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

# Load necessary libraries
pacman::p_load(pacman, ggplot2, dplyr, tidyr, gridExtra, knitr)

# 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 bar plot for Grand Total
create_bar_plot_total <- function(data, title) {
  ggplot(data, aes(x = Area, y = Grand_Total, fill = Area)) +
    geom_bar(stat = "identity") +
    coord_flip() +  
    labs(title = title, x = "Area", y = "Weapon with a Blade or Point") +
    theme() +
    theme(legend.position = "none")
}

# Function to create bar plot for Rate per 100,000
create_bar_plot_rate <- function(data, title) {
  ggplot(data, aes(x = Area, y = Rate_per_100000, fill = Area)) +
    geom_bar(stat = "identity") +
    coord_flip() +  
    labs(title = title, x = "Area", y = "Rate per 100,000") +
    theme() +
    theme(legend.position = "none")
}

# Function to create heatmap 
create_heatmap <- function(data_long, title) {
  ggplot(data_long, aes(x = Year, y = Area, fill = Knives_Found)) +
    geom_tile() +
    scale_fill_gradient(low = "pink", high = "red") +  
    labs(title = title, x = "Year", y = "Area") +
    theme()
}

# 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 plots for top 25
bar_plot_total_top <- create_bar_plot_total(top_25, "Total No. of Weapons with a Blade or Point Found by Area (Top 25):2021-2024")
bar_plot_rate_top <- create_bar_plot_rate(top_25, "Weapon with a Blade or Point Found Per Capita by Area (Top 25):2021-2024")
heatmap_top <- create_heatmap(top_25_long, "Heatmap of Possession of a Weapon with a Blade or Point by Area (Top 25)")

# Create plots for bottom 25
bar_plot_total_bottom <- create_bar_plot_total(bottom_25, "Total No. of Weapons with a Blade or Point Found by Area (BTM 25):2021-2024")
bar_plot_rate_bottom <- create_bar_plot_rate(bottom_25, "Weapon with a Blade or Point Found Per Capita by Area (BTM 25):2021-2024")
heatmap_bottom <- create_heatmap(bottom_25_long, "Heatmap of Possession of a Weapon with a Blade or Point by Area (BTM 25)")

# 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")

# Arrange plots for top 25 in a 2x2 grid
grid.arrange(bar_plot_total_top, stacked_bar_plot_top, heatmap_top, bar_plot_rate_top, ncol = 2)

# Arrange plots for bottom 25 in a 2x2 grid
grid.arrange(bar_plot_total_bottom, stacked_bar_plot_bottom, heatmap_bottom, bar_plot_rate_bottom, ncol = 2)

# 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")

# 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))

# 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