1994 – 2014

Steps to download the data:

• Go to NZ.Stats
• Select Crime
• Select Calendar
• Select Annual Recorded Offences for the latest Calendar Years (ANZSOC)
• Select Customise
• Deselct all offences
• Select only the offences included under Aggravated sexual assault

Default selection also includes

• All years
• All police districts (Location)
• Only Total Scene
• Both Measures: Recorded and resolved

Download the this data

For comparison serious assault causing injury data was also downloaded from StatsNZ and is here

This analysis only considers sexual assaults against adults, therefore all offences against need to be removed.

stats_data <- read.csv('TABLECODE7405_Data_4257896b-fd89-4dac-bf13-b591c410f76f.csv')
offences <-read.csv('aggrv_offences.txt')
kable(stats_data %>% group_by(Offence) %>%
  summarise() %>%
  mutate(Included = ifelse(Offence %in% offences$Offence, 'Yes', 'No')) %>%
  arrange(desc(Included),Offence))

statsdata_adult <- stats_data %>%
  filter(Offence %in% offences$Offence & Location != 'Total New Zealand')

2015 –

Data downloaded from policedata.nz Victimisations demographics report. Only selected Aggravated Sexual Assault.

Data download details

The following filters are applied to the data:

• After 1 Jan, 2015
• Before 31 Aug, 2017 (2017 is truncated so that data more than 180 days from last data release)
• Victim age is 15 or above
• Outcome of investigation is not No crime or Withdrawn by victim

Download aggravated sexual assaults extract and serious assault causing injury extract. The serious assaults extract was extracted in a similar way to the aggravated sexual assaults data.

children <- c("0 - 4 years inclusive","5 - 9 years inclusive","10 - 14 years inclusive")
remove <- c("No crime", "Withdrawn by victim")
resolved <- c("Court action, n.f.d.", "Formal caution or formal warning", "Informal warning or informal caution", "Non-court action, n.f.d.")

policedata <- read.csv('victimisations.csv', sep='\t')
policedata_adult <- policedata %>% 
  filter(!(Age.Group.5Yr.Band %in% children)) %>%
  filter(!(Outcome.Of.Investigation.180 %in% remove)) %>%
  mutate(date = as.Date(Year.Month, format='%d/%m/%Y')) %>%
  mutate(
    Year = case_when(
      date >= '2015-01-01' & date < '2016-01-01' ~ as.integer(2015),
      date >= '2016-01-01' & date < '2017-01-01' ~ as.integer(2016),
      date >= '2017-01-01' & date < '2017-08-31' ~ as.integer(2017),
      TRUE ~ as.integer(-1))) %>%
  filter(Year != -1)


assaults_police <- read.csv('assaults.csv', sep='\t') %>%
  filter(!(Age.Group.5Yr.Band %in% children)) %>%
  filter(!(Outcome.Of.Investigation.180 %in% remove)) %>%
  mutate(date = as.Date(Year.Month, format='%d/%m/%Y')) %>%
  mutate(
    Year = case_when(
      date >= '2015-01-01' & date < '2016-01-01' ~ as.integer(2015),
      date >= '2016-01-01' & date < '2017-01-01' ~ as.integer(2016),
      date >= '2017-01-01' & date < '2017-08-31' ~ as.integer(2017),
      TRUE ~ as.integer(-1))) %>%
  filter(Year != -1)
assaults_police_agg <- assaults_police %>%
  group_by(Year) %>%
  summarise(
    Total = sum(Victimisations),
    Resolved  = sum(Victimisations[Outcome.Of.Investigation.180 %in% resolved])
   ) %>%
  mutate(Rate = round((Total - Resolved) / Total * 100,1),
         Unresolved = Total - Resolved,
         Source = 'police')

Male rapes female, 2015 –

The data released on policedata.nz does not include the specific offence male rapes female over 16. This data was requested from the police via an OIA

Download this data

oia <- readxl::read_xlsx('oia.xlsx', range="Victimisations!A2:D71") %>% 
  rename(Area=X__1, Outcome=X__2) %>% 
  fill(Area) %>% 
  mutate(`2015`=as.integer(`2015`)) %>%
  mutate(`2016`=as.integer(`2016`)) %>%
  gather(Year, Count, -Area, -Outcome) %>%
  filter(Outcome != 'Sub-total') 
oia_agg <- oia %>%
  filter(Outcome != 'No crime' & !is.na(Count)) %>%
  group_by(Year) %>%
  summarise(Total = sum(Count),
            Resolved = sum(Count[Outcome == 'Court action' | Outcome == 'Non-court action n.f.d.'])) %>%
  mutate(Unresolved = Total - Resolved, Rate = round((Total - Resolved) / Total * 100,1), Source='oia', Year=as.integer(Year))
kable(oia_agg)

2016 resolved/unresolved

The following table shows which investigation outcomes after 180 days are considered resolved

kable(policedata_adult %>%
      group_by(Outcome.Of.Investigation.180) %>%
      summarise() %>%
      mutate(Resolved = ifelse(Outcome.Of.Investigation.180 %in% resolved, 'Yes', 'No')) %>%
      arrange(desc(Resolved),Outcome.Of.Investigation.180))

Unresolved aggravated sexual assaults over time

statsdata_adult_agg <- statsdata_adult %>% 
  group_by(Year) %>%
  summarise(Resolved = sum(Value[Measure=='Resolved']),
            Total= sum(Value[Measure=='Recorded'])) %>%
  mutate(Rate = round((Total - Resolved)/Total*100,1),
         Unresolved = Total - Resolved,
         Source = 'stats')

policedata_adult_agg <- policedata_adult %>%

  group_by(Year) %>%
  summarise(
    Total = sum(Victimisations),
    Resolved  = sum(Victimisations[Outcome.Of.Investigation.180 %in% resolved])
   ) %>%
  mutate(Rate = round((Total - Resolved) / Total * 100,1),
         Unresolved = Total - Resolved,
         Source = 'police')

combined <- union(policedata_adult_agg, statsdata_adult_agg) %>% arrange(Year)
exp1 <- combined %>% select(Year, Rate, Source, Total)
write.csv(exp1, file='aggravated-over-time.csv',
          row.names=FALSE)
list(exp1 %>% filter(Year <= 2014),
     exp1 %>% filter(Year > 2014),
     exp1 %>% filter(Year == 2014 | Year == 2015)
     ) %>% 
  toJSON() %>% write_lines('../graphics/src/aggravated-over-time.json')
kable(exp1)

Unresolved male rapes female over 16 over time

stats_rape <- statsdata_adult %>% filter(Offence == 'Males Rapes Female Over 16') %>%
  group_by(Year) %>%
  summarise(Resolved = sum(Value[Measure=='Resolved']),
            Total= sum(Value[Measure=='Recorded'])) %>%
  mutate(Rate = round((Total - Resolved)/Total*100,1),
         Unresolved = Total - Resolved,
         Source = 'stats')
rape = union(stats_rape, oia_agg) %>% select(Year, Rate, Source, Total)

Unresolved serious assault causing injury over time

assault <- read.csv('TABLECODE7405_Data_093506f6-703c-4eb1-b327-3431ab5ba7cb.csv') %>%
  group_by(Year)  %>%
  summarise(Resolved = sum(Value[Measure=='Resolved']),
            Total= sum(Value[Measure=='Recorded'])) %>%
  mutate(Rate = round((Total - Resolved)/Total*100,1),
         Unresolved = Total - Resolved,
         Source = 'stats')

list(exp1 %>% filter(Year <= 2014),
     exp1 %>% filter(Year > 2014),
     exp1 %>% filter(Year == 2014 | Year == 2015),
     rape %>% filter(Year <= 2014),
     rape %>% filter(Year > 2014),
     rape %>% filter(Year == 2014 | Year == 2015),
     assault %>% select(Year, Rate, Source, Total),
     assaults_police_agg %>% select(Year, Rate, Source, Total),
     union(assault, assaults_police_agg) %>% select(Year, Rate, Source, Total) %>% filter(Year == 2014 | Year == 2015)
     ) %>% toJSON() %>% write_lines('../graphics/src/aggravated-vs-rape.json')

Regional differences

p2016 <- policedata_adult %>% filter(Year == 2016) %>%
  group_by(Police.District) %>%
  summarise(
    Total = sum(Victimisations),
    Resolved  = sum(Victimisations[Outcome.Of.Investigation.180 %in% resolved])
   ) %>%
  mutate(Rate = round((Total - Resolved) / Total * 100,1),
         Unresolved = Total - Resolved,
         Source = 'police') %>%
  arrange(Rate)
p2016 %>% select(Police.District, Rate) %>%
  toJSON() %>% write_lines('../graphics/src/police-districts.json')