Mapping of Illiteracy and Information and Communication Technology Indicators Using Geographically Weighted Regression
Paper in Journal of Mathematics and Statistics Vol.10(2) p. 130-138 by Science Publications, DOI : 10.3844/jmssp.2014.130.138
Rokhana Dwi Bekti, Andiyono and Edy Irwansyah
Geographically Weighted Regression (GWR) is a technique that brings the
framework of a simple regression model into a weighted regression model. Each
parameter in this model is calculated at each point geographical location. The
significantly parameter can be used for mapping. In this research GWR model use
for mapping Information and Communication Technology (ICT) indicators which
influence on illiteracy. This problem was solved by estimation GWR model. The
process was developing optimum bandwidth, weighted by kernel bisquare and
parameter estimation. Mapping of ICT indicators was done by P-value. This
research use data 29 regencies and 9 cities in East Java Province, Indonesia.
GWR model compute the variables that significantly affect on illiteracy (? = 5%)
in some locations, such as percent households members with a mobile phone (x2),
percent of household members who have computer (x3) and the percent of
households who access the internet at school in the last month (x4). Ownership
of mobile phone was significant (? = 5%) at 20 locations. Ownership of computer
and internet access were significant at 3 locations. Coefficient determination
at all locations has R2 between 73.05-92.75%. The factors which affecting
illiteracy in each location was very diverse. Mapping by P-value or critical
area shows that ownership of mobile phone significantly affected at southern
part of East Java. Then, the ownership of computer and internet access were
significantly affected on illiteracy at northern area. All the coefficient
regression in these locations was negative. It performs that if the number of
mobile phone ownership, computer ownership and internet access were high then
illiteracy will be decrease.
Rokhana Dwi Bekti, Andiyono and Edy Irwansyah
Geographically Weighted Regression (GWR) is a technique that brings the
framework of a simple regression model into a weighted regression model. Each
parameter in this model is calculated at each point geographical location. The
significantly parameter can be used for mapping. In this research GWR model use
for mapping Information and Communication Technology (ICT) indicators which
influence on illiteracy. This problem was solved by estimation GWR model. The
process was developing optimum bandwidth, weighted by kernel bisquare and
parameter estimation. Mapping of ICT indicators was done by P-value. This
research use data 29 regencies and 9 cities in East Java Province, Indonesia.
GWR model compute the variables that significantly affect on illiteracy (? = 5%)
in some locations, such as percent households members with a mobile phone (x2),
percent of household members who have computer (x3) and the percent of
households who access the internet at school in the last month (x4). Ownership
of mobile phone was significant (? = 5%) at 20 locations. Ownership of computer
and internet access were significant at 3 locations. Coefficient determination
at all locations has R2 between 73.05-92.75%. The factors which affecting
illiteracy in each location was very diverse. Mapping by P-value or critical
area shows that ownership of mobile phone significantly affected at southern
part of East Java. Then, the ownership of computer and internet access were
significantly affected on illiteracy at northern area. All the coefficient
regression in these locations was negative. It performs that if the number of
mobile phone ownership, computer ownership and internet access were high then
illiteracy will be decrease.
Document URL : http://thescipub.com/abstract/10.3844/jmssp.2014.130.138
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