Exploratory Data Analysis of Forest Fire Data
Kathan Vakharia
28/07/2020
Introduction
- Forest fires can create ecological problems and endanger human lives and property. Understanding when they occur and what causes them is important for managing them.
Visualization is one of the most important skills that every Data Analyst should possess. In this project, I perform the most simple form of Exploratory data analyses of Forest Fire data set.
This data-set about forest fires occurring in Portugal and based on this scientific research paper.
Main Goal:To find our patterns in occurrence of forest fire
Initial Setup
#importing data
library(tidyverse)
forest_fire = read_csv("forestfires.csv")Getting premliminary isights
glimpse(forest_fire)## Rows: 517
## Columns: 13
## $ X <dbl> 7, 7, 7, 8, 8, 8, 8, 8, 8, 7, 7, 7, 6, 6, 6, 6, 5, 8, 6, 6, 6...
## $ Y <dbl> 5, 4, 4, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4...
## $ month <chr> "mar", "oct", "oct", "mar", "mar", "aug", "aug", "aug", "sep"...
## $ day <chr> "fri", "tue", "sat", "fri", "sun", "sun", "mon", "mon", "tue"...
## $ FFMC <dbl> 86.2, 90.6, 90.6, 91.7, 89.3, 92.3, 92.3, 91.5, 91.0, 92.5, 9...
## $ DMC <dbl> 26.2, 35.4, 43.7, 33.3, 51.3, 85.3, 88.9, 145.4, 129.5, 88.0,...
## $ DC <dbl> 94.3, 669.1, 686.9, 77.5, 102.2, 488.0, 495.6, 608.2, 692.6, ...
## $ ISI <dbl> 5.1, 6.7, 6.7, 9.0, 9.6, 14.7, 8.5, 10.7, 7.0, 7.1, 7.1, 22.6...
## $ temp <dbl> 8.2, 18.0, 14.6, 8.3, 11.4, 22.2, 24.1, 8.0, 13.1, 22.8, 17.8...
## $ RH <dbl> 51, 33, 33, 97, 99, 29, 27, 86, 63, 40, 51, 38, 72, 42, 21, 4...
## $ wind <dbl> 6.7, 0.9, 1.3, 4.0, 1.8, 5.4, 3.1, 2.2, 5.4, 4.0, 7.2, 4.0, 6...
## $ rain <dbl> 0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0...
## $ area <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...So there are 517 and 13 in our data set.Let’s see some of the rows.
head(forest_fire, n=11)## # A tibble: 11 x 13
## X Y month day FFMC DMC DC ISI temp RH wind rain area
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7 5 mar fri 86.2 26.2 94.3 5.1 8.2 51 6.7 0 0
## 2 7 4 oct tue 90.6 35.4 669. 6.7 18 33 0.9 0 0
## 3 7 4 oct sat 90.6 43.7 687. 6.7 14.6 33 1.3 0 0
## 4 8 6 mar fri 91.7 33.3 77.5 9 8.3 97 4 0.2 0
## 5 8 6 mar sun 89.3 51.3 102. 9.6 11.4 99 1.8 0 0
## 6 8 6 aug sun 92.3 85.3 488 14.7 22.2 29 5.4 0 0
## 7 8 6 aug mon 92.3 88.9 496. 8.5 24.1 27 3.1 0 0
## 8 8 6 aug mon 91.5 145. 608. 10.7 8 86 2.2 0 0
## 9 8 6 sep tue 91 130. 693. 7 13.1 63 5.4 0 0
## 10 7 5 sep sat 92.5 88 699. 7.1 22.8 40 4 0 0
## 11 7 5 sep sat 92.5 88 699. 7.1 17.8 51 7.2 0 0Description of Various Columns
Here are descriptions of the variables in the data set and the range of values for each taken from the paper:
- X: X-axis spatial coordinate within the Montesinho park map: 1 to 9
- Y: Y-axis spatial coordinate within the Montesinho park map: 2 to 9
- month: Month of the year: ‘jan’ to ‘dec’
- day: Day of the week: ‘mon’ to ‘sun’
- FFMC: Fine Fuel Moisture Code index from the FWI system: 18.7 to 96.20
- DMC: Duff Moisture Code index from the FWI system: 1.1 to 291.3
- DC: Drought Code index from the FWI system: 7.9 to 860.6
- ISI: Initial Spread Index from the FWI system: 0.0 to 56.10
- temp: Temperature in Celsius degrees: 2.2 to 33.30
- RH: Relative humidity in percentage: 15.0 to 100
- wind: Wind speed in km/h: 0.40 to 9.40 rain: Outside rain in mm/m2 : 0.0 to 6.4
- area: The burned area of the forest (in ha): 0.00 to 1090.84
The X and Y variables are coordinates of fire locations.
The acronym FWI stands for “fire weather index”, a method used by scientists to quantify risk factors for forest fires.
Visualizing relation b/w forest fire and Weekdays & Months
#generic function to create bar graphs
create_bar <- function(dframe,x, y){
ggplot(data = dframe,
aes_string(x, y)
)+
geom_bar(stat="identity")+
labs(y="Total Forest Fires")+
theme(panel.background = element_rect(fill = "white"))
}forest_fire_by_month <- forest_fire %>%
group_by(month) %>%
summarise(
count = n(),
.groups="drop"
)%>%
mutate(
month = factor(month,
levels=c("jan", "feb", "mar", "apr", "may",
"jun", "jul", "aug", "sep", "oct",
"nov", "dec")
)
)forest_fire_by_day <- forest_fire %>%
group_by(day) %>%
summarise(
count = n(),
.groups="drop"
)%>%
mutate(
day = factor(day,
levels=c("sun", "mon", "tue", "wed", "thu",
"fri", "sat")
)
)#plotting the bar graphs
create_bar(forest_fire_by_day,"day", "count")
create_bar(forest_fire_by_month, "month", "count")
Examining spread of various factors using Box plots
create_box <- function( x, y){
ggplot(data=forest_fire,
aes_string(x=x,
y=y
)
)+
geom_boxplot()+
theme(panel.background = element_rect("white"))
}#Creating various box plots by vectorization
x_var_month <- names(forest_fire)[3] ## month
x_var_day <- names(forest_fire)[4] ## day
y_var <- names(forest_fire)[5:12]
month_box <- map2(x_var_month, y_var, create_box)
day_box <- map2(x_var_day, y_var, create_box)month_box## [[1]]
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Examining the relation b/w Area and various factors
#generic function to create scatter
create_scatter <- function(x, y){
ggplot(data=forest_fire,
aes_string(
x=x,
y=y
))+
geom_point()+
theme(panel.background = element_rect(fill = "white"))
}x_var_scatter = names(forest_fire)[5:12]
y_var_scatter = names(forest_fire)[13] ##area
scatters <- map2(x_var_scatter, y_var_scatter, create_scatter)
scatters## [[1]]
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We can notice that the scatter plot is clustered in bottom because there are very few high values of area and more values are spread in lower range.
- Let’s try to see the range of
areacolumn
summary(forest_fire$area)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.52 12.85 6.57 1090.84Looking at the 3rd quartile we can conclude that more than 75% of the values of area is less than 6.57.
For more clarity let’s plot a histogram of area column
Conclusion
We conclude that how important it is to visualize data.It will help us in making various statistical models for performing predictions.