E3S Web Conf.
Volume 76, 2019The 4th International Conference on Science and Technology (ICST 2018)
|Number of page(s)||7|
|Section||Disaster Mitigation & Management|
|Published online||15 January 2019|
Modeling (Im) mobility: the decision to stay in disaster prone area amongs fishermen community in Semarang
Geography Doctoral Program, Universitas Gadjah Mada, Indonesia
* Corresponding author: email@example.com
The existing literature on population immobility, especially immobility associated with climate change-related disaster, is very finite. Consequently, the understanding of population immobility in disaster-prone areas is still low. This article adds to the literature on population immobility by modeling decision to stay in the disaster-prone area amongst fishermen community in Tambak Lorok, Semarang. The survey was conducted among the residents of Kampung Tambak Lorok Semarang, which is prone to 3 disasters simultaneously i.e. sea level rise, land subsidence, and tidal inundation. The study sample was 235 heads of households selected using proportional sampling area technique. This study constructs three factors: place valuation, disaster adaptation, and stakeholder intervention. These three factors used as explanatory variables for modeling the decision to stay. The study employed a Confirmatory Factor Analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyses the data and examines the logical relationship between those three factors in staying decision. Our results suggest that the place valuation and disaster adaptation significantly influence the decision to stay, while stakeholder interventions are influential but not significant. We concur that residents with positive place valuation and good disaster adaptation tend to stay although threatening by disaster. More broadly, this study contributes to our understanding of population immobility in the disaster-prone area by modeling the decision to stay.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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