library(readr)
library(dplyr)
library(ggplot2)
library(plotly)
library(lubridate)
library(tidyr)
library(stringr)
library(tools)
library(leaflet)
library(rfishnet2)
library(chron)
library(readxl)
library(leaflet.extras)
library(summarytools)
library(skimr)
library(tinytex)
library(knitr)
library(kableExtra)
library(htmlTable)
load("final_project_AM.RData")Final_project_DACSS_601
Libraries
Introduction:
Crime in the United States has been recorded since its founding. Crime rates have varied over time, with a sharp rise after 1900 and reaching a broad bulging peak between the 1970s and early 1990s. After 1992, crime rates began to fall year by year and have since declined significantly. This trend lasted until 2015, when crime rates began to rise slightly, which reversed in 2018 and 2019.Despite the increase in violent crime, particularly murders, between 2020 and 2021, the quantity of overall crime is still far below the peak of crime seen in the United States during the late 1980s and early 1990s, as other crimes such as rape, property crime and robbery continued to decline.
The Boston Police Department (BPD) is the primary law enforcement agency for the city of Boston, Massachusetts. It was established in 1838 and is one of the oldest police departments in the United States.The BPD is responsible for protecting and serving the residents of Boston, as well as maintaining order and enforcing the law within the city limits. The BPD is organized into several divisions, including patrol, detective, and special operations, and it has a number of specialized units, such as the bomb squad and the marine unit.The Boston Police Depurtment divides the City into twelve police districts, each covering a major geographic area, namely:
- A1-Downtown, North End & Waterfront, West End, China Town, Theater District, Boston Commons,
- A15-Beacon Hill Charlestown
- A7-East Boston
- B2-Roxbury
- B3-Mattapan
- C6-South Boston
- C11-Dorchester
- D4-South End
- D14-Allston & Brighton
- E5-West Roxbury & Roslindale
- E13-Jamaica Plain
- E18-Hyde Park
In this analysis report Boston Crime Data from Year 2015 to date is analyzed, the analysis consists of exploratory, timeseries and geographic analysis of crime incidents pertaining to different crime categories to identify major crimes being held in different police districts of Boston Police Department.Crime incidents are classified into 303 different crime categories.
Research Questions:
- What is trend of crime incidents, if the overall trend of crimes of increasing or decreasing?
- What crimes are more prominent in Boston Police Districts, and requires targeted approach from authorities to address the crime incidents?
- Which crime incidents are higher in which Months, Day, or Hours?
- How does crime vary by neighborhood or police district in Boston?
- Which Boston Police Districts generate highest number of crime incidents, and requires attention from authorities
- Which Street in Boston generate highest number of crime incidents, and requires attention from authorities?
Boston Police Department Dataset
The Boston Crime Data is a dataset that contains records of crimes reported in the city of Boston, Massachusetts. The BPD publishes crime data on its website through the CrimeStat system. CrimeStat allows users to view and analyze crime data for the city of Boston, including incidents of violent crime, property crime, and drug offenses.The Boston Crime Data contains records of crimes reported in the city of Boston, Massachusetts. The dataset is collected by the Boston Police Department and is made available to the public through the city’s open data portal.
This is a dataset containing records from the new crime incident report system. The dataset includes information about the type of crimes, the location of the crime, the time and date the crime occurred, and other relevant details. The Boston Crime Data is useful in analysing trends in crime rates over time or identifying patterns in the types of crimes that are committed in different parts of the city. It can also be used to inform public safety policies and procedures.The dataset includes the following fields:
- INCIDENT_NUMBER: A unique identifier for each crime incident
- OFFENSE_CODE: A code that represents the type of crime that was committed
- OFFENSE_CODE_GROUP: A broader category that groups related offenses together
- OFFENSE_DESCRIPTION: A description of the offense
- DISTRICT: The police district where the crime occurred
- REPORTING_AREA: The specific area within the district where the crime occurred
- SHOOTING: A flag indicating whether a shooting was involved in the incident
- OCCURRED_ON_DATE: The date and time when the crime occurred
- YEAR: The year when the crime occurred
- MONTH: The month when the crime occurred
- DAY_OF_WEEK: The day of the week when the crime occurred
- HOUR: The hour when the crime occurred
- UCR_PART: The part of the Uniform Crime Reporting system that the crime falls under
- STREET: The street where the crime occurred (if applicable)
- LATITUDE: The latitude of the location where the crime occurred
- LONGITUDE: The longitude of the location where the crime occurred
The dataset has only has Boston Police Districts codes but BPD district names are not available, One more column is integrated to include Boston Police Districts name as well, mapping of district code wise ‘District Name’ is given below:
- A1: Downtown, North End & Waterfront, West End, China Town, Theater District, Boston Commons, Beacon Hill
- A15: Charlestown
- A7: East Boston
- B2: Roxbury
- B3: Mattapan
- C6: South Boston
- C11: Dorchester
- D4: South End
- D14: Allston & Brighton
- E5: West Roxbury & Roslindale
- E13: Jamaica Plain
- E18: Hyde Park
The dataset contains 644,843 rows, and 16 columns. 303 Offense types (OFFENSE_DESCRIPTION) have been renamed and merged into 107 Offense classes for better representation of graphs. Each row in the dataset represent single crime incidents,
Dataset Read
# crime <- read_excel("boston data.xlsx") %>%
# mutate(OFFENSE_DESCRIPTION = str_trim(OFFENSE_DESCRIPTION))
# area_codes <- read_csv("boston_area_codes.csv")
# # write.csv(unique(crime$OFFENSE_DESCRIPTION), "offense_descriptions.csv", row.names = FALSE)
# crime_classes <- read_csv("offense_descriptions.csv") %>%
# mutate(OFFENSE_DESCRIPTION=str_trim(OFFENSE_DESCRIPTION),
# Class = str_trim(Class))
# crime <- merge(crime, area_codes, by = "DISTRICT", all.x = TRUE)
# crime <- merge(crime, crime_classes, by = "OFFENSE_DESCRIPTION", all = TRUE)# skimr::skim(crime)# crime$OFFENSE_CODE_GROUP <- toTitleCase(crime$OFFENSE_CODE_GROUP)
# crime$OFFENSE_DESCRIPTION <- toTitleCase(crime$OFFENSE_DESCRIPTION)
# crime$STREET <- toTitleCase(crime$STREET)
# crime$Class <- toTitleCase(crime$Class)
# save.image("final_project_AM.RData")1. What is trend of crime incidents, if the overall trend of crimes of increasing or decreasing?
Year wise Trend of Crime Incidents
crime_yearwise <- crime %>%
group_by(YEAR) %>%
summarise(frequency = n())
fig <- plot_ly(crime_yearwise, x = ~YEAR, y = ~frequency, type = 'scatter', mode = 'lines', line = list(dash = "solid"),
text = ~paste("Year:", YEAR, "<br>Crime:", frequency),
textfont = list(color = 'red', size = 12)) %>%
layout(
xaxis = list(title = 'Timeline', tickangle = -90, showgrid = TRUE, zeroline = TRUE, showline = TRUE, tickfont = list(size = 12)),
yaxis = list(title = 'Crime Incidents', showgrid = TRUE, zeroline = TRUE, showline = TRUE, tickfont = list(size = 12)),
title = 'Crime incidents over the years',
font = list(family = 'sans-serif', size = 15, color = '#444'),
plot_bgcolor = "#FFFFFF",
paper_bgcolor = "#FFFFFF"
)
# Display the graph
figThe Boston Police Department (BPD) has made progress in reducing crime rates over the past few years, according to data from the department. The overall crime rate has decreased since 2017, with the exception of a slight 1.17% increase in 2021. While this small increase may be concerning, it is worth noting that the overall trend in crime rates has been downward since 2017.
There has been a significant year-on-year decline in crime rates from 2018 to 2019 (11.84%), and an even larger decline from 2019 to 2020 (18.68%). The additional decline in 2020 may be attributed to the COVID-19 pandemic and the lockdown measures that were implemented to slow the spread of the virus. However, there was a slight increase in crime rates in 2021, which may be due to the economic downturn caused by the pandemic and the resulting unemployment. There was a further decline in crime rates in 2022, which may be attributed to the recovery in the economy or to controlling measures put in place by the BPD.
It is also important to consider the context of the significant increase in crime rates between 2015 and 2017. According to the BPD data, there was an 89.07% increase in crime incidents over this two-year period, with the number of incidents rising from 53597 in 2015 to 101338 in 2017. This spike in crime is likely due to a variety of factors, including social and economic conditions, changes in law enforcement policies and strategies, and other external influences.
Despite this increase in crime, the Boston Police Department has been able to bring crime rates down since 2017. It is likely that the department has implemented new strategies and techniques to address crime and improve public safety, such as community policing, crime prevention programs, and targeted enforcement efforts. This is a positive development for the city of Boston and its residents, as it indicates that the department is effectively working to reduce crime and maintain public safety.
3. Which crime incidents are higher in which Months, Day, or Hours?
Month wise distribution of Crime Incidents
crime_Month<- crime %>%
group_by(MONTH) %>%
summarise(frequency = n()) %>%
plot_ly(x = ~MONTH, y = ~frequency, type = 'bar', name = ~MONTH) %>%
layout(xaxis = list(title = 'Month', tickangle = -45), yaxis = list(title = 'No. of Crime Incidents'), barmode = 'group', showlegend=FALSE)
crime_MonthSeasonality in projection of Number of Crime Incidents can be observed from June to November, in the month wise projection of Number of Crime Incidents from Year. There are several factors that can contribute to seasonality in crime rates. Some of the most common factors include:
Weather: Crime rates often increase during the warmer months, when people are more likely to be outside and there are more opportunities for criminal activity.
Holidays: Crime rates may also increase during holiday periods, when there are more people out and about and there may be more opportunities for criminal activity.
Economic conditions: Economic downturns can lead to increases in crime, as people may turn to criminal activity in an effort to make money or support themselves. 2015-2022.
Weekday wise distribution of Crime Incidents
crime_weekday<- crime %>%
group_by(DAY_OF_WEEK) %>%
summarise(frequency = n()) %>%
plot_ly(x = ~DAY_OF_WEEK, y = ~frequency, type = 'bar', name = ~DAY_OF_WEEK) %>%
layout(xaxis = list(title = 'Week day', tickangle = -45), yaxis = list(title = 'No. of Crime Incidents'), barmode= 'group', showlegend = FALSE)
crime_weekday Weekday-wise projections of the number of crime incidents suggest that there are more incidents on Friday and Thursday and fewer on Sunday. Possible reasons for this pattern include:
Alcohol consumption: Crime rates may be higher on weekends, when people are more likely to be drinking alcohol and may be more prone to engaging in risky or aggressive behavior.
Social activities: Crime rates may also be influenced by social activities that occur on Friday and Thursday. For example, there may be more opportunities for criminal activity at events such as parties or sporting events on these days.
Economic factors: The distribution of work and leisure time can also affect crime rates. For example, crime rates may be higher on weekdays when more people are at work and fewer people are at home, while crime rates may be lower on Sunday when more people are at home.
Hour wise distribution of Crime Incidents
crime_hour<- crime %>%
group_by(HOUR) %>%
summarise(frequency = n()) %>%
plot_ly(x = ~HOUR, y = ~frequency, type = 'bar', name = 'frequency', color = 'blue') %>%
layout(xaxis = list(title = 'Hour wise Crime', tickangle = -90), yaxis = list(title = 'No. of Crime Incidents'))
crime_hourHour wise projection of Number of Crime Incidents, suggest that there are most number of crime incidents during the day from 8 AM to 12 AM, and there is a sudden surge at 12 AM, where 36,380 crime incidents are reported between 11PM-12 AM.
87 % of the crime incidents happen between 8 AM to 12 AM duration
5 % of the crime incidents happen between 11 PM to 12 AM duration
It’s possible that the high number of crime incidents during the day from 8 AM to 12 AM and the sudden surge at 12 AM (midnight) could be due to a variety of factors, potential reasons for this pattern could include:
Legal and law enforcement factors: The patterns of crime may also be influenced by legal and law enforcement factors, such as the presence of police patrols and the effectiveness of law enforcement in preventing and responding to crime.The 12 AM surge might highlight lack of police patrol in certain areas of Boston Police Department
Economic factors: Crime rates may be higher during the day when more people are out and about and there are more opportunities for criminal activity.
Lack of Cohesion in the Neighborhood: Community cohesion refers to the level of social connection and mutual trust within a community. When a community has a high level of cohesion, its members are more likely to work together and support each other, and they may be less likely to engage in criminal activity. On the other hand, when a community has a low level of cohesion, its members may be more isolated and less likely to help each other or intervene in situations that could lead to crime. In this case, a lack of cohesion among neighbors might lead to indifference towards any mishappenings such as crime incidents in the neighborhood, which could contribute to an increase in crime incidents.
4. How does Crime Vary by Neighborhood or Police District in Boston?
- Which Boston Police Districts generate highest number of crime incidents, and requires attention from authorities
- Which Street in Boston generate highest number of crime incidents, and requires attention from authorities?Police District wise Distribution of Crime Incidents
crime <- crime %>% filter(!is.na(district_area))
crime$district_area <- iconv(crime$district_area, to = "UTF-8")
dist_crime_frequency <- crime %>%
group_by(district_area) %>%
summarise(count = n())%>%
rename(labels = district_area, values = count)%>%
top_n(15)
dist_crime_frequency$labels <- str_trunc(dist_crime_frequency$labels, width = 20)
# Create the pie chart using the pie_data data frame
plot_ly(dist_crime_frequency, labels = ~labels, values = ~values, type = 'pie', hole = 0.3) %>%
layout(xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))The above Pie Chart explains the distribution of crime incidents between Boston Police Department Districts
# Create a new data frame with the labels and values for the table
dist_crime_frequency = arrange(dist_crime_frequency,desc(values))
colnames(dist_crime_frequency) <- c("BPD District", "No. of Crime Incidents")
# Create table using the kable function
dist_crime_frequency_table <- kable(dist_crime_frequency, "html")
# Use the kable_styling function to apply formatting to the table
dist_crime_frequency_table %>%
kable_styling(bootstrap_options = "striped")| BPD District | No. of Crime Incidents |
|---|---|
| Roxbury | 98657 |
| Dorchester | 85120 |
| South End | 83875 |
| Downtown, North E... | 71862 |
| Mattapan | 71336 |
| South Boston | 48898 |
| Allston & Brighton | 42777 |
| Hyde Park | 36149 |
| Jamaica Plain | 36043 |
| West Roxbury & Ro... | 28634 |
| East Boston | 27493 |
| Charlestown | 13471 |
Police District Wise Crime Categories, where maximum crime incidents Occurred
street_and_crimes_dist <- crime %>%
group_by(district_area, Class) %>%
summarise(frequency = n()) %>%
filter(!is.na(district_area)) %>%
arrange(desc(frequency)) %>%
slice(1)%>%
ungroup()%>%
arrange(desc(frequency))%>%
slice(1:12)
street_and_crimes_dist$district_area <- str_trunc(street_and_crimes_dist$district_area, width = 20)
plot_ly(street_and_crimes_dist, x = ~district_area, y = ~frequency, text = ~Class,
type = 'bar', mode = 'markers', name= ~Class, showlegend= FALSE)%>%
layout(xaxis = list(title = 'District Name', tickangle = -90), yaxis = list(title = 'No. of Crime Incidents'), showlegends = FALSE)It’s worth noting that Roxbury, Dorchester, and South End together make up 41.5% of total crime incidents. This suggests that these three districts may have higher crime rates compared to the other districts.
Larceny, which includes crimes such as theft and shoplifting, is often more common in areas with a high concentration of retail businesses and foot traffic. South End and “Downtown, North End-” may have more incidents of larceny due to their urban, commercial character. These areas may have a higher concentration of retail establishments and a larger number of people visiting these areas for shopping and other activities, which can create opportunities for larceny.
On the other hand, motor vehicle crimes, such as car theft and joyriding, may be more common in neighborhoods with a higher concentration of cars and parking areas. This could explain the higher prevalence of motor vehicle crimes in the other districts, these districts may have more cars and parking areas, which can create opportunities for motor vehicle crimes.
Roxbury District Crime Distribution
crime <- crime %>% filter(!is.na(DISTRICT))
crime$district_area <- iconv(crime$district_area, to = "UTF-8")
district_B2_crimes <- crime %>%
filter(district_area %in% c("Roxbury")) %>%
group_by(Class) %>%
summarise(count = n())%>%
rename(labels = Class, values = count)%>%
top_n(15)
district_B2_crimes$labels <- str_trunc(district_B2_crimes$labels, width = 30)
# Create the pie chart using the pie_data data frame
plot_ly(district_B2_crimes, labels = ~labels, values = ~values, type = 'pie', hole = 0.3) %>%
layout(xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), title="Roxbury District Crime Distribution")Dorchester District Crime Distribution
district_C11_crimes <- crime %>%
filter(district_area %in% c("Dorchester")) %>%
group_by(Class) %>%
summarise(count = n())%>%
rename(labels = Class, values = count)%>%
top_n(15)
district_C11_crimes$labels <- str_trunc(district_C11_crimes$labels, width = 30)
# Create the pie chart using the pie_data data frame
plot_ly(district_C11_crimes, labels = ~labels, values = ~values, type = 'pie', hole = 0.3) %>%
layout(xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), title="Dorchester District Crime Distribution")South End District Crime Distribution
district_D4_crimes <- crime %>%
filter(district_area %in% c("South End")) %>%
group_by(Class) %>%
summarise(count = n())%>%
rename(labels = Class, values = count)%>%
top_n(15)
district_D4_crimes$labels <- str_trunc(district_D4_crimes$labels, width = 30)
# Create the pie chart using the pie_data data frame
plot_ly(district_D4_crimes, labels = ~labels, values = ~values, type = 'pie', hole = 0.3) %>%
layout(xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), title="South End District Crime Distribution")Street wise Number of Crime Incidents
street_crime_frequency <- crime %>%
group_by(STREET) %>%
filter(!is.na(STREET)) %>%
summarise(count = n())%>%
rename(labels = STREET, values = count)%>%
top_n(10)
street_crime_frequency$labels = str_to_title(street_crime_frequency$labels)
street_crime_frequency$labels <- str_trunc(street_crime_frequency$labels, width = 30)
# Create the pie chart using the pie_data data frame
plot_ly(street_crime_frequency, labels = ~labels, values = ~values, type = 'pie', hole = 0.3) %>%
layout(xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), title="Street wise Crime Distribution")Street wise, largest Single Contributer to Crime Incidents
street_and_crimes <- crime %>%
group_by(STREET, Class) %>%
summarise(frequency = n()) %>%
filter(!is.na(STREET)) %>%
arrange(desc(frequency)) %>%
slice(1)%>%
ungroup()%>%
arrange(desc(frequency))%>%
slice(1:20)
street_and_crimes$STREET = str_to_title(street_and_crimes$STREET)
plot_ly(street_and_crimes, x = ~STREET, y = ~frequency, text = ~Class,
type = 'bar', mode = 'markers', name= ~Class, showlegend= FALSE)%>%
layout(xaxis = list(title = 'Street Name', tickangle = -90), yaxis = list(title = 'No. of Crime Incidents'), showlegends = FALSE, title="Street wise Most Common Crime")Washington Street, Blue Hill Avenue, and Boylston Street are three of the busiest streets in Boston, and unfortunately, they also have a high number of crime incidents. According to the data provided, these three streets alone contribute to 48.91% of the total crime incidents in the city. This is a significant portion and highlights the need for increased attention and efforts to address the crime problem in these areas.
Larceny and motor vehicle crimes are the major contributors to the overall crime rate on these streets. Larceny, which includes theft of personal property, is particularly prevalent on Washington Street and Boylston Street, with 5705 and 4140 incidents respectively. Motor vehicle crimes, such as car thefts and break-ins, are more common on Blue Hill Avenue, with 2406 incidents reported.
Overall, it is clear that Washington Street, Blue Hill Avenue, and Boylston Street have a significant crime problem that needs to be addressed.
Washington Streeet Crime Distribution
washington_street_crimes <- crime %>%
filter(STREET %in% c("WASHINGTON ST")) %>%
group_by(Class) %>%
summarise(count = n())%>%
rename(labels = Class, values = count)%>%
top_n(10)
washington_street_crimes$labels = str_to_title(washington_street_crimes$labels)
washington_street_crimes$labels <- str_trunc(washington_street_crimes$labels, width = 30)
# Create the pie chart using the pie_data data frame
plot_ly(washington_street_crimes, labels = ~labels, values = ~values, type = 'pie', hole = 0.3) %>%
layout(xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), title="Washington Street Crime Distribution")Washington Street Month wise Crime Trend, for Year 2020
crime_year_prt_WST_all_2020 <- crime %>%
filter(YEAR %in% c(2020), STREET=='WASHINGTON ST')%>%
group_by(MONTH, Class) %>%
summarise(frequency = n()) %>%
top_n(10) %>%
pivot_wider(names_from = Class, values_from = frequency)
crime_year_prt_WST_all_2020 <- crime_year_prt_WST_all_2020 %>%
select(assault = `Assault/Battery`,`Investigate Person/Property/Other`, Larceny, `Motor Vehicle`, Property)
plot_ly(crime_year_prt_WST_all_2020, x = ~MONTH, y = ~assault, name = "Assault / Battery",
type = 'scatter', mode = 'line') %>%
add_trace(y = ~`Investigate Person/Property/Other`, name = 'Investigate Person/Property/Other', mode = 'line') %>%
add_trace(y = ~Larceny, name = 'Larceny', mode = 'line') %>%
add_trace(y = ~`Motor Vehicle`, name = 'Motor Vehicle', mode = 'line') %>%
add_trace(y = ~Property, name = 'Property', mode = 'line') %>%
layout(xaxis = list(title = 'Year-2020 Crimes Washington Street'), yaxis = list(title = 'No. of Crime Incidents'), barmode = 'group')It can be clearly interpreted that crime incidents declined in the months of March, April, May, and June, and then picked up again in July. This pattern of crime incident trends may be related to the COVID-19 pandemic and the lockdown measures that were implemented in Boston.
The lockdown measures, which were implemented in order to slow the spread of the virus, likely had a significant impact on crime rates. Many businesses and schools were closed, and people were asked to stay at home as much as possible, which may have reduced the opportunities for crime to occur. Additionally, the economic downturn caused by the pandemic may have led to an increase in financial stress and desperation among some individuals, which could have contributed to an increase in crime.
Boylston street Crime Distribution
Boylston_street_crimes <- crime %>%
filter(STREET %in% c("BOYLSTON ST")) %>%
group_by(Class) %>%
summarise(count = n())%>%
rename(labels = Class, values = count)%>%
top_n(10)
Boylston_street_crimes$labels = str_to_title(Boylston_street_crimes$labels)
Boylston_street_crimes$labels <- str_trunc(Boylston_street_crimes$labels, width = 30)
# Create the pie chart using the pie_data data frame
plot_ly(Boylston_street_crimes, labels = ~labels, values = ~values, type = 'pie', hole = 0.3) %>%
layout(xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), title="Boylston Street Ave Crime Distribution")Geo-Graphic Maps: Street wise, Day-Time wise, Crime Distribution
Geographic maps that show crime incidents by location and time of day can be a valuable tool for the Boston Police Department (BPD) and the citizens of Boston. These maps can help the BPD to identify patterns in crime and to focus their efforts on areas where crime is more likely to occur.
For citizens, these maps can be helpful in making informed decisions about their safety when traveling to certain areas. By showing the location and frequency of crime incidents, citizens can choose routes that may be safer and take additional precautions to protect themselves.
In addition, it’s important to remember that crime can occur anywhere and that no area is completely immune to it. It’s always a good idea for individuals to be aware of their surroundings and to take appropriate precautions to protect themselves and their property.
breaks <- hour(hm("00:00", "2:59", "5:59", "8:59", "11:59","14:59", "17:59","20:50", "23:59"))
crime$OCCURRED_ON_DATE <- ymd_hms(crime$OCCURRED_ON_DATE)
labels <- c("After Midnight","Toward Morning" ,"Early Morning","Late Morning", "Early Afternoon","Late Afternoon", "Evening", "Late Evening")
crime$Time_of_day <- cut(x=hour(crime$OCCURRED_ON_DATE), breaks = breaks, labels = labels, include.lowest=TRUE)
crime$OFFENSE_DESCRIPTION <- str_replace_all(crime$OFFENSE_DESCRIPTION,"^[a-zA-Z]\\s", "")
crime$label_month <- month(crime$OCCURRED_ON_DATE, label = TRUE)dtmap1 <- crime %>%
select(STREET,Time_of_day, Lat, Long, OFFENSE_DESCRIPTION) %>%
filter(!is.na(STREET)) %>%
filter(!is.na(Lat)) %>%
filter(!(Lat %in% c(-1,1))) %>%
filter(!is.na(Long)) %>%
filter(!(Long %in% c(-1,1))) %>%
group_by(STREET,OFFENSE_DESCRIPTION,Time_of_day, Lat, Long ) %>%
summarise(total =n()) %>%
filter(total > 1)
dtmap1$content <- paste("<b>Street:</b>",dtmap1$STREET,"<br/>",
"<b>Type of Offense:</b>", dtmap1$OFFENSE_DESCRIPTION,"<br/>",
"<b>Time of offense:</b>",dtmap1$Time_of_day,"<br/>",
"<b>Frequency:</b>",dtmap1$total )
colors <- c("green", "orange", "red")
pal <- colorFactor(colors, dtmap1$total)
leaflet() %>%
setView(lng = -71.0589, lat = 42.3601, zoom = 14) %>%
addTiles() %>%
addCircleMarkers(data = dtmap1,
lat = ~Lat,
lng = ~Long,
popup = ~as.character(content),
label = ~as.character(STREET),
color = ~pal(total),
clusterOptions = markerClusterOptions())%>%
addHeatmap(data = dtmap1,lng=~Long, lat=~Lat, blur=15, max =10, radius = 7)