Search results

1 – 2 of 2
Per page
102050
Citations:
Loading...
Access Restricted. View access options
Article
Publication date: 18 November 2020

Dian Purworini, Desi Puji Hartuti and Dini Purnamasari

Sociocultural aspects of populations residing in disaster-prone areas have not often been discussed in disaster evacuation studies. Therefore, the main purpose of this paper is to…

324

Abstract

Purpose

Sociocultural aspects of populations residing in disaster-prone areas have not often been discussed in disaster evacuation studies. Therefore, the main purpose of this paper is to describe the sociocultural factors affecting evacuation decision-making.

Design/methodology/approach

This was an exploratory research study which used in-depth semi-structured interviews to collect the data. Selection of the informants was also fulfilled via the purposive sampling method with regard to specific criteria. The informants consisted of 20 villagers that had faced a disaster and eight staff members of the Regional Board of Disaster Management of the Republic of Indonesia which is Badan Penanggulangan Bencana Daerah (BPBD), Ponorogo, who had managed it. The data analysis was ultimately performed through thematic coding.

Findings

The results of the coding analysis revealed that sociocultural aspects were among the primary reasons for evacuation decisions before disasters. In this paper, sociocultural factors shaping evacuation decision behavior could be a result of norms, roles, language, leadership, rules, habits, jobs, perceptions, family engagement, as well as other behaviors demonstrated by individuals and the community.

Research limitations/implications

This study is not analyzing the role of the social organization or a religious one and also the economic aspect in the evacuation decision-making.

Practical implications

This paper includes implications for the local government and the BPBD Ponorogo to establish an efficient communication strategy persuading villagers to evacuate. In general, formal policies cannot always be implemented in managing disaster; therefore, visible dedication and solidarity of the members are always needed in order to manage evacuation problems.

Originality/value

This paper meets needs for a study delineating sociocultural factors affecting evacuation decisions before disasters strike. Sociocultural theory could also describe real aspects of culture inherent in the daily lives of populations living in disaster-prone areas.

Details

Disaster Prevention and Management: An International Journal, vol. 30 no. 2
Type: Research Article
ISSN: 0965-3562

Keywords

Access Restricted. View access options
Article
Publication date: 3 January 2024

Abba Suganda Girsang and Bima Krisna Noveta

The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity…

98

Abstract

Purpose

The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet.

Design/methodology/approach

This research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM.

Findings

Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected.

Research limitations/implications

This study implements in Indonesia region.

Originality/value

(a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 17 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

1 – 2 of 2
Per page
102050