Visualizing 2018 lombok earthquake in Indonesia using crowdsouring data

How people experience it

Abstract

Along with the development of science and technology, using big data, map makers can take advantage of crowdsourcing social media data on Twitter to obtain user location when uploading tweets, which can be called geolocated tweets. Earthquakes that occur very often in Indonesia often grab people's attention, especially netizens who use social media like Twitter. One of the major earthquakes that occurred in Indonesia in 2018 was the Lombok earthquake, which occurred twice in a row from July to August 2018. Using Twitter data, information and social responses related to the 2018 Lombok earthquake can be obtained, which can be used as evaluation material for public handling and responding. The information is then visualized in various forms, and one of the best visualization methods is selected.
This study uses Twint package in Python as a way of obtaining location data from Twitter. The method used to collect Twitter data is a case study on the social impact of the Lombok earthquake in Indonesia in 2018. The data observation method used is a simulation of several types of map visualization and survey methods in selecting the best type of visualization. The method of analysis used is by mapping the data on the number of tweets as the main object using various types of maps, as well as calculating survey results by scoring each group of questions.
The results of spatial data extraction from Twitter in this study obtained 2032 tweets that had been selected and cleaned from 11,584 tweets. Map visualization with the theme of the social impact of the Lombok earthquake in 2018 was compiled using five types of visualization, namely choropleth maps, proportional symbol maps, dot maps, hexagonal tessellation maps, and heat maps. Based on the results of the survey on selecting the best visualization, it was found that the choropleth map is the best visualization method according to respondents with a cartography background and respondents who are unfamiliar with cartography because the information displayed is easier to read and understand.