scholarly journals Eksplorasi efektifitas model spasial untuk menjelaskan hubungan antara penduduk dan infrastruktur terhadap kesejahteraan masyarakat Kota Manado

AITI ◽  
2021 ◽  
Vol 17 (2) ◽  
pp. 130-142
Author(s):  
Winsy Christo Deilan Weku
Keyword(s):  
Error Model ◽  
Weighted Regression ◽  
Spatial Error Model ◽  
Spatial Error

Penelitian ini dilakukan untuk menentukan model kesejahteraan keluarga yang dipengaruhi oleh jumlah penduduk dan infrastruktur di kota Manado menggunakan regresi spasial dan regresi terboboti spasial, menganalisis faktor-faktor yang mempengaruhinya serta mengkaji efektifitas metode regresi spasial dalam menganalisis kasus tersebut. Analisis yang digunakan yaitu Spatial Error Model (SEM) dilanjutkan dengan Geographicaly Weighted Regression (GWR). Hasil analisis menunjukkan bahwa variabel prediktor yang mempengaruhi variabel respon adalah jumlah penduduk, jumlah sekolah dan sarana kesehatan. Kriteria penilaian lebih memilih Model GWR untuk menjelaskan kesejahteraan keluarga karena memiliki nilai AIC lebih kecil dibandingkan menggunakan model SEM.  

scholarly journals Spatial Modeling for Poverty: The Comparison of Spatial Error Model and Geographic Weighted Regression

2021 ◽  
Vol 12 (1) ◽  
pp. 237-251
Author(s):  
Achi Rinaldi ◽  
Yuni Susianto ◽  
Budi Santoso ◽  
Wahyu Kusumaningtyas
Keyword(s):  
Spatial Modeling ◽  
Spatial Regression ◽  
Evaluation Criteria ◽  
Spatial Models ◽  
Error Model ◽  
Weighted Regression ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Explanatory Variables ◽  
The Relationship

This study aims to analyze poverty using spatial models. The researchers also compared the Spatial Error Model (SEM) and Geographically Weighted Regression (GWR). The comparison of the two models was based on the estimation evaluation criteria and the constructed spatial associations. Spatial regression is considered very appropriate to be used to model the relationship pattern between poverty and explanatory variables when the observed data has a spatial effect caused by the proximity between the observation areas. The spatial dependence of errors on observational data can be overcome using SEM, while the effect of heterogeneity of spatial variance can overcome using GWR.

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

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.

Spatial Error Model

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

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.

Kernel-based spatial error model for analyzing spatial panel data

2020 ◽  
Vol 15 (3) ◽  
pp. 239-248
Author(s):  
Jooyong Shim ◽  
Sang Bum Lee ◽  
Daiwon Kim ◽  
Jung-Suk Yu ◽  
Chanha Hwang
Keyword(s):  
Panel Data ◽  
Error Model ◽  
Support Vector ◽  
Spatial Error Model ◽  
Varying Coefficient Model ◽  
Spatial Error ◽  
Spatial Panel ◽  
Varying Coefficient ◽  
Proposed Model ◽  
Spatial Panel Data

Spatial panel data model captures spatial interactions across spatial units and over time. Lots of effort have been devoted to develop effective estimation methods for parametric and nonparametric spatial panel data models. Varying coefficient model has received a great deal of attention as an important tool for modeling panel data. In this paper we propose a kernel-based spatial error model for the purpose of analyzing spatial panel data. This model is based on the idea of fixed effect time-varying coefficient model and the kernel technique of support vector machine along with the technique of regularization. A generalized cross validation method is also considered for choosing the hyperparameters which affect the performance of the proposed model. The proposed model is evaluated through numerical studies.

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