is characterized by a spatial weight matrix that describes the coordinates of the region at each location. The spatial weight matrix is determined outside the model. This study examines the spatial weight matrix determined based on rook contiguity, bishop contiguity, queen contiguity, inverse distance and inverse distance squared, and compares the application of the spatial weight matrix to the SAR-X model with the Casetti approach for the description and prediction of people's habits and behavior towards culture in Java Island. The description and prediction results obtained are measured using the Root Mean Square Error (RMSE) value. The results of data processing show that the best spatial weight matrix in the SAR-X model with the Casetti approach to community habits and behavior in Java Island is the inverse distance squared spatial weight matrix, supported by the calculation of the minimum RMSE value and the coefficient of determination above 60%.

Assessment of the Spatial Variation of the Economic Benefits of Urban Green Spaces in a Highly Urbanized Area

Urban green spaces play a vital role in improving the quality of life and well-being of urban residents. However, their economic benefits in different spatial contexts within highly urbanized areas remain a critical yet understudied topic. This study delves into the economic value of urban green spaces in Cheongju City, Republic of Korea, and investigates the distance-decay features associated with the proximity of green spaces to residential properties. Two spatial econometric models were employed to address these questions: the spatially autoregressive (SAR) model and the generalized additive model (GAM). The SAR model was used to assess the economic benefits of urban green spaces, whereas the distance decay of these benefits was examined with the GAM. Empirical analyses revealed that small-sized parks or forests under 20 ha hold greater economic value when in proximity to residential areas compared to medium-sized parks or forests between 20 and 200 ha. Conversely, large parks or forests over 200 ha appeared to have a disamenity effect, negatively impacting property prices when in close proximity. The GAM’s smooth functions illustrated that the distance-decay effect was shorter for small-sized green spaces and exhibited an inverted U-shape for large-sized ones, resulting in a negative benefit of proximity. Our findings suggest that urban green spaces have a positive impact on property prices, but this effect may not apply uniformly to large-sized parks or forests. Therefore, to enhance the residents’ welfare, green infrastructure policies should prioritize the provision of accessible small- and/or medium-sized parks or forests near residential areas.

Estimating spatial autoregressions under heteroskedasticity without searching for instruments

This paper proposes estimating higher-order spatial autoregressions with spatial autoregressive errors and heteroskedastic error innovations without searching for instruments by explicitly exploiting the endogeneity of spatial lags in the outcome and error equations. The resulting estimator is shown to be consistent and asymptotically normal. Monte Carlo experiments demonstrate that it possesses better finite-sample properties than existing estimators. An empirical study of venture capital funding for biotechnology firms illustrates that spatial correlation stretches as far as 20 miles and that the number of venture capital firms in close proximity has stronger impact on the level of funding than as reported in an existing study.

Modeling Factors Influencing Covid-19 Cases in South Sulawesi Using Bayesian Conditional Autoregressive Localised

South Sulawesi Province is listed as the province with the highest number of Covid-19 cases in the Sulawes island. Research on Covid-19 modeling has been carried out by many researchers, but until now, there has been no research using the Bayesian spatial Conditional Autoregressive Localized model which involves a combination of factors such as distance to the provincial capital, population density, and the number of elderly people in each district in South Sulawesi Province. The aim of this research is to get the best Bayesian Conditional Autoregressive Localized model. The best model is based on four criteria, namely: Deviance Information Criteria, Watanabe Akaike Information Criteria, residuals from Modified Moran's I, and the number of areas included in a group. It was found that model with G=3 by including population density covariates was the best model. A significant factor influencing the increase in Covid-19 cases is the population density factor which has a positive effect. This shows that the more densely populated an area is, the greater the chance of being infected with Covid-19. Makassar has the highest relative risk value for Covid-19 followed by Toraja district and Pare-Pare City. Meanwhile, Bone district has the lowest relative risk value for Covid-19, followed by Wajo district and Enrekang district.

Spillover effects of microcredit on the GDP of surrounding cantons in Ecuador

PurposeThe aim of this paper is to analyse the spillover effects of microcredit on the economy of Ecuador, with a particular focus on its potential as a poverty alleviation mechanism.Design/methodology/approachTo address our research questions, we take into account the distance between cantons (Ecuador’s own administrative distribution) by adopting a spatial autoregressive (SAR) model. To this end, a database will be constructed with macroeconomic information about the country, broken down by canton (administrative division of Ecuador), and in a 2019 cross section, with a total of 1,331 microcredit operations in all 221 of Ecuador’s cantons.FindingsWe find a positive effect of microcredit on these neighbouring regions in terms of wealth generation.Research limitations/implicationsWe acknowledge that this study is limited to Ecuadorian cantons. Nonetheless, it is crucial to emphasize that focussing on an under-represented developing country like Ecuador adds significant value to the research.Practical implicationsFacilitating access to microcredit is one of the main solutions to address the goals proposed in the sustainable development goals (SDGs).Social implicationsMicrocredit activity contributes to the creation of value and wealth in Ecuador, exerting a spillover effect in neighbouring areas that can generate positive multiplier effects and alleviate poverty. For all of the above reasons, our proposal for the country is to support and promote microcredit as one of the main solutions to address the goals proposed in the SDGs.Originality/valueThe novelty of this study lies in the use of spatial econometrics to observe the indirect effects of microcredit on the regions bordering the canton in which it was issued, thus examining the spatial effects of microcredit on wealth distribution.

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.

Coal price shock propagation through sectoral financial interconnectedness in China's stock market: Quantile coherency network modelling and shock decomposition analysis

The long and continuing coal-dominated energy structure in China makes it important to investigate the impact of coal price shocks on China's financial markets. This study identifies whether volatilities in coal market may propagate between sectoral equity markets through the heterogeneous connectedness between these markets, and even further contribute to larger scale overall instabilities. We first apply the cross-spectral quantile coherency (QC) to identify the time-frequency interconnectedness among returns of 28 sectors in China's equity market. A spatial autoregressive (SAR) framework based on the QC network is further utilized to identify the indirect effect propagating through the heterogeneous interconnectedness between 28 sectoral equity markets. The empirical results indicate significant risk contagion effects during market turmoil, while strong risk absorbing effects are confirmed for the tranquil case. The significantly varying sectoral interconnectedness along with the corresponding heterogeneous pattern of shock propagation under various market specifications may provide evidence for the spillover effects to be the key mechanism and the sectoral interconnectedness as an important channel for coal price shock propagation, which is essential to the effectiveness of portfolio diversification and financial stabilizing policy.

MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION

Spatial analysis of count data is important in epidemiology and other domains to identify spatial patterns. While Bayesian spatial models are a popular approach, they do require detailed knowledge of the process for model fitting, checking, and visualising results. Although a number of R packages are available to simplify running the model, there are still complexities when checking the model. This paper aims to provide a user-friendly and interactive R Shiny web application for the analysis of spatial data using Bayesian spatial Conditional Autoregressive Leroux models. The web application is built with the integration of the R packages shiny and CARBayes. The required data are the number of cases, population, and optionally some covariates for each region. In this case, we used Covid-19 data in 2021 in South Sulawesi province, Indonesia. This application enables fitting a Bayesian spatial CAR Leroux model under several hyperpriors and selecting the most appropriate through comparing several goodness of fit measures. The application also enables checking convergence, plus obtaining and visualising in an interactive map the relative risk of disease for each region.

Is air pollution exposure linked to household income? Spatial analysis of Community Multiscale Air Quality Model results for Madrid

This study explores the potential correlation between income and exposure to air pollution for the city of Madrid, Spain and its neighboring municipalities. Madrid is a well-known European air pollution hotspot with a high mortality burden attributable to nitrogen dioxide (NO2) and fine particulate matter (PM2.5). Statistical analyses were carried out using electoral district level data on gross household income (GHI), and NO2 and PM2.5 concentrations in air obtained from a mesoscale air quality model for the study area. We applied linear regression, bivariate spatial correlation analysis, spatial autoregression and geographically weighted regression to explore the relationship between contaminants and income. Three different strategies were adopted to harmonize data for analysis. While some strategies suggested a link between income and air pollution, others did not, highlighting the need for multiple different approaches where uncertainty is high. Our findings offer important lessons for future spatial geographical studies of air pollution in cities worldwide. In particular we highlight the limitations of census-scale socio-economic data and the lack of non-model derived high-resolution air quality measurement data for many cities and offers lessons for policy makers on improving the integration of these types of essential public information.

Local Walsh-average-based Estimation and Variable Selection for Spatial Single-index Autoregressive Models

Local Walsh-average-based Estimation and Variable Selection for Spatial Single-index Autoregressive Models

Rent growth near rail stations after the Great Recession

This study provides an incisive analysis of U.S. rail station area rent trends from 2012 to 2016, post-Great Recession. Utilizing Ordinary Least Squares (OLS), Hierarchical Spatial Autoregressive (HSAR), and Geographically Weighted Regression (GWR) models, it uncovers complex interplays between rent changes and socioeconomic, as well as built environment factors. Key findings reveal that increased rents are associated with walkability, higher housing density, and predominantly White neighborhoods. Conversely, rent decreases correlate with diverse land use, higher Black population percentages, and locations further from rail stations or business districts. These results are critical for understanding rent dynamics in transit-oriented development areas, impacting diverse housing market sectors, including workforce and affordable housing. The study’s regional analysis highlights the need for geographically tailored approaches. This research informs policy on housing development and affordability, aiming to mitigate displacement risks for low-income, transit-dependent communities in gentrifying urban areas.

Panel Data Spatial Regression Modeling with a Rook Contiguity Weighting Function on the Human Development Index in West Sumatera Province

The achievement of the level of welfare of a region or country can be seen from the level of human development as measured by the Human Development Index (HDI). West Sumatra is one of the provinces with HDI achievements above the national average. However, there are still regencies/cities in West Sumatra Province that have HDI achievements below the national average and HDI achievements in West Sumatra Province Regencies/Cities have changed in 2017-2021. Therefore, in this study, spatial analysis of panel data was used. The aim of this research is to find out the general description of the HDI of West Sumatra Province, obtain a panel data spatial regression model and obtain variables that significantly influence on HDI in West Sumatra Province 2017─2021because differences in HDI achievement were suspected to have influences from areas that were side by side and the area was observed more than once. The model formed from this analysis using the rook contigutiy weighting function is Random Effect Spatial Autoregressive because the spatial interactions formed in human development index data in West Sumatra Province are real at lag. This model is a suitable model based on panel spatial model selection and has an R2 value of 92.94%. Analysis of human development index data in regencies/cities in West Sumatra Province using spatial regression panel data obtained results that expectations of school length (X1), average length of schooling (X2), and population density (X3) significantly directly influenced the human development index in regencies/cities in West Sumatra Province.

Data forecasting performance evaluation of threshold spatial vector autoregressive with exogenous variables

One time series model developed to predict economic data is Spatial Vector Autoregressive with Exogenous Variables (SpVARX). This model can accommodate simultaneously the interrelationships between variables, the impact of exogenous variables, and Inter-regional linkages. However, this model has not adjusted the nonlinearity relationships between variables. The relationship between economic variables is usually not linear. Therefore, we introduce the Threshold Spatial Vector Autoregressive with exogenous variables (TSpVARX). This paper assesses the forecasting performance of TSpVARX and compares it with SpVARX models. We conducted a simulation study by generating 100 times the simulation data with twelve scenarios. We found that the forecasting performance of the TSpVARX model is better than SpVARX when there is a nonlinear relationship between variables. In addition, we find that the forecasting performance of TSpVARX models will improve as the sample size increases.

Spatial Vector Autoregressive with Metric Exogenous Variable (SpVARX) for Inflation and Outflow Forecasting

Forecasting inflation and outflow is very important for the government to make price control policies. We propose SpVARX be applied to inflation and outflow forecasting. SpVARX can simultaneously accommodate interrelationships between variables, the influence of metric exogenous variables, and spatial aspects. Our study shows SpVARX has better forecasting performance than SpVAR, as most of SpVARX's Root Mean Square Errors (RMSEs) are smaller than SpVAR's. Based on the forecast, the highest inflation in Semarang and Solo will occur in November 2023, while the highest inflation in Yogyakarta will occur in December 2023. The highest outflow forecast for all these cities is in April 2023.