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Indonesian Earthquake Visualization and Early Warning System

Explore earthquake data in Indonesia, visualize its impact, and provide early warnings using real-time information from various sources. This dashboard aims to save lives and minimize future damages.

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Indonesian Earthquake Visualization and Early Warning System

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  1. Introduction • Indonesia: • Population : 267 Million • (4th most populous country on earth) • Island : 17,000 • Language : 700 • (2nd largest number of languages on earth) • Province : 34 • (Capital: Jakarta)

  2. It takes more than 20 hours to fly from Papua to Southampton . . . To pursue my dream studying at University of Southampton

  3. Do you remember these? Earthquake in the media . . .

  4. Source: https://news.sky.com/story/dozens-of-students-found-dead-in-church-after-indonesia-earthquake-11514901 Indonesia is located in the Ring of Fire which is at the meeting point of several tectonic plates

  5. Using Exploratory Data Analysis method for an early warning in Indonesia’s earthquakes disaster. Presented by Yuliagnis Transver Wijaya Master Candidate of Data Analytics for Government MDATAGOV Symposium Manchester, 27th March 2019 Statistics Indonesia

  6. The Main Objective To construct a centralized repository of data on earthquakes, their impact, and the characteristics of the population, as well as an interactive visualisation tool. In the case of a new event, to publish real time information obtained scraping data from Twitter accounts of organisations such as the Meteorology, Climatology and Geophysics Agency and the National Disaster Management Agency. Automatically retweet the relevant information as an early warning for the general public.

  7. Data Source (National Disaster Management Agency) (Indonesian Agency for Meteorology, Climatology and Geophysics) Statistics Indonesia

  8. Dashboard Framework [Real-time] Historical Repository Data: [Automatic re-tweet] Real-time Twitter Information Explore and Visualize Data Repository Data • Local Government • Public Data Manipulation Data Collection Early Warning [Export Data] Real-time Social Media Data: Real-time Information Note: [ Next Development]

  9. The packages are in use Data Manipulation Twitter Scraping The Front End reticulate Data Visualization tweepy

  10. Application Back-end Architecture User sets parameter Using the side bar or inside tab renderLeaflet() To display the earthquake map renderPlotly() To display the interactive graphics These can be seen in earthquake visualization tab renderDataTable() To display manipulated data in dataset tab Data is manipulated using user’s parameter The manipulated data will be visualized into leaflet map and other graphics

  11. DEMO PLATFORM

  12. A User Interface

  13. The Earthquake Visualization

  14. The Earthquake’s Impact History

  15. The Official Statistics Data

  16. The Official Statistics Data (2)

  17. The Social Media Update

  18. What’s Next? Using real-time earthquake data from Indonesia Agency of Meteorology, Climatology and Geophysics: Scraping the website and to gather the data. Implementing Automatic retweet to National Disaster Management Agency in Indonesia with keyword “#gempabumi”,”#earthquake”, etc. to response the warning from Meteorology, Climatology and Geophysics agency Using other supporting data, such as News media channel for monitoring the progress of disaster management.

  19. Conclusion I hope this dashboard can be used to save people’s life and minimize the future damages

  20. Thank You

  21. Learning Resources https://bps.go.id/ https://bnpb.cloud/ https://dataonline.bmkg.go.id/ https://dplyr.tidyverse.org/ http://docs.tweepy.org/en/v3.5.0/index.html https://plot.ly/r/shiny-tutorial/ https://plot.ly/ggplot2/extending-ggplotly/ http://shiny.rstudio.com/articles/ https://shiny.rstudio.com/gallery/lego-set.html https://shiny.rstudio.com/gallery/superzip-example.html https://r4ds.had.co.nz/ http://r-statistics.co/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html https://rstudio.github.io/leaflet/shiny.html https://tweepy.readthedocs.io/en/v3.5.0/api.html#tweepy-api-twitter-api-wrapper James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning: with Applications in R. New York: Springer. Keon-Woong Moon, V. (2016). Learn ggplot2 Using Shiny App. Switzerland: Springer.

  22. Application Import Data Back-end User inserts csv file with same structure App will read the file and combine the data with the old one renderLeaflet() To display the earthquake map renderPlotly() To display the interactive graphics These can be seen in earthquake visualization tab renderDataTable() display the new data in dataset tab updatesliderinput() To update the date The new data will be visualized into leaflet map and other graphics App will be manipulated into the same date format

  23. Social Media Tab Back-end Application User clicks renew tweet button Combine the new with the old tweets and display using renderDataTable() R will call python command The data frame is sent back into R tweepy reticulate Get new tweet using tweepy and convert into data frame using pandas

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