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leeds_crime_study.Rmd
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---
title: "Leeds Crime Study"
output:
html_document:
df_print: paged
---
```{r}
################################################################################
# The study of Leeds crimes in space and time
# University of Sheffield
# MSc Data Science
# Registration No: 180234862
# Item page: https://github.com/bobleer/Leeds_crime_study
################################################################################
library("tidyverse")
library("dplyr")
library("grid")
library("gridExtra")
library("leaflet")
library("rgdal")
library("tmap")
library("ggcorrplot")
library("ggmap")
library("ggplot2")
library("cowplot")
library("zoo")
options(scipen = 200)
################################################################################
# Show all crime data in the UK (The huge data need a ton of RAM.)
################################################################################
#Set file directory
all_crimes_dir <- "./all-street/"
#Traversal
all_crimes_csv <-
list.files(
path = all_crimes_dir,
pattern = "*.csv",
recursive = TRUE,
all.files = TRUE,
full.names = TRUE
)
#Set output filename
all_combined_filename <- "[Combined]all-street_crimes.csv"
#First apply read.csv, then rbind
read_all_csv <- lapply(all_crimes_csv,
function(x)
read.csv(x, stringsAsFactors = F, header = T))
#Output combined csv
all_crimes_path <- paste0(all_crimes_dir, all_combined_filename)
#Save combined csv
do.call(rbind, read_all_csv) %>%
write.csv(all_crimes_path)
#Read combined csv
all_combined_crimes <- read.csv(all_crimes_path, header = TRUE)
#Sum by month
all_crimes_by_month <-
all_combined_crimes %>% group_by(Month) %>% summarise(crime_sum = n())
ggplot() +
geom_point(
aes(x = as.Date(as.yearmon(Month)),
y = crime_sum),
size = all_crimes_by_month$crime_sum / 60000,
color = "#999999",
data = all_crimes_by_month
) +
geom_smooth(
aes(x = as.Date(as.yearmon(Month)),
y = crime_sum),
data = all_crimes_by_month,
method = lm,
size = 0.7,
color = "#ffffff",
fill = "#FF635C",
se = TRUE
) +
geom_hline(
yintercept = max(all_crimes_by_month$crime_sum),
color = "#eeeeee",
size = 1,
alpha = 0.5,
linetype = "dashed"
) +
ggtitle("The trend of crimes in the UK") +
scale_x_date(
date_labels = "%Y-%m",
date_breaks = "3 months",
date_minor_breaks = "1 months"
) +
xlab("Month (over 36 months)") +
ylab("The number of crimes") +
theme(
plot.title = element_text(
size = 16,
color = "#ffffff",
hjust = 0,
face = "bold"
),
axis.title = element_text(
size = 14,
color = "#888888",
hjust = 0.5,
face = "bold"
),
axis.text.x = element_text(
size = 12,
color = "#ffffff",
hjust = 0.5,
angle = 45,
face = "bold"
),
axis.text.y = element_text(
size = 12,
angle = 45,
face = "bold",
color = "#ffffff"
),
plot.background = element_rect(fill = "#2E2E2E",
color = NA),
plot.margin = unit(c(0.4, 0.4, 0.4, 0.4), "inches"),
panel.background = element_rect(fill = "#5E5E5E"),
panel.grid = element_line(color = "#888888"),
panel.grid.minor = element_line(color = "#727272")
)
#Will crack brower
###leaflet(all_combined_crimes) %>% addTiles() %>%
### addMarkers(clusterOptions = markerClusterOptions())
```
```{r}
################################################################################
# Read West Yorkshire crime data and combine with Leeds LSOA data
################################################################################
leedsShape <-
readOGR(dsn = "./leeds_lsoa", layer = "england_lsoa_2011")
deprivation2015 <-
read.csv("./Deprivation2015.csv",
header = TRUE)
deprivation2015_sl <- deprivation2015 %>%
select(
LSOA.name..2011.,
LSOA.code..2011.,
Total.population..mid.2012..excluding.prisoners.,
Income.Score..rate.,
Employment.Score..rate.,
Education..Skills.and.Training.Score,
Living.Environment.Score,
Crime.Score
)
names(deprivation2015_sl)[names(deprivation2015_sl) == "LSOA.name..2011."] <-
"LSOA_name"
names(deprivation2015_sl)[names(deprivation2015_sl) == "LSOA.code..2011."] <-
"LSOA_code"
names(deprivation2015_sl)[names(deprivation2015_sl) == "Total.population..mid.2012..excluding.prisoners."] <-
"Total_population"
names(deprivation2015_sl)[names(deprivation2015_sl) == "Income.Score..rate."] <-
"Income"
names(deprivation2015_sl)[names(deprivation2015_sl) == "Employment.Score..rate."] <-
"Employment"
names(deprivation2015_sl)[names(deprivation2015_sl) == "Education..Skills.and.Training.Score"] <-
"Education"
names(deprivation2015_sl)[names(deprivation2015_sl) == "Living.Environment.Score"] <-
"Living_ENV"
names(deprivation2015_sl)[names(deprivation2015_sl) == "Crime.Score"] <-
"Crime_Score"
leedsShape@data <- left_join(leedsShape@data,
deprivation2015_sl,
by = c('code' = 'LSOA_code'))
transformed_leedsShape <- spTransform(leedsShape,
CRS(
"+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
))
tmap_mode("view")
tm_shape(leedsShape) + tm_borders(lwd = 2, alpha = 0.5, col = "#666666") + tm_basemap(leaflet::providers$OpenMapSurfer.Roads)
leeds_pop_map <- tm_shape(leedsShape) +
tm_fill(
"Total_population",
alpha = 0.7,
style = "kmeans",
title = "Population@Leeds"
) +
tm_borders(alpha = 0.5, col = "#eeeeee") +
tm_view(view.legend.position = c("right", "bottom")) +
tm_basemap(leaflet::providers$OpenMapSurfer.Roads)
leeds_pop_map
leeds_income_map <- tm_shape(leedsShape) +
tm_fill("Income",
alpha = 0.7,
style = "kmeans",
title = "Income@Leeds") +
tm_borders(alpha = 0.5, col = "#eeeeee") +
tm_view(view.legend.position = c("right", "bottom")) +
tm_basemap(leaflet::providers$OpenMapSurfer.Roads)
leeds_income_map
leeds_employment_map <- tm_shape(leedsShape) +
tm_fill("Employment",
alpha = 0.7,
style = "kmeans",
title = "Employment@Leeds") +
tm_borders(alpha = 0.5, col = "#eeeeee") +
tm_view(view.legend.position = c("right", "bottom")) +
tm_basemap(leaflet::providers$OpenMapSurfer.Roads)
leeds_employment_map
leeds_education_map <- tm_shape(leedsShape) +
tm_fill("Education",
alpha = 0.7,
style = "kmeans",
title = "Education@Leeds") +
tm_borders(alpha = 0.5, col = "#eeeeee") +
tm_view(view.legend.position = c("right", "bottom")) +
tm_basemap(leaflet::providers$OpenMapSurfer.Roads)
leeds_education_map
leeds_living_map <- tm_shape(leedsShape) +
tm_fill("Living_ENV",
alpha = 0.7,
style = "kmeans",
title = "Living Environment<br>@Leeds") +
tm_borders(alpha = 0.5, col = "#eeeeee") +
tm_view(view.legend.position = c("right", "bottom")) +
tm_basemap(leaflet::providers$OpenMapSurfer.Roads)
leeds_living_map
################################################################################
# Merge all 36 months data from West Yorkshire Police
################################################################################
#Set file directory
crimes_dir = "./west-yorkshire-street_crimes/"
crimes_csv <-
list.files(path = crimes_dir,
pattern = "*.csv",
full.names = TRUE)
#Set output filename
combined_filename = "[Combined]west-yorkshire-street_crimes.csv"
#First apply read.csv, then rbind
read_all_csv <- lapply(crimes_csv,
function(x)
read.csv(x, stringsAsFactors = F, header = T))
#Save combined csv
do.call(rbind, read_all_csv) %>%
write.csv(paste0(crimes_dir, combined_filename))
################################################################################
# Visualise Leeds crime data by LSOA
################################################################################
combined_crimes <-
read.csv(paste0(crimes_dir, combined_filename), header = TRUE)
numCrimesByLSOA <- combined_crimes %>%
select(LSOA.code, Crime.ID) %>%
group_by(LSOA.code) %>%
summarise(Num_crimes = n())
leedsShape@data <- left_join(leedsShape@data,
numCrimesByLSOA,
by = c('code' = 'LSOA.code'))
leedsShape@data[is.na(leedsShape@data$Num_crimes)] <- 0
tmap_mode("view")
leeds_crime_map <- tm_shape(leedsShape) +
tm_fill("Num_crimes",
alpha = 0.7,
style = "kmeans",
title = "Crimes@Leeds<br>Over past 36 months") +
tm_borders(alpha = 0.5, col = "#eeeeee") +
tm_view(view.legend.position = c("right", "bottom")) +
tm_basemap(leaflet::providers$OpenMapSurfer.Roads)
leeds_crime_map
################################################################################
# Correlation among the crime and other social elements
################################################################################
nation_data <- deprivation2015_sl %>%
select(LSOA_name,
Total_population,
Income,
Employment,
Education,
Living_ENV,
Crime_Score)
rownames(nation_data) <- nation_data[, 1]
nation_data <- nation_data[, -1]
nation_p.mat <- cor_pmat(nation_data)
cor_nation <- cor(nation_data)
leeds_data_by_nation <- leedsShape@data %>%
select(LSOA_name,
Total_population,
Income,
Employment,
Education,
Living_ENV,
Crime_Score)
rownames(leeds_data_by_nation) <- leeds_data_by_nation[, 1]
leeds_data_by_nation <- leeds_data_by_nation[, -1]
leeds_by_nation_p.mat <- cor_pmat(leeds_data_by_nation)
cor_leeds_by_nation <- cor(leeds_data_by_nation)
leeds_data <- leedsShape@data %>%
select(LSOA_name,
Total_population,
Income,
Employment,
Education,
Living_ENV,
Num_crimes)
rownames(leeds_data) <- leeds_data[, 1]
leeds_data <- leeds_data[, -1]
leeds_p.mat <- cor_pmat(leeds_data)
cor_leeds <- cor(leeds_data)
ggcorrplot(
title = "The UK crimes p-value significance",
cor_nation,
method = "square",
outline.color = "white",
type = "full",
ggtheme = ggplot2::theme_get,
lab = TRUE,
lab_size = 6,
tl.srt = 45,
pch.cex = 24,
p.mat = nation_p.mat,
colors = c("#6D9EC1", "white", "#E46726")
) + theme(text = element_text(face = "bold"))
ggcorrplot(
title = "Leeds crimes p-value significance (Data from deprivation2015)",
cor_leeds_by_nation,
method = "square",
outline.color = "white",
type = "full",
ggtheme = ggplot2::theme_get,
lab = TRUE,
lab_size = 6,
tl.srt = 45,
pch.cex = 24,
p.mat = leeds_by_nation_p.mat,
colors = c("#6D9EC1", "white", "#E46726")
) + theme(text = element_text(face = "bold"))
ggcorrplot(
title = "Leeds crimes p-value significance",
cor_leeds,
method = "square",
outline.color = "white",
type = "full",
ggtheme = ggplot2::theme_get,
lab = TRUE,
lab_size = 6,
tl.srt = 45,
pch.cex = 24,
p.mat = leeds_p.mat,
colors = c("#6D9EC1", "white", "#E46726")
) + theme(text = element_text(face = "bold"))
numCrimesbyLSOA <- combined_crimes %>%
select(Month, Crime.type, LSOA.code) %>%
group_by(Month, Crime.type, LSOA.code) %>%
na.omit() %>%
summarise(Num_crimes = n())
leedsShape <-
readOGR(dsn = "./leeds_lsoa", layer = "england_lsoa_2011")
numCrimesbyType <- left_join(leedsShape@data,
numCrimesbyLSOA,
by = c('code' = 'LSOA.code')) %>%
group_by(Month, Crime.type) %>%
summarise(crime_sum = sum(Num_crimes))
numCrimesbyMonth <- numCrimesbyType %>%
select(Month, crime_sum) %>%
group_by(Month) %>%
summarise(crime_sum = sum(crime_sum))
dataset_path <- "./Leeds_City_Council_Spending/"
leeds_council_dataset <- list.files(path = dataset_path,
pattern = "*.csv",
full.names = TRUE)
read_all_csv <- lapply(leeds_council_dataset,
function(x)
read.csv(x, stringsAsFactors = F, header = T))
combined = ''
for (leeds_council_month in leeds_council_dataset) {
single_month_data <- read.csv(leeds_council_month, header = TRUE)
combining <-
select(single_month_data, Effective.Date, Amount)[which(single_month_data$Benificiary.Name == "P & Cc For West Yorkshire"),]
combined <- rbind(combined, combining)
}
leeds_council_spending <- combined %>%
group_by(Effective.Date) %>%
summarise(Amount_sum = sum(as.numeric(Amount))) %>%
na.omit()
leeds_council_spending <-
leeds_council_spending[order(as.Date(leeds_council_spending$Effective.Date, format = "%d/%m/%Y")),]
crime_and_spending <-
cbind(numCrimesbyMonth, leeds_council_spending)[-3]
rownames(crime_and_spending) <- crime_and_spending[, 1]
crime_and_spending <- crime_and_spending[, -1]
cor(crime_and_spending)
summary(lm(formula = crime_sum ~ Amount_sum, data = crime_and_spending))
ggplot(data = crime_and_spending,
aes(x = crime_sum,
y = as.integer(Amount_sum))) +
geom_point(size = 4.5,
color = "#999999") +
geom_smooth(
method = lm,
size = 0.7,
color = "#ffffff",
fill = "#2469FF",
se = TRUE
) +
ggtitle("The correlation between the number of crimes and the amount of police in Leeds") +
xlab("The number of crimes@Leeds (over past 36 months)") +
ylab("The amount of spending@West Yorkshire Police") +
theme(
plot.title = element_text(
size = 16,
color = "#ffffff",
hjust = 0,
face = "bold"
),
axis.title = element_text(
size = 14,
color = "#888888",
hjust = 0.5,
face = "bold"
),
axis.text.x = element_text(
size = 12,
color = "#ffffff",
hjust = 0.5,
angle = 45,
face = "bold"
),
axis.text.y = element_text(
size = 12,
angle = 45,
face = "bold",
color = "#ffffff"
),
plot.background = element_rect(fill = "#2E2E2E",
color = NA),
plot.margin = unit(c(0.4, 0.4, 0.4, 0.4), "inches"),
panel.background = element_rect(fill = "#5E5E5E"),
panel.grid = element_line(color = "#888888"),
panel.grid.minor = element_line(color = "#727272")
)
################################################################################
# Visualise Leeds crime data by month
################################################################################
crimes_csv <-
list.files(path = crimes_dir,
pattern = "*.csv",
full.names = TRUE)
# remove combined csv
crimes_csv <-
crimes_csv[-(grep(pattern = "Combined", x = crimes_csv))]
for (single_month in crimes_csv) {
crimes <- read.csv(single_month, header = TRUE)
crimes_month <- substr(single_month, 33, 39)
numCrimesByLSOA <- crimes %>%
select(LSOA.code, Longitude, Latitude, Crime.type) %>%
group_by(Crime.type, LSOA.code) %>%
summarise(Num_crimes = n())
leeds_crimes_location <- left_join(leedsShape@data,
numCrimesByLSOA,
by = c('code' = 'LSOA.code')) %>%
na.omit()
crimes_location <- crimes %>%
select(LSOA.code, Longitude, Latitude, Crime.type)
leeds_crimes_location <- left_join(leedsShape@data,
crimes_location,
by = c('code' = 'LSOA.code')) %>%
na.omit()
plot01 <- qmplot(
Longitude,
Latitude,
data = leeds_crimes_location,
geom = "blank",
darken = .7,
zoom = 12,
maptype = "toner",
legend = "bottomright"
) +
stat_density_2d(
aes(fill = ..level..),
bins = 50,
geom = "polygon",
alpha = .2,
color = NA
) +
scale_fill_gradient2(
paste0("Leeds Crimes\nPropensity\n", crimes_month),
low = "white",
mid = "yellow",
high = "red",
midpoint = 100
) +
theme(
plot.background = element_rect(fill = "#5E5E5E",
color = "#5E5E5E"),
panel.background = element_rect(fill = "#5E5E5E",
color = "#5E5E5E")
)
crime_colors <- c(
"Anti-social behaviour" = "#A172FF",
"Bicycle theft" = "#72A1FF",
"Burglary" = "#0074D9",
"Criminal damage and arson" = "#39CCCC",
"Drugs" = "#1B7B00",
"Other crime" = "#111111",
"Other theft" = "#EEEEEE",
"Possession of weapons" = "#85144b",
"Public order" = "#F012BE",
"Robbery" = "#3333cc",
"Shoplifting" = "#01FF70",
"Theft from the person" = "#E4FF00",
"Vehicle crime" = "#FFB800",
"Violence and sexual offences" = "#FF4136"
)
plot02 <- qmplot(
Longitude,
Latitude,
data = leeds_crimes_location,
geom = "blank",
darken = .7,
zoom = 12,
maptype = "toner",
legend = "bottomright"
) +
stat_bin2d(
aes(Longitude, Latitude,
fill = Crime.type),
size = 0,
bins = 150,
alpha = 1,
drop = TRUE,
na.rm = TRUE,
show.legend = FALSE,
data = leeds_crimes_location
) +
scale_fill_manual(values = crime_colors) +
theme(
plot.background = element_rect(fill = "#5E5E5E",
color = "#5E5E5E"),
panel.background = element_rect(fill = "#5E5E5E",
color = "#5E5E5E")
)
crime_type_sum <- leeds_crimes_location %>%
select(Crime.type) %>%
group_by(Crime.type) %>%
summarise(Num_crimes = n()) %>%
na.omit()
crime_type_sum$Percent <- round(crime_type_sum$Num_crimes /
sum(crime_type_sum$Num_crimes) * 100,
2)
plot03 <- ggplot(crime_type_sum,
aes(
x = reorder(Crime.type, Num_crimes),
y = as.numeric(Num_crimes),
fill = Crime.type
)) +
geom_bar(stat = "identity", show.legend = FALSE) +
geom_text(
label = paste0(crime_type_sum$Percent, '%'),
hjust = -0.05,
color = "#ffffff",
fontface = "bold"
) +
geom_hline(
yintercept = max(crime_type_sum$Num_crimes),
color = "#eeeeee",
size = 1
) +
scale_fill_manual(values = crime_colors) +
scale_y_continuous(limits = c(0, max(crime_type_sum$Num_crimes) * 1.05),
breaks = sort(c(
seq(
min(crime_type_sum$Num_crimes),
max(crime_type_sum$Num_crimes),
length.out = 6
),
max(crime_type_sum$Num_crimes)
))) +
geom_text(
aes(
x = 0,
y = max(crime_type_sum$Num_crimes) * 1.05,
label = paste0("Leeds Crime Types\n", crimes_month),
hjust = 1,
vjust = 0
),
fontface = "bold.italic",
size = 8,
color = "#2E2E2E",
show.legend = FALSE,
nudge_x = 1.2
) +
coord_flip() +
labs(x = "", y = "the number of crimes") +
theme(
plot.title = element_text(
size = 12,
color = "#888888",
hjust = 0,
face = "bold"
),
axis.title = element_text(
size = 11,
color = "#888888",
hjust = 0.95,
face = "bold"
),
axis.text.x = element_text(
size = 11,
color = "#ffffff",
hjust = 0
),
axis.text.y = element_text(size = 11,
color = "#ffffff"),
plot.background = element_rect(fill = "#2E2E2E",
color = NA),
panel.background = element_rect(fill = "#5E5E5E"),
panel.grid = element_line(color = "#888888")
)
#ggplot2.multiplot(plot01, plot02, plot03, cols = 3)
jpeg(
filename = paste0("./images/", crimes_month, "_leeds_crimes.jpeg"),
width = 1920,
height = 580,
units = "px",
pointsize = 24,
bg = "#2E2E2E"
)
print(
cowplot::ggdraw(grid.arrange(plot01, plot02, plot03, ncol = 3)) +
theme(
plot.background = element_rect(fill = "#5E5E5E",
color = "#5E5E5E"),
panel.background = element_rect(fill = "#5E5E5E",
color = "#5E5E5E")
)
)
dev.off()
}
################################################################################
plot04 <- ggplot() +
geom_area(aes(
x = as.Date(as.yearmon(Month)),
y = crime_sum,
group = reorder(Crime.type,-crime_sum),
fill = reorder(Crime.type,-crime_sum)
),
data = numCrimesbyType) +
geom_point(aes(x = as.Date(as.yearmon(Month)),
y = crime_sum),
color = "#2E2E2E",
data = numCrimesbyMonth) +
geom_smooth(
aes(x = as.Date(as.yearmon(Month)),
y = crime_sum),
data = numCrimesbyMonth,
method = lm,
size = 0.7,
color = "white",
se = TRUE
) +
geom_hline(
yintercept = max(numCrimesbyMonth$crime_sum),
color = "#eeeeee",
size = 1,
alpha = 0.5,
linetype = "dashed"
) +
geom_text(
aes(
x = as.Date(as.yearmon(Month)),
y = crime_sum,
label = crime_sum
),
data = numCrimesbyMonth,
hjust = 1,
vjust = 0.5,
angle = 90,
color = "#ffffff",
size = 4
) +
scale_fill_manual(values = crime_colors,
name = "Leeds crime types") +
scale_x_date(
date_labels = "%Y-%m",
date_breaks = "3 months",
date_minor_breaks = "1 months"
) +
xlab("Month (over 36 months)") +
ylab("The number of crime") +
theme(
plot.title = element_text(
size = 12,
color = "#888888",
hjust = 0,
face = "bold"
),
axis.title = element_text(
size = 12,
color = "#888888",
hjust = 0.5,
face = "bold"
),
axis.text.x = element_text(
size = 12,
color = "#ffffff",
hjust = 0.5,
angle = 45,
face = "bold"
),
axis.text.y = element_text(
size = 12,
angle = 45,
face = "bold",
color = "#ffffff"
),
legend.title = element_text(size = 12,
hjust = 0,
face = "bold"),
legend.text = element_text(#size = 12,
hjust = 0,
face = "bold"),
plot.background = element_rect(fill = "#2E2E2E",
color = NA),
plot.margin = unit(c(0.4, 0.4, 0.4, 0.4), "inches"),
panel.background = element_rect(fill = "#5E5E5E"),
panel.grid = element_line(color = "#888888"),
panel.grid.minor = element_line(color = "#727272")
)
plot05 <-
ggplot(
numCrimesbyType,
aes(
x = as.Date(as.yearmon(Month)),
y = crime_sum,
group = reorder(Crime.type,-crime_sum),
fill = reorder(Crime.type,-crime_sum),
color = reorder(Crime.type,-crime_sum)
)
) +
geom_point(show.legend = FALSE) +
geom_smooth(
method = lm,
color = "white",
size = 0.7,
show.legend = FALSE
) +
facet_wrap( ~ reorder(Crime.type,-crime_sum),
scales = "free",
ncol = 2) +
scale_discrete_manual(aesthetics = c("colour", "fill"),
values = crime_colors) +
xlab("") +
ylab("") +
theme(
axis.text.x = element_text(
color = "#ffffff",
hjust = 0,
face = "bold"
),
axis.text.y = element_text(face = "bold",
color = "#ffffff"),
plot.background = element_rect(fill = "#2E2E2E",
color = NA),
plot.margin = unit(c(0.4, 0.4, 0.4, 0.4), "inches"),
panel.background = element_rect(fill = "#5E5E5E"),
panel.grid = element_line(color = "#888888"),
panel.grid.minor = element_line(color = "#727272"),
strip.background = element_rect(fill = "#242424"),
strip.text = element_text(color = "#ffffff",
face = "bold")
)
filename = "./images/leeds_crimes_36_months.jpeg"
jpeg(
filename = filename,
width = 1919,
height = 1014,
units = "px",
pointsize = 12,
bg = "#2E2E2E"
)
print(
cowplot::ggdraw(grid.arrange(
plot05, plot04, ncol = 2, widths = c(1, 2.5)
)) +
theme(
plot.background = element_rect(fill = "#5E5E5E",
color = "#5E5E5E"),
panel.background = element_rect(fill = "#5E5E5E",
color = "#5E5E5E")
)
)
dev.off()
```
