Determinants of Telecommunication Infrastructure Index (TII) through Geographically Weighted Regression (GWR) in the Philippines

Information and Communication Technology (ICT) continues to be a growing sector around the world. In the past, quality levels of ICT access, such as through Telecommunication Infrastructure Index (TII), were investigated by nations to help determine a country’s ICT infrastructure capacity. As such,...

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Main Authors: Manuel, Renz Marquee Calazan, Menor, Jilian Vergara, Ramos, Romeo Clarence Carranza, Jr.
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Language:English
Published: Animo Repository 2022
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Online Access:https://animorepository.dlsu.edu.ph/etdb_math/12
https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1012&context=etdb_math
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etdb_math-10122022-07-22T02:18:53Z Determinants of Telecommunication Infrastructure Index (TII) through Geographically Weighted Regression (GWR) in the Philippines Manuel, Renz Marquee Calazan Menor, Jilian Vergara Ramos, Romeo Clarence Carranza, Jr. Information and Communication Technology (ICT) continues to be a growing sector around the world. In the past, quality levels of ICT access, such as through Telecommunication Infrastructure Index (TII), were investigated by nations to help determine a country’s ICT infrastructure capacity. As such, the Department of Information and Communications Technology conducted the first National ICT Household Survey in 2019 to investigate the current state of ICT in the Philippines and see if there is a digital divide. The research aimed to determine how province locations play a role in TII and how this can be incorporated into a model that predicts TII levels in the country using Geographically Weighted Regression (GWR). The GWR model assigned different weights and models for each province based on the values of their nearby neighbors. Moran’s I showed that significant spatial autocorrelations exist across provinces on TII, percentage of household heads with secondary education, and income per capita among others. Results showed that the percentage of household heads with secondary education per province is a common determinant of TII of all provinces in the Philippines. Furthermore, income per capita was a significant determinant in many provinces with high TII. Lastly, the GWR model provided better results than other models tested with the baseline Ordinary Least Squares (OLS) model, with lowest Mean Square Error (MSE), lower Mean Absolute Error (MAE), and higher adjusted R2. Keywords: ICT Access, TII, GWR 2022-07-11T07:00:00Z text application/pdf https://animorepository.dlsu.edu.ph/etdb_math/12 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1012&context=etdb_math Mathematics and Statistics Bachelor's Theses English Animo Repository Information technology Mathematical models Mathematics
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Information technology
Mathematical models
Mathematics
spellingShingle Information technology
Mathematical models
Mathematics
Manuel, Renz Marquee Calazan
Menor, Jilian Vergara
Ramos, Romeo Clarence Carranza, Jr.
Determinants of Telecommunication Infrastructure Index (TII) through Geographically Weighted Regression (GWR) in the Philippines
description Information and Communication Technology (ICT) continues to be a growing sector around the world. In the past, quality levels of ICT access, such as through Telecommunication Infrastructure Index (TII), were investigated by nations to help determine a country’s ICT infrastructure capacity. As such, the Department of Information and Communications Technology conducted the first National ICT Household Survey in 2019 to investigate the current state of ICT in the Philippines and see if there is a digital divide. The research aimed to determine how province locations play a role in TII and how this can be incorporated into a model that predicts TII levels in the country using Geographically Weighted Regression (GWR). The GWR model assigned different weights and models for each province based on the values of their nearby neighbors. Moran’s I showed that significant spatial autocorrelations exist across provinces on TII, percentage of household heads with secondary education, and income per capita among others. Results showed that the percentage of household heads with secondary education per province is a common determinant of TII of all provinces in the Philippines. Furthermore, income per capita was a significant determinant in many provinces with high TII. Lastly, the GWR model provided better results than other models tested with the baseline Ordinary Least Squares (OLS) model, with lowest Mean Square Error (MSE), lower Mean Absolute Error (MAE), and higher adjusted R2. Keywords: ICT Access, TII, GWR
format text
author Manuel, Renz Marquee Calazan
Menor, Jilian Vergara
Ramos, Romeo Clarence Carranza, Jr.
author_facet Manuel, Renz Marquee Calazan
Menor, Jilian Vergara
Ramos, Romeo Clarence Carranza, Jr.
author_sort Manuel, Renz Marquee Calazan
title Determinants of Telecommunication Infrastructure Index (TII) through Geographically Weighted Regression (GWR) in the Philippines
title_short Determinants of Telecommunication Infrastructure Index (TII) through Geographically Weighted Regression (GWR) in the Philippines
title_full Determinants of Telecommunication Infrastructure Index (TII) through Geographically Weighted Regression (GWR) in the Philippines
title_fullStr Determinants of Telecommunication Infrastructure Index (TII) through Geographically Weighted Regression (GWR) in the Philippines
title_full_unstemmed Determinants of Telecommunication Infrastructure Index (TII) through Geographically Weighted Regression (GWR) in the Philippines
title_sort determinants of telecommunication infrastructure index (tii) through geographically weighted regression (gwr) in the philippines
publisher Animo Repository
publishDate 2022
url https://animorepository.dlsu.edu.ph/etdb_math/12
https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1012&context=etdb_math
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