SDPD Annual Service Call Count by Beat

library(tidyverse)
library(lubridate)
library(leaflet)
library(geojsonio)
library(sf)
library(viridis)
library(ggExtra)

San Diego Police Department Total Calls for Service by Beat

Leaflet map displays each SDPD beat and the total number of calls for service from the public for each year from 2016 to 2024. Data are directly loaded from the San Diego Open Data portal into the RMD doc where it is cleaned and the map generated.

R Code

# Load datasets dynamically and combine
years <- 2016:2024
pd_data_list <- lapply(years, function(year) {
  url <- paste0("https://seshat.datasd.org/police_calls_for_service/pd_calls_for_service_", year, "_datasd.csv")
  read_csv(url, col_types = cols(
    incident_num = col_character(), 
    date_time = col_datetime(format = "%Y-%m-%d %H:%M:%S"), 
    day_of_week = col_integer(), 
    address_number_primary = col_integer(), 
    address_dir_primary = col_character(), 
    address_road_primary = col_character(), 
    address_sfx_primary = col_character(), 
    address_dir_intersecting = col_character(), 
    address_road_intersecting = col_character(), 
    address_sfx_intersecting = col_character(), 
    call_type = col_character(), 
    disposition = col_character(), 
    beat = col_integer(), 
    priority = col_integer()
  )) %>%
  mutate(year = year)
})

# Combine all years into a single dataframe
pd_combined <- bind_rows(pd_data_list)

# Aggregate call counts by beat and year
crime_beat_sums <- pd_combined %>% 
  group_by(beat, year) %>% 
  summarize(call_count = n(), .groups = 'drop') %>%
  pivot_wider(names_from = year, values_from = call_count, names_prefix = "ct_")

# Load geojson data
geojson <- geojson_sf("https://seshat.datasd.org/gis_police_beats/pd_beats_datasd.geojson")

# Merge with spatial data
m <- geojson %>%
  left_join(crime_beat_sums, by = 'beat')

# Create labels
mytext <- apply(m, 1, function(row) {
  paste(
    "Beat: ", row["name"], "<br/>",
    paste0("Total Calls ", years, ": ", row[paste0("ct_", years)], collapse = "<br/>")
  )
}) %>%
  lapply(htmltools::HTML)

# Create a color palette based on the 2024 call count data
pal <- colorNumeric(palette = viridis_pal()(100), domain = m$ct_2024)

sd_crime_map <- leaflet(m) %>%
  addTiles() %>%
  addPolygons(stroke = TRUE, 
              color = "black", 
              weight = 1, 
              fillOpacity = 0.5, 
              smoothFactor = 0.3, 
              label = mytext,
              popup = mytext, # Adding popup
              highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = TRUE),
              fillColor = ~pal(ct_2024))

sd_crime_map