bohdanszymanik · August 1, 2025 04:08
diff --git a/wgtn_cctv_vtp.R b/wgtn_cctv_vtp.R
 library(tidyverse)
 library(sf)
 library(tictoc)
 library(future)
 library(units)

 #library(remotes)
 #install_github("yonghah/esri2sf")
 library("esri2sf")



 library(dplyr)
 library(purrr)

 data <- tibble(
  x = 1:3,
  y = 4:6,
  z = 7:9
 )

 # Define a function that takes multiple arguments
 my_function <- function(arg1, arg2) {
  arg1 * arg2
 }

 # Apply the function row-wise using rowwise() and pmap()
 result <- data %>%
  rowwise() %>%
  mutate(a = pmap_dbl(list(x, y), my_function)) %>%
  ungroup() # Ungroup to remove the rowwise attribute

 print(result)











 #
 # 1. First we get the CCTV camera locations, and more importantly, the viewsheds
 #

 sf_use_s2(TRUE)


 wgtn_cctv_url <- "https://services1.arcgis.com/CPYspmTk3abe6d7i/arcgis/rest/services/CCTV_Viewsheds_(View_layer)/FeatureServer/0"

 wgtn_cctv <- esri2sf(wgtn_cctv_url)

 glimpse(wgtn_cctv)

 # take a peek at the geometry column - normal glimpse won't show me enough
 sf::st_as_text(wgtn_cctv$geoms)
 wgtn_cctv |> slice_head(n = 2) |> mutate(wkt = st_as_text(geoms))

 ggplot(wgtn_cctv) +
  geom_sf() +
  coord_sf(xlim = c(174.77, 174.78), ylim = c(-41.275, -41.30)) +
  geom_sf_label(aes(label = Camera_Name), size = 2)

 #
 # 2. Next we load in the meshblocks 2013 for wellington from https://datafinder.stats.govt.nz/layer/8347-meshblock-2013/
 # I suggest cropping down to just the Wellington region first otherwise you're just wasting memory and compute
 #
 mb2013_wgtn <- st_read("C:\\Users\\...\\wd\\Areas\\statsnz-meshblock-2013-SHP-wgtn\\meshblock-2013.shp")

 ggplot(data = mb2013_wgtn) +
  geom_sf() +
  theme_minimal()

 # I could clip meshblocks to the coastline... but actually I'm focussed on inner city areas where that's not a significant issue for me
 # however, for the sake of completeness... let's do it. We use https://data.linz.govt.nz/layer/51153-nz-coastlines-and-islands-polygons-topo-150k/
 # (it needs to be the polygons dataset not one with polylines!)
 # I strongly suggest clipping that down inside the Koordinates viewer to eg the Wellington Region TA
 nz_coastline <- st_read("C:\\Users\\...\\Downloads\\lds-nz-coastlines-and-islands-polygons-topo-150k-SHP\\nz-coastlines-and-islands-polygons-topo-150k.shp")

 # clip to wellington region
 wgtn_bbox <- st_as_sfc(mb2013_wgtn |> st_bbox())
 # ggplot(data = wgtn_bbox) +
 #   geom_sf() +
 #   theme_minimal()

 tic()
 nz_coastline_wgtn <- st_intersection(wgtn_bbox, nz_coastline)
 toc()

 ggplot(data = nz_coastline_wgtn) +
  geom_sf() +
  theme_minimal()



 # the meshblocks come as multipolygons - for handling lakes etc?
 # but this slows down geometric operations so if we can get away with
 # polygons then this will speed things up - actually based on numbers here
 # probably not necessary
 # 1.08s
 tic()
 mb2013_wgtn_p <- st_make_valid(st_cast(mb2013_wgtn, "POLYGON"))
 toc()

 # 5.15 sec
 tic()
 mb2013_wgtn_p_clip <- st_intersection(mb2013_wgtn_p, nz_coastline_wgtn)
 toc()

 ggplot(data = mb2013_wgtn_p_clip) +
  geom_sf() +
  theme_minimal()


 # 5.77 sec
 tic()
 mb2013_wgtn_clip <- st_intersection(mb2013_wgtn, nz_coastline_wgtn)
 toc()

 ggplot(data = mb2013_wgtn_clip) +
  geom_sf() +
  theme_minimal()


 # it appears we have some LandAreaSQ measures that don't tally with what is calculated by st_area() so let's add a calculated area
 mb2013_wgtn_clip$LandAreaSQ_calc <- mb2013_wgtn_clip |> st_area()


 # let's union together all those overlaying viewsheds to simplify the geometry
 wgtn_cctv_union <- wgtn_cctv |> st_union()

 ggplot(data = wgtn_cctv_union) +
  geom_sf() +
  theme_minimal()

 # and now remove the viewsheds from the meshblocks
 wgtn_mbs_less_viewsheds <- st_difference(mb2013_wgtn_clip, wgtn_cctv_union) # a tiny bit faster with mb2013_wgtn_small

 # won't be able to see the impact at the regional level so zoom into central wellington
 # where Lambton Quay meets Willis St
 ggplot(data = wgtn_mbs_less_viewsheds) +
  geom_sf() +
  coord_sf(xlim = c(174.772, 174.778), ylim = c(-41.281, -41.285)) +
  theme_minimal()


 #################################################################

 # Next task is to figure out by how much the area of a meshblock has decreased as a result of



 wgtn_mbs_less_viewsheds$AreaMinusViewshed <- st_area(wgtn_mbs_less_viewsheds)
 wgtn_mbs_less_viewsheds$ChangeInArea <- wgtn_mbs_less_viewsheds$LandAreaSQ_calc - wgtn_mbs_less_viewsheds$AreaMinusViewshed
 wgtn_mbs_less_viewsheds$ChangeInAreaRatio <- 1 - drop_units(wgtn_mbs_less_viewsheds$AreaMinusViewshed / wgtn_mbs_less_viewsheds$LandAreaSQ_calc)


 #################################################################

 # and now need to join to the crime data
 vtp <- read_csv("C:\\Users\\...\\wd\\VictimisationByMeshblock\\vtp_jul2025.csv",
                col_types = cols(
                  `Meshblock` = col_character()
                ))
 glimpse(vtp)

 vtp$year <- as.numeric( vtp$`Month Year` |> str_sub(-4) )

 vtp_year_mb <-
  vtp |>
  group_by(year, meshblock = Meshblock) |>
  summarise( sum_v = sum(Victimisations))

 glimpse(vtp_year_mb)

 wgtn_mbs_less_viewsheds_vtp <-
  wgtn_mbs_less_viewsheds |>
  select(MeshblockN, ChangeInArea, ChangeInAreaRatio, AreaMinusViewshed, LandAreaSQ_calc) |>
  left_join(vtp_year_mb, join_by(MeshblockN == meshblock))

 glimpse(wgtn_mbs_less_viewsheds_vtp)

 # the vtp data only goes back to june 2021 - that sux as it's impacted by covid and it's a midyear value
 wgtn_mbs_less_viewsheds_vtp_2022_vrs_2024 <-
  wgtn_mbs_less_viewsheds_vtp |> filter(year %in% c(2022, 2024)) |>
  pivot_wider(
    names_from = year,
    values_from = sum_v
  )

 wgtn_mbs_less_viewsheds_vtp_2022_vrs_2024$v_2022_2024 <- wgtn_mbs_less_viewsheds_vtp_2022_vrs_2024$`2024` - wgtn_mbs_less_viewsheds_vtp_2022_vrs_2024$`2022`

 # not sure there's a significant movement below the 0 line
 ggplot(data = wgtn_mbs_less_viewsheds_vtp_2022_vrs_2024) +
  geom_point(mapping = aes(x = ChangeInAreaRatio, y = v_2022_2024)) +
  labs(x = "Proportion of Meshblock in Viewshed", y = "Victimisations 2024 minus victimisations 2022")


 ## using the ANZSOC Division as an asthetic to colour the points?

 vtp_year_mb_anzsocd <-
  vtp |>
  group_by(year, meshblock = Meshblock, anzsoc_division = `ANZSOC Division`) |>
  summarise( sum_v = sum(Victimisations))

 glimpse(vtp_year_mb)

 wgtn_mbs_less_viewsheds_vtp_anzsocd <-
  wgtn_mbs_less_viewsheds |>
  select(MeshblockN, ChangeInArea, ChangeInAreaRatio, AreaMinusViewshed, LandAreaSQ_calc) |>
  left_join(vtp_year_mb_anzsocd, join_by(MeshblockN == meshblock))

 wgtn_mbs_less_viewsheds_vtp_anzsocd_2022_vrs_2024 <-
  wgtn_mbs_less_viewsheds_vtp_anzsocd |> filter(year %in% c(2022, 2024)) |>
  pivot_wider(
    names_from = year,
    values_from = sum_v
  )

 wgtn_mbs_less_viewsheds_vtp_anzsocd_2022_vrs_2024$v_2022_2024 <- wgtn_mbs_less_viewsheds_vtp_anzsocd_2022_vrs_2024$`2024` - wgtn_mbs_less_viewsheds_vtp_anzsocd_2022_vrs_2024$`2022`

 # the ANZSOC Division colours again don't show a significant distribution one way or the other
 ggplot(data = wgtn_mbs_less_viewsheds_vtp_anzsocd_2022_vrs_2024) +
  geom_point(mapping = aes(x = ChangeInAreaRatio, y = v_2022_2024, colour = anzsoc_division)) +
  labs(x = "Proportion of Meshblock in Viewshed", y = "Victimisations 2024 minus victimisations 2022")


 # another way to look at the data is the absolute change in victimisations
 # from 2022 to 2024
 # split by there being a noticeable camera deployment versus no deployment
 # using an area change of 0.5% as a measure of CCTV deployment 
 # (remember these viewsheds are only a small part of a meshblock's area)
 wgtn_mbs_less_viewsheds_vtp_2022_2024_abs <-
  wgtn_mbs_less_viewsheds_vtp |> 
  filter(year %in% c(2022, 2024)) |>
  group_by(CameraImpact = ChangeInAreaRatio > 0.01, year) |>
  summarise(sum_sum_v = sum(sum_v)) 

  wgtn_mbs_less_viewsheds_vtp_2022_2024_abs |>
  st_drop_geometry() |>
  pivot_wider(
    names_from = year,
    values_from = sum_sum_v
  ) |>
  mutate(change_ratio = (`2024` - `2022`)/`2022`)

  # CameraImpact      `2022` `2024` change_ratio
  #   <lgl>           <dbl>  <dbl>        <dbl>
  #   1 FALSE         20050  21815       0.0880
  #   2 TRUE           4606   5022       0.0903
  
 # seems very strange that there is no benefit at all from the CCTV deployment?
 # Let's look at the different ANZSOC Divisions and see if that shows anything

 wgtn_mbs_less_viewsheds_vtp_2022_2024_anzsocd_abs <-
  wgtn_mbs_less_viewsheds_vtp_anzsocd |>
  filter(year %in% c(2022, 2024)) |>
  group_by(CameraImpact = ChangeInAreaRatio > 0.05, year, anzsoc_division) |>
  summarise(sum_sum_v = sum(sum_v)) 

 wgtn_mbs_less_viewsheds_vtp_2022_2024_anzsocd_abs |>
  st_drop_geometry() |>
  pivot_wider(
    names_from = year,
    values_from = sum_sum_v
  ) |>
  mutate(change_ratio = (`2024` - `2022`)/`2022`) |>
  arrange(anzsoc_division)

 # # Groups:   CameraImpact [2]
 # CameraImpact anzsoc_division                                                   `2022` `2024` change_ratio
 # <lgl>              <chr>                                                              <dbl>  <dbl>        <dbl>
 #   1   FALSE        Abduction, Harassment and Other Related Offences Against a Person     22     18      -0.182 
 #   2   TRUE         Abduction, Harassment and Other Related Offences Against a Person      4      3      -0.25  
 #   3   FALSE        Acts Intended to Cause Injury                                       1784   2119       0.188 
 #   4   TRUE         Acts Intended to Cause Injury                                        680    731       0.075 
 #   5   FALSE        Robbery, Extortion and Related Offences                              194    224       0.155 
 #   6   TRUE         Robbery, Extortion and Related Offences                               31     55       0.774 
 #   7   FALSE        Sexual Assault and Related Offences                                  144    165       0.146 
 #   8   TRUE         Sexual Assault and Related Offences                                   47     57       0.213 
 #   9   FALSE        Theft and Related Offences                                         14945  16040       0.0733
 #   10  TRUE         Theft and Related Offences                                          2455   2785       0.134 
 #   11  FALSE        Unlawful Entry With Intent/Burglary, Break and Enter                3924   4292       0.0938
 #   12  TRUE         Unlawful Entry With Intent/Burglary, Break and Enter                 426    348      -0.183
	library(tidyverse)
	library(sf)
	library(tictoc)
	library(future)
	library(units)

	#library(remotes)
	#install_github("yonghah/esri2sf")
	library("esri2sf")



	library(dplyr)
	library(purrr)

	data <- tibble(
	x = 1:3,
	y = 4:6,
	z = 7:9
	)

	# Define a function that takes multiple arguments
	my_function <- function(arg1, arg2) {
	arg1 * arg2
	}

	# Apply the function row-wise using rowwise() and pmap()
	result <- data %>%
	rowwise() %>%
	mutate(a = pmap_dbl(list(x, y), my_function)) %>%
	ungroup() # Ungroup to remove the rowwise attribute

	print(result)











	#
	# 1. First we get the CCTV camera locations, and more importantly, the viewsheds
	#

	sf_use_s2(TRUE)


	wgtn_cctv_url <- "https://services1.arcgis.com/CPYspmTk3abe6d7i/arcgis/rest/services/CCTV_Viewsheds_(View_layer)/FeatureServer/0"

	wgtn_cctv <- esri2sf(wgtn_cctv_url)

	glimpse(wgtn_cctv)

	# take a peek at the geometry column - normal glimpse won't show me enough
	sf::st_as_text(wgtn_cctv$geoms)
	wgtn_cctv \|> slice_head(n = 2) \|> mutate(wkt = st_as_text(geoms))

	ggplot(wgtn_cctv) +
	geom_sf() +
	coord_sf(xlim = c(174.77, 174.78), ylim = c(-41.275, -41.30)) +
	geom_sf_label(aes(label = Camera_Name), size = 2)

	#
	# 2. Next we load in the meshblocks 2013 for wellington from https://datafinder.stats.govt.nz/layer/8347-meshblock-2013/
	# I suggest cropping down to just the Wellington region first otherwise you're just wasting memory and compute
	#
	mb2013_wgtn <- st_read("C:\\Users\\...\\wd\\Areas\\statsnz-meshblock-2013-SHP-wgtn\\meshblock-2013.shp")

	ggplot(data = mb2013_wgtn) +
	geom_sf() +
	theme_minimal()

	# I could clip meshblocks to the coastline... but actually I'm focussed on inner city areas where that's not a significant issue for me
	# however, for the sake of completeness... let's do it. We use https://data.linz.govt.nz/layer/51153-nz-coastlines-and-islands-polygons-topo-150k/
	# (it needs to be the polygons dataset not one with polylines!)
	# I strongly suggest clipping that down inside the Koordinates viewer to eg the Wellington Region TA
	nz_coastline <- st_read("C:\\Users\\...\\Downloads\\lds-nz-coastlines-and-islands-polygons-topo-150k-SHP\\nz-coastlines-and-islands-polygons-topo-150k.shp")

	# clip to wellington region
	wgtn_bbox <- st_as_sfc(mb2013_wgtn \|> st_bbox())
	# ggplot(data = wgtn_bbox) +
	# geom_sf() +
	# theme_minimal()

	tic()
	nz_coastline_wgtn <- st_intersection(wgtn_bbox, nz_coastline)
	toc()

	ggplot(data = nz_coastline_wgtn) +
	geom_sf() +
	theme_minimal()



	# the meshblocks come as multipolygons - for handling lakes etc?
	# but this slows down geometric operations so if we can get away with
	# polygons then this will speed things up - actually based on numbers here
	# probably not necessary
	# 1.08s
	tic()
	mb2013_wgtn_p <- st_make_valid(st_cast(mb2013_wgtn, "POLYGON"))
	toc()

	# 5.15 sec
	tic()
	mb2013_wgtn_p_clip <- st_intersection(mb2013_wgtn_p, nz_coastline_wgtn)
	toc()

	ggplot(data = mb2013_wgtn_p_clip) +
	geom_sf() +
	theme_minimal()


	# 5.77 sec
	tic()
	mb2013_wgtn_clip <- st_intersection(mb2013_wgtn, nz_coastline_wgtn)
	toc()

	ggplot(data = mb2013_wgtn_clip) +
	geom_sf() +
	theme_minimal()


	# it appears we have some LandAreaSQ measures that don't tally with what is calculated by st_area() so let's add a calculated area
	mb2013_wgtn_clip$LandAreaSQ_calc <- mb2013_wgtn_clip \|> st_area()


	# let's union together all those overlaying viewsheds to simplify the geometry
	wgtn_cctv_union <- wgtn_cctv \|> st_union()

	ggplot(data = wgtn_cctv_union) +
	geom_sf() +
	theme_minimal()

	# and now remove the viewsheds from the meshblocks
	wgtn_mbs_less_viewsheds <- st_difference(mb2013_wgtn_clip, wgtn_cctv_union) # a tiny bit faster with mb2013_wgtn_small

	# won't be able to see the impact at the regional level so zoom into central wellington
	# where Lambton Quay meets Willis St
	ggplot(data = wgtn_mbs_less_viewsheds) +
	geom_sf() +
	coord_sf(xlim = c(174.772, 174.778), ylim = c(-41.281, -41.285)) +
	theme_minimal()


	#################################################################

	# Next task is to figure out by how much the area of a meshblock has decreased as a result of



	wgtn_mbs_less_viewsheds$AreaMinusViewshed <- st_area(wgtn_mbs_less_viewsheds)
	wgtn_mbs_less_viewsheds$ChangeInArea <- wgtn_mbs_less_viewsheds$LandAreaSQ_calc - wgtn_mbs_less_viewsheds$AreaMinusViewshed
	wgtn_mbs_less_viewsheds$ChangeInAreaRatio <- 1 - drop_units(wgtn_mbs_less_viewsheds$AreaMinusViewshed / wgtn_mbs_less_viewsheds$LandAreaSQ_calc)


	#################################################################

	# and now need to join to the crime data
	vtp <- read_csv("C:\\Users\\...\\wd\\VictimisationByMeshblock\\vtp_jul2025.csv",
	col_types = cols(
	`Meshblock` = col_character()
	))
	glimpse(vtp)

	vtp$year <- as.numeric( vtp$`Month Year` \|> str_sub(-4) )

	vtp_year_mb <-
	vtp \|>
	group_by(year, meshblock = Meshblock) \|>
	summarise( sum_v = sum(Victimisations))

	glimpse(vtp_year_mb)

	wgtn_mbs_less_viewsheds_vtp <-
	wgtn_mbs_less_viewsheds \|>
	select(MeshblockN, ChangeInArea, ChangeInAreaRatio, AreaMinusViewshed, LandAreaSQ_calc) \|>
	left_join(vtp_year_mb, join_by(MeshblockN == meshblock))

	glimpse(wgtn_mbs_less_viewsheds_vtp)

	# the vtp data only goes back to june 2021 - that sux as it's impacted by covid and it's a midyear value
	wgtn_mbs_less_viewsheds_vtp_2022_vrs_2024 <-
	wgtn_mbs_less_viewsheds_vtp \|> filter(year %in% c(2022, 2024)) \|>
	pivot_wider(
	names_from = year,
	values_from = sum_v
	)

	wgtn_mbs_less_viewsheds_vtp_2022_vrs_2024$v_2022_2024 <- wgtn_mbs_less_viewsheds_vtp_2022_vrs_2024$`2024` - wgtn_mbs_less_viewsheds_vtp_2022_vrs_2024$`2022`

	# not sure there's a significant movement below the 0 line
	ggplot(data = wgtn_mbs_less_viewsheds_vtp_2022_vrs_2024) +
	geom_point(mapping = aes(x = ChangeInAreaRatio, y = v_2022_2024)) +
	labs(x = "Proportion of Meshblock in Viewshed", y = "Victimisations 2024 minus victimisations 2022")


	## using the ANZSOC Division as an asthetic to colour the points?

	vtp_year_mb_anzsocd <-
	vtp \|>
	group_by(year, meshblock = Meshblock, anzsoc_division = `ANZSOC Division`) \|>
	summarise( sum_v = sum(Victimisations))

	glimpse(vtp_year_mb)

	wgtn_mbs_less_viewsheds_vtp_anzsocd <-
	wgtn_mbs_less_viewsheds \|>
	select(MeshblockN, ChangeInArea, ChangeInAreaRatio, AreaMinusViewshed, LandAreaSQ_calc) \|>
	left_join(vtp_year_mb_anzsocd, join_by(MeshblockN == meshblock))

	wgtn_mbs_less_viewsheds_vtp_anzsocd_2022_vrs_2024 <-
	wgtn_mbs_less_viewsheds_vtp_anzsocd \|> filter(year %in% c(2022, 2024)) \|>
	pivot_wider(
	names_from = year,
	values_from = sum_v
	)

	wgtn_mbs_less_viewsheds_vtp_anzsocd_2022_vrs_2024$v_2022_2024 <- wgtn_mbs_less_viewsheds_vtp_anzsocd_2022_vrs_2024$`2024` - wgtn_mbs_less_viewsheds_vtp_anzsocd_2022_vrs_2024$`2022`

	# the ANZSOC Division colours again don't show a significant distribution one way or the other
	ggplot(data = wgtn_mbs_less_viewsheds_vtp_anzsocd_2022_vrs_2024) +
	geom_point(mapping = aes(x = ChangeInAreaRatio, y = v_2022_2024, colour = anzsoc_division)) +
	labs(x = "Proportion of Meshblock in Viewshed", y = "Victimisations 2024 minus victimisations 2022")


	# another way to look at the data is the absolute change in victimisations
	# from 2022 to 2024
	# split by there being a noticeable camera deployment versus no deployment
	# using an area change of 0.5% as a measure of CCTV deployment
	# (remember these viewsheds are only a small part of a meshblock's area)
	wgtn_mbs_less_viewsheds_vtp_2022_2024_abs <-
	wgtn_mbs_less_viewsheds_vtp \|>
	filter(year %in% c(2022, 2024)) \|>
	group_by(CameraImpact = ChangeInAreaRatio > 0.01, year) \|>
	summarise(sum_sum_v = sum(sum_v))

	wgtn_mbs_less_viewsheds_vtp_2022_2024_abs \|>
	st_drop_geometry() \|>
	pivot_wider(
	names_from = year,
	values_from = sum_sum_v
	) \|>
	mutate(change_ratio = (`2024` - `2022`)/`2022`)

	# CameraImpact `2022` `2024` change_ratio
	# <lgl> <dbl> <dbl> <dbl>
	# 1 FALSE 20050 21815 0.0880
	# 2 TRUE 4606 5022 0.0903

	# seems very strange that there is no benefit at all from the CCTV deployment?
	# Let's look at the different ANZSOC Divisions and see if that shows anything

	wgtn_mbs_less_viewsheds_vtp_2022_2024_anzsocd_abs <-
	wgtn_mbs_less_viewsheds_vtp_anzsocd \|>
	filter(year %in% c(2022, 2024)) \|>
	group_by(CameraImpact = ChangeInAreaRatio > 0.05, year, anzsoc_division) \|>
	summarise(sum_sum_v = sum(sum_v))

	wgtn_mbs_less_viewsheds_vtp_2022_2024_anzsocd_abs \|>
	st_drop_geometry() \|>
	pivot_wider(
	names_from = year,
	values_from = sum_sum_v
	) \|>
	mutate(change_ratio = (`2024` - `2022`)/`2022`) \|>
	arrange(anzsoc_division)

	# # Groups: CameraImpact [2]
	# CameraImpact anzsoc_division `2022` `2024` change_ratio
	# <lgl> <chr> <dbl> <dbl> <dbl>
	# 1 FALSE Abduction, Harassment and Other Related Offences Against a Person 22 18 -0.182
	# 2 TRUE Abduction, Harassment and Other Related Offences Against a Person 4 3 -0.25
	# 3 FALSE Acts Intended to Cause Injury 1784 2119 0.188
	# 4 TRUE Acts Intended to Cause Injury 680 731 0.075
	# 5 FALSE Robbery, Extortion and Related Offences 194 224 0.155
	# 6 TRUE Robbery, Extortion and Related Offences 31 55 0.774
	# 7 FALSE Sexual Assault and Related Offences 144 165 0.146
	# 8 TRUE Sexual Assault and Related Offences 47 57 0.213
	# 9 FALSE Theft and Related Offences 14945 16040 0.0733
	# 10 TRUE Theft and Related Offences 2455 2785 0.134
	# 11 FALSE Unlawful Entry With Intent/Burglary, Break and Enter 3924 4292 0.0938
	# 12 TRUE Unlawful Entry With Intent/Burglary, Break and Enter 426 348 -0.183