SPATIAL ANALYSIS INF LUENCE OF ROAD CLASS ON LAND COVER CHANGE WITH LOGISTIC BINARY REGRESSION (CASE STUDY: WEST JA VA)

<p align="justify">Uncontrolled land cover changes negatively affect several social, economic, and environmental aspects, so that the impact on the environment is significant both locally and globally. One of the causes of land use change is a factor of human activity involving physi...

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Main Author: SITI AISAH ROBIANSAH - Nim: 25117020 , SUCI
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/31118
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:31118
spelling id-itb.:311182018-07-03T18:52:37ZSPATIAL ANALYSIS INF LUENCE OF ROAD CLASS ON LAND COVER CHANGE WITH LOGISTIC BINARY REGRESSION (CASE STUDY: WEST JA VA) SITI AISAH ROBIANSAH - Nim: 25117020 , SUCI Indonesia Theses INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/31118 <p align="justify">Uncontrolled land cover changes negatively affect several social, economic, and environmental aspects, so that the impact on the environment is significant both locally and globally. One of the causes of land use change is a factor of human activity involving physical action on land cover, for example by the construction of road network. The road network can provide easy access to workplaces as well as educational, health, social and entertainment facilities. To facilitate the management, the road is grouped into various classes. This road grouping is intended to realize legal certainty of road operation in accordance with the authority of the Government and local government. Public roads according to their status are grouped into national roads, provincial roads, district roads, city roads and village roads. Many studies also suggest that road is one of the factors affecting land change. However, no studies have shown which class of roads are more influential on changes in land cover. Therefore, an effort is needed to understand how this land cover change phenomenon occurs. Efforts to understand land cover changes are by modeling. One of the modeling methods that can be used in predicting land cover change is Binary Logistic Regression Model (BLR). Regression is a mathematical method that aims to evaluate the relationship between one dependent variable (response) with one or more independent variables (predictor / explanatory). In this study, the independent variables used are the National Road class, Provincial Road, and Other Roads. The result showed that the model of land cover change with road class factor based on the RLB method has the best accuracy of 34,51%, the road factor only affects about 2.90% of all land cover changes and the more influential road class to the change of cover land is a national road. <p align="justify"> <br /> text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description <p align="justify">Uncontrolled land cover changes negatively affect several social, economic, and environmental aspects, so that the impact on the environment is significant both locally and globally. One of the causes of land use change is a factor of human activity involving physical action on land cover, for example by the construction of road network. The road network can provide easy access to workplaces as well as educational, health, social and entertainment facilities. To facilitate the management, the road is grouped into various classes. This road grouping is intended to realize legal certainty of road operation in accordance with the authority of the Government and local government. Public roads according to their status are grouped into national roads, provincial roads, district roads, city roads and village roads. Many studies also suggest that road is one of the factors affecting land change. However, no studies have shown which class of roads are more influential on changes in land cover. Therefore, an effort is needed to understand how this land cover change phenomenon occurs. Efforts to understand land cover changes are by modeling. One of the modeling methods that can be used in predicting land cover change is Binary Logistic Regression Model (BLR). Regression is a mathematical method that aims to evaluate the relationship between one dependent variable (response) with one or more independent variables (predictor / explanatory). In this study, the independent variables used are the National Road class, Provincial Road, and Other Roads. The result showed that the model of land cover change with road class factor based on the RLB method has the best accuracy of 34,51%, the road factor only affects about 2.90% of all land cover changes and the more influential road class to the change of cover land is a national road. <p align="justify"> <br />
format Theses
author SITI AISAH ROBIANSAH - Nim: 25117020 , SUCI
spellingShingle SITI AISAH ROBIANSAH - Nim: 25117020 , SUCI
SPATIAL ANALYSIS INF LUENCE OF ROAD CLASS ON LAND COVER CHANGE WITH LOGISTIC BINARY REGRESSION (CASE STUDY: WEST JA VA)
author_facet SITI AISAH ROBIANSAH - Nim: 25117020 , SUCI
author_sort SITI AISAH ROBIANSAH - Nim: 25117020 , SUCI
title SPATIAL ANALYSIS INF LUENCE OF ROAD CLASS ON LAND COVER CHANGE WITH LOGISTIC BINARY REGRESSION (CASE STUDY: WEST JA VA)
title_short SPATIAL ANALYSIS INF LUENCE OF ROAD CLASS ON LAND COVER CHANGE WITH LOGISTIC BINARY REGRESSION (CASE STUDY: WEST JA VA)
title_full SPATIAL ANALYSIS INF LUENCE OF ROAD CLASS ON LAND COVER CHANGE WITH LOGISTIC BINARY REGRESSION (CASE STUDY: WEST JA VA)
title_fullStr SPATIAL ANALYSIS INF LUENCE OF ROAD CLASS ON LAND COVER CHANGE WITH LOGISTIC BINARY REGRESSION (CASE STUDY: WEST JA VA)
title_full_unstemmed SPATIAL ANALYSIS INF LUENCE OF ROAD CLASS ON LAND COVER CHANGE WITH LOGISTIC BINARY REGRESSION (CASE STUDY: WEST JA VA)
title_sort spatial analysis inf luence of road class on land cover change with logistic binary regression (case study: west ja va)
url https://digilib.itb.ac.id/gdl/view/31118
_version_ 1822923483048312832