v0.2 of the application.

The analysis and discussion over the last few months on data integrity have finally positioned me to do some basic analysis of the NOAA data. Undoubtedly, as you look through the data in the interactive program below, you will see things that cause you to question the data. If you have the time, and the interest, please go back and read through my earlier posts on Data Integrity: The Rest of the Story Part II. In those discussions, data from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database were used to demonstrate some fundamental principles for managing data integrity. There is still a lot of work to do on these data, but for now, it is in somewhat of a usable state.

Unfortunately, historical data have been lost, and, without considerable expense to recover it, appears to be lost forever. For example, there doesn’t appear to be any realistic casualty numbers for the Katrina Hurricane that struck New Orleans in 2005. In addition, NOAA did not track casualties from any storm other than Tornadoes until the 80’s.

As NOAA began tracking more types of weather events, they didn’t exercise control over their reference data. There were numerous classifications which I summarized into more meaningful groups (at least from my perspective). For example, what is the difference between a “Tornado” and “Tornadoes”, or, “Heat”, and “Excessive Heat”? This type of grouping causes the actual numbers to be diluted. You can see how I modified the groupings below if you are interested, but there are now 11 Event Types as opposed to 72.

NOAA Storm Data Distribution Interactive Application

The purpose of this application is to identify storms that caused casualties and categorize them by: 1) Decade, 2) Region, and 3) Event Type. Casualties are considered either injury or fatality. Injury and fatality numbers are provided as well as total casualty numbers.

If you have questions, or comments, please pass them along. This is my first Shiny application, and I personally think it is a great framework - especially if you like to program in R.

My apologies, but we can't use iframe on this site, so I am providing a link to the application on Shiny. It is only the application depicting the NOAA data as detailed above. Please click here to view the NOAA Storm Data Distribution Interactive Application.

Program Notes/Instructions:

  1. One thing that might cause confusion while using the application is that the location (longitude and latitude) for many of the storms is not provided by NOAA. I assume the reason for this is that many events occur over a wide area, such as a cold front, or hurricane. Instead of omitting this data, the center of the state in which the event occurred was used for the location. I use the term Center of Mass, and it is abbreviated as COM. 
  2. The point here is to use caution when selecting the COM checkbox. While the COM checkbox is selected, all Events will be available for selection. If you have selected the event "Cold", for example, and then uncheck the COM checkbox, a message will appear reminding you that this event type is only available using Center of Mass data. Hopefully, this explains this little quirk. I couldn't think of an easier way to manage it. 
  3. The ZOOM feature only works for the Heat Maps, and the longitude and latitudes can only be adjusted once the ZOOM feature is enabled. The reason for this is the the ZOOM level is atutomatically calculated by Google Maps, and is usually sufficient unless you have an interest in roaming around.

Please feel free to comment and pass along suggestions for improvements.

Event Type Groupings:

```{r Groupings, cache=TRUE, echo=TRUE}
Tornado <- c("Tornado", "High Wind", 'Thunderstorm Wind', 'Strong Wind',
'TORNADOES, TSTM WIND, HAIL', 'Marine High Wind',
'Waterspout', 'Funnel Cloud')

Heat <- c("Heat", "Excessive Heat")

Flood<- c("Flood", "Flash Flood", "Heavy Rain", 'Landslide')

Hurricane <- c("Hurricane (Typhoon)", "Tropical Storm", "Tropical Depression")

Cold <- c("Winter Weather", "Ice Storm", "Winter Storm", 'Cold/Wind Chill',
'Heavy Snow','Blizzard','Avalanche', 'Extreme Cold/Wind Chill',
'Lake-Effect Snow', 'Freezing Fog', 'Sleet', 'Frost/Freeze' )

Wildfire <- c("Wildfire")

Lightning <- c("Lightning")

Hail <- c('Hail')

OceanCurrent <- c('Rip Current','High Surf', 'Coastal Flood', 'Storm Surge/Tide',
'Astronomical Low Tide', 'Sneakerwave','Tsunami')

OTHER <- c('OTHER', 'Drought','Debris Flow', 'Dust Storm', 'Dust Devil',
'Dense Fog', "Dense Smoke")

D. Kahle and H. Wickham. ggmap: Spatial Visualization with ggplot2. The R
Journal, 5(1), 144-161. URL

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Tags: Big, Data


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