Predicting town vulnerability in coastal Semarang, Indonesia: Spatial data analysis of land subsidence in relation to geomorphological factors

Authors: Juliette Bateman*, Boston University
Topics: Geographic Information Science and Systems, Spatial Analysis & Modeling, Human-Environment Geography
Keywords: GIS, spatial analysis, Semarang, land subsidence, coastal flooding
Session Type: Poster
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded

Semarang city, the highly populated capital of Central Java Province, Indonesia has been severely impacted by coastal flooding, inundation, and land subsidence in recent years. This study focuses on the spatial relationship between the depth of land subsidence (cm) in coastal Semarang and the following four factors: elevation (m), distance to the coastline, and the presence of alluvial soil and building infrastructure. In addition to a thorough literature review, data preparation and analyses were completed using ArcMap 10.6 and R Studio. Specifically, analyses were completed on 5 sets of 400 randomly sampled points of land subsidence in coastal Semarang using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) in R Studio. Maps were created that visualize predictions of the most vulnerable Semarang towns to land subsidence; the final maps indicate that the most vulnerable towns in the Semarang region are concentrated in the coastal northern zone. This preliminary research study relates to future research focused on the use of remote sensing techniques to identify areas and relative rates of land loss in the coastal Semarang region. Additionally, these results support previous research and provides critical information that could assist decisions makers in implementing policies that reduce harmful, subsidence inducing practices in these particularly vulnerable areas, or apply policies that could help combat these harmful flooding and subsidence recurrences.

Abstract Information

This abstract is already part of a session. View the session here.

To access contact information login