Geospatial location characteristics and global subdivision grid analysis of multiple disaster data

2018 ◽  
Author(s):  
xuefeng lv ◽  
Yannan Sun
Keyword(s):  
Grid Analysis ◽  
Disaster Data

PENERAPAN ALGORITMA K-MEANS UNTUK PENGELOMPOKAN DAERAH RAWAN BENCANA DI INDONESIA

2019 ◽  
Vol 1 (2) ◽  
pp. 1-10
Author(s):  
Amril Mutoi Siregar
Keyword(s):  
Natural Disasters ◽  
Accurate Data ◽  
Disaster Victims ◽  
Central Java ◽  
North Sulawesi ◽  
Natural Beauty ◽  
Or Groups ◽  
Disaster Data ◽  
The Government ◽  
Almost All

Indonesia is a country located in the equator, which has beautiful natural. It has a mountainous constellation, beaches and wider oceans than land, so that Indonesia has extraordinary natural beauty assets compared to other countries. Behind the beauty of natural it turns out that it has many potential natural disasters in almost all provinces in Indonesia, in the form of landslides, earthquakes, tsunamis, Mount Meletus and others. The problem is that the government must have accurate data to deal with disasters throughout the province, where disaster data can be in categories or groups of regions into very vulnerable, medium, and low disaster areas. It is often found when a disaster occurs, many found that the distribution of long-term assistance because the stock for disaster-prone areas is not well available. In the study, it will be proposed to group disaster-prone areas throughout the province in Indonesia using the k-means algorithm. The expected results can group all regions that are very prone to disasters. Thus, the results can be Province West java, central java very vulnerable categories, provinces Aceh, North Sumatera, West Sumatera, east Java and North Sulawesi in the medium category, provinces Bengkulu, Lampung, Riau Island, Babel, DIY, Bali, West Kalimantan, North Kalimantan, Central Sulawesi, West Sulawesi, Maluku, North Maluku, Papua, west Papua including of rare categories. With the results obtained in this study, the government can map disaster-prone areas as well as prepare emergency response assistance quickly. In order to reduce the death toll and it is important to improve the services of disaster victims. With accurate data can provide prompt and appropriate assistance for victims of natural disasters.

Efektivitas Layanan Darurat Jakarta Siaga 112 dalam Penanganan Kebakaran oleh UPT. Pusat Data & Informasi Kebencanaan BPBD Provinsi DKI Jakarta

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Musa Musa
Keyword(s):  
Emergency Service ◽  
Fire Management ◽  
Emergency Services ◽  
The Public ◽  
Information Center ◽  
Disaster Data ◽  
In Fire ◽  
Document Review

This research was conducted to determine the Effectiveness of Jakarta Siaga 112 Emergency Services in Fire Management by UPT. Disaster Data & Information Center of BPBD DKI Jakarta Province by paying attention to aspects contained in the Effectiveness of the Jakarta Siaga Emergency Service Program 112. The research method was carried out with a case study method with data collection techniques using interview methods and document review. Interviews were conducted on 10 (ten) key informants, document review focused on documents related to the Jakarta Emergency Alert Service 112 Effectiveness research in Fire Management. The results showed that the Effectiveness of Jakarta Siaga 112 Emergency Services in Fire Management by UPT. The Center for Disaster Data & Information BPBD DKI Jakarta Province Its effectiveness is still low, due to the Implementation of Emergency Services Jakarta Standby 112 in Fire Management implemented by UPT. Disaster Data & Information Center of BPBD DKI Jakarta Province in terms of the Target Group Understanding of the Program, the Achievement of the Program Objectives aspects, and the Program Follow-up aspects. It is recommended to continue to disseminate this Emergency Service to the public, it is necessary to increase the firm commitment of the Head of 8 SKPD related to fire management so that all units play a role in accordance with the Standard Operating Procedures (SOPs) for Fire Management and the evaluation and follow-up of program services that are held periodically 3 once a month.Keywords: Effectiveness, Emergency Services, Fire Handling

Geodiversity evaluation and geoconservation using grid analysis: case study, north of Ilam Province

2021 ◽  
Author(s):  
Mehdi Ahmadi ◽  
Davoud Mokhtari ◽  
Masood Khodadadi ◽  
Himan Shahabi
Keyword(s):  
Grid Analysis

scholarly journals Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Open Data ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
Knowledge Graph ◽  
Cross Domain ◽  
Disaster Data ◽  
Knowledge Graphs ◽  
Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

Small-grid analysis of discrete model for evacuation from a hall

2007 ◽  
Vol 374 (2) ◽  
pp. 821-826 ◽  
Author(s):  
W.G. Weng ◽  
L.L. Pan ◽  
S.F. Shen ◽  
H.Y. Yuan
Keyword(s):  
Discrete Model ◽  
Grid Analysis

Grid analysis: Continuing the search for a metric of shape

1987 ◽  
Vol 31 (4) ◽  
pp. 338-365 ◽  
Author(s):  
Ernest Greene ◽  
Peter Waksman
Keyword(s):  
Grid Analysis
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