Assessing Indonesia Spatial Data Infrastructure Using R for Disaster Management

Abstract

Indonesia is a country with high disaster risk, entitled to ring of fire as one of the countries, which are surrounded by tectonic plates. The country has been exposed to natural hazards for decades. However, every time the natural hazard hits, the number of casualties remains huge. There is a serious question on how disaster management is going on in Indonesia and what knowledge could help to minimize or even prevent such huge casualties in any disaster occurrences. Big data can be a new approach towards natural disaster management mainly because it has the ability to visualize, analyze, and predict natural disasters. In the openly big data era, it is rather easy to process data with open-source software for managing the post-disaster as well as pre-disaster effect. The question remaining is how well the data is, which will be processed. This paper aims to appraise the quality of Indonesia's spatial data infrastructure using the R programming language in order to address natural disaster management and eventually lower the impact of the disaster. Some of the assessment criteria used in this paper are metadata of the data, positional accuracy, and completeness. The article concludes that Indonesia, as a country with high level of exposure to natural hazards, still lacks in NSDI quality, especially in providing disaster data. The most damaged buildings in Palu, with 2,416 damaged buildings affected. The sub-district of Nunu has the lowest number, with only 1 building damaged. The number of sub-districts with damaged buildings in Palu is 14 of the total 43 sub-districts.