Damage to the economy of the United States due to climatic events

Visualization per state

Fabien Tarrade
Quantitative Analyst - Data Scientist - Researcher

Introduction and goal

Introduction

Storms and other severe weather events can cause economic problems for communities and municipalities. Many severe events can result crop and property damage. This shiny application allow you to select a period of time, two type of economic damage and to see a map of the United States with the breakdown of the damage in billions of USD per state.

Goal of this `shiny` application

This shiny application allow you to:

1.  Select a period of time between 1950 and 2011

2.  Select one or two type(s) of economic damage 

3.  Display a map with the breakdown of the damage in billions of USD per state

4.  Display the numeric of the values per state

Using a map of the United States with R

As an example we use USArrests R dataset to show how data are display for each state. The data of the Shine application are too big to be used here. Some part of the code is masked to fit on the slide. Unfortunately plotly for such map is not implemented to get interactive map. The full R code for the Shiny application can be found on Github

us <- map_data("state")
arr <- USArrests %>% add_rownames("region") %>% mutate(region=tolower(region))
gg <- ggplot()
gg <- gg + geom_map(data=us, map=us,aes(x=long, y=lat, map_id=region))

plot of chunk unnamed-chunk-3

Demonstration and results

A demonstration version of the tool to visualize the damage to the economy of the United States due to climatic events can be found on the Shiny.io server:

https://fabien-tarrade.shinyapps.io/Project1

Below an example of the results:

This tool is offered under the standard Beerware license

References

You can find below the references for the Storm Events Database [1-2] as well as the one for R[3], shiny[4] and the previous study [5]

[1] Storm Data FAQ Page. Storm Events Database. National Climatic Data Center. Retrieved 2014-04-14.

[2] National Weather Service Instruction 10-1605 concerning Storm Data preparation, August 17, 2007.

[3] R Core Team (2014), R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. http://www.R-project.org/.

[4] RStudio, Inc (2015). shiny: Easy web applications in R. http://shiny.rstudio.com .

[5] Study of the impact of climatic events on population health and its economic consequences across the United States. https://rpubs.com/tarrade/127056 .