Transportation and Urban Development

2018 ◽  
pp. 283-305
Author(s):  
Mehmet Akif Kara
Keyword(s):  
Spillover Effect ◽  
Error Model ◽  
Transportation Infrastructure ◽  
Spatial Error Model ◽  
Test Statistic ◽  
Spatial Econometric ◽  
Spatial Error ◽  
Infrastructure Investments ◽  
Regional Economic Performance ◽  
The Impact

It is noteworthy that there is a substantial literature review that examines the impact of transportation infrastructure on urban and regional economic performance. It is observed that such infrastructure investments are focused on the economic growth as well as the spillover effect in applied studies carried out in this respect. In this study, in which the effects of highway transportation infrastructure on urban output and the spillover effect of these investments are determined using the spatial econometric method, 81 cities in Turkey have been taken into consideration, and according to the results of the study, transportation infrastructure investments in Turkey have been found to contribute positively to urban output. Also, while the Moran's I test statistic reveals the spatial dependence of such investments, the Lagrange multiplier test results also determine the need to use the spatial error model. The spatial error model results reveal the existence of the positive spillover effect of transportation infrastructure investments.

scholarly journals Regression Models for Spatial Data: An Example from Gross Domestic Regional Bruto in Province Central Java

2017 ◽  
Vol 18 (2) ◽  
pp. 213
Author(s):  
Abdul Karim ◽  
Akhmad Faturohman ◽  
Suhartono Suhartono ◽  
Dedy Dwi Prastyo ◽  
Budi Manfaat
Keyword(s):  
Economic Growth ◽  
Spatial Data ◽  
Spatial Modeling ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Error Model ◽  
Transportation Infrastructure ◽  
Spatial Error ◽  
Central Java ◽  
Transportation Infrastructures

The important role of a region's transportation infrastructure strongly affects the economic growth of the region and tends to affect the surrounding areas. The effect is called spillover effect. The aim of the research was to recognize the direct effect and spillover effect (indirect) of transportation infrastructure on the economic growth in Central Java. To identify the spillover effects, it is necessary to recognize the different characteristics of each region which have the implications on the various transportation infrastructures at each region in Central Java. Therefore, the spatial modeling was conducted. In this study, the spatial modeling employed was Spatial Durbin Error Model (SDEM). The SDEM is another form of Spatial Error Model (SEM). It does not allow for lag effects of endogenous variables, but it allows for spatial error and spatial lag on exogenous variables in which it simplifies the interpretations on direct effects and spillover effect. According to SDEM estimates, the transportation infrastructures at the districts/municipalities in Central Java had no significant effect on the outputs at each region where the infrastructures were located and their neighboring districts/cities

scholarly journals Effects of Land Use on Land Surface Temperature: A Case Study of Wuhan, China

2021 ◽  
Vol 18 (19) ◽  
pp. 9987
Author(s):  
Youpeng Lu ◽  
Wenze Yue ◽  
Yaping Huang
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Error Model ◽  
Spatial Error Model ◽  
Residential Areas ◽  
Spatial Error ◽  
Commercial Area ◽  
Urban Heat ◽  
Use Factors ◽  
The Impact

In this study, we aim to understand the impact of land use on the urban heat island (UHI) effect across an urban area. Considering the case study of Wuhan, China, land use factors and land surface temperatures (LSTs) of 589 planning management units were quantified in order to identify the spatial autocorrelation of LST, which indicated that a traditional regression would be invalid. By investigating the relationships between land use factors and the LST in summer, based on spatial regression models including the spatial lag model and the spatial error model, four conclusions were derived. First, the spatial error model effectively explains the relationships between LST and land use factors. Second, the impact on LST of the percentage of industrial areas is significant even though the impacts of land cover and building-group morphology indicators are combined, indicating that anthropogenic heat emission of industrial production contributes to high LSTs. Third, the relationship between the percentage of commercial area and LST is significant in the Pearson correlation analysis and traditional regression models, while not significant in spatial error model, suggesting that the urban heat environment of a commercial area is determined by the land use factors of the surrounding area. Fourth, the UHI effect in industrial and commercial areas could be precisely mitigated by not locating industrial areas beside residential areas, and setting up buffer zones between commercial areas and surrounding traditional residential areas. Overall, the results of this study innovatively deepen the understanding of the impact of the percentage of different urban land use types on the urban heat environment at the scale of planning management units, which is conducive to formulating precise regulation measures for mitigating UHI effects and improving public health.

scholarly journals Spatial Econometric Model on Economic Growth in West Nusa Tenggara

Jurnal Varian ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 153-158
Author(s):  
Siti Soraya ◽  
Baiq Candra Herawati ◽  
Muttahid Shah ◽  
Syaharuddin Syaharuddin
Keyword(s):  
Economic Growth ◽  
Spatial Dependence ◽  
Econometric Model ◽  
Error Model ◽  
Spatial Error Model ◽  
Spatial Econometric ◽  
Spatial Error ◽  
Response Variable ◽  
Spatial Econometric Model ◽  
Capital Value

Gross Regional Domestic Product (GRDP) is a reflection of a region's economic growth. West Nusa Tenggara (NTB) is one of the provinces that contributes to good GRDP for Indonesia. The purpose of this research is to modeling GRDP in NTB using spatial econmetrics. The data used is the GRDP data of each district / city in NTB Province as a response variable and factors that affect the number of workers, capital value and electrification ratio as predictor variables. The results showed that there is a spatial dependence on the district / city GRDP in NTB Province on the error model so that the model formed is the Spatial Error Model (SEM) with a rho of 71.1% and an AIC value of 173.34.

Recursive Estimation of the Spatial Error Model

2022 ◽  
Author(s):  
Chiara Ghiringhelli ◽  
Gianfranco Piras ◽  
Giuseppe Arbia ◽  
Antonietta Mira
Keyword(s):  
Error Model ◽  
Recursive Estimation ◽  
Spatial Error Model ◽  
Spatial Error

Spatial Error Model

2008 ◽  
pp. 1101-1101
Author(s):  
Shashi Shekhar ◽  
Hui Xiong
Keyword(s):  
Error Model ◽  
Spatial Error Model ◽  
Spatial Error

Does China’s transportation infrastructure have an impact on employment in the service sector?

Kybernetes ◽  
2020 ◽  
Vol 49 (11) ◽  
pp. 2737-2753
Author(s):  
Hui Wang ◽  
Meiqing Zhang
Keyword(s):  
Large Scale ◽  
Service Industry ◽  
Spillover Effect ◽  
Transportation Infrastructure ◽  
Content Type ◽  
Inland Waterways ◽  
Spatial Spillover ◽  
Positive Effects ◽  
The Impact ◽  
Spatial Spillover Effect

Purpose The large-scale construction of China’s transportation infrastructure has driven the flow of elements between regions, which has provided convenient conditions for the accumulation of advantageous resources. Design/methodology/approach Based on the panel data of 31 provinces in China in the past 2003-2017 years, this paper applies the spatial econometric model and partial differential method and empirically analyzes the spatial spillover effect of transportation infrastructure on employment in the service industry under four spatial weighting matrices. Findings The results show that for every 1 per cent increase in the level of transportation infrastructure, the employment density of the service industry in the region can be increased by 0.1274 per cent. It is worth noting that roads promote the employment of the service industry more than railways and inland waterways. However, inland waterways have not shown positive effects. The results on spatial spillover of transportation infrastructure indicate that railway has obvious promotion effect on the employment level of service industry in the surrounding area, while the highway has hindered the effect. The spatial spillover effect of inland waterway is not obvious. Originality/value The value of this paper is to consider the impact of China’s transportation infrastructure on employment in a particular industry, especially in the service industry. The research will help to provide empirical evidence for policymakers. The government needs to invest and build transportation infrastructure based on the stage and development potential of the employment development of the regional service industry.

scholarly journals Model Regresi Spasial dan Aplikasinya pada Kasus Tingkat Kemiskinan Kabupaten Soppeng

2018 ◽  
Vol 4 (2) ◽  
pp. 102
Author(s):  
Anggi Ananda Putri ◽  
Wahidah Sanusi ◽  
Sukarna Sukarna
Keyword(s):  
Social Issues ◽  
Well Being ◽  
Error Model ◽  
Poor Household ◽  
Spatial Dependency ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Sar Model ◽  
Spatial Autoregressive ◽  
Independent Variable

Poverty is one of the major problem that frequently faced by human. Begin from poverty, consequently emerged several social issues, such as homeless, beggar, defendant, and prostitution. On this research were conducted modeling poverty degree in Soppeng with using number of poor household as the dependent variable. Modeling were done by using area approach which is a Spatial Autoregressive (SAR) model and Spatial Error Model (SEM). As for the independent variable used on this research is the number of health services, school facility, population density, social well being disable, and the distance on village and centre of Soppeng.  Regarding to the analysis of Spatial Autoregressive (SAR) and Spatial Error Model (SEM) shows that there is a spatial dependency lag and error on number of poor household variable. As for the independent variable which have the significancy account for 5% on Spatial Autoregressive (SAR) and Spatial Error Model (SEM) are every variables with a number R2= 90,9% on SAR and R2= 90,1% on SEM.

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