Analysis of traffic Accidents in Newyork

library(rgdal)
library(UScensus2010)
library(UScensus2010county)

UScensus2010county: US Census 2010 County Level Shapefiles and Additional
Demographic Data 
Version 1.00 created on 2011-11-06 
copyright (c) 2011, Zack W. Almquist, University of California-Irvine
Type help(package="UScensus2010county") to get started.

For citation information, type citation("UScensus2010county").

shp <- data("new_york.county10")
shp <- new_york.county10
df_1 <- shp@data

Study Area

library(tmap)
qtm(shp,fill = "P0010001")

P0010001

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dt <- readOGR(dsn = ".",layer = "deatht")

OGR data source with driver: ESRI Shapefile 
Source: ".", layer: "deatht"
with 2385 features
It has 12 fields

Data

library(datasets)
dt_1 <- dt@data
df2 <- dt_1[, c(6,7,8,9,10,11,12)]
library(DT)
datatable(df2, options = list(pageLength = 5))

m <- lm(injury ~ ., data = df2)
m

Call:
lm(formula = injury ~ ., data = df2)

Coefficients:
(Intercept)     numfatal     speeding     conszone          age  
   3.291200     0.089382     0.333959    -0.020767     0.001359  
     alcres          sex  
  -0.025312     0.267218  

summary(m)

Call:
lm(formula = injury ~ ., data = df2)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9715 -1.2287 -0.0115  0.6792  7.7590 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  3.2911997  0.0922655  35.671  < 2e-16 ***
numfatal     0.0893815  0.0536148   1.667   0.0956 .  
speeding     0.3339590  0.0687210   4.860 1.25e-06 ***
conszone    -0.0207673  0.1086711  -0.191   0.8485    
age          0.0013592  0.0002616   5.195 2.22e-07 ***
alcres      -0.0253118  0.0006962 -36.355  < 2e-16 ***
sex          0.2672177  0.0381353   7.007 3.16e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.473 on 2378 degrees of freedom
Multiple R-squared:  0.3938,    Adjusted R-squared:  0.3923 
F-statistic: 257.5 on 6 and 2378 DF,  p-value: < 2.2e-16

Variable Inflation Factor

library(graphics)
boxplot(injury~sex, data = df2)

library(car)

Warning in sample.int(.Machine$integer.max - 1L, 1L): '.Random.seed' is not
an integer vector but of type 'NULL', so ignored

vif(m)

numfatal speeding conszone      age   alcres      sex 
1.098672 1.043408 1.045882 2.106586 1.029146 2.125943 

Regression Equation

b1 <- lm(formula = injury ~  alcres + age + speeding + sex, data = df2)
summary(b1)

Call:
lm(formula = injury ~ alcres + age + speeding + sex, data = df2)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9986 -1.2310 -0.0261  0.6723  7.7583 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  3.3913843  0.0701987  48.311  < 2e-16 ***
alcres      -0.0254391  0.0006920 -36.763  < 2e-16 ***
age          0.0013441  0.0002615   5.140 2.98e-07 ***
speeding     0.3559688  0.0674589   5.277 1.43e-07 ***
sex          0.2696557  0.0381108   7.076 1.95e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.473 on 2380 degrees of freedom
Multiple R-squared:  0.3931,    Adjusted R-squared:  0.3921 
F-statistic: 385.4 on 4 and 2380 DF,  p-value: < 2.2e-16

f_crit <- qf(0.95,df1 = 4,df2 = 2380)
f_crit

[1] 2.375667

print("Null Hypotheisis:There is no relationship between the independent and dependent variable")

[1] "Null Hypotheisis:There is no relationship between the independent and dependent variable"

print("Alternative Hypotheisis:There is relationship between the independent and dependent variable")

[1] "Alternative Hypotheisis:There is relationship between the independent and dependent variable"

Data visualization

library(leaflet)
df <- read.csv("data.csv",header=T)
leaflet(data = df) %>% 
  addTiles() %>%
  addMarkers(~POINT_X, ~POINT_Y,popup= "Accident",clusterOptions = markerClusterOptions())

leaflet(df,width = "100%",height = 800) %>% 
  addTiles(group = "OSM (default)") %>%
  addProviderTiles(provider = "Esri.WorldStreetMap",group = "World StreetMap") %>%
  addProviderTiles(provider = "Esri.WorldImagery",group = "World Imagery") %>%
  addProviderTiles(provider = "NASAGIBS.ViirsEarthAtNight2012",group = "Nighttime Imagery") %>%
    addProviderTiles(provider = "NASAGIBS.ModisTerraBands367CR",group = "Hybrid Imagery") %>%
      addTiles() %>% fitBounds(147,90,-101,-90) %>% 
       addMarkers(~POINT_X,~POINT_Y,popup = "Accident",clusterOptions = markerClusterOptions())  %>%
      addLayersControl(
    baseGroups = c("OSM (default)","World StreetMap", "Nighttime Imagery","World Imagery","Hybrid Imagery"),
    options = layersControlOptions(collapsed = FALSE)
  )