Impossible to draw a correlation ?

Recorded Knife Crime Trends Over Time: England & Wales (Including Greater Manchester Police (GMP)), Last Year Excluding GMP, Apr 2010 - Mar 2024, Source Home Office

Change in Ethnic Group Percentages by Group in England & Wales (2011 vs 2021)

Ethnic Group Populations in England & Wales (2011 vs 2021)

Possession of a Weapon with a Blade or Point by Area: England & Wales 2021-2024 Top & Bottom 25

Ethnicity Distribution by Age 2021 in the Top & Bottom 9 Areas for Knife Possession 2021-2024 in England & Wales

Full Table of Areas from Worst to Best based on Grand Total of Weapons with a Blade or Point Found

How life has changed in Birmingham: Census 2021.link

How life has changed in Oadby and Wigston: Census 2021.link

# Load necessary libraries
pacman::p_load(pacman, readr, ggplot2, dplyr, tidyverse, scales, gridExtra, knitr)

# Data for the first table (F3a) - England and Wales (excluding GMP)
data_f3a <- data.frame(
  Selected_offences = c("Attempted murder", "Threats to kill", 
                        "Assault with injury and assault with intent to cause serious harm", 
                        "Robbery", "Rape", "Sexual assault", 
                        "Total selected offences", "Homicide", 
                        "Total selected offences including homicide"),
  `Apr 2010 to Mar 2011` = c(217, 1424, 14647, 17187, 240, 85, 33800, 220, 34020),
  `Apr 2011 to Mar 2012` = c(234, 1172, 13130, 16978, 219, 71, 31804, 200, 32004),
  `Apr 2012 to Mar 2013` = c(180, 1155, 11839, 13692, 174, 81, 27121, 182, 27303),
  `Apr 2013 to Mar 2014` = c(226, 1295, 12203, 12451, 234, 92, 26501, 193, 26694),
  `Apr 2014 to Mar 2015` = c(260, 1696, 13836, 11025, 285, 121, 27223, 178, 27401),
  `Apr 2015 to Mar 2016` = c(336, 2297, 16187, 11496, 309, 106, 30731, 196, 30927),
  `Apr 2016 to Mar 2017` = c(338, 2918, 19455, 14329, 401, 169, 37610, 201, 37811),
  `Apr 2017 to Mar 2018` = c(369, 3385, 21765, 19138, 436, 157, 45250, 265, 45515),
  `Apr 2018 to Mar 2019` = c(409, 3954, 22821, 21226, 525, 157, 49092, 250, 49342),
  `Apr 2019 to Mar 2020` = c(460, 4746, 22873, 22727, 646, 277, 51729, 253, 51982),
  `Apr 2020 to Mar 2021` = c(459, 4790, 20589, 14842, 559, 210, 41449, 222, 41671),
  `Apr 2021 to Mar 2022` = c(416, 5551, 22610, 15657, 694, 385, 45313, 257, 45570),
  `Apr 2022 to Mar 2023` = c(415, 5801, 22169, 18787, 713, 298, 48183, 226, 48409),
  `Apr 2023 to Mar 2024` = c(401, 5411, 22167, 21226, 751, 321, 50277, 233, 50510)
)

# Data for the second table (F4a) - Greater Manchester Police
data_f4a <- data.frame(
  Selected_offences = c("Attempted murder", "Threats to kill", 
                        "Assault with injury and assault with intent to cause serious harm", 
                        "Robbery", "Rape", "Sexual assault", 
                        "Total selected offences", "Homicide", 
                        "Total selected offences including homicide"),
  `Apr 2010 to Mar 2011` = c(23, 110, 812, 1061, 18, 8, 2032, 16, 2048),
  `Apr 2011 to Mar 2012` = c(12, 80, 729, 888, 18, 1, 1728, 8, 1736),
  `Apr 2012 to Mar 2013` = c(18, 84, 643, 818, 16, 7, 1586, 13, 1599),
  `Apr 2013 to Mar 2014` = c(22, 84, 636, 859, 27, 5, 1633, 10, 1643),
  `Apr 2014 to Mar 2015` = c(15, 154, 806, 735, 36, 7, 1753, 8, 1761),
  `Apr 2015 to Mar 2016` = c(17, 135, 897, 696, 24, 14, 1783, 14, 1797),
  `Apr 2016 to Mar 2017` = c(26, 111, 734, 730, 29, 13, 1643, 14, 1657),
  `Apr 2017 to Mar 2018` = c(33, 117, 789, 947, 38, 11, 1935, 17, 1952),
  `Apr 2018 to Mar 2019` = c(30, 240, 1168, 1641, 69, 12, 3160, 9, 3169),
  `Apr 2019 to Mar 2020` = c(34, 189, 1215, 1692, 38, 8, 3176, 12, 3188),
  `Apr 2020 to Mar 2021` = c(23, 256, 1382, 1346, 26, 10, 3043, 13, 3056),
  `Apr 2021 to Mar 2022` = c(23, 359, 1748, 1467, 26, 15, 3638, 21, 3659),
  `Apr 2022 to Mar 2023` = c(28, 344, 1498, 1257, 42, 18, 3187, 15, 3202)
)

# Combine the data by summing E&W excl GMP and GMP for each year except the last year
data_combined <- data_f3a
for (col in names(data_f3a)[2:(ncol(data_f3a)-1)]) {
  data_combined[[col]] <- data_f3a[[col]] + data_f4a[[col]]
}

# Add the last year's E&W excl GMP data separately
data_combined <- data_combined %>%
  mutate(`Apr 2023 to Mar 2024` = data_f3a$`Apr 2023 to Mar 2024`)

# Reshape the data for plotting
df_long <- data_combined %>%
  pivot_longer(cols = -Selected_offences, names_to = "Year", values_to = "Count") %>%
  mutate(Region = ifelse(Year == "Apr 2023 to Mar 2024", "E&W excl GMP (2023-2024)", "E&W incl GMP"))

# Create the visualisations
# Line plot to compare trends over the years
ggplot(df_long, aes(x = Year, y = Count, group = Selected_offences, color = Region)) +
  geom_line(data = df_long %>% filter(Region == "E&W incl GMP"), aes(group = Region), linewidth = 1) +
  geom_point(data = df_long %>% filter(Region == "E&W incl GMP"), size = 2) +
  geom_point(data = df_long %>% filter(Region == "E&W excl GMP (2023-2024)"), size = 4, color = "red") + 
  facet_wrap(~ Selected_offences, scales = "free_y") +
  theme() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Recorded Knife Crime Trends Over Time: England & Wales (Including Greater Manchester Police (GMP)), Last Year Excluding GMP, Apr 2010 - Mar 2024, Source Home Office.",
       x = "Year",
       y = "Number of Offences",
       color = "Region")

Ethnic_group_England_and_Wales_Census_2011_2021 <-  read_csv("Census_2011_2021.csv")

## Rows: 5 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Ethnic Group
## dbl (2): 2011 %, 2021 %
## num (2): 2011, 2021
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Pivot the data to long format for easier plotting
data_long <- Ethnic_group_England_and_Wales_Census_2011_2021 %>%
  pivot_longer(cols = c(`2011`, `2021`), names_to = "Year", values_to = "Population")

# Create a bar plot with non-exponential y-axis labels
ggplot(data_long, aes(x = `Ethnic Group`, y = Population, fill = Year)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Ethnic Group Populations in England & Wales (2011 vs 2021)",
       x = "Ethnic Group",
       y = "Population",
       fill = "Census Year") +
  scale_y_continuous(labels = comma) +  # Apply comma formatting to y-axis labels
  theme() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Pivot the percentage columns to long format
data_percentage_long <- Ethnic_group_England_and_Wales_Census_2011_2021 %>%
  pivot_longer(cols = c(`2011 %`, `2021 %`), names_to = "Year", values_to = "Percentage")

# Facet plot to show each ethnic group separately
ggplot(data_percentage_long, aes(x = Year, y = Percentage, group = 1)) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 3) +
  facet_wrap(~ `Ethnic Group`, scales = "free_y") +
  labs(title = "Change in Ethnic Group Percentages by Group in England & Wales (2011 vs 2021)",
       x = "Census Year",
       y = "Percentage (%)") +
  theme()

# Load the data
data_cL49V <- read.csv("data-cL49V.csv")

# Convert columns 
data_cL49V <- data_cL49V %>%
  rename(
    Area = X.1,
    `2021/22` = `X2021.22`,
    `2022/23` = `X2022.23`,
    `2023/24` = `X2023.24`,
    Grand_Total = `Grand.Total`,
    Population = Population,
    Rate_per_100000 = `Rate.per.100.000`
  )

# Ensure that Area column has unique values
data_cL49V$Area <- make.unique(as.character(data_cL49V$Area))

# Gather the year columns into key-value pairs
data_long <- data_cL49V %>%
  gather(key = "Year", value = "Knives_Found", `2021/22`, `2022/23`, `2023/24`)

# Convert Year to a factor with levels in reverse chronological order
data_long$Year <- factor(data_long$Year, levels = rev(c("2021/22", "2022/23", "2023/24")))

# Split data into top 25 and bottom 25 based on Grand_Total
top_25 <- data_cL49V %>% arrange(desc(Grand_Total)) %>% head(25)
bottom_25 <- data_cL49V %>% arrange(Grand_Total) %>% head(25)

top_25_long <- top_25 %>%
  gather(key = "Year", value = "Knives_Found", `2021/22`, `2022/23`, `2023/24`)

# Convert Year to a factor with levels in reverse chronological order for top 25
top_25_long$Year <- factor(top_25_long$Year, levels = rev(c("2021/22", "2022/23", "2023/24")))

bottom_25_long <- bottom_25 %>%
  gather(key = "Year", value = "Knives_Found", `2021/22`, `2022/23`, `2023/24`)

# Convert Year to a factor with levels in reverse chronological order for bottom 25
bottom_25_long$Year <- factor(bottom_25_long$Year, levels = rev(c("2021/22", "2022/23", "2023/24")))

# Function to create stacked bar plot for Knives Found by Year
create_stacked_bar_plot <- function(data_long, title) {
  ggplot(data_long, aes(x = Area, y = Knives_Found, fill = Year)) +
    geom_bar(stat = "identity", position = "stack") +
    coord_flip() +  
    labs(title = title, x = "Area", y = "Knives Found") +
    theme()
}

# Create stacked bar plots for top 25 and bottom 25
stacked_bar_plot_top <- create_stacked_bar_plot(top_25_long, "Total No. of Weapons with a Blade or Point Found by Area (Top 25):2021-2024")
stacked_bar_plot_bottom <- create_stacked_bar_plot(bottom_25_long, "Total No. of Weapons with a Blade or Point Found by Area (BTM 25):2021-2024")

# Plot stacked bar plots for top 25 and bottom 25
print (stacked_bar_plot_top) 

print (stacked_bar_plot_bottom)

# Create a table from worst to best excluding the first column (CSP.Code)
worst_to_best <- data_cL49V %>% 
  select(-CSP.Code) %>% 
  arrange(desc(Grand_Total))

# Load the "Bottom 9" dataset
Ethnicity_Statistics_From_Administrative_Data_E_amp_W_2021_Bot_9_Areas_by_Knife_Possession_Age_Ethnicity <- read_csv(
  "Age_Ethnicity_Bot9.csv"
)

## Rows: 90 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): Local Authority District Name, Age Band, Sex, Asian, Black, Mixed, ...
## dbl (1): White
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Load the "Top 9" dataset
Ethnicity_Statistics_From_Administrative_Data_E_amp_W_2021_Top_9_Areas_by_Knife_Possession_Age_Ethnicity <- read_csv(
  "Age_Ethnicity_Top9.csv"
)

## Rows: 90 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Local Authority District Name, Age Band, Sex
## dbl (5): Asian, Black, Mixed, White, Other
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Convert columns in the "Bottom 9" dataset to numeric
Ethnicity_Statistics_From_Administrative_Data_E_amp_W_2021_Bot_9_Areas_by_Knife_Possession_Age_Ethnicity <- 
  Ethnicity_Statistics_From_Administrative_Data_E_amp_W_2021_Bot_9_Areas_by_Knife_Possession_Age_Ethnicity %>%
  mutate(across(c(Asian, Black, Mixed, White), as.numeric))  # Remove "Other" as it doesn't exist in this dataset

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(c(Asian, Black, Mixed, White), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.

# Convert columns in the "Top 9" dataset to numeric
Ethnicity_Statistics_From_Administrative_Data_E_amp_W_2021_Top_9_Areas_by_Knife_Possession_Age_Ethnicity <- 
  Ethnicity_Statistics_From_Administrative_Data_E_amp_W_2021_Top_9_Areas_by_Knife_Possession_Age_Ethnicity %>%
  mutate(across(c(Asian, Black, Mixed, White, Other), as.numeric))

# Reshape the data for the "Top 9" areas
top9_long_data <- Ethnicity_Statistics_From_Administrative_Data_E_amp_W_2021_Top_9_Areas_by_Knife_Possession_Age_Ethnicity %>%
  pivot_longer(cols = c(Asian, Black, Mixed, White, Other), 
               names_to = "Ethnicity", 
               values_to = "Percentage") 

# Reshape the data for the "Bottom 9" areas
bot9_long_data <- Ethnicity_Statistics_From_Administrative_Data_E_amp_W_2021_Bot_9_Areas_by_Knife_Possession_Age_Ethnicity %>%
  pivot_longer(cols = c(Asian, Black, Mixed, White),  # Only include columns present in the dataset
               names_to = "Ethnicity", 
               values_to = "Percentage")

# Filter out any rows with NA in Percentage or Local Authority District Name for the bottom 9 dataset
bot9_long_data <- bot9_long_data %>%
  filter(!is.na(Percentage) & !is.na(`Local Authority District Name`))

# Combine sexes into one plot
top9_long_data_combined <- top9_long_data %>%
  group_by(`Local Authority District Name`, `Age Band`, Ethnicity) %>%
  summarize(Percentage = mean(Percentage, na.rm = TRUE))

## `summarise()` has grouped output by 'Local Authority District Name', 'Age
## Band'. You can override using the `.groups` argument.

bot9_long_data_combined <- bot9_long_data %>%
  group_by(`Local Authority District Name`, `Age Band`, Ethnicity) %>%
  summarize(Percentage = mean(Percentage, na.rm = TRUE))

## `summarise()` has grouped output by 'Local Authority District Name', 'Age
## Band'. You can override using the `.groups` argument.

# Custom colour palette for ethnicity groups
ethnicity_colors <- c(
  "Asian" = "#cc0033",
  "Black" = "#56B4E9",
  "Mixed" = "#666666",
  "White" = "#339900",
  "Other" = "#CC79A7"
)

# Create the faceted plot for the "Top 9" areas
ggplot(top9_long_data_combined, aes(x = `Age Band`, y = Percentage, fill = Ethnicity)) +
  geom_bar(stat = "identity", position = "dodge") +
  facet_wrap(~ `Local Authority District Name`, ncol = 3) +
  scale_fill_manual(values = ethnicity_colors) +
  labs(title = "Ethnicity Distribution by Age 2021 in the Top 9 Areas for Knife Possession 2021-2024 in England & Wales",
       x = "Age Band", y = "Percentage",
       fill = "Ethnicity") +
  theme() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        strip.text = element_text(size = 10))

# Create the faceted plot for the "Bottom 9" areas, excluding NA district
ggplot(bot9_long_data_combined, aes(x = `Age Band`, y = Percentage, fill = Ethnicity)) +
  geom_bar(stat = "identity", position = "dodge") +
  facet_wrap(~ `Local Authority District Name`, ncol = 3) +
  scale_fill_manual(values = ethnicity_colors) +
  labs(title = "Ethnicity Distribution by Age 2021 in the BTM 9 Areas for Knife Possession 2021-2024 in England & Wales",
       x = "Age Band", y = "Percentage",
       fill = "Ethnicity") +
  theme() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        strip.text = element_text(size = 10))

# Print the table
knitr::kable(worst_to_best, caption = "Table of Areas from Worst to Best based on Grand Total of Weapons with a Blade or Point Found")

Table of Areas from Worst to Best based on Grand Total of Weapons with a Blade or Point Found

Area	2021/22	2022/23	2023/24	Grand_Total	Population	Rate_per_100000
Birmingham	1871	2174	1835	5880	1157603	507.9
Manchester	620	759	670	2049	568996	360.1
Sandwell	423	437	435	1295	344210	376.2
Coventry	390	458	385	1233	355600	346.7
Nottingham	364	415	410	1189	328513	361.9
Sheffield	346	387	402	1135	566242	200.4
Crawley	129	470	528	1127	119509	943.0
Leeds	367	407	341	1115	822483	135.6
Wolverhampton	316	395	355	1066	267651	398.3
Walsall	324	336	310	970	286105	339.0
Salford	237	356	281	874	278064	314.3
Dudley	244	302	290	836	324969	257.3
Liverpool	253	288	249	790	496770	159.0
Northampton	214	282	227	723	240930	300.1
Southampton	210	262	239	711	252689	281.4
Doncaster	209	252	207	668	311027	214.8
Stoke-on-Trent	160	232	269	661	259965	254.3
Bristol, City of	169	208	255	632	479024	131.9
Bradford	217	223	184	624	552644	112.9
Leicester	194	182	230	606	373399	162.3
Blackpool	158	221	226	605	141574	427.3
Bolton	199	192	207	598	298903	200.1
Newcastle upon Tyne	186	203	195	584	307565	189.9
Tameside	212	211	159	582	232753	250.1
Portsmouth	189	192	196	577	208420	276.8
Solihull	181	207	179	567	217678	260.5
Plymouth	190	186	189	565	266862	211.7
Rochdale	212	187	165	564	226992	248.5
Westminster	173	209	170	552	211365	261.2
Cornwall	162	196	193	551	575413	95.8
Wigan	146	198	197	541	334110	161.9
Derby	179	193	160	532	263490	201.9
Brighton and Hove	174	182	169	525	277965	188.9
Oldham	156	189	179	524	243912	214.8
Kirklees	158	195	148	501	437593	114.5
Cardiff	170	170	154	494	372089	132.8
Preston	133	187	164	484	151582	319.3
North Yorkshire	132	179	166	477	623501	76.5
Rotherham	156	149	163	468	268354	174.4
Croydon	157	147	135	439	392224	111.9
Norwich	116	150	157	423	144525	292.7
Luton	118	169	134	421	226973	185.5
County Durham	130	153	135	418	528127	79.1
Barnsley	132	151	128	411	246482	166.7
North Hampshire	125	138	139	402	389787	103.1
Lambeth	167	139	92	398	316812	125.6
Middlesbrough	134	142	116	392	148285	264.4
Somerset	118	128	143	389	576852	67.4
Lewisham	167	116	102	385	298653	128.9
Milton Keynes	110	145	128	383	292180	131.1
Stockport	125	137	117	379	297107	127.6
Medway	120	120	132	372	282702	131.6
Stockton-on-Tees	119	128	124	371	199966	185.5
Peterborough	121	126	118	365	217705	167.7
North Worcestershire	99	135	129	363	289536	125.4
Western Suffolk	110	142	102	354	382228	92.6
Tower Hamlets	124	130	98	352	325789	108.0
Southwark	127	114	104	345	311913	110.6
Telford and Wrekin	88	122	135	345	188871	182.7
Trafford	122	113	109	344	236301	145.6
Kingston upon Hull, City of	128	118	98	344	268852	128.0
South Warwickshire	100	106	137	343	289741	118.4
South Worcester	98	131	112	341	319680	106.7
Bury	117	95	127	339	194606	174.2
Wiltshire	100	125	111	336	515885	65.1
Wakefield	123	108	99	330	357729	92.2
Greenwich	131	106	83	320	291080	109.9
Blackburn with Darwen	83	96	140	319	155762	204.8
Southend-on-Sea	86	122	104	312	180915	172.5
Newham	125	107	80	312	358645	87.0
Sunderland	88	114	109	311	277354	112.1
Nuneaton and Bedworth	87	111	107	305	135481	225.1
Swindon	90	110	101	301	235657	127.7
Cheshire East	62	116	122	300	406527	73.8
Basildon	91	117	91	299	188848	158.3
South Nottinghamshire	103	92	104	299	351425	85.1
Hackney	113	100	82	295	261491	112.8
Ipswich	80	107	94	281	139247	201.8
Haringey	107	97	72	276	261811	105.4
Gateshead	77	100	96	273	197722	138.1
Brent	121	78	73	272	341221	79.7
Enfield	110	93	65	268	327224	81.9
Cheshire West and Chester	51	94	118	263	361694	72.7
Northumberland	76	95	87	258	324362	79.5
Shropshire	52	97	103	252	327178	77.0
Mansfield	59	101	89	249	111117	224.1
Calderdale	95	81	73	249	207699	119.9
Dartford and Gravesham	81	90	76	247	225790	109.4
Gloucester	57	89	98	244	133522	182.7
Great Yarmouth	72	90	80	242	99862	242.3
Reading	62	99	79	240	174820	137.3
Islington	82	91	62	235	220373	106.6
Oxford	67	78	90	235	163257	143.9
Cambridge	82	78	74	234	146995	159.2
Lancaster	67	76	91	234	144446	162.0
Kettering	64	87	78	229	106875	214.3
Wirral	85	75	67	227	322453	70.4
Exeter	75	79	72	226	134939	167.5
Waltham Forest	88	80	57	225	275887	81.6
Sefton	86	68	69	223	281027	79.4
Torbay	71	71	80	222	139479	159.2
Colchester	63	69	89	221	194394	113.7
Camden	94	69	58	221	218049	101.4
Ealing	91	75	54	220	369937	59.5
Lincoln	67	72	78	217	102545	211.6
Cwm Taf	60	61	95	216	297901	72.5
North Tyneside	69	62	83	214	210487	101.7
Swansea	54	68	91	213	241282	88.3
Dorset	64	68	79	211	433164	48.7
Havering	64	74	72	210	264703	79.3
Bournemouth	74	71	64	209	198162	105.5
North East Lincolnshire	70	65	74	209	157754	132.5
Thanet	71	76	60	207	140689	147.1
Wellingborough	68	77	61	206	84406	244.1
King’s Lynn and West Norfolk	53	65	87	205	155741	131.6
Slough	51	72	81	204	159182	128.2
Barking and Dagenham	69	66	67	202	219992	91.8
Eastbourne	61	58	83	202	102247	197.6
City of York	65	62	75	202	204551	98.8
North Somerset	62	63	74	199	219145	90.8
Carlisle	55	62	80	197	111418	176.8
Harlow	52	71	74	197	94409	208.7
Bromley	81	70	45	196	329578	59.5
Halton	36	63	96	195	128964	151.2
Hounslow	66	65	63	194	290488	66.8
Arun	48	64	82	194	166366	116.6
Redbridge	63	69	61	193	310911	62.1
Ashfield	40	76	76	192	127179	151.0
Burnley	52	67	70	189	95553	197.8
South Tyneside	68	66	55	189	148667	127.1
St. Helens	67	71	49	187	184728	101.2
Warrington	30	73	81	184	211580	87.0
Redcar and Cleveland	36	67	81	184	137175	134.1
Maidstone	61	56	64	181	180428	100.3
Isle of Wight	61	52	66	179	140794	127.1
Hastings	65	64	50	179	90622	197.5
Thurrock	65	55	58	178	176877	100.6
Corby	58	62	57	177	76583	231.1
Denbighshire	51	61	64	176	96558	182.3
Uttlesford	31	68	76	175	92578	189.0
Hillingdon	73	52	50	175	310681	56.3
Wandsworth	68	63	44	175	329035	53.2
Canterbury	52	59	61	172	157550	109.2
Bedford	60	50	61	171	187466	91.2
Hammersmith and Fulham	56	72	42	170	185238	91.8
Havant	59	57	51	167	124854	133.8
Hartlepool	46	51	68	165	93861	175.8
Barnet	69	56	40	165	389101	42.4
Breckland	52	66	47	165	143479	115.0
Darlington	50	57	56	163	109469	148.9
Daventry and South Northamptonshire	38	64	59	161	188083	85.6
Herefordshire, County of	44	48	68	160	188719	84.8
Chelmsford	40	64	55	159	183326	86.7
Chorley	51	65	43	159	118624	134.0
South Devon and Dartmoor	56	56	47	159	283954	56.0
South Ribble	47	51	60	158	112166	140.9
Hyndburn	30	65	60	155	83213	186.3
East and Mid Devon	48	56	50	154	238286	64.6
Knowsley	52	60	41	153	157103	97.4
Newport	41	67	45	153	161506	94.7
South Gloucestershire	50	41	61	152	294765	51.6
Huntingdonshire	45	52	54	151	184052	82.0
New Forest	39	49	62	150	175942	85.3
Tendring	43	44	62	149	151451	98.4
Swale	51	43	54	148	154619	95.7
Cherwell	36	52	60	148	164155	90.2
Flintshire	49	50	49	148	155319	95.3
Newcastle-under-Lyme	41	44	62	147	125297	117.3
Worthing	58	40	49	147	112044	131.2
Waveney	31	59	52	142	116633	121.7
Pendle	28	48	64	140	96110	145.7
North Lincolnshire	44	45	50	139	170042	81.7
Bassetlaw	40	53	45	138	120012	115.0
Barrow-In-Furness	33	53	51	137	67347	203.4
Dover	43	35	59	137	117473	116.6
Aylesbury Vale	41	47	49	137	208101	65.8
Windsor and Maidenhead	28	46	62	136	154738	87.9
Wycombe	34	49	52	135	182368	74.0
Wrexham	54	36	41	131	135394	96.8
Stafford	21	52	57	130	138670	93.7
Conwy	44	44	42	130	114290	113.7
East Riding of Yorkshire	40	38	51	129	346309	37.2
Rugby	35	52	42	129	116436	110.8
Cheltenham	37	47	44	128	119434	107.2
Allerdale	24	52	51	127	96555	131.5
Gosport	33	52	42	127	82285	154.3
Test Valley	40	46	41	127	132871	95.6
Wyre	31	44	52	127	114809	110.6
Chichester	33	39	55	127	126103	100.7
Kensington and Chelsea	46	35	45	126	146154	86.2
Sutton	50	44	32	126	210053	60.0
Bexley	41	52	30	123	247835	49.6
West Lancashire	22	55	45	122	119367	102.2
East Northamptonshire	34	43	45	122	95544	127.7
Eastleigh	46	43	30	119	138935	85.7
East Lindsey	39	42	38	119	144415	82.4
South Kesteven	41	42	36	119	144249	82.5
Braintree	31	41	46	118	157681	74.8
Folkestone and Hythe	39	36	42	117	110237	106.1
North Devon	35	33	49	117	169140	69.2
Charnwood	45	34	36	115	184748	62.2
Newark and Sherwood	31	36	47	114	125089	91.1
Pembrokeshire	32	46	36	114	124367	91.7
Poole	30	42	41	113	153846	73.5
Watford	39	39	35	113	103031	109.7
Merton	44	32	37	113	214709	52.6
East Staffordshire	31	33	49	113	125760	89.9
Carmarthenshire	37	45	31	113	189117	59.8
Chesterfield	38	33	41	112	104110	107.6
Gwynedd	28	43	41	112	117591	95.2
Caerphilly	32	34	42	108	176130	61.3
Ashford	34	32	41	107	135610	78.9
Kingston upon Thames	48	37	20	105	168302	62.4
Boston	33	45	26	104	70806	146.9
Broadland	19	45	40	104	133872	77.7
South Lakeland	17	37	49	103	104807	98.3
Erewash	32	35	36	103	113080	91.1
Tamworth	18	35	50	103	79639	129.3
Bridgend	32	41	29	102	146136	69.8
Dacorum	46	32	23	101	156123	64.7
Central Bedfordshire	37	31	33	101	301501	33.5
Harrow	35	46	19	100	261185	38.3
Lichfield	15	53	32	100	108352	92.3
Winchester	25	40	32	97	130268	74.5
Vale of White Horse	35	28	34	97	142116	68.3
Bath and North East Somerset	22	29	45	96	195618	49.1
Wealden	29	24	43	96	163012	58.9
South Norfolk	28	28	39	95	144593	65.7
Stevenage	34	36	23	93	89737	103.6
Cannock Chase	14	35	43	92	101140	91.0
Spelthorne	20	31	41	92	103551	88.8
Welwyn Hatfield	40	27	24	91	120213	75.7
Mid Sussex	24	29	36	89	154930	57.4
Epping Forest	23	28	37	88	134989	65.2
Fareham	33	20	35	88	114547	76.8
Powys	21	36	31	88	133891	65.7
Amber Valley	31	31	25	87	126944	68.5
City of London	22	21	43	86	10847	792.8
Tunbridge Wells	26	22	37	85	116028	73.3
Reigate and Banstead	24	31	30	85	153629	55.3
Horsham	21	25	39	85	148696	57.2
Neath Port Talbot	35	23	27	85	142158	59.8
Fenland	27	26	31	84	103035	81.5
West Berkshire	22	26	35	83	162215	51.2
East Hampshire	32	22	28	82	127319	64.4
Rossendale	27	30	25	82	71169	115.2
Guildford	20	28	33	81	145673	55.6
North Warwickshire	24	32	25	81	65946	122.8
West Lindsey	17	24	37	78	96817	80.6
Suffolk Coastal	21	26	31	78	130447	59.8
North West Leicestershire	31	21	24	76	107672	70.6
Vale of Glamorgan	24	21	30	75	133492	56.2
Copeland	23	19	32	74	67417	109.8
Wokingham	20	17	37	74	180967	40.9
South Derbyshire	17	29	25	71	111133	63.9
St Albans	18	30	23	71	148358	47.9
Fylde	27	17	26	70	83008	84.3
Isle of Anglesey	23	24	23	70	69049	101.4
Tewkesbury	24	19	26	69	97000	71.1
Tonbridge and Malling	15	28	26	69	133661	51.6
Ceredigion	22	27	20	69	71610	96.4
Sevenoaks	15	27	26	68	121106	56.1
North Norfolk	19	21	28	68	103227	65.9
Runnymede	21	24	23	68	88524	76.8
South Holland	22	21	24	67	96983	69.1
Forest of Dean	22	18	26	66	87937	75.1
Broxbourne	24	16	26	66	99103	66.6
Castle Point	17	27	21	65	89731	72.4
South Cambridgeshire	16	25	22	63	165633	38.0
Stroud	21	16	26	63	123205	51.1
Lewes	22	15	26	63	100677	62.6
South Oxfordshire	14	25	24	63	151820	41.5
West Oxfordshire	17	18	28	63	116928	53.9
Torfaen	18	13	30	61	92860	65.7
Bracknell Forest	19	18	23	60	126881	47.3
Bolsover	21	18	20	59	81553	72.3
Hinckley and Bosworth	22	19	18	59	114298	51.6
Rother	22	13	24	59	94162	62.7
Hertsmere	12	27	18	57	108106	52.7
North Hertfordshire	27	17	13	57	134159	42.5
Richmond upon Thames	30	13	14	57	194894	29.2
Staffordshire Moorlands	15	16	26	57	95899	59.4
South Staffordshire	13	22	21	56	111527	50.2
Rochford	10	22	22	54	87216	61.9
Adur	21	15	18	54	64688	83.5
Eden	11	22	20	53	55489	95.5
High Peak	18	16	18	52	91109	57.1
East Hertfordshire	16	23	13	52	151635	34.3
Melton	23	12	15	50	52433	95.4
Elmbridge	21	13	15	49	140024	35.0
Woking	17	17	15	49	104179	47.0
Blaenau Gwent	18	16	14	48	67014	71.6
Epsom and Ewell	15	16	14	45	81184	55.4
Derbyshire Dales	16	9	19	44	71752	61.3
Waverley	9	19	16	44	130063	33.8
Chiltern	9	18	16	43	97702	44.0
Maldon	12	14	16	42	67554	62.2
Three Rivers	12	14	15	41	94123	43.6
North East Derbyshire	13	12	14	39	103783	37.6
Cotswold	9	14	16	39	91311	42.7
Blaby	15	9	15	39	104182	37.4
Tandridge	12	11	15	38	88707	42.8
North Kesteven	11	14	12	37	119709	30.9
Brentwood	12	14	10	36	77332	46.6
Harborough	6	17	11	34	100481	33.8
East Cambridgeshire	11	16	6	33	89394	36.9
Mole Valley	11	10	12	33	87769	37.6
South Bucks	10	10	13	33	72238	45.7
Ribble Valley	5	12	14	31	63107	49.1
Monmouthshire	10	8	12	30	93886	32.0
Surrey Heath	10	5	13	28	91237	30.7
Oadby and Wigston	9	8	8	25	58341	42.9
Rutland	6	2	5	13	41151	31.6
Isles of Scilly	0	1	2	3	2281	131.5