The Arcview And Geoda Application In Optimization Of Spatial Regression Estimate


SCOLASTIKA MARIANI, FMIPA Matematika and WARDONO, - and MASRUKAN, - and FATKHUROKHMAN FAUZI, - (2017) The Arcview And Geoda Application In Optimization Of Spatial Regression Estimate. Journal of Theoretical and Applied Information Technology, 95 (5). ISSN 1992-8645

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Abstract

ArcView is a geographic information system software product produced by and GeoDa is a software tool developed to implement techniques for exploratory spatial data to the analysis (ESDA) on a lattice of data (points and polygons). There were spatial diversity between the regions on the human development index (HDI) variable. This research would look for the best spatial regression model for the HDI in Central Java, Indonesia. Testing spatial effects were done by looking at the value Moran`s I, there were five variables with positive autocorrelation, and a negative autocorrelation variable is unemployment. The next test with the test Lagrange Multiplier (LM), only Lagrange Multiplier (error) which is included in the admission criteria, then the regression model used is the Spatial Error Model (SEM). In this study used two kinds of weighting matrix, i.e. rock contiguity and queen contiguity. The next step research was comparing the value of R-Square and the value of Akaike Info Criterion (AIC) and then obtained best regression model i.e.Spatial Error Model (SEM) with weighting rock contiguity with the value of R-Square is 99.8119 % and the smallest Akaike Info Criterion (AIC) was 4.6362. The spatial regression equation was : ��� � 4.5565 0.4176 �� 0.8445 ��� 1.4262 ��� 0.0011 ��� with ��� : human development index, �� : life expectancy, ��� : hope and period school, ��� : average length of school, ��� : per capita spending

Item Type: Article
Uncontrolled Keywords: Arcview, Geoda, Optimization, Spatial Regression Estimate
Subjects: Q Science > QA Mathematics
Fakultas: Fakultas Matematika dan Ilmu Pengetahuan Alam > Matematika, S1
Depositing User: mahargjo hapsoro adi
Date Deposited: 06 Apr 2022 04:47
Last Modified: 06 Apr 2022 04:47
URI: http://lib.unnes.ac.id/id/eprint/49525

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