Spatial analysis of crime incidents in New York City: comparison between 2012 and 2025
Your name
1. Introduction
This project analyses the spatial distribution of crime incidents in New York City using spatial point pattern analysis. Each crime incident is represented as a point in space using its geographical coordinates.
The aim is to compare crime patterns between 2012 and 2025 and investigate whether crime incidents are randomly distributed or clustered in specific areas.
2. Literature review
Spatial point pattern analysis is widely used in geography, criminology and urban studies to analyse the spatial distribution of events. In crime studies, these methods help identify whether criminal incidents are randomly distributed or concentrated in specific areas of a city.
Previous research has shown that crime often forms spatial clusters, commonly known as hotspots. These concentrations are usually related to urban and socio-economic factors such as population density, commercial activity and transportation networks.
Methods such as kernel density estimation and Ripley’s K function are commonly applied to study spatial clustering and compare observed patterns with Complete Spatial Randomness (CSR).
3. Packages
requiredPackages = c("sf", "spatstat", "ggplot2", "dplyr")
for(i in requiredPackages){
if(!require(i, character.only = TRUE)) install.packages(i)
}
for(i in requiredPackages){
library(i, character.only = TRUE)
}
Sys.setenv(LANG="en")4. Filtering and preparing data for New York City
The crime data come from the NYPD Complaint Data Historic dataset. For this analysis, only observations with latitude and longitude are used. The data are filtered into two separate years: 2012 and 2025.
# NYC borough map
nyc_map <- st_read(
"https://raw.githubusercontent.com/dwillis/nyc-maps/master/boroughs.geojson",
quiet = TRUE
)
nyc_map <- st_make_valid(nyc_map)
# Function to download crime data by year
download_crime_year <- function(year, limit = 700) {
start_date <- paste0(year, "-01-01T00:00:00")
end_date <- paste0(year, "-12-31T23:59:59")
where_query <- paste0(
"latitude IS NOT NULL AND longitude IS NOT NULL ",
"AND cmplnt_fr_dt between '", start_date, "' and '", end_date, "'"
)
url <- paste0(
"https://data.cityofnewyork.us/resource/qgea-i56i.csv?",
"$limit=", limit,
"&$select=cmplnt_fr_dt,ofns_desc,boro_nm,latitude,longitude",
"&$where=", URLencode(where_query, reserved = TRUE)
)
read.csv(url)
}
crime_2012 <- download_crime_year(2012)
crime_2025 <- download_crime_year(2025)
nrow(crime_2012)## [1] 700nrow(crime_2025)## [1] 700The dataset is downloaded directly from NYC Open Data. The variables used are the date of the complaint, type of offence, borough, latitude and longitude. The coordinates allow each crime incident to be treated as a spatial point.
5. Creating spatial objects
Following the structure used in class, the data frame is converted
into an sf object using st_as_sf(). Then, both
the points and the New York City boundary are transformed into a
projected coordinate system.
crime_2012_sf <- st_as_sf(
crime_2012,
coords = c("longitude", "latitude"),
crs = 4326
)
crime_2025_sf <- st_as_sf(
crime_2025,
coords = c("longitude", "latitude"),
crs = 4326
)
# Transform to planar projection
nyc_map_proj <- st_transform(nyc_map, crs = 3857)
crime_2012_proj <- st_transform(crime_2012_sf, crs = 3857)
crime_2025_proj <- st_transform(crime_2025_sf, crs = 3857)
# Keep only points inside New York City
crime_2012_proj <- st_intersection(crime_2012_proj, nyc_map_proj)
crime_2025_proj <- st_intersection(crime_2025_proj, nyc_map_proj)This step follows the same logic used in class: the original coordinate system is geographic, so the data are transformed into a projected coordinate system before distance-based analysis.
6. Study area
plot(st_geometry(nyc_map_proj),
main = "Study area: New York City",
col = "gray90",
border = "black")
The study area is New York City, formed by five boroughs: Manhattan, Brooklyn, Queens, The Bronx and Staten Island.
7. Visualisation of crime incidents
plot(st_geometry(nyc_map_proj),
main = "Crime incidents in New York City, 2012",
col = "white",
border = "black")
plot(st_geometry(crime_2012_proj),
add = TRUE,
col = "blue",
pch = 16,
cex = 0.4)
plot(st_geometry(nyc_map_proj),
main = "Crime incidents in New York City, 2025",
col = "white",
border = "black")
plot(st_geometry(crime_2025_proj),
add = TRUE,
col = "red",
pch = 16,
cex = 0.4)
Each point represents one reported crime incident. The maps provide a first visual comparison between the spatial distribution of crimes in 2012 and 2025.
8. Crime distribution by borough
boro_2012 <- crime_2012 %>%
count(boro_nm)
boro_2025 <- crime_2025 %>%
count(boro_nm)
ggplot(boro_2012,
aes(x = reorder(boro_nm, n), y = n, fill = boro_nm)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Crime incidents by borough, 2012",
x = "Borough",
y = "Number of crimes") +
theme_minimal() +
theme(legend.position = "none")
ggplot(boro_2025,
aes(x = reorder(boro_nm, n), y = n, fill = boro_nm)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Crime incidents by borough, 2025",
x = "Borough",
y = "Number of crimes") +
theme_minimal() +
theme(legend.position = "none")
This section adds a simple quantitative and socio-economic interpretation. Differences between boroughs may reflect population density, commercial activity, tourism and other urban characteristics.
9. Creating point pattern objects
The sf points are converted into ppp
objects, following the approach used in class. The observation window is
created from the New York City boundary using
as.owin().
# Create observation window
W <- as.owin(nyc_map_proj)
# Extract coordinates
xy_2012 <- st_coordinates(crime_2012_proj)
xy_2025 <- st_coordinates(crime_2025_proj)
# Create ppp objects
ppp_2012 <- ppp(
x = xy_2012[,1],
y = xy_2012[,2],
window = W
)
ppp_2025 <- ppp(
x = xy_2025[,1],
y = xy_2025[,2],
window = W
)
# Remove duplicated points if necessary
ppp_2012 <- unique(ppp_2012)
ppp_2025 <- unique(ppp_2025)
summary(ppp_2012)## Planar point pattern: 350 points
## Average intensity 2.558407e-07 points per square unit
##
## Coordinates are given to 9 decimal places
##
## Window: polygonal boundary
## 64 separate polygons (1 hole)
## vertices area relative.area
## polygon 1 21 1.71662e+04 1.25e-05
## polygon 2 5101 9.58781e+07 7.01e-02
## polygon 3 4 8.05116e+02 5.89e-07
## polygon 4 5 1.47236e+03 1.08e-06
## polygon 5 15 4.95573e+03 3.62e-06
## polygon 6 5 7.95246e+02 5.81e-07
## polygon 7 7 1.74311e+03 1.27e-06
## polygon 8 5 3.34093e+04 2.44e-05
## polygon 9 4 8.46849e+03 6.19e-06
## polygon 10 4 2.67616e+03 1.96e-06
## polygon 11 136 8.12964e+03 5.94e-06
## polygon 12 106 3.12758e+03 2.29e-06
## polygon 13 128 3.62314e+03 2.65e-06
## polygon 14 4 4.37037e+03 3.19e-06
## polygon 15 190 4.91014e+04 3.59e-05
## polygon 16 96 7.81569e+05 5.71e-04
## polygon 17 1400 1.94910e+06 1.42e-03
## polygon 18 5 1.46725e+04 1.07e-05
## polygon 19 5 1.26636e+04 9.26e-06
## polygon 20 7 8.81355e+04 6.44e-05
## polygon 21 4 1.30235e+04 9.52e-06
## polygon 22 11 1.11055e+05 8.12e-05
## polygon 23 243 3.80942e+06 2.78e-03
## polygon 24 11 3.86041e+04 2.82e-05
## polygon 25 508 2.94551e+06 2.15e-03
## polygon 26 29 1.88748e+04 1.38e-05
## polygon 27 144 1.02374e+06 7.48e-04
## polygon 28 17 1.33687e+03 9.77e-07
## polygon 29 29 1.94843e+05 1.42e-04
## polygon 30 279 1.25032e+06 9.14e-04
## polygon 31 47 1.03542e+05 7.57e-05
## polygon 32 15 1.61910e+03 1.18e-06
## polygon 33 30898 7.50536e+08 5.49e-01
## polygon 34 8909 2.61582e+08 1.91e-01
## polygon 35 57 1.24307e+05 9.09e-05
## polygon 36 1438 7.93930e+06 5.80e-03
## polygon 37 7 1.28578e+06 9.40e-04
## polygon 38 5 5.64214e+05 4.12e-04
## polygon 39 30 6.70779e+05 4.90e-04
## polygon 40 30 1.97498e+06 1.44e-03
## polygon 41 7 1.48721e+06 1.09e-03
## polygon 42 31 1.43027e+06 1.05e-03
## polygon 43 (hole) 9 -1.94339e+03 -1.42e-06
## polygon 44 6 4.09039e+05 2.99e-04
## polygon 45 15 1.56820e+06 1.15e-03
## polygon 46 15 9.70082e+05 7.09e-04
## polygon 47 9432 3.66054e+07 2.68e-02
## polygon 48 9 1.30499e+05 9.54e-05
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## polygon 52 4 2.17558e+04 1.59e-05
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## polygon 56 38 9.10120e+05 6.65e-04
## polygon 57 4 3.08903e+04 2.26e-05
## polygon 58 1659 5.69806e+05 4.17e-04
## polygon 59 3 9.20719e+03 6.73e-06
## polygon 60 405 6.50783e+05 4.76e-04
## polygon 61 46 1.11728e+06 8.17e-04
## polygon 62 8 8.18035e+04 5.98e-05
## polygon 63 46 2.06765e+04 1.51e-05
## polygon 64 6155 1.87344e+08 1.37e-01
## enclosing rectangle: [-8266095, -8204247] x [4938301, 4999891] units
## (61850 x 61590 units)
## Window area = 1368040000 square units
## Fraction of frame area: 0.359summary(ppp_2025)## Planar point pattern: 623 points
## Average intensity 4.553964e-07 points per square unit
##
## Coordinates are given to 9 decimal places
##
## Window: polygonal boundary
## 64 separate polygons (1 hole)
## vertices area relative.area
## polygon 1 21 1.71662e+04 1.25e-05
## polygon 2 5101 9.58781e+07 7.01e-02
## polygon 3 4 8.05116e+02 5.89e-07
## polygon 4 5 1.47236e+03 1.08e-06
## polygon 5 15 4.95573e+03 3.62e-06
## polygon 6 5 7.95246e+02 5.81e-07
## polygon 7 7 1.74311e+03 1.27e-06
## polygon 8 5 3.34093e+04 2.44e-05
## polygon 9 4 8.46849e+03 6.19e-06
## polygon 10 4 2.67616e+03 1.96e-06
## polygon 11 136 8.12964e+03 5.94e-06
## polygon 12 106 3.12758e+03 2.29e-06
## polygon 13 128 3.62314e+03 2.65e-06
## polygon 14 4 4.37037e+03 3.19e-06
## polygon 15 190 4.91014e+04 3.59e-05
## polygon 16 96 7.81569e+05 5.71e-04
## polygon 17 1400 1.94910e+06 1.42e-03
## polygon 18 5 1.46725e+04 1.07e-05
## polygon 19 5 1.26636e+04 9.26e-06
## polygon 20 7 8.81355e+04 6.44e-05
## polygon 21 4 1.30235e+04 9.52e-06
## polygon 22 11 1.11055e+05 8.12e-05
## polygon 23 243 3.80942e+06 2.78e-03
## polygon 24 11 3.86041e+04 2.82e-05
## polygon 25 508 2.94551e+06 2.15e-03
## polygon 26 29 1.88748e+04 1.38e-05
## polygon 27 144 1.02374e+06 7.48e-04
## polygon 28 17 1.33687e+03 9.77e-07
## polygon 29 29 1.94843e+05 1.42e-04
## polygon 30 279 1.25032e+06 9.14e-04
## polygon 31 47 1.03542e+05 7.57e-05
## polygon 32 15 1.61910e+03 1.18e-06
## polygon 33 30898 7.50536e+08 5.49e-01
## polygon 34 8909 2.61582e+08 1.91e-01
## polygon 35 57 1.24307e+05 9.09e-05
## polygon 36 1438 7.93930e+06 5.80e-03
## polygon 37 7 1.28578e+06 9.40e-04
## polygon 38 5 5.64214e+05 4.12e-04
## polygon 39 30 6.70779e+05 4.90e-04
## polygon 40 30 1.97498e+06 1.44e-03
## polygon 41 7 1.48721e+06 1.09e-03
## polygon 42 31 1.43027e+06 1.05e-03
## polygon 43 (hole) 9 -1.94339e+03 -1.42e-06
## polygon 44 6 4.09039e+05 2.99e-04
## polygon 45 15 1.56820e+06 1.15e-03
## polygon 46 15 9.70082e+05 7.09e-04
## polygon 47 9432 3.66054e+07 2.68e-02
## polygon 48 9 1.30499e+05 9.54e-05
## polygon 49 12 3.03573e+05 2.22e-04
## polygon 50 17 8.87032e+05 6.48e-04
## polygon 51 5 3.32429e+04 2.43e-05
## polygon 52 4 2.17558e+04 1.59e-05
## polygon 53 12 6.41352e+04 4.69e-05
## polygon 54 105 3.29170e+05 2.41e-04
## polygon 55 5 1.37126e+04 1.00e-05
## polygon 56 38 9.10120e+05 6.65e-04
## polygon 57 4 3.08903e+04 2.26e-05
## polygon 58 1659 5.69806e+05 4.17e-04
## polygon 59 3 9.20719e+03 6.73e-06
## polygon 60 405 6.50783e+05 4.76e-04
## polygon 61 46 1.11728e+06 8.17e-04
## polygon 62 8 8.18035e+04 5.98e-05
## polygon 63 46 2.06765e+04 1.51e-05
## polygon 64 6155 1.87344e+08 1.37e-01
## enclosing rectangle: [-8266095, -8204247] x [4938301, 4999891] units
## (61850 x 61590 units)
## Window area = 1368040000 square units
## Fraction of frame area: 0.359The ppp object is the main structure used in
spatstat for point pattern analysis. It contains the
coordinates of the events and the observation window.
10. Rescaling
As in the class scripts, the point patterns are rescaled to make distances easier to interpret.
area.owin(W)## [1] 1368038987# Rescale from metres to kilometres
ppp_2012_km <- rescale(ppp_2012, 1000, "km")
ppp_2025_km <- rescale(ppp_2025, 1000, "km")
summary(ppp_2012_km)## Planar point pattern: 350 points
## Average intensity 0.2558407 points per square km
##
## Coordinates are given to 12 decimal places
##
## Window: polygonal boundary
## 64 separate polygons (1 hole)
## vertices area relative.area
## polygon 1 21 1.71662e-02 1.25e-05
## polygon 2 5101 9.58781e+01 7.01e-02
## polygon 3 4 8.05116e-04 5.89e-07
## polygon 4 5 1.47236e-03 1.08e-06
## polygon 5 15 4.95573e-03 3.62e-06
## polygon 6 5 7.95246e-04 5.81e-07
## polygon 7 7 1.74311e-03 1.27e-06
## polygon 8 5 3.34093e-02 2.44e-05
## polygon 9 4 8.46849e-03 6.19e-06
## polygon 10 4 2.67616e-03 1.96e-06
## polygon 11 136 8.12964e-03 5.94e-06
## polygon 12 106 3.12758e-03 2.29e-06
## polygon 13 128 3.62314e-03 2.65e-06
## polygon 14 4 4.37037e-03 3.19e-06
## polygon 15 190 4.91014e-02 3.59e-05
## polygon 16 96 7.81569e-01 5.71e-04
## polygon 17 1400 1.94910e+00 1.42e-03
## polygon 18 5 1.46725e-02 1.07e-05
## polygon 19 5 1.26636e-02 9.26e-06
## polygon 20 7 8.81355e-02 6.44e-05
## polygon 21 4 1.30235e-02 9.52e-06
## polygon 22 11 1.11055e-01 8.12e-05
## polygon 23 243 3.80942e+00 2.78e-03
## polygon 24 11 3.86041e-02 2.82e-05
## polygon 25 508 2.94551e+00 2.15e-03
## polygon 26 29 1.88748e-02 1.38e-05
## polygon 27 144 1.02374e+00 7.48e-04
## polygon 28 17 1.33687e-03 9.77e-07
## polygon 29 29 1.94843e-01 1.42e-04
## polygon 30 279 1.25032e+00 9.14e-04
## polygon 31 47 1.03542e-01 7.57e-05
## polygon 32 15 1.61910e-03 1.18e-06
## polygon 33 30898 7.50536e+02 5.49e-01
## polygon 34 8909 2.61582e+02 1.91e-01
## polygon 35 57 1.24307e-01 9.09e-05
## polygon 36 1438 7.93930e+00 5.80e-03
## polygon 37 7 1.28578e+00 9.40e-04
## polygon 38 5 5.64214e-01 4.12e-04
## polygon 39 30 6.70779e-01 4.90e-04
## polygon 40 30 1.97498e+00 1.44e-03
## polygon 41 7 1.48721e+00 1.09e-03
## polygon 42 31 1.43027e+00 1.05e-03
## polygon 43 (hole) 9 -1.94339e-03 -1.42e-06
## polygon 44 6 4.09039e-01 2.99e-04
## polygon 45 15 1.56820e+00 1.15e-03
## polygon 46 15 9.70082e-01 7.09e-04
## polygon 47 9432 3.66054e+01 2.68e-02
## polygon 48 9 1.30499e-01 9.54e-05
## polygon 49 12 3.03573e-01 2.22e-04
## polygon 50 17 8.87032e-01 6.48e-04
## polygon 51 5 3.32429e-02 2.43e-05
## polygon 52 4 2.17558e-02 1.59e-05
## polygon 53 12 6.41352e-02 4.69e-05
## polygon 54 105 3.29170e-01 2.41e-04
## polygon 55 5 1.37126e-02 1.00e-05
## polygon 56 38 9.10120e-01 6.65e-04
## polygon 57 4 3.08903e-02 2.26e-05
## polygon 58 1659 5.69806e-01 4.17e-04
## polygon 59 3 9.20719e-03 6.73e-06
## polygon 60 405 6.50783e-01 4.76e-04
## polygon 61 46 1.11728e+00 8.17e-04
## polygon 62 8 8.18035e-02 5.98e-05
## polygon 63 46 2.06765e-02 1.51e-05
## polygon 64 6155 1.87344e+02 1.37e-01
## enclosing rectangle: [-8266.095, -8204.247] x [4938.301, 4999.891] km
## (61.85 x 61.59 km)
## Window area = 1368.04 square km
## Unit of length: 1 km
## Fraction of frame area: 0.359summary(ppp_2025_km)## Planar point pattern: 623 points
## Average intensity 0.4553964 points per square km
##
## Coordinates are given to 12 decimal places
##
## Window: polygonal boundary
## 64 separate polygons (1 hole)
## vertices area relative.area
## polygon 1 21 1.71662e-02 1.25e-05
## polygon 2 5101 9.58781e+01 7.01e-02
## polygon 3 4 8.05116e-04 5.89e-07
## polygon 4 5 1.47236e-03 1.08e-06
## polygon 5 15 4.95573e-03 3.62e-06
## polygon 6 5 7.95246e-04 5.81e-07
## polygon 7 7 1.74311e-03 1.27e-06
## polygon 8 5 3.34093e-02 2.44e-05
## polygon 9 4 8.46849e-03 6.19e-06
## polygon 10 4 2.67616e-03 1.96e-06
## polygon 11 136 8.12964e-03 5.94e-06
## polygon 12 106 3.12758e-03 2.29e-06
## polygon 13 128 3.62314e-03 2.65e-06
## polygon 14 4 4.37037e-03 3.19e-06
## polygon 15 190 4.91014e-02 3.59e-05
## polygon 16 96 7.81569e-01 5.71e-04
## polygon 17 1400 1.94910e+00 1.42e-03
## polygon 18 5 1.46725e-02 1.07e-05
## polygon 19 5 1.26636e-02 9.26e-06
## polygon 20 7 8.81355e-02 6.44e-05
## polygon 21 4 1.30235e-02 9.52e-06
## polygon 22 11 1.11055e-01 8.12e-05
## polygon 23 243 3.80942e+00 2.78e-03
## polygon 24 11 3.86041e-02 2.82e-05
## polygon 25 508 2.94551e+00 2.15e-03
## polygon 26 29 1.88748e-02 1.38e-05
## polygon 27 144 1.02374e+00 7.48e-04
## polygon 28 17 1.33687e-03 9.77e-07
## polygon 29 29 1.94843e-01 1.42e-04
## polygon 30 279 1.25032e+00 9.14e-04
## polygon 31 47 1.03542e-01 7.57e-05
## polygon 32 15 1.61910e-03 1.18e-06
## polygon 33 30898 7.50536e+02 5.49e-01
## polygon 34 8909 2.61582e+02 1.91e-01
## polygon 35 57 1.24307e-01 9.09e-05
## polygon 36 1438 7.93930e+00 5.80e-03
## polygon 37 7 1.28578e+00 9.40e-04
## polygon 38 5 5.64214e-01 4.12e-04
## polygon 39 30 6.70779e-01 4.90e-04
## polygon 40 30 1.97498e+00 1.44e-03
## polygon 41 7 1.48721e+00 1.09e-03
## polygon 42 31 1.43027e+00 1.05e-03
## polygon 43 (hole) 9 -1.94339e-03 -1.42e-06
## polygon 44 6 4.09039e-01 2.99e-04
## polygon 45 15 1.56820e+00 1.15e-03
## polygon 46 15 9.70082e-01 7.09e-04
## polygon 47 9432 3.66054e+01 2.68e-02
## polygon 48 9 1.30499e-01 9.54e-05
## polygon 49 12 3.03573e-01 2.22e-04
## polygon 50 17 8.87032e-01 6.48e-04
## polygon 51 5 3.32429e-02 2.43e-05
## polygon 52 4 2.17558e-02 1.59e-05
## polygon 53 12 6.41352e-02 4.69e-05
## polygon 54 105 3.29170e-01 2.41e-04
## polygon 55 5 1.37126e-02 1.00e-05
## polygon 56 38 9.10120e-01 6.65e-04
## polygon 57 4 3.08903e-02 2.26e-05
## polygon 58 1659 5.69806e-01 4.17e-04
## polygon 59 3 9.20719e-03 6.73e-06
## polygon 60 405 6.50783e-01 4.76e-04
## polygon 61 46 1.11728e+00 8.17e-04
## polygon 62 8 8.18035e-02 5.98e-05
## polygon 63 46 2.06765e-02 1.51e-05
## polygon 64 6155 1.87344e+02 1.37e-01
## enclosing rectangle: [-8266.095, -8204.247] x [4938.301, 4999.891] km
## (61.85 x 61.59 km)
## Window area = 1368.04 square km
## Unit of length: 1 km
## Fraction of frame area: 0.359Rescaling allows the analysis to be interpreted in kilometres instead of metres.
11. Kernel density estimation
density_2012 <- density(ppp_2012_km, sigma = 1)
plot(density_2012,
main = "Kernel density of crime incidents, 2012")
points(ppp_2012_km,
pch = 16,
cex = 0.2)
density_2025 <- density(ppp_2025_km, sigma = 1)
plot(density_2025,
main = "Kernel density of crime incidents, 2025")
points(ppp_2025_km,
pch = 16,
cex = 0.2)
Kernel density estimation identifies areas with higher concentrations of crime incidents. These areas can be interpreted as crime hotspots.
12. Nearest neighbour distances
nn_2012 <- nndist(ppp_2012_km)
nn_2025 <- nndist(ppp_2025_km)
summary(nn_2012)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.02169 0.42615 0.66350 0.86054 1.12995 5.04776summary(nn_2025)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.004503 0.244618 0.400617 0.581354 0.651926 6.559123hist(nn_2012,
main = "Nearest neighbour distances, 2012",
xlab = "Distance in km",
col = "lightblue")
hist(nn_2025,
main = "Nearest neighbour distances, 2025",
xlab = "Distance in km",
col = "lightcoral")
Nearest neighbour distances measure how close each crime incident is to its closest neighbouring event. Shorter distances suggest stronger clustering.
13. Ripley’s K function
K_2012 <- Kest(ppp_2012_km)
plot(K_2012,
main = "Ripley's K function, 2012")
K_2025 <- Kest(ppp_2025_km)
plot(K_2025,
main = "Ripley's K function, 2025")
Ripley’s K function analyses clustering at different distance scales. If the observed curve is above the theoretical CSR curve, this suggests clustering.
16. Conclusions
This project analysed the spatial distribution of crime incidents in New York City using point pattern analysis. The comparison between 2012 and 2025 showed how crime incidents are distributed across the city and whether they tend to cluster in specific areas.
The visual maps and kernel density estimations suggest that crime is not evenly distributed across New York City. Instead, crime incidents tend to concentrate in certain areas, forming spatial hotspots.
Nearest neighbour distances and Ripley’s K function provide additional quantitative evidence about the spatial structure of the pattern. The simulation envelopes allow comparison with Complete Spatial Randomness.
From a socio-economic perspective, the spatial concentration of crime may reflect urban factors such as population density, commercial activity, tourism, transportation hubs and social inequality. Therefore, spatial crime analysis can provide useful information for urban planning, public safety policies and resource allocation.