SDPD Beat Counts

library(tidyverse)
library(readr)
library(lubridate)
library(data.table)
library(dplyr)
library(leaflet)
library(geojsonio)
library(jsonlite)
library(raster)
library(sf)
library(geojsonsf)
library(ggplot2)

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 of 2018, 2019, and 2020. With more time, I would like to obtain the lat/lon positions of each call for service to give a more detailed map in the future. Data is directly loaded from the San Diego Open Data portal into the RMD doc where it is cleaned and the map generated.

R Code

# import multple datasets and join together before eda
pd_2020 <- read_csv("https://seshat.datasd.org/pd/pd_calls_for_service_2020_datasd.csv",
#pd_2020 <- read_csv("pd_calls_for_service_2020_datasd.csv", 
            #col_types = cols(date_time = col_datetime(format = "%Y-%m-%d %H:%M:%S")))
              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()))

pd_2019 <- read_csv("https://seshat.datasd.org/pd/pd_calls_for_service_2019_datasd.csv", 
              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()))

pd_2018 <- read_csv("https://seshat.datasd.org/pd/pd_calls_for_service_2018_datasd.csv", 
              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()))

# load in geojson data as spacial data frame
geojson <- geojson_sf("https://seshat.datasd.org/sde/pd/pd_beats_datasd.geojson")

# working with the geojson beats data, let's aggregate crimes in each beat
# for the map display

crime_beat_sums_2020 <- pd_2020 %>% 
  group_by(beat) %>% 
  count(name = 'ct_2020') %>%
  ungroup(beat) %>%
  arrange(beat, desc(ct_2020), by_group = TRUE)

crime_beat_sums_2019 <- pd_2019 %>% 
  group_by(beat) %>% 
  count(name = 'ct_2019') %>%
  ungroup(beat) %>%
  arrange(beat, desc(ct_2019), by_group = TRUE)

crime_beat_sums_2018 <- pd_2018 %>% 
  group_by(beat) %>% 
  count(name = 'ct_2018') %>%
  ungroup(beat) %>%
  arrange(beat, desc(ct_2018), by_group = TRUE)

# now merge the aggregated call data with the spacial data frame
m <- sp::merge(geojson,crime_beat_sums_2020, by = 'beat')
m <- sp::merge(m,crime_beat_sums_2019, by = 'beat')
m <- sp::merge(m,crime_beat_sums_2018, by = 'beat')
               
# create variable with lable information pulled from geojson data
mytext <- paste(
            "Beat: ", m$name, "<br/>",
            "Total Calls 2020: ", m$ct_2020, 
            "<br/> Total Calls 2019: ", m$ct_2019, 
            "<br/> Total Calls 2018: ", m$ct_2018 , 
            sep = " "
        ) %>%
        lapply(htmltools::HTML)

# finally we create the map using leaflet
sd_crime_map <- leaflet(m) %>%
  addTiles() %>%
  addPolygons(stroke = TRUE, 
              smoothFactor = 0.3, 
              label = mytext,
              highlightOptions = highlightOptions(color = "white", weight = 2,
                                                  bringToFront = TRUE))

sd_crime_map