spatial autoregressive
Recently Published Documents


TOTAL DOCUMENTS

564
(FIVE YEARS 239)

H-INDEX

38
(FIVE YEARS 5)

2021 ◽  
Vol 6 ◽  
Author(s):  
Etty Puji Lestari ◽  
Caroline Caroline

The ASEAN Economic Community provides opportunities for foreign workers to enter Indonesia, including Central Java Province. The existence of these foreign workers tends to negatively and positively impact the regional economic growth of the country. Therefore, this study aims to analyze the effects of foreign workers’ human capital spillover inflow on the economic growth of Central Java. The Euclidean distance spatial weight matrix was used to calculate the spatial autoregressive model from 2015 to 2020. These results indicate that the presence of skilled foreign workers positively impacts increasing economic growth in Central Java Province. The influx of foreign workers, along with the influx of investment, encourages local workers to follow the performance of foreign workers. This study suggests a policy to encourage technology transfer from foreign workers to local workers. The government is also expected to strengthen local workers’ productivity to compete with foreign workers.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 5
Author(s):  
Tianjian Yu ◽  
Fan Gao ◽  
Xinyuan Liu ◽  
Jinjun Tang

Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, but rarely both. To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling relationships change across the distribution of crash rates considering the effects of spatial autocorrelation. Three types of explanatory variables, i.e., demographic, traffic networks and volumes, and land-use patterns, were considered. Using data collected in New York City from 2017 to 2019, the results show that: (1) the SARQ model outperforms the traditional quantile regression model in prediction and fitting performance; (2) the effects of variables vary with the quantiles, mainly classifying three types: increasing, unchanged, and U-shaped; (3) at the high tail of crash rate distribution, the effects commonly have sudden increases/decrease. The findings are expected to provide strategies for reducing the crash rate and improving road traffic safety.


2021 ◽  
Vol 13 (24) ◽  
pp. 13968
Author(s):  
Wenming Liang ◽  
Azhong Ye

Investment in China’s transport infrastructure has contributed to its rapid economic growth, which also consumes a great deal of energy and generates a significant amount of carbon emissions. In these circumstances, it is worthwhile to discuss the internal influence mechanisms behind these two outcomes’ similar growth trends. This paper selects panel data from 30 regions in China from 2009 to 2019 and uses the threshold spatial autoregressive (TSAR) model to analyze the impact of transport infrastructure investment on the energy intensity due to fiscal decentralization. While studies of the relationship between transport infrastructure investment and energy intensity exist, few studies examine the non-linear spatial relationship between the two. This paper fills this gap by using the TSAR Model. The results show the following: (1) the effect of transport infrastructure investment on the energy intensity under fiscal decentralization and heterogeneity expresses non-linear characteristic; (2) there is a positive relationship between infrastructure investment and energy intensity when the degree of attenuation is low, but when the degree of attenuation is higher than a particular threshold value, transport infrastructure investment negatively impacts energy intensity; (3) rising energy prices, increasing investment in technological innovation costs, and increasing foreign trade will help to drive the decline in energy intensity.


2021 ◽  
Author(s):  
Libo Xia ◽  
Zhiliang Wang ◽  
Shuang Du ◽  
Decun Tian ◽  
Feng Chen

Abstract This article has carried out a statistical analysis of the industrial wastewater discharge (IWD) and gross regional product (GRP) of 79 cities in the Yellow River Basin from 2003 to 2019. By calculating the Moran index of IWD and GRP, the study has found that a certain spatial autoregressive in space. There is an environmental Kuznets curve (EKC) between the environmental pollution and economic development of cities in the Yellow River Basin, and a spatial autoregressive is modelled by a set of random effects that are assigned a conditional autoregressive prior distribution. In the Bayesian environment, Markov chain Monte Carlo (MCMC) is used for inferencing, and the spatial weight matrix is ​​selected to be U-shaped matrix, and the error of the model is minimized. The parameter posterior distribution results of the model showed that the GRP did not show a significant decline. The modified EKC showed that the discharge of industrial wastewater in the entire Yellow River Basin will be reduced. Generally, cities with high pollutant emissions should learn from other cities to reduce emissions, and cities with low GRP need to increase local economic development.


PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261144
Author(s):  
Xiaowen Dai ◽  
Libin Jin

This paper considers the quantile regression model with individual fixed effects for spatial panel data. Efficient minimum distance quantile regression estimators based on instrumental variable (IV) method are proposed for parameter estimation. The proposed estimator is computational fast compared with the IV-FEQR estimator proposed by Dai et al. (2020). Asymptotic properties of the proposed estimators are also established. Simulations are conducted to study the performance of the proposed method. Finally, we illustrate our methodologies using a cigarettes demand data set.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Huiping Hu ◽  
Xinqun Huang ◽  
Majed Ahmad Suhaim ◽  
Hui Zhang

Abstract To reduce the probability of violent crimes, the deep learning (DL) technology and linear spatial autoregressive models (ARMs) are utilised to estimate the model parameters through different penalty functions. In addition, under a determinate space, the influences of environmental factors on violent crimes are discussed. By taking campus violence cases as examples, the major influencing factors of violent crimes are found through data analysis. The results show that campus violence cases are usually caused by the complex surrounding environments and persons. Also, campus security measures only cover a small range, and the security management is difficult. In the meantime, due to the younger ages and lack of self-protection awareness, students may easily become the targets of criminals. Therefore, the results have a positive significance for authorities to analyse the crime rates in a determinate area and take preventive measures against violent crimes.


2021 ◽  
Author(s):  
Yingyu Zhu ◽  
Yan Zhang ◽  
Huilan Piao

Abstract It has important theoretical value and practical significance to study the impact of agricultural mechanization (AM) on agriculture environment efficiency (AEE), as AM is an important way to improve the level of rural modernization and accelerate the high-quality development of agriculture, while the increase of energy consumption of AM has brought greenhouse gas emissions. Using the panel data of 30 provinces in China from 2001 to 2019, this article adopts stochastic frontier analysis method with output oriented distance function to measure AEE based on net carbon sink, and empirically analyzes the impact of AM on AEE. The empirical analysis finds that the AEE of the whole country and all provinces shows an upward trend with time, and has significant spatial positive autocorrelation characteristics. There is a Kuznets inverted "U" relationship between AM and AEE. Meanwhile, AM has spatial spillover effect and time cumulative effect on AEE, and this basic conclusion is still robust after using instrumental variables, spatial autoregressive model, sub sample regression, changing spatial weight matrix and independent. Further research shows that the effect of AM on AEE depends on the input effect and output effect caused by AM, and the mechanism is mainly reflected in agricultural technology progress, expansion of the scale of agricultural operation, optimization of resource allocation and spatial spillover. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.


Author(s):  
Levi Pérez ◽  
Ana Rodríguez ◽  
Andrey Shmarev

AbstractCities are certainly a key factor in the location of gambling facilities. This paper aims to map the location of gambling outlets in urban areas and to examine potential links between neighborhoods socioeconomic and demographic characteristics and gambling supply, taking into account spatial dependencies of neighboring areas. This correlation is of interest because neighborhood characteristics may attract sellers, and because the presence of gambling sellers may cause changes in neighborhood demographics. Using detailed official data from the city of Madrid for the year 2017, three spatial econometric approaches are considered: spatial autoregressive (SAR) model, spatial error model (SEM) and spatial lag of X (explicative variables) model (SLX). Empirical analysis finds a strong correlation between neighborhoods characteristics and co-location of gambling outlets, highlighting a specific geographic patterning of distribution within more disadvantaged urban areas. This may have interesting implications for gambling stakeholders and for local governments when it comes to the introduction and/or increase of gambling availability.


Sign in / Sign up

Export Citation Format

Share Document