Reports: How did the Christchurch Mosque Attacks Affect Attitudes to Muslims in New Zealand?

Joseph Bulbulia

Show code

#import data
df <- readRDS(here::here("data", "df"))

Show code

# prepare data
dt <- df %>%
  dplyr::filter(YearMeasured == 1)  %>%
  dplyr::filter(
    Wave == 2019 |
      Wave == 2018 |
      Wave == 2017 |
      Wave == 2016 |
      Wave == 2015 |
      Wave == 2014 |
      Wave == 2013 |
      Wave == 2012
  ) %>%
  dplyr::group_by(Id) %>% filter(n() > 2) %>%
  dplyr::ungroup(Id)  %>%
  dplyr::select(Id, TSCORE, Warm.Muslims) %>%
  dplyr::filter(!is.na(Warm.Muslims)) %>%
  dplyr::mutate(
    wm_s = scale(Warm.Muslims),
    days = as.integer(TSCORE),
    yr_0 = ((days - min(days)) / 365),
    yrs = (days / 365),
    yr_a = 3545 / 365,
  ) %>%
  dplyr::mutate(Pre_PostATTACK = factor(ifelse(
    TSCORE >= 3545, "post_attack",
    "pre_attack"
  ))) %>%
  dplyr::mutate(Pre_PostATTACK = forcats::fct_relevel(Pre_PostATTACK, c("pre_attack", "post_attack")))


## below are dataframes used for the supplemental timelines in the appendix


# For Maori timline
dm <- df %>%
  dplyr::filter(YearMeasured == 1)  %>%
  dplyr::filter(
    Wave == 2019 |
      Wave == 2018 |
      Wave == 2017 |
      Wave == 2016 |
      Wave == 2015 |
      Wave == 2014 |
      Wave == 2013 |
      Wave == 2012
  ) %>%
  dplyr::group_by(Id) %>% filter(n() > 2) %>%
  dplyr::ungroup(Id)  %>%
  dplyr::select(Id, TSCORE, Warm.Maori) %>%
  dplyr::filter(!is.na(Warm.Maori)) %>%
  dplyr::mutate(
    wm_s = scale(Warm.Maori),
    days = as.integer(TSCORE),
    yr_0 = ((days - min(days)) / 365),
    yrs = (days / 365),
    yr_a = 3545 / 365,
  ) %>%
  dplyr::mutate(Pre_PostATTACK = factor(ifelse(
    TSCORE >= 3545, "post_attack",
    "pre_attack"
  ))) %>%
  dplyr::mutate(Pre_PostATTACK = forcats::fct_relevel(Pre_PostATTACK, c("pre_attack", "post_attack")))


# For Migrant timline
di <- df %>%
  dplyr::filter(YearMeasured == 1)  %>%
  dplyr::filter(
    Wave == 2019 |
      Wave == 2018 |
      Wave == 2017 |
      Wave == 2016 |
      Wave == 2015 |
      Wave == 2014 |
      Wave == 2013 |
      Wave == 2012
  ) %>%
  dplyr::group_by(Id) %>% filter(n() > 2) %>%
  dplyr::ungroup(Id)  %>%
  dplyr::select(Id, TSCORE, Warm.Immigrants) %>%
  dplyr::filter(!is.na(Warm.Immigrants)) %>%
  dplyr::mutate(
    wm_s = scale(Warm.Immigrants),
    days = as.integer(TSCORE),
    yr_0 = ((days - min(days)) / 365),
    yrs = (days / 365),
    yr_a = 3545 / 365,
  ) %>%
  dplyr::mutate(Pre_PostATTACK = factor(ifelse(
    TSCORE >= 3545, "post_attack",
    "pre_attack"
  ))) %>%
  dplyr::mutate(Pre_PostATTACK = forcats::fct_relevel(Pre_PostATTACK, c("pre_attack", "post_attack")))

dnz <- df %>%
  dplyr::filter(YearMeasured == 1)  %>%
  dplyr::filter(
    Wave == 2019 |
      Wave == 2018 |
      Wave == 2017 |
      Wave == 2016 |
      Wave == 2015 |
      Wave == 2014 |
      Wave == 2013 |
      Wave == 2012
  ) %>%
  dplyr::group_by(Id) %>% filter(n() > 2) %>%
  dplyr::ungroup(Id)  %>%
  dplyr::select(Id, TSCORE, Warm.NZEuro) %>%
  dplyr::filter(!is.na(Warm.NZEuro)) %>%
  dplyr::mutate(
    wm_s = scale(Warm.NZEuro),
    days = as.integer(TSCORE),
    yr_0 = ((days - min(days)) / 365),
    yrs = (days / 365),
    yr_a = 3545 / 365,
  ) %>%
  dplyr::mutate(Pre_PostATTACK = factor(ifelse(
    TSCORE >= 3545, "post_attack",
    "pre_attack"
  ))) %>%
  dplyr::mutate(Pre_PostATTACK = forcats::fct_relevel(Pre_PostATTACK, c("pre_attack", "post_attack")))


# For Refugee timline

drefugees <- df %>%
  dplyr::filter(YearMeasured == 1)  %>%
  dplyr::filter(
    Wave == 2019 |
      Wave == 2018 |
      Wave == 2017 |
      Wave == 2016 |
      Wave == 2015 |
      Wave == 2014 |
      Wave == 2013 |
      Wave == 2012
  ) %>%
  dplyr::group_by(Id) %>% filter(n() > 2) %>%
  dplyr::ungroup(Id)  %>%
  dplyr::select(Id, TSCORE, Warm.Refugees) %>%
  dplyr::filter(!is.na(Warm.Refugees)) %>%
  dplyr::mutate(
    wm_s = scale(Warm.Refugees),
    days = as.integer(TSCORE),
    yr_0 = ((days - min(days)) / 365),
    yrs = (days / 365),
    yr_a = 3545 / 365,
  ) %>%
  dplyr::mutate(Pre_PostATTACK = factor(ifelse(
    TSCORE >= 3545, "post_attack",
    "pre_attack"
  ))) %>%
  dplyr::mutate(Pre_PostATTACK = forcats::fct_relevel(Pre_PostATTACK, c("pre_attack", "post_attack")))


# For Chinese timline

dchinese <- df %>%
  dplyr::filter(YearMeasured == 1)  %>%
  dplyr::filter(
    Wave == 2019 |
      Wave == 2018 |
      Wave == 2017 |
      Wave == 2016 |
      Wave == 2015 |
      Wave == 2014 |
      Wave == 2013 |
      Wave == 2012
  ) %>%
  dplyr::group_by(Id) %>% filter(n() > 2) %>%
  dplyr::ungroup(Id)  %>%
  dplyr::select(Id, TSCORE, Warm.Chinese) %>%
  dplyr::filter(!is.na(Warm.Chinese)) %>%
  dplyr::mutate(
    wm_s = scale(Warm.Chinese),
    days = as.integer(TSCORE),
    yr_0 = ((days - min(days)) / 365),
    yrs = (days / 365),
    yr_a = 3545 / 365,
  ) %>%
  dplyr::mutate(Pre_PostATTACK = factor(ifelse(
    TSCORE >= 3545, "post_attack",
    "pre_attack"
  ))) %>%
  dplyr::mutate(Pre_PostATTACK = forcats::fct_relevel(Pre_PostATTACK, c("pre_attack", "post_attack")))


# For Indians timline


dindians <- df %>%
  dplyr::filter(YearMeasured == 1)  %>%
  dplyr::filter(
    Wave == 2019 |
      Wave == 2018 |
      Wave == 2017 |
      Wave == 2016 |
      Wave == 2015 |
      Wave == 2014 |
      Wave == 2013 |
      Wave == 2012
  ) %>%
  dplyr::group_by(Id) %>% filter(n() > 2) %>%
  dplyr::ungroup(Id)  %>%
  dplyr::select(Id, TSCORE, Warm.Indians) %>%
  dplyr::filter(!is.na(Warm.Indians)) %>%
  dplyr::mutate(
    wm_s = scale(Warm.Indians),
    days = as.integer(TSCORE),
    yr_0 = ((days - min(days)) / 365),
    yrs = (days / 365),
    yr_a = 3545 / 365,
  ) %>%
  dplyr::mutate(Pre_PostATTACK = factor(ifelse(
    TSCORE >= 3545, "post_attack",
    "pre_attack"
  ))) %>%
  dplyr::mutate(Pre_PostATTACK = forcats::fct_relevel(Pre_PostATTACK, c("pre_attack", "post_attack")))




# For Pacific Peoples timline

dpacific <- df %>%
  dplyr::filter(YearMeasured == 1)  %>%
  dplyr::filter(
    Wave == 2019 |
      Wave == 2018 |
      Wave == 2017 |
      Wave == 2016 |
      Wave == 2015 |
      Wave == 2014 |
      Wave == 2013 |
      Wave == 2012
  ) %>%
  dplyr::group_by(Id) %>% filter(n() > 2) %>%
  dplyr::ungroup(Id)  %>%
  dplyr::select(Id, TSCORE, Warm.Pacific) %>%
  dplyr::filter(!is.na(Warm.Pacific)) %>%
  dplyr::mutate(
    wm_s = scale(Warm.Pacific),
    days = as.integer(TSCORE),
    yr_0 = ((days - min(days)) / 365),
    yrs = (days / 365),
    yr_a = 3545 / 365,
  ) %>%
  dplyr::mutate(Pre_PostATTACK = factor(ifelse(
    TSCORE >= 3545, "post_attack",
    "pre_attack"
  ))) %>%
  dplyr::mutate(Pre_PostATTACK = forcats::fct_relevel(Pre_PostATTACK, c("pre_attack", "post_attack")))

drefugees <- df %>%
  dplyr::filter(YearMeasured == 1)  %>%
  dplyr::filter(
    Wave == 2019 |
      Wave == 2018 |
      Wave == 2017 |
      Wave == 2016 |
      Wave == 2015 |
      Wave == 2014 |
      Wave == 2013 |
      Wave == 2012
  ) %>%
  dplyr::group_by(Id) %>% filter(n() > 2) %>%
  dplyr::ungroup(Id)  %>%
  dplyr::select(Id, TSCORE, Warm.Refugees) %>%
  dplyr::filter(!is.na(Warm.Refugees)) %>%
  dplyr::mutate(
    wm_s = scale(Warm.Refugees),
    days = as.integer(TSCORE),
    yr_0 = ((days - min(days)) / 365),
    yrs = (days / 365),
    yr_a = 3545 / 365,
  ) %>%
  dplyr::mutate(Pre_PostATTACK = factor(ifelse(
    TSCORE >= 3545, "post_attack",
    "pre_attack"
  ))) %>%
  dplyr::mutate(Pre_PostATTACK = forcats::fct_relevel(Pre_PostATTACK, c("pre_attack", "post_attack")))

Show code

 ord_attack <- c("bsline_w09","pre_w10", "pst_w10","pst_w11")

# dataset for models

km2 <- df %>%
  dplyr::select(
    Id,
    Age,
    TSCORE,
    EthnicCats,
    Warm.Overweight,
    Warm.Elderly,
    Warm.MentalIllness,
    Warm.Muslims,
    Warm.Immigrants,
    Warm.Asians,
    Warm.Refugees,
    Wave,
    Warm.Maori,
    Warm.NZEuro,
    Warm.Indians,
    Warm.Chinese,
    Warm.Refugees,
    Warm.Pacific,
    Muslim,
    Euro,
    Relid,
    Religious,
    Pol.Orient,
    TSCORE,
    WSCORE, 
    YearMeasured
  ) %>%
dplyr::filter(Wave == 2017 & YearMeasured == 1 |  Wave == 2018 & YearMeasured == 1| Wave == 2019 & YearMeasured == 1) %>%
   droplevels() %>%
  group_by(Id)%>%
  filter(n() == 3) %>%
  dplyr::mutate(Pre_PostATTACK = as.factor(ifelse(
    TSCORE >= 3545 & Wave == 2018,
    "pst_w10",
    ifelse(
      Wave == 2019,
      "pst_w11",
      ifelse(TSCORE < 3545 & Wave == 2018, "pre_w10",
             "bsline_w09") )
  ))) %>%
  dplyr::mutate(Attack = ifelse(TSCORE >= 3545, "post_attack", "before_attack"))%>%
  dplyr::mutate(days = as.integer(TSCORE),
                years = days / 365) %>%
 # dplyr::filter(Muslim == 0) %>%
  dplyr::group_by(Id) %>%
  dplyr::add_tally()%>%
  dplyr::ungroup()%>%
  filter(n == 3)%>%
  dplyr::mutate(
    cons_s = scale(Pol.Orient),
    r_s = scale(Relid),
    a_s = scale(Age),
    yearS = years - min(years) )%>%
  dplyr::mutate(Pre_PostATTACK = forcats::fct_relevel(Pre_PostATTACK, ord_attack))

#library(table1)  # make pretty tables quickly
#table1::table1(~Muslim|Wave, data = km, overall=F)# there are 31 muslims in 2017

Show code

library(lme4)
library(mgcv)
library(splines)
library(ggeffects)
# Check model
#parameters::model_parameters(m1)
## 1.55 years to shooting
#parameters::model_parameters(m1)  estimate expected effect at day of shooting
#4.04 + .2 + .1*1.55


## models below

# muslims
m1 <- lmer(Warm.Muslims ~  Pre_PostATTACK +
             (1 | Id) ,
           data = km2)


p1 <- ggeffects::ggpredict(m1,
                           terms = "Pre_PostATTACK")

p1p <- plot(p1,
            add.data = TRUE,
            dot.alpha = .05,
            jitter = .8) +
  ylab("Warmth to Muslims (1-7)") + labs(title = "Warm Muslims") +
  geom_hline(yintercept = 4.395, color = "green") +
  # scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()

## For intro
p1pp <- plot(p1,
            add.data = TRUE,
            dot.alpha = .05,
            jitter = .8) +
  ylab("Warmth to Muslims (1-7)") + labs(title = "Warm Muslims") +
  geom_hline(yintercept = 4.395, color = "green") +
  # scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()


## euro
m2 <- lmer(Warm.NZEuro  ~    Pre_PostATTACK +
             (1 | Id) ,
           data = km2)

p2 <- ggeffects::ggpredict(m2,
                           terms = "Pre_PostATTACK")


p2p <- plot(p2,
            add.data = TRUE,
            dot.alpha = .05,
            jitter = .8) +
  ylab("Warmth to NZ Europeans (1-7)") + labs(title = "Warm NZ Europeans") +
  geom_hline(yintercept = 4.395, color = "green") +
  # scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() +
  xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()


# maori
m3 <- lmer(Warm.Maori ~     Pre_PostATTACK  +
             (1 | Id) ,
           data = km2)
p3 <- ggeffects::ggpredict(m3,
                           terms = "Pre_PostATTACK")


p3p <- plot(p3,
            add.data = TRUE,
            dot.alpha = .05,
            jitter = .8) +
  ylab("Warmth to Māori (1-7)") + labs(title = "Warmth to Māori") +
  geom_hline(yintercept = 4.395, color = "green") +
  #  scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()

# migrants

m4 <- lmer(Warm.Immigrants ~    Pre_PostATTACK  +
             (1 | Id) ,
           data = km2)

p4 <- ggeffects::ggpredict(m4,
                           terms = "Pre_PostATTACK")

p4p <- plot(p4,
            add.data = TRUE,
            dot.alpha = .05,
            jitter = .8) +
  ylab("Warmth to Immigrants (1-7)") + labs(title = "Warmth to Immigrants") +
  geom_hline(yintercept = 4.395, color = "green") +
  #  scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()


# asians

m5 <- lmer(Warm.Asians ~ Pre_PostATTACK +
             (1 | Id) ,
           data = km2)

p5 <- ggeffects::ggpredict(m5,
                           terms = "Pre_PostATTACK")

p5p <- plot(p5,
            add.data = TRUE,
            dot.alpha = .05,
            jitter = .8) +
  ylab("Warmth to Asians (1-7)") + labs(title = "Warmth to Asians") +
  geom_hline(yintercept = 4.395, color = "green") +
  #  scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()


# indians

m6 <- lmer(Warm.Indians ~ Pre_PostATTACK +
             (1 | Id) ,
           data = km2)

p6 <- ggeffects::ggpredict(m6,
                           terms = "Pre_PostATTACK")

p6p <- plot(p6,
            add.data = TRUE,
            dot.alpha = .05,
            jitter = .8) +
  ylab("Warmth to Indians (1-7)") + labs(title = "Warmth to Indians") +
  geom_hline(yintercept = 4.395, color = "green") +
  #  scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()


# Chinese

m7 <- lmer(Warm.Chinese  ~  Pre_PostATTACK  +
             (1 | Id) ,
           data = km2)

p7 <- ggeffects::ggpredict(m7,
                           terms = "Pre_PostATTACK")

p7p <- plot(p7,
            add.data = TRUE,
            dot.alpha = .05,
            jitter = .8) +
  ylab("Warmth to Chinese (1-7)") + labs(title = "Warmth to Chinese") +
  geom_hline(yintercept = 4.395, color = "green") +
  #  scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()


## pacific

m8 <- lmer(Warm.Pacific  ~  Pre_PostATTACK  +
             (1 | Id) ,
           data = km2)


p8 <- ggeffects::ggpredict(m8,
                           terms = "Pre_PostATTACK")

p8p <- plot(p8,
            add.data = TRUE,
            dot.alpha = .05,
            jitter = .8) +
  ylab("Warmth to Pacific (1-7)") + labs(title = "Warmth to Pacific Peoples") +
  geom_hline(yintercept = 4.395, color = "green") +
  #  scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()


## Elderly (contrast)

m9 <- lmer(Warm.Elderly  ~  Pre_PostATTACK +
             (1 | Id),
           data = km2)


p9 <- ggeffects::ggpredict(m9,
                           terms = "Pre_PostATTACK")

p9p <- plot(p9,
            add.data = TRUE,
            dot.alpha = .05,
            jitter = .8) +
  ylab("Warmth to Elderly (1-7)") + labs(title = "Warmth to Elderly") +
  geom_hline(yintercept = 4.395, color = "green") +
  #  scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()


## mental illness

m10 <- lmer(Warm.MentalIllness  ~  Pre_PostATTACK +
              (1 | Id) ,
            data = km2)

p10 <- ggeffects::ggpredict(m10,
                            terms = "Pre_PostATTACK")

p10p <- plot(p10,
             add.data = TRUE,
             dot.alpha = .05,
             jitter = .8) +
  ylab("Warmth to the Mentally ill (1-7)") + labs(title = "Warmth to the Mentally ill") +
  geom_hline(yintercept = 4.395, color = "green") +
  #  scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()


### Overweight

m11 <- lmer(Warm.Overweight  ~ Pre_PostATTACK +
              (1 | Id) ,
            data = km2)


p11 <- ggeffects::ggpredict(m11,
                            terms = "Pre_PostATTACK")

p11p <- plot(p11,
             add.data = TRUE,
             dot.alpha = .05,
             jitter = .8) +
  ylab("Warmth to Overweight (1-7)") + labs(title = "Warmth to Overweight") +
  geom_hline(yintercept = 4.395, color = "green") +
  #  scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()


## Refugees

m12 <- lmer(Warm.Refugees  ~  Pre_PostATTACK +
              (1 | Id) ,
            data = km2)

p12 <- ggeffects::ggpredict(m12,
                            terms = "Pre_PostATTACK")

p12p <- plot(p12,
             add.data = TRUE,
             dot.alpha = .05,
             jitter = .8) +
  ylab("Warmth to Refugees (1-7)") + labs(title = "Warmth to Refugees") +
  geom_hline(yintercept = 4.395, color = "green") +
  #  scale_y_continuous(limits = c(1, 7)) +
  scale_colour_viridis_d() + xlab("Attack timeline") +
  # geom_vline(xintercept = c(2.8), colour = "red", linetype = "dashed") +
  theme_classic()

## For graphs
desc <-
  'Large boost in Muslim acceptance following shootings; However Muslim acceptance still lags behind.'
p1a <-
  p1p #+   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack', description = desc), colour = "red") + theme_classic()
p2a <-
  p2p # +   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()
p3a <-
  p3p # +   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()
p4a <-
  p4p #+   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()

p5a <-
  p5p #+   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()


p6a <-
  p6p #+   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()

p7a <-
  p7p #+   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()


p8a <-
  p8p #+   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()

p9a <-
  p9p #+   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()

p10a <-
  p10p #+   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()

p11a <-
  p11p #+   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()

p12a <-
  p12p #+   ggforce::geom_mark_ellipse(aes(label = 'Pre/Post Attack')) + theme_classic()

lds3 <-
  (p1a +
     p4a +
     p5a +
     p6a +
     p7a +
     p12a) +
  plot_annotation(tag_levels = 'a',
                  title = "Attitudes to Muslims and Non-Pacific Minorities",
                  subtitle = "pre/post attacks") +
  plot_layout(guides = 'collect', ncol = 1) +
  theme_classic()

lds4 <-
  (p2a +
     p3a +
     p8a +
     p9a +
     p10a +
     p11a) + plot_annotation(tag_levels = 'a',
                             title = "Attitudes to contrast groups",
                             subtitle = "Pacific/Maori/non-Ethnic minorities: pre/post attacks") +  plot_layout(guides = 'collect', ncol = 1)


 
# RESULTS BELOW
# parameters::model_parameters(m1)
# parameters::model_parameters(m2)
# parameters::model_parameters(m2)
# parameters::model_parameters(m2)
# parameters::model_parameters(m2)
# parameters::model_parameters(m2)
# parameters::model_parameters(m2)
# parameters::model_parameters(m2)
# parameters::model_parameters(m2)
# parameters::model_parameters(m11)
# parameters::model_parameters(m10)
# parameters::model_parameters(m11)

Show code

if (!require(xaringanExtra)) {
  devtools::install_github("gadenbuie/xaringanExtra")
}

rlang (1.0.0 -> 1.0.1) [CRAN]

The downloaded binary packages are in
    /var/folders/yg/51026mdn5qx17sxynkkp_xdh0000gq/T//RtmpwlAaie/downloaded_packages
* checking for file ‘/private/var/folders/yg/51026mdn5qx17sxynkkp_xdh0000gq/T/RtmpwlAaie/remotes22f6710391f1/gadenbuie-xaringanExtra-ee5092d/DESCRIPTION’ ... OK
* preparing ‘xaringanExtra’:
* checking DESCRIPTION meta-information ... OK
* checking for LF line-endings in source and make files and shell scripts
* checking for empty or unneeded directories
Omitted ‘LazyData’ from DESCRIPTION
* building ‘xaringanExtra_0.5.5.tar.gz’

Talk

Click here to go to the lecture slides.

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xaringanExtra::embed_xaringan(url = "slides.html", ratio = "16:9")

Summary

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p1pp

$\label{fig:muslimboost} Change in acceptance of Muslims following the Christchurch Mosque attacks March 15, 2019. Green horizonal line indicates peak acceptance of Muslims, which occurred following the attacks (N = 11272, Years = 2017-2020, Source = New Zealand Attitudes and Values Study)$

Figure 1: Change in acceptance of Muslims following the Christchurch Mosque attacks March 15, 2019. Green horizonal line indicates peak acceptance of Muslims, which occurred following the attacks (N = 11272, Years = 2017-2020, Source = New Zealand Attitudes and Values Study)

Following the Christchurch Mosque attacks, warmth towards Muslim grew.
Despite this growth, warmth to Muslims lags behind all other groups (except overweight people.)
Following the mosque attacks non-Pacific/Māori minority groups with Muslims also received a boost in warmth.
The Muslim acceptance gap remains large.

Introduction

We investigate warmth to Muslims before and after the 15 March 2019 mosque attacks in a nationally diverse longitudinal sample of New Zealanders (N = 11,272), and clarify how levels of warmth changed to Muslims and other groups.¹

Findings

Following the attacks there was a substantial increase in warmth to Muslims (Figure 2a)
Following the attacks, there was also a small boost in warmth to Immigrants (Figure 2b), Asians (Figure 2c), Indians (Figure 2d), Chinese people (Figure 2e), and Refugees (Figure 2f.)
The boost in warmth was specific to Muslims and non-Pacific minorities (Figure 2a-f.)
Despite the post-attack boost in warmth to Muslims, warmth to Muslims still lags behind warmth to other groups. (Figure 2a-f).
Whether the in boost to warmth to Muslims and ethnic groups associated with Muslims persists remains unclear.

Muslims and groups non-Pacific ethnic minorities

Show code

lds3

$\label{fig:graphsmodels} Increase in Warmth to Muslims After Shootings (red-dashed line), however, the acceptance of Muslims lags behind other minority groups. Green horizonal line indicates peak acceptance of Muslims, (N = 11272, Years = 2017-2020, Source = New Zealand Attitudes and Values Study)$

Figure 2: Increase in Warmth to Muslims After Shootings (red-dashed line), however, the acceptance of Muslims lags behind other minority groups. Green horizonal line indicates peak acceptance of Muslims, (N = 11272, Years = 2017-2020, Source = New Zealand Attitudes and Values Study)

Contrast groups that are not associated with Muslims.

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lds4

$\label{fig:graphsmodels2} There is no evidence for substantial change in prejudice pre-post shootings among groups that are not associated with Muslims. Green horizonal line indicates peak acceptance of Muslims, which occurred following the attacks (N = 11272, Years = 2017-2020, Source = New Zealand Attitudes and Values Study)$

Figure 3: There is no evidence for substantial change in prejudice pre-post shootings among groups that are not associated with Muslims. Green horizonal line indicates peak acceptance of Muslims, which occurred following the attacks (N = 11272, Years = 2017-2020, Source = New Zealand Attitudes and Values Study)

Importance of findings

Although the purpose of the Mosque attacks was to incite division, the attacks caused a strong boost in acceptance.
The reasons for the boost in Muslim acceptance are unclear.
Speculating, the government’s response, media’s response (Shaver et al. 2017), and non-Muslim church responses (Shaver et al. 2016) likely played a role in boosting Muslim acceptance.
Whether growth in acceptance will be sustained remains unclear
Whether acceptance reverts to pre-shooting levels remains unclear.

Appendix 1: Design

Participants responded to waves 9, 10, and 11 of the New Zealand Attitudes and Values Study. In total there were 11482 participants who responded to all three waves. See Figure 4, Table 1

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km2 %>%
  select(Pre_PostATTACK) %>%
  group_by(Pre_PostATTACK) %>%
  summarise(n = n()) %>%
  kbl(caption = "Participant numbers divided by condition: baseline, wave 10 pre/shootings, wave 10 post-shooting, and wave 11") %>%
   kable_classic_2(c("striped", "hover"), full_width = TRUE)

Table 1: Participant numbers divided by condition: baseline, wave 10 pre/shootings, wave 10 post-shooting, and wave 11
Pre_PostATTACK	n
bsline_w09	11482
pre_w10	8120
pst_w10	3362
pst_w11	11482

Show code

## timeline
rarep<-df%>%
  dplyr::filter(YearMeasured == 1) %>%
  dplyr::filter(Wave == 2017 | Wave == 2018 | Wave == 2019) %>%
  dplyr::group_by(Id) %>% filter(n() == 3) %>%
  dplyr::filter(Wave == 2017 | Wave == 2018 | Wave == 2019) %>%
  droplevels() %>%
  dplyr::ungroup(Id) %>%
  dplyr::mutate(timeline = make_date(year = 2009, month = 6, day = 30)+ TSCORE)%>%
  dplyr:::count(day = floor_date(timeline, "day"))%>%
  dplyr::mutate(Condition = factor(ifelse(day < "2018-06-18", -1, 
                                          ifelse(day >="2018-06-18" & day < "2019-03-15", 0,
                                                 ifelse( day >="2019-03-15" & day < "2019-06-18",1 , 
                                                         2))),
                                                 labels= c("PRE-ATTACK Baseline wv9 N = 11,481","PRE-ATTACK CONTROL rep measure wv10: n = 8120","IMMEDIATE POST-ATTACK rep measure wv10: PostAttack n = 3361", "DELAY POST-ATTACK Repeated measure post attack 2019: N= 11,481")))%>%
  arrange(day)
#table(rarep$Condition)
# get data of change of baseline
dates_vline1<- as.Date("2018-06-18")

table(km2$Pre_PostATTACK)


bsline_w09    pre_w10    pst_w10    pst_w11 
     11482       8120       3362      11482

Show code


# get attack date
dates_vline2<- as.Date("2019-03-15")
dates_vline3<- as.Date("2019-06-18")

# for line in a graph
dates_vline2b<-which(rarep$day %in% dates_vline2)
dates_vline1b<-which(rarep$day %in% dates_vline1)
dates_vline3b<-which(rarep$day %in% dates_vline3)

lds2 <- ggplot(rarep, aes(day, n)) + geom_col(aes(fill = Condition)) + scale_x_date(date_labels = "%b/%Y",
                                                                                    limits = c(as.Date("2017-08-25"), as.Date("2020-10-15")))  +
  scale_y_continuous(expand = c(0, 0), limits = c(0, NA)) +
  geom_vline(xintercept = as.numeric(rarep$day[dates_vline2b]),
             col = "red",
             linetype = "dashed") +
  xlab("NZAVS Waves years 2017 - 2020 daily counts") + ylab("Count of Responses")+
  theme_classic() +
  annotate(
    "rect",
    xmin = as.Date("2017-08-25"),
    xmax = dates_vline1,
    ymin = 0,
    ymax = 900,
    alpha = 0
  ) +
  annotate(
    "rect",
    xmin = dates_vline2,
    xmax = dates_vline3,
    ymin = 0,
    ymax = 900,
    alpha = .3,
    fill = "darkred"
  ) +
  annotate(
    "rect",
    xmin = dates_vline1,
    xmax = dates_vline2,
    ymin = 0,
    ymax = 900,
    alpha = .3
  ) +
  annotate(
    "rect",
    xmin = dates_vline3,
    xmax = as.Date("2020-10-15"),
    ymin = 0,
    ymax = 900,
    alpha = .05,
    fill = "red"
  ) +
  annotate(
    "text",
    x = as.Date("2018-10-15"),
    y = 980,
    label = "Wave 10
           repeated pre-attacks"
  ) +
  annotate(
    "text",
    x = as.Date("2018-01-15"),
    y = 980,
    label = "Wave 9
           baseline (control)"
  ) +
  annotate(
    "text",
    x = as.Date("2019-04-30"),
    y = 980,
    label = "Wave 10
           repeated post attacks"
  ) +
  annotate(
    "text",
    x = as.Date("2020-02-15"),
    y = 980,
    label = "Wave 11
           repeated during the year following attacks"
  ) +
  annotate(
    geom = "curve",
    x = as.Date("2018-02-15"),
    y = 750,
    xend = as.Date("2018-10-15"),
    yend = 750,
    curvature = -.3,
    arrow = arrow(length = unit(2, "mm"))
  ) +
  annotate(
    geom = "curve",
    x = as.Date("2018-02-15"),
    y = 750,
    xend = as.Date("2019-05-15"),
    yend = 750,
    curvature = -.3,
    arrow = arrow(length = unit(2, "mm"))
  ) +
  annotate("text",
           x = as.Date("2018-02-01"),
           y = 750,
           label = "0") +
  annotate("text",
           x = as.Date("2018-11-05"),
           y = 750,
           label = "+1") +
  annotate(
    geom = "curve",
    x = as.Date("2019-05-15"),
    y = 800,
    xend = as.Date("2020-05-15"),
    yend = 750,
    curvature = -.3,
    arrow = arrow(length = unit(2, "mm"))
  ) +
  annotate("text",
           x = as.Date("2019-06-05"),
           y = 750,
           label = "+1") +
  annotate("text",
           x = as.Date("2020-05-30"),
           y = 750,
           label = "+2") +
  annotate(
    geom = "curve",
    x = as.Date("2018-11-15"),
    y = 800,
    xend = as.Date("2020-05-15"),
    yend = 750,
    curvature = -.3,
    arrow = arrow(length = unit(2, "mm"))
  ) +
  theme(
    legend.position = "top",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  scale_fill_viridis_d(option = "plasma")

### USE GRAPH OF TIMELINE
lds2

$\label{fig:pptimeline} Within-participant repeated measures covering the years before and following the attacks allow precise estimates of attitudinal change within-people (N = 11,481).$

Figure 4: Within-participant repeated measures covering the years before and following the attacks allow precise estimates of attitudinal change within-people (N = 11,481).

Appendix 2: Background

New Zealand ranks among the world’s most tolerant societies (OECD 2011). However, Muslims are New Zealand’s least accepted minority group (Shaver et al. 2016, 2017; Hawi et al. 2019, 2019; Yogeeswaran et al. 2019; Greaves et al. 2020; Sibley et al. 2020; Highland et al. 2019).

In the context of New Zealand history, the relatively high levels of prejudice against Muslim, as well as the confusion of Muslims with Arabs, might seem surprising.

First, New Zealand’s cultural diversity exceeds that of Australia, the United Kingdom, France, Germany, the Netherlands, and Scandinavia. Muslims are only a small part of this diversity. In 2018, New Zealand’s Muslim, just over 1.5% of the total population. The Islamic community has been present in New Zealand since at least 1874 (Shepard, 2006). The New Zealand Muslim community is of diverse ethnic and cultural origins — of those that affiliated with Islam in 2013, 26.9% were born in Asia, 25.7% in New Zealand, 23.3% in the Middle East or Africa, and 21% in the Pacific Islands (Center, 2013). Thus, fewer than a quarter of New Zealand Muslims can be classified as ‘Arab.’

Another reason that anti-Muslim in New Zealand is puzzling is that New Zealand is one of the world’s most socially progressive and tolerant societies (OECD 2011), which has a long tradition of religious tolerance and accommodation of religious diversity.

A final source of puzzlement about anti-Muslim prejudice in New Zealand is that there is no record of any extremist Muslim group in New Zealand, nor have there been any reported attacks of Muslims on non-Muslims By contrast many attacks against the Muslim minority were reported prior to 15 March 2019, when 51 Muslims were murdered at two mosques in a single day. Members of Islamic communities in New Zealand have long reported verbal and physical harassment, vandalism, negative media portrayal, and/or have faced difficulties in the labour market @. Thus, while Muslims in New Zealand have a basis for concern about their experiences of anti-Muslim prejudice, there is no basis from in experience for anti-Muslim prejudice among the non-Muslim majority

Sources of anti-Muslim prejudice

In previous research, we found that levels of media exposure predicted lower warmth towards Muslims; notably, this effect held at both the low and high end of the political conservativism scale (Shaver et al. 2017). Whether changes in media coverage of Muslims contributed to rising Muslim acceptance following the attacks is a question that our future research will consider.

We also found that prior to the attacks, religiously identified New Zealanders were on average more accepting of Muslims (Shaver et al. 2016). Whether this difference held after the attacks is another question that we will consider in future research.

Appendix 3: Prejudice trends from 2012-2020 reveal a longstanding “Muslim Acceptance Gap”

Figure 5 shows the longer term trend warmth towards Muslims, begining in 2012, when the NZAVS first included questions about about attitudes to Muslims. As evident in the figure, despite gradually increasing warmth towards Muslims, the group as a whole has experienced greater prejudice than other groups. Prejudice of any kind, towards Muslims or other groups, should be addressed because prejudice unfairly affects life experiences for those who are the targets of it. Experiences of unfairness conflict with New Zealand’s aspirations to extend manaakitanga, and whanaungatanga to all New Zealanders.

For more analysis of prejudice trends in New Zealand see: (Sibley et al. 2020)

Show code


ld <- ggplot(data = dt,
             aes(x = yrs,
                 y = Warm.Muslims))

lds_0 <-ld + geom_line(alpha = .005) +
  stat_summary(
    geom = "point",
    fun = mean,
    size = .1,
    alpha = .2
  ) +
  geom_smooth(method ="gam" ) +  theme_minimal() + labs("")   + theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) + scale_colour_viridis_d(option = "plasma") +   geom_vline(xintercept= 9.71, linetype="dashed", color = "red") +
  geom_hline(yintercept= 4.395, color = "green") +
  ylab("Warmth (1-7)") +
  labs(title="Warmth to Muslims") # +
#  xlab("Years 2011-2020, N = 22,254")

li <- ggplot(data = di,
             aes(x = yrs,
                 y = Warm.Immigrants))

li_0 <-li + geom_line(alpha = .005) +
  stat_summary(
    geom = "point",
    fun = mean,
    size = .1,
    alpha = .2
  ) +
  geom_smooth(method = "gam") +
  theme_minimal() + labs("")   + theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) + scale_colour_viridis_d(option = "plasma") +
  geom_vline(xintercept= 9.71, linetype="dashed", color = "red") +
  geom_hline(yintercept= 4.4, color = "green") +
  ylab("Warmth (1-7)") +
  labs(title="Warmth to Immigrants") #+
 # xlab("Years 2011-2020, N = 22,254")

## indians
lm <- ggplot(data = dindians,
             aes(x = yrs,
                 y = Warm.Indians))

lm_m <-lm + geom_line(alpha = .005) +
  stat_summary(
    geom = "point",
    fun = mean,
    size = .1,
    alpha = .2
  ) +
  geom_smooth( method = "gam" ) +
  theme_minimal() + labs("")   + theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) + scale_colour_viridis_d(option = "plasma") +
  geom_vline(xintercept= 9.71, linetype="dashed", color = "red") +
  geom_hline(yintercept= 4.4, color = "green") +
  ylab("Warmth (1-7)") +
  labs(title="Warmth to Indians")
  #+
 # xlab("Years 2011-2020, N = 22,254")

# Chinese
lnz <- ggplot(data = dchinese,
              aes(x = yrs,
                  y = Warm.Chinese))

lnz_0 <-lnz + geom_line(alpha = .005) +
  stat_summary(
    geom = "point",
    fun = mean,
    size = .1,
    alpha = .2
  ) +
  geom_smooth( method = "gam" ) +
  theme_classic() + labs("")   + theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) + scale_colour_viridis_d(option = "plasma") +
  geom_vline(xintercept= 9.71, linetype="dashed", color = "red") +
  geom_hline(yintercept= 4.4, color = "green") +
  ylab("Warmth (1-7)") +
  labs(title="Warmth to Chinese") +
  xlab("Years 2011-2020, N = 22,254")

lds_0 + li_0 + lm_m + lnz_0 +  plot_layout(guides = 'collect', nrow = 1, byrow = FALSE) +   plot_annotation(title = "Timeline of attitudes to different groups in New Zealand; years 2011-2020: Source: NZAVS, N = 22,254", subtitle = "Green line = maximum level of warmth for Muslims, red line = Mosque attacks; Alpha for points on graph were set to .02 to avoid overplotting; method for estimation", tag_levels = 'a')

$\label{fig:warmthtime} Within-person prejudice change pre/post attacks towards (a) Muslims, (b) Immigrants, (c) Māori, (d) NZ Europeans. Redline indicates mosque attacks. Greenline indicates maximum warmth to Muslims, which was strongly boosted post-attacks, yet lags behind other groups$

Figure 5: Within-person prejudice change pre/post attacks towards (a) Muslims, (b) Immigrants, (c) Māori, (d) NZ Europeans. Redline indicates mosque attacks. Greenline indicates maximum warmth to Muslims, which was strongly boosted post-attacks, yet lags behind other groups

Show code


m6 <- lmer(Warm.Muslims ~  cons_s * Attack +  EthnicCats + 
             a_s +
             r_s + (1|Id) , # dependence owing to the week of response
           data=km2)

p6 <- plot(ggeffects::ggpredict(m6, terms = c("Attack [all]", "cons_s[all]"), ci = .89)) + xlab("Conservativism") + ylab("Warmth Muslims") +  scale_colour_viridis_d()

p7 <- plot(ggeffects::ggpredict(m6, terms = c("cons_s[all]","Attack [all]"), ci = .89), add.data = TRUE, dot.alpha = 0.02, jitter = .2 ) + scale_y_continuous(limits = c(.5,7.5)) +
  xlab("Political Conservativism (SD)") + ylab("Warmth Muslims (1-7") + 
  labs(title = "Warmth to Muslims") + scale_colour_viridis_d()   +
  theme(
    # Change legend background color
    # legend.background = element_rect(fill = "darkgray"),
    #  legend.key = element_rect(fill = "lightblue", color = NA),
    # Change legend key size and key width
    legend.key.size = unit(0.2, "cm"),
     legend.direction = "vertical",,
    legend.key.width = unit(0.2, "cm"),
  ) + 
  guides(size = 1.5)

desc <- "Post Shooting Difference"

p71<-p7 + annotate("rect", 
              xmin = 2., 
              xmax = 2.5, 
              ymin = 3.5, 
              ymax = 4.1,
              alpha = .2, 
              colour = "red") + 
  annotate(
    "rect",
    xmin = -2.,
    xmax = -1.5,
    ymin = 4.4,
    ymax = 4.8,
    alpha = .2,
    colour = "red"
  ) + 
  annotate(
    "text",
    x = 0,
    y = 7.2,
    label = " Greater relative growth in 
    acceptance among conservatives",
    colour = "brown3"
  ) + 
  annotate(
    geom = "curve",
    x = -1,
    y = 6.7,
    xend = -1.75,
    yend = 4.9,
    curvature = 0,
    arrow = arrow(length = unit(2, "mm")),
    colour = "red"
  ) +
   annotate(
    geom = "curve",
    x = 1,
    y = 6.7,
    xend = 2.25,
    yend = 4.2,
    curvature = 0,
    arrow = arrow(length = unit(2, "mm")),
    colour = "red"
  ) 

## Compare this to warmth to Maori
m8 <- lmer(Warm.Maori ~  cons_s * Attack +  EthnicCats + 
             a_s +
             r_s +(1|Id) , # dependence owing to the week of response
           data=km2)

p8 <- plot(ggeffects::ggpredict(m8, terms = c("cons_s[all]","Attack [all]"), ci = .89), 
          add.data = TRUE, dot.alpha = 0.02, jitter = .2 ) + scale_y_continuous(limits = c(.5,7.5)) +
  xlab("Political Conservativism (SD)") + ylab("Warmth Māori (1-7") + 
  labs(title = "Warmth to Māori") + scale_colour_viridis_d()+ 
    theme(
    # Change legend background color
    # legend.background = element_rect(fill = "darkgray"),
    #  legend.key = element_rect(fill = "lightblue", color = NA),
    # Change legend key size and key width
    legend.key.size = unit(0.2, "cm"),
    legend.direction = "vertical",
    legend.key.width = unit(0.2, "cm")
  )+ 
  guides(size = 1.5)

## Warmth to NZEuro
m9 <- lmer(Warm.NZEuro ~  cons_s * Attack +  EthnicCats + 
             a_s +
             r_s +(1|Id) , # dependence owing to the week of response
           data=km2)

p9 <- plot(ggeffects::ggpredict(m9, terms = c("cons_s[all]","Attack [all]"), ci = .89), 
          add.data = TRUE, dot.alpha = 0.02, jitter = .2 ) + scale_y_continuous(limits = c(.5,7.5)) +
  xlab("Political Conservativism (SD)") + ylab("Warmth NZ Europeans (1-7") + 
  labs(title = "Warmth to NZ Europeans") + scale_colour_viridis_d() + 
    theme(
    # Change legend background color
    # legend.background = element_rect(fill = "darkgray"),
    #  legend.key = element_rect(fill = "lightblue", color = NA),
    # Change legend key size and key width
    legend.key.size = unit(0.2, "cm"),
     legend.direction = "vertical",
    legend.key.width = unit(0.2, "cm")
  )+ 
  guides(size = 1.5)

m10 <- lmer(Warm.Immigrants ~  cons_s * Attack +  EthnicCats + 
             a_s +
             r_s +(1|Id) , # dependence owing to the week of response
           data=km2)

p10 <-
  plot(
    ggeffects::ggpredict(
      m10,
      terms = c("cons_s[all]", "Attack [all]"),
      ci = .89
    ),
    add.data = TRUE,
    dot.alpha = 0.02,
    jitter = .2
  ) + scale_y_continuous(limits = c(.5, 7.5)) +
  xlab("Political Conservativism (SD)") + ylab("Warmth Immigrants (1-7") +
  labs(title = "Warmth to Immigrants") + scale_colour_viridis_d()+
  theme(
    # Change legend background color
    # legend.background = element_rect(fill = "darkgray"),
    #  legend.key = element_rect(fill = "lightblue", color = NA),
    # Change legend key size and key width
    legend.key.size = unit(0.2, "cm"),
    legend.key.width = unit(0.2, "cm"),
    legend.direction = "vertical") +
  guides(size = 1.5) +
  annotate(
    "rect",
    xmin = 2.,
    xmax = 2.5,
    ymin = 4.2,
    ymax = 4.4,
    alpha = .2,
    colour = "red"
  ) + 
  annotate(
    "rect",
    xmin = -2.,
    xmax = -1.5,
    ymin = 4.75,
    ymax = 4.95,
    alpha = .2,
    colour = "red"
  ) + 
  annotate(
    "text",
    x = 0,
    y = 7.2,
    label = "Boost to Immigrant Warmth was linear across political conservativism",
    colour = "brown3"
  ) + 
  annotate(
    geom = "curve",
    x = -1,
    y = 6.7,
    xend = -1.75,
    yend = 4.95,
    curvature = 0,
    arrow = arrow(length = unit(2, "mm")),
    colour = "red"
  ) +
   annotate(
    geom = "curve",
    x = 1,
    y = 6.7,
    xend = 2.25,
    yend = 4.4,
    curvature = 0,
    arrow = arrow(length = unit(2, "mm")),
    colour = "red"
  )

Appendix 4: Political conservatives also grew more accepting of Muslims after the 2019 Mosque attacks.

Generally, greater political conservativism predicts greater minority group prejudice. In absolute terms, however, growth in Muslim acceptance following the shootings was stronger at the conservative end of the scale (see Figure 6.

Show code

lds4 <-  p71 + p10 + p8 + p9 +  plot_annotation(tag_levels = 'a') +   plot_layout(ncol = 2,  byrow = FALSE)  +  plot_layout(guides = 'collect')
lds4

$\label{fig:graphsmodelsPol} Warmth to Muslims (y-axis) and other groups by political conservativism (x-axis) pre/post shootings (the solid lines). The shift betweenand the purple/blue lines (pre-attacks) and the yellow/green lines (post-attacks) shows the magnitude of growth in minority group acceptance. Here we plot all expected values of the model as points. The darkness of the areas on the graph reflect densities of responses. This approach allows us to visualise the variance around average responses. Results reveal that the mosque attacks led to a greater increase in warmth towards Muslims among political conservatives. However, this population had more headroom for growth in warmth. Whether this boost in Muslim warmth is sustained remains to be seen: (N = 11272, Years = 2017-2020, Source = New Zealand Attitudes and Values Study)$

Figure 6: Warmth to Muslims (y-axis) and other groups by political conservativism (x-axis) pre/post shootings (the solid lines). The shift betweenand the purple/blue lines (pre-attacks) and the yellow/green lines (post-attacks) shows the magnitude of growth in minority group acceptance. Here we plot all expected values of the model as points. The darkness of the areas on the graph reflect densities of responses. This approach allows us to visualise the variance around average responses. Results reveal that the mosque attacks led to a greater increase in warmth towards Muslims among political conservatives. However, this population had more headroom for growth in warmth. Whether this boost in Muslim warmth is sustained remains to be seen: (N = 11272, Years = 2017-2020, Source = New Zealand Attitudes and Values Study)

Appendix 5: Regression Discontinuity Analysis

Show code


df_rdd <- df %>%
    dplyr::filter(Wave == 2018) %>%
  dplyr::select(
    Id,
    Age,
    Male,
    TSCORE,
    EthnicCats,
    Warm.Overweight,
    Warm.Elderly,
    Warm.MentalIllness,
    Warm.Muslims,
    Warm.Immigrants,
    Warm.Asians,
    Warm.Refugees,
    Wave,
    Warm.Maori,
    Warm.NZEuro,
    Warm.Indians,
    Warm.Chinese,
    Warm.Refugees,
    Warm.Pacific,
    Muslim,
    Euro,
    Relid,
    Religious,
    Pol.Orient,
    TSCORE,
    WSCORE,
    Terrorism.Anxiety,
    KESSLER6sum,
    KESSLER6,
    BELONG,
    PWI,
    SWB.SoC01,
    YearMeasured,
    WSCORE
  ) %>%
  dplyr::filter(TSCORE >= 3395 & TSCORE <= 3695
  )%>%
  dplyr::mutate(Pre_Post_attack = as.factor(ifelse(
    TSCORE >= 3545, "Post_Attack", "Pre_Attack"
  ))) %>%
  dplyr::mutate(Neighbourhood_Community = SWB.SoC01) %>%
  dplyr::mutate(Personal_Wellbeing = PWI) %>%
  dplyr::mutate(Pre_Post_attack = forcats::fct_relevel(Pre_Post_attack, c("Pre_Attack", "Post_Attack"))) %>%
  dplyr::mutate(days = as.integer(TSCORE),
                years = days / 365) %>%
  dplyr::mutate(days_0 = as.integer(days - min(days))) %>%
  dplyr::filter(Muslim == 0)%>%
  dplyr::mutate(Timeline = make_date(year = 2009, month = 6, day = 30)+ TSCORE)


length(unique(df_rdd$Id))

[1] 17228

Show code

plot(df_rdd$days_0)

table1::table1(~ Age + Male + EthnicCats, data = df_rdd)

	Overall (N=17228)
Age
Mean (SD)	52.7 (13.6)
Median [Min, Max]	54.4 [19.0, 99.1]
Missing	1 (0.0%)
Male
Not_Male	10797 (62.7%)
Male	6386 (37.1%)
Missing	45 (0.3%)
EthnicCats
Euro	14018 (81.4%)
Maori	1980 (11.5%)
Pacific	318 (1.8%)
Asian	642 (3.7%)
Missing	270 (1.6%)

Show code





df_rdd2 <- df %>%
    dplyr::filter(Wave == 2018) %>%
  dplyr::select(
    Id,
    Age,
    Male,
    TSCORE,
    EthnicCats,
    Warm.Overweight,
    Warm.Elderly,
    Warm.MentalIllness,
    Warm.Muslims,
    Warm.Immigrants,
    Warm.Asians,
    Warm.Refugees,
    Wave,
    Warm.Maori,
    Warm.NZEuro,
    Warm.Indians,
    Warm.Chinese,
    Warm.Refugees,
    Warm.Pacific,
    Muslim,
    Euro,
    Relid,
    Religious,
    Pol.Orient,
    TSCORE,
    WSCORE,
    Terrorism.Anxiety,
    KESSLER6sum,
    KESSLER6,
    BELONG,
    PWI,
    SWB.SoC01,
    YearMeasured,
    WSCORE
  ) %>%
  dplyr::filter(TSCORE >= 3288 # July 1 2018
  )%>%
  dplyr::mutate(Pre_Post_attack = as.factor(ifelse(
    TSCORE >= 3545, "Post_Attack", "Pre_Attack"
  ))) %>%
  dplyr::mutate(Neighbourhood_Community = SWB.SoC01) %>%
  dplyr::mutate(Personal_Wellbeing = PWI) %>%
  dplyr::mutate(Pre_Post_attack = forcats::fct_relevel(Pre_Post_attack, c("Pre_Attack", "Post_Attack"))) %>%
  dplyr::mutate(days = as.integer(TSCORE),
                years = days / 365) %>%
  dplyr::mutate(days_0 = as.integer(days - min(days))) %>%
  dplyr::filter(Muslim == 0)%>%
  dplyr::mutate(Timeline = make_date(year = 2009, month = 6, day = 30)+ TSCORE)


length(unique(df_rdd$Id))

[1] 17228

Show code

plot(df_rdd$days_0)

Show code


table1::table1(~ Age + Male + EthnicCats, data = df_rdd)

	Overall (N=17228)
Age
Mean (SD)	52.7 (13.6)
Median [Min, Max]	54.4 [19.0, 99.1]
Missing	1 (0.0%)
Male
Not_Male	10797 (62.7%)
Male	6386 (37.1%)
Missing	45 (0.3%)
EthnicCats
Euro	14018 (81.4%)
Maori	1980 (11.5%)
Pacific	318 (1.8%)
Asian	642 (3.7%)
Missing	270 (1.6%)

Show code




# TERRORISM ANXIETY
df$YearMeasured

    [1]  1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1  1  0  0  0  1  1
   [18]  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1
   [35]  0  0  0  0  0  0  1  0  1  1  1  1  0  0  0  0  0
   [52]  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  0
   [69]  0  1  0  0  0  0  0  0  0  1  1  0  1  0  0  0  0
   [86]  0  1  0  1  0  0  0  0  0  0 -1 -1 -1 -1  1  1  1
  [103]  1  1  1  1  1  0  1  0  1  0  0  1  0  0  0  0  0
  [120]  0  0  1  1  0  0  1  0  0  0  0  0  0  1  0  0  0
  [137]  1  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1
  [154]  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  [171]  1  1  1  1  0  1  1  0  0  0  0  0  0  0  0  0  0
  [188]  1  0  0  1  1  1  1  1  1  0  0  1  1  1  1  1  1
  [205]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  [222]  0  1  0  1  1  1  1  1  1  1  1  1  0  1  1  1  0
  [239]  1  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1
  [256]  1  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1
  [273]  0  0  0  1  1  1  1  1  1  1  1  0  0  1  1  1 -1
  [290] -1 -1 -1 -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  1
  [307]  1  1  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1
  [324]  1  1  1  1  1  1  1  1  1  1  1  0  0  0 -1 -1 -1
  [341] -1  1  1  1  1  1  1  1  0  0  0  0  1  1  1  1  1
  [358]  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1
  [375]  1  0  0  0  0  0  0  0  0  0  0  1  0  1  0  1  1
  [392]  1  1  0  1  1  1  1  1  0  0  0  0 -1 -1 -1 -1  1
  [409]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  [426]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  [443]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  [460]  0  1  1  1  0  0  0  0  0  0  0  0  0  0  1  1  1
  [477]  1  1  1  1  1  1  1  0  1  1  0  0  0  0  0  0  0
  [494]  0  0  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0
  [511]  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1
  [528]  1  1  1  1  1  1  1  1 -1 -1 -1 -1  1  1  0  0  0
  [545]  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1
  [562]  1  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0
  [579]  0  0  0  0  0  1  1  1  0  1  0  0  0  0  0  0  1
  [596]  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1
  [613]  1  1  1  1  1  0  0  0  1  0  0  0  1  0  0  1  1
  [630]  0  0  1  1  0  0  0  0  0  1  1  1  1  1  1  1  1
  [647]  1  1  1  1  1  1  1  1  1  1  1  1  0  0  1  1  1
  [664]  0  0  0  0  0  0  0  0  1  1  0  0  0  0  0  0  0
  [681]  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1
  [698]  1  1  1  1  1  1  1  1  1  0  0  0  0  1  0  0  0
  [715]  0  1  1  1  1  1  1  0  1  0  1  0  1  0  0  0  0
  [732] -1 -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1
  [749]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
  [766]  0  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1
  [783]  1  1  1  1  1  1  1  0  0  0  1  1  1  0  0  0  0
  [800] -1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1
  [817]  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1
  [834]  1  1  1  1  1  1 -1 -1 -1 -1 -1 -1 -1 -1  1  1  1
  [851]  1  0  1  1  1  1  1  1  1  0  0  0  0  0  1  1  0
  [868]  0  0  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1
  [885]  0  1  1  1  0  0  0  1  1  1  0  0  1  1  0  0  0
  [902]  0  1  1  1  1  1  1  1  0  0  0  0  1  0  0  0  0
  [919]  0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  0  0
  [936]  1  1  0  0  1  1  0  1  0  1  1  1  1  1  1  1  1
  [953]  1  1  0  1  0  1  1  1  1  0  1  0  0  1  1  1  1
  [970]  0  0  0  0  0  0  0  0  0  0  1  1  1  0  1  1  1
  [987]  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1  0
 [1004]  1  0  0  0  0  0  0  1  0  1  1  1  1  1  1  1  1
 [1021]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [1038]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0
 [1055]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [1072]  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  0  0
 [1089]  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1
 [1106]  1  0  0  0  0  0  1  1  0  0  0  0  0  0  0  0  0
 [1123]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [1140]  1  1  1  1  1  1  1  1  0  1  0  0  0  0  0  0  1
 [1157]  1  1  1  1  1  1  1  1  1  1  1  0  0  0  1  0  1
 [1174]  0  0  0  1  1  1  0  0  1  0  0  0  0  1  1  1  0
 [1191]  0  0  0  0  0  0  0  0  0  1  0  1  1  1  1  0  0
 [1208]  0  0  0  1  1  1  1  1  1  0  0  0  0  0  1  1  1
 [1225]  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0
 [1242]  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [1259]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [1276]  1  1  1  0  1  0  1  1  1 -1 -1 -1  1  1  0  1  0
 [1293]  1  0  1  0  1  0  1  1  0  1  1  1  1  0  1  1  1
 [1310]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [1327]  0  0  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1
 [1344]  0  0  0  1  1  0  0  0  0  0  1  1  1  1  1  1  1
 [1361]  1  1  1  0  1  0  0  1  1  1  0  0  0  0  0  1  0
 [1378]  0  0  0  0  0  0  0  1  0  1  0  0  0  0  0  0  0
 [1395]  0  0  0  1  0  1  0  1  0  0  0  0  0  0  1  1  1
 [1412]  1  1  1  1  1  1 -1 -1  1  1  1  1  1  1  1  1  1
 [1429]  1  1  1  0  1  0  1  1  1  1  1  0  0  1  0  0  0
 [1446]  0  0  0  0  0  0  0  1  0  0  1  1  0  0  0  0  0
 [1463]  0  1  1  1  1  1  1  0  0  0  0  0  1  1  1  1  0
 [1480]  1  0  1  0  0  0  1  1  0  1  0  0  0  0  0  1  0
 [1497]  1  1  1  1  1  1  1  1  1  1  1  1  1  0  1  0  0
 [1514]  0  0  0  0  0  1  1  0  0  0  0  0 -1 -1 -1 -1  1
 [1531]  0  0  0  0  0  1  0  1  0  1  1  0  0  0  0  0  0
 [1548]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1
 [1565]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  0  1
 [1582]  1  0  0  1  1  1  0  1  1  1  1  1  1  1  1  0  0
 [1599]  0  0  0  0  0  0  0  0  1  1  0  0  0  0  0  0  0
 [1616]  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1
 [1633]  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1
 [1650]  1  1  1  1  1  1  0  0  1  0  0  0  1  1  1  1  1
 [1667]  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0
 [1684]  1  1  1  1  1  0  0  0  0  0  0  1  0  0  0  0  0
 [1701]  1  0  0  0  0  1  1  1  1  1  1  0  0  0  0  0  1
 [1718]  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0
 [1735]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  0
 [1752]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1
 [1769]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [1786]  1  1  1  0  0  0  0  0  1  1  1  1  1  1  0  0  0
 [1803]  1  0  1  0  0  0  0  1  1  1  0  0  0  1  0  0  0
 [1820]  0  0  1  1  1  1  0  1  1  0  0  0  0  0  0  0  0
 [1837]  0  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1
 [1854]  1  1  1  1  1  1  1  1  0  1  1  0  0  0  0  0  0
 [1871]  1  1  1  1  1  0  0  1  0  1  0  1  1  1  0  1  1
 [1888]  1  1  0  0  0  1  1  1  1  1  1  0  1  1  1  1  1
 [1905]  1  0  1  0  0  0  0  0  0  0  1  0  0  1  1  0  1
 [1922]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  0
 [1939]  0  0  1  0  1  0  0  0  1  1  1  1  1 -1 -1 -1 -1
 [1956] -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [1973]  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1
 [1990]  1  1  1  1  0  0  0  1  1  1  0  1  1  1  1  1  1
 [2007]  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0
 [2024]  0  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1
 [2041]  0  0  1  0  1  0  1  1  1  1  0  1  1  1  1  1  1
 [2058]  1  0  0  1  1  1  1  1  1  0  0  1  1  1  1  1  1
 [2075]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [2092]  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  1  0
 [2109]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1
 [2126]  0  0  1  0  1  0  0  1  0  1  0  0  0  0  0  0  0
 [2143]  0  0  0  1  1  1  1  1  1  0  0  0  0  0  1  1  1
 [2160]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  1
 [2177]  1  0  1  0  0  1  0  0  0  1  0  0  0  1  1  1  1
 [2194]  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1  1
 [2211]  1  1  1  0  0  0  0  0  0  0  0  0  1  1  1  1  1
 [2228]  1  1  1  0  1  1  1  1  0 -1 -1 -1 -1 -1 -1 -1 -1
 [2245]  1  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0
 [2262]  0  0  0  0  0  1  0  0  0  0  0  0  0  0  1  0  1
 [2279]  1  1  1  1  1  1  1  0  1  1  1  1  1  0  0  0  0
 [2296] -1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [2313]  1  1  0  0  0  0  0  0  0  1  1  1  1  0  0  0  0
 [2330]  0  0  0  1  1  1  0  1  1  0  1  0  0  0  1  1  1
 [2347]  1  1  1  1  1  0  0  0  1  1  0  0 -1 -1 -1 -1 -1
 [2364] -1 -1  1  0  0  0  0  0  0  0  0  0  0  1  0  0  0
 [2381]  0  0  0  0  0  0  0  1  0  0  0  0  0  0 -1 -1 -1
 [2398] -1  1  1  1  1  1  1  0  0  0  0  0  1  1  1  1  1
 [2415]  1  0  1  0  0  1  1  0  0  0  0  0  0  0  0  0  0
 [2432]  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1
 [2449]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [2466]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [2483]  1  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1
 [2500]  1  1  1  1  0  0  0  0  0  1  1  0  0  1  0  0 -1
 [2517] -1 -1 -1  1  1  1  1  1  1  1  1  1  1  0  1  1  1
 [2534]  1  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1
 [2551]  1  0  1  1  1  0  1  1  1  1  0  1  1  1  1  1  0
 [2568]  0  0  0  0  0  1  0  1  1  1  0 -1 -1 -1 -1 -1 -1
 [2585] -1  1  0  0  0  0  0  0  0  0  0  0  1  1  1  0  1
 [2602]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [2619]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0
 [2636]  0  1  1  1  1  1  0  0  1  0  0  0  0  0  1  0  1
 [2653]  0  1  0  1  0  0  0  1  0  0  1  0  0  0  1  0  0
 [2670]  0  0  0  0  1  1  1  1  1  0  0  0  0  0  0  1  0
 [2687]  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [2704]  1  1  1  1  1  0  0  0  0  0  0  0  0  0  1  0  0
 [2721]  0  0  0  0  0  0  0  0  1  1  1  1  1  1  0  1  1
 [2738]  1  1  1  1  1  1  0  0  0  0  0  1  0  1  1  1  1
 [2755]  1  1  1  0  0  0  0  1  1  1  1  1  1  1  1  1  1
 [2772]  1  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1
 [2789]  1  1  1  1  1  1  1  1  1  1  1  1  1 -1 -1 -1 -1
 [2806]  1  0  0  1  1  0  0  0  0  0  0  1  1  1  1  0  1
 [2823]  1  1  1  1  0  1  0  0  1  1  0  0  0  0  0  0  1
 [2840]  1  0  1  1  1  0  0  0  0  0  1  1  0  0  1  1  1
 [2857]  1  1  1  1  1  1  1 -1 -1 -1 -1 -1 -1 -1 -1  1  1
 [2874]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [2891]  1  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1
 [2908]  0  1  1  1  1  0  1  1  1  1  0  0  0  0  0  0  0
 [2925]  1  1  1  1  1  0  0  0  0  0  0  0  0  1  1  1  1
 [2942]  1  1  1  1  1  1  1  1  1  0  1  1  0  0  0  0  0
 [2959]  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [2976]  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1
 [2993]  1  1  0  0  0  1  0  0  0  0  0  1  0  0  0  0  0
 [3010]  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1
 [3027]  0  0  0  0  0  1  0  0  0  0  1  0  1  0  0  0  1
 [3044]  1  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1
 [3061]  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [3078]  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  0
 [3095]  0  0  0  1  1  1  0  1  1  1  1  1  0  0  1  1  1
 [3112]  1  0  1  1  0  0  0  0  0  0  0  0  0  1  0  0  0
 [3129]  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0
 [3146]  0  1  1  0  0  0  0  0  0  0  0  0  1  0  0  0  0
 [3163]  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
 [3180]  1  1  1  1  1  1  1  1  1  0  1  1  0  0  0  0  0
 [3197]  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1
 [3214]  1  0  0  0  0  0  0  0  0  0  1  1  1  1  0  0  0
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 [3248]  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1
 [3265]  1  1  0  1  0  0  0  0  0  0  0  0  0  0  1  1  0
 [3282]  1  0 -1 -1 -1 -1 -1 -1  1  1  1 -1 -1 -1 -1 -1 -1
 [3299] -1 -1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0
 [3316]  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1
 [3333]  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  1  1
 [3350]  1  1  1  1  1  0  1  0  1  0  1  1  1  1  1  0  1
 [3367]  1  1  1  1  1  1  1  0  0  0  0  1  1  1  1  1  1
 [3384]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [3401]  0  0  0  0  0  0  0  0  0  0  1  1  1  0  1  1  1
 [3418]  1  0  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
 [3435]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [3452]  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1  0  0
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 [3486]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [3503]  0  1  1  0  0  0  0  1  1  0  0  0  0  0  0  0  1
 [3520]  0  1  1  1  0  0  0  0  0  0  0  0  1  0  1  1  0
 [3537]  1  0  0  1  1  0  1  1  0  1  1 -1 -1 -1 -1 -1 -1
 [3554]  1  1  0  0  0  0  0  0  0  0  0  1  1  1  1  0  1
 [3571]  1  1  0  1  0  1  0  0  0  0  0  0  0  0  0  0  1
 [3588]  1  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1
 [3605]  1  1  1  1  1  1 -1 -1 -1 -1 -1 -1 -1 -1 -1  1  1
 [3622]  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0
 [3639]  0  0  0  1  0  1  0  0  0  0  0  1  0  1  1  1  1
 [3656]  1  1  1  1  1  1  1  1  1  1  1  0  1  0  0  0  1
 [3673]  0  0  1  1  0  0  0  0  0  0  0  0  0  1  1  0  0
 [3690]  0  1  0  1  0  0  0  1  1  1  1  1  1  1  1  1  1
 [3707]  1  1  1  1  0  0 -1 -1 -1 -1 -1 -1  1  1  1  1  1
 [3724]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [3741]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [3758]  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1
 [3775]  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1
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 [3860]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [3877]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0
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 [3911]  0  1  1  0  0  0  1  1  1  1 -1 -1 -1 -1 -1 -1 -1
 [3928]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
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 [4013]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0
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 [4098]  1  1  1  1  0  1  1  1  0  1  1  1  1  1  1  1  1
 [4115]  1  1  1  1  0  0  1  0  1  1  1  1  1  1  1  1  1
 [4132]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [4149]  1  1  1  1  0  1  1  0  0  0  1  1  1  1  1  1  1
 [4166]  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1  0
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 [4217] -1 -1 -1 -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  1
 [4234]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [4251]  1  1 -1 -1 -1 -1 -1  1  1  0  0  0  0  0  0  0  0
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 [4285]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [4302]  1  0  0  1  1  1  1  1  0  1  1  1  1  0  0  0  1
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 [4336]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
 [4353]  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1  1  1
 [4370]  1  1  1  1  1  1  0  1  0  1  1  1  1  1  1  1  1
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 [4523]  0 -1 -1 -1 -1 -1 -1 -1 -1 -1  1  0  0  0  0  0  0
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 [5152]  1  1  1  1  1  1  0  0  1  1  1  1  0  1  1  1  0
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 [5968] -1 -1 -1 -1 -1 -1  1  0  0  0  0  1  1  1  1  1  1
 [5985]  1  1  1  1  1  0  0  0  0  0  0  1  1  1  1  1  1
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 [9538]  1  1 -1 -1 -1 -1 -1 -1 -1 -1 -1  1  1  1  1  1  1
 [9555]  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1
 [9572]  1  1  1  0  1  0  0  0  0  0  1  1  1  1  1  1  0
 [9589] -1 -1 -1 -1  1  0  0  0  0  0  0  0  0  0  0  1  1
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 [9708]  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1
 [9725]  1  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1
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 [9827]  1  1  1  0  1  0  0  0  1  0  0  1  1  0  1  1  0
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 [9963]  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1  1
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[10014]  1  1  0  0  0  0  0  0  1  1  0  1  1  0  1  1  0
[10031]  0  1  1  1  1  0  0  1  0  0  0  0  0  1  1  1  1
[10048]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[10065]  1  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  0
[10082]  1  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
[10099]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0 -1 -1
[10116] -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1  1
[10133]  1  1  1  1  0  0  0  0  0  0  1  1  1  1  1  1  0
[10150]  1  0  0  0  1  1  1  1  1  1  0  0  0  0  0  1  0
[10167]  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  1
[10184]  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  0  1
[10201]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[10218]  1  1  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1
[10235]  1  1  1  1  0  1  0  1  1  1  1  1  1  1  1  1  1
[10252]  1  1  0  0  1  1  1  1  1  0  1  1  1  1  1  0  0
[10269]  0 -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1
[10286]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[10303]  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  1
[10320]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[10337]  1  0  1  1  1  1  1  1  1  1  1  1  0  0  1  1  1
[10354]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[10371]  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1  1  1
[10388]  1  1  1  0  1  0  0  0  1  1  0  0  0  1  1  1  1
[10405]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0
[10422]  0  0  1  0  1  0  0  1  1  0  0  1  0  0  1  0  0
[10439]  1  1  0  0  1  1  1  1  1  0  0  0  1  0  0  0  0
[10456]  0  0  0  1  1  1  1  0  1  0  1  0  0  1  0  1  0
[10473]  1  1  0  1  0  1  0  1  0  0  1  1  1  1  1  1  1
[10490]  1  1  1  1 -1  1  0  0  0  0  0  0  0  0  0  0  1
[10507]  1  1  1  1  1  1  1  1  1  1  1  1  0  1  1  0  0
[10524]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1
[10541]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[10558]  1  1  1  1  1  0  1  0  0  0  0  0  0  0  1  0  1
[10575]  1  1  1  1  1  0  1  0  1  1  0  0  0  0  1  0  0
[10592]  0  0  1  0  0  0  1  0  0  0  0  0  0  1  1  1  1
[10609]  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1 -1 -1
[10626] -1  1  1  0  0  0  1  1  1  0  0  0  1  0  0  1  1
[10643]  0  0  0  0  0  0  1  1  1  1  0  0  0  0  0  0  0
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[10677]  0  1  1  0  0  1  1  1  1  1  1  1  1  1  1  0  1
[10694]  0  1  1  1  1  0  1  0  1  0  1  0  1  0  0  0  0
[10711]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1
[10728]  1  1  1  1  1  1  1  1  1  1  0  0  0  1  0  0  0
[10745]  0  0  0  1  1  1  1  1  1  0  1  0  1  0  1 -1 -1
[10762] -1 -1 -1 -1 -1 -1 -1 -1  1  0  0  0  1  1  0  1  0
[10779]  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[10796]  1  1  1  1  1  1  1  1  0  0  1  0  0  0  0  0  0
[10813]  0  1  1  1  1  1  1  1  1  1  1  1  1  0  1  1  1
[10830]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[10847]  1  0  0  1  0  0  0  0  0  0  0  1  1  1  1  1  1
[10864]  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1
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[10898]  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1  1  1
[10915]  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0
[10932]  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1
[10949]  1  1  1  1  1  0  0  0  1  1  0  0  0  0  0  0  0
[10966]  0  0  1  1  1  0  0  0  0 -1 -1 -1 -1  1  1  1  1
[10983]  1  0  1  0  0  0  0  1  1  1  1  1  0  0  0  0  0
[11000]  0  1  1  1  1  1  1  0  0  0  0  0  1  0  0  1  1
[11017]  1  1  1  1  1  1  1  1  1  1  1  1 -1 -1 -1 -1 -1
[11034]  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0
[11051]  0  0  0  1  0  1  1  1  1  1  1  1  1  1  1  1  1
[11068]  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1  1  1
[11085]  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  1  1
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[11119]  1  1  1  1  1  1  1  1  1 -1 -1 -1 -1 -1  1  1  1
[11136]  1  1  1  1  1  1  1  1  1  1  0  0  0  0  1  0  0
[11153]  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0
[11170]  1  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1
[11187]  1  1  1  1  0  1  0  1  1  0  0  1  1  1  1  1  1
[11204]  1  1  1  1  0  0  1  1  1  1  1  1  1  1  0  1  1
[11221]  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1
[11238]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[11255]  1  1  1  1  1  1  1  1  1  1  1  0  1  0  0  0  1
[11272]  0  0  0  0  1  1  1  0  0  0  0  0  0  0  0  1  1
[11289]  0  1  1  1  0  1  0  1  1  1  1  1  0  1  0  0  0
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[11323]  0  0  0  0  0  0  0  0  1  0  1  1  0  0  0  0  0
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[11680]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[11697]  1  1  1  1  1  1  1 -1  1  1  1  1  1  0  0  0  0
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[11969]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
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[12224]  1  1  1  0  1  1  0  1  1  1  0 -1 -1 -1 -1 -1 -1
[12241] -1 -1 -1  1  1  1  1  1  1  1  0  0  0  0  1  0  0
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[16355]  1  1  1  1  1  0  0  0  0  0  0  0  1  0  1  1  1
[16372]  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1
[16389]  1  1  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1
[16406]  1  1  1  1  1  1  0  1  0  0  1  0  0  0  0  0  0
[16423]  0  1  1  1  1  1  1  1  1  1  1  0  1  1  1 -1 -1
[16440] -1 -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  0  0  0
[16457]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[16474]  1  1  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1
[16491]  1  1  1  1  1  0  1  0  1  1  1  1  1  1  1  1  1
[16508]  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1
[16525]  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1
[16542]  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  0  0
[16559]  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1
[16576]  1  0  1  0  0  0  0  0  0  0  0  0  0  1  0  0  1
[16593]  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1
[16610]  1  1  1  1  0  1  0  0  0  0  0  0  1  1  1  1  1
[16627]  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0
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[16661]  0  0  0  0  0  1  1  1  1  0  0  0  0  0  0  0  1
[16678]  0  0  0  1  0  1  1  1  1  1  1  1  1  1  1  1  0
[16695]  0  0  0  0  1  1  0  1  0  0  0  0  0  0  0  1  1
[16712]  1  1  1  1  1  1  0  0  0  1  0  0  0  0  0  0  0
[16729]  0  0  0  1  1  0  1  1  1  1  1  1  1  1  1  1  0
[16746]  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0
[16763]  1  0  1  1  1  1  1  1  1  1  1  1  1  1  0  1  0
[16780]  0  0  0  0  0  0  0  1  1  0  1  1  1  0  0  0  0
[16797]  0  1  1  1  0  0  0  1  1  1  1  1  1  1  1  1  1
[16814]  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0
[16831]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  1  1
[16848]  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  1
[16865]  1  1  1  1  1  1  1  1  1  0  1  1  0  1  1  0  1
[16882]  1  0  0  0  1  1  1  0  1  0  1  1  0  1  1  1  1
[16899]  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0
[16916]  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1
[16933]  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1
[16950]  1  1  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1
[16967]  1  1  1  1  1  1  1  1  1  1  1  1  0  1  1  0  1
[16984]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0
[17001]  0  0  0  0  0  0  1  0  1  1  1  1  1  1  1  0  1
[17018]  1  0  1  1  1  0  0  0  0  0  0  1  1  1  0  1  1
[17035]  1  1  1  1  0  1  1  1  1  1  1  1  1  1  0  0  1
[17052]  0  0  0  0  0  0  0  0  0  0  1  0  0  1  0  0  0
[17069]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1
[17086]  0  1  0  1  1  0  0  1  1  1  1  1  1  1  1  1  1
[17103]  1  0  1  1  1  1  1  1 -1 -1 -1 -1 -1 -1  1  0  0
[17120]  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1
[17137]  1  1  1  1  1  1  1  0  1  1  0  0  0  1  1 -1 -1
[17154] -1 -1 -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  0  1
[17171]  1  1  0  0  0  0  0  0  0  0  0  0  1  1 -1 -1 -1
[17188] -1 -1 -1 -1 -1 -1  1  1  1  1  1  0  1  0  0  0  1
[17205]  1  1  0  0  0  0  0  0  0  1  0  1  1  1  1  1  1
[17222]  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1
[17239]  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  0  0
[17256]  0  0  0  0  1  1  1  1  1  1 -1 -1 -1 -1 -1  1  1
[17273]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[17290]  1  1  0  1  1  0  0  0  0  0  0  0  0  0  1  1  1
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[17324]  1  1  1  1  1  1  1  1  0  0  0  0  0  1  1  1  0
[17341]  1  0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  0
[17358]  0  1  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[17375]  1  1  1  1  1  1  1  1  0  0 -1 -1 -1 -1 -1 -1 -1
[17392]  1  1  1  0  0  1  1  1  1  1  1  1  0  0  1  0  0
[17409]  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1
[17426]  1  0  1  1  1  1  1  1  1  0  1  1  1  1  1  1  0
[17443]  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  0
[17460]  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  1
[17477]  1  1  0  1  1  1  1  1  1  1 -1 -1 -1 -1  1  0  0
[17494]  0  0  0  0 -1 -1 -1 -1  1  0  0  1  0  0  0  0  0
[17511]  0  0  1  1  1  1  1  1  1  1  0  0  0  1  1  1  0
[17528]  1  1  0  1  1  1  1  1  1  1  1  1  1  0  1  1  1
[17545]  0  1  1  1  1  1  1  1  1  0  1  1  1  0  0  0  0
[17562]  0  0  0  0  0  0  1  1  1  1  1  1  1  0  1  1  0
[17579]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[17596]  1  1  1  1  1  1  1  0  0  0  0  0 -1 -1 -1 -1  1
[17613]  1  1  1  1  1  1  1  0  0  1  1  1  0  1  1  0  1
[17630]  1  0  0  0  1  1  1  1  1  0  1  1  0  0  0  1  1
[17647]  1  1  1  0  1  1  0  0  0  1 -1 -1 -1 -1 -1 -1 -1
[17664] -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1  1  0  0
[17681]  1  1  0  0  1  0  1  1  1  1  0  1  1  1  1  1  1
[17698]  1  1  1  1  1  1  1  1  1  1  1  1  0  1  0  1  1
[17715]  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0
[17732]  0  1  0  0  1  1  0  0  0  0  0  0  1  0  0  1  0
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[17800]  0  1  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1
[17817]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1
[17834]  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0
[17851]  0  0  0  1  0  0  1  0 -1 -1 -1 -1 -1 -1  1  1  0
[17868]  0  0  1  1  0  1  0  0  1  0  0  0  0  0  0  0  0
[17885]  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1
[17902]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[17919]  1  1  1  1  1  1  1  1  1  1  1  1  1  0  1  0  1
[17936]  0  0  0  1  1  1  1  1  1  1  0  1  1  1  0  1  1
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[17970]  0  0  0  0  0  1  1  1  1  0  0  1  1  1  0  0  1
[17987]  1  1  0  1  1  1  0  0  0  0  1  0  0  0  0  0  1
[18004]  1  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  0
[18021]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1
[18038]  1  1  1  1  1  1  0  1  1  0  0  1  0  0  1  0  0
[18055]  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1
[18072]  1  1  1  0  0  1  0  0  0  0  0  0  0  1  1  1  0
[18089]  1  1  1  1  0  1  1  1  1  1  1  1  0  1  1  1  1
[18106]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[18123]  1  1  1  0  0  1  1  1  1  1  1  1  1  1  1  1  1
[18140]  1  1  1  1  1  1 -1 -1 -1 -1 -1  1  1  1  1  1  1
[18157]  0  0  0  0  0  1  0  1  0  0  0  0  0  0  0  0  1
[18174]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[18191]  0  1  1  0  1  0  0  0  0  0  0  0  0  1  1  1  1
[18208]  1  1  1  1 -1 -1 -1 -1 -1  1  1  1  1  1  1  0  0
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[18242]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
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[18293]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0
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[18344] -1 -1 -1 -1 -1  1  0  0  0  1  1  1  1  1  0  0  1
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[18514]  1  1  1  1  1  1  1  1  1  1  1  1  1  0  1  0 -1
[18531] -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1 -1  1
[18548]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[18565]  1  1  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1
[18582]  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1  1  1
[18599]  1  1  1  1  1  0  1  0  0  1  1  1  1  1  1  0  0
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[18667]  1  1  1  0 -1 -1 -1 -1 -1 -1 -1 -1  1  1  1  1  1
[18684] -1 -1 -1 -1 -1 -1  1  1  1  1  1  1  0  1  1  0  0
[18701]  1  1  1  1  1  1  0  0  0  0  1  1  1  1  1  1  1
[18718]  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1
[18735]  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1
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[18786]  1  1  1  1  1  1  1  1  1  1  1  1  1  0  1  0  1
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[18837]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  1
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[18871]  1  1  0  1  1  0  1  0  1  1  0  1  1  1  0  1  0
[18888]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[18905]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[18922]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[18939]  1  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1
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[18990]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[19007]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0
[19024]  1  1  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1
[19041]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[19058] -1 -1 -1 -1 -1 -1  1  0  0  1  1  1  1  1  1  1  0
[19075]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[19092]  1  1  1  1  1  1  0  0  1  1  1  1  1  1  0  0  1
[19109]  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0
[19126]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  0
[19143]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1
[19160]  1  1  1  1  1  1  1  1  1  1  1  0  0  0  1  0  0
[19177]  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1
[19194]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0
[19211]  0  0  0  0  0  0  0  1  1  1  0  0  0  0  0  0  1
[19228]  1  1  1  1  1  1  1  0  1  1  1  1  1  0  0  1  0
[19245]  0  0  0  0  0  0  1  1  1  1  1  1 -1 -1 -1 -1 -1
[19262]  1  1  1  1  1  1  1  1  0  1  1  1  0 -1 -1 -1 -1
[19279] -1 -1 -1 -1 -1  1  0  0  0  0 -1 -1 -1 -1 -1 -1  1
[19296]  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0
[19313]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  0
[19330]  1  1  1  1  0  0  0  0  0  1  1  1  1  1  1  1  1
[19347]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[19364]  1  1  1  1  1  1 -1 -1  1  1  1  1  1  1  1  1  1
[19381]  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1  0
[19398] -1 -1 -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1
[19415]  1  1  1  1  1  1  1  0  0  0  0  0  1  1  1  1  0
[19432]  0  0  0  0  0  0  1  0  1  1  1  0  1  0  0  0  0
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[19466]  1  1  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1
[19483]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[19500]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[19517]  1  1  1  1  1 -1 -1 -1 -1  1  1  0  0  0  1  1 -1
[19534] -1 -1 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[19551]  1  1  1  1  1  0  0  0  1  0  0  0  1  0  1  0  0
[19568]  0  0  1  1  1  0  0  0  0  0  0  0  0  1  0  1  1
[19585]  1  1 -1 -1 -1 -1 -1  1  0  0  0  0  0  0  1  0  0
[19602]  0  1  0  1  1  1  1  1  1  1  1  1  1  1  0  0  0
[19619]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1
[19636]  1  0  0  0  0  0  0  0  0  0  0  1  0  0  0  1  0
[19653]  1  0  0  0  0  1  0  0  1  1  1  1  1 -1 -1 -1  1
[19670]  1  1  1  1  0  0  0  0  0  0  1  0  0  0  1  1  0
[19687]  1  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1
[19704]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1
[19721] -1 -1 -1  1  1  0  1  1  1  0  1  1  1  0  1  0  0
[19738]  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1
[19755]  0  0  1  0  0  1  0  1  1  1  0  1  1  1  1  1  1
[19772]  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  1  0
[19789]  0  1  1  1  1  1  1  0  0  1  1  0  1  1  1  1  1
[19806]  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1  1  1
[19823]  1  1  1  1  1  1  1  1  1  1  1  1  0  0  1  0  0
[19840]  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1
[19857]  0  0  0  1  0  0  0  0  0  0  1  1  1  1  1  1  1
[19874]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[19891]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[19908]  1  1  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1
[19925]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[19942]  1  1  1  1  0  0  0  0  0  0  0  0  0  1  1  1  1
[19959]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1
[19976] -1  1  1  1  1  1  1  1  1  0  0  0  1  1  1  1  1
[19993]  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0
[20010]  1  0  0  1  1  0  0  0  0  1  0  1  1  1  0  0  1
[20027]  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1
[20044]  0  1  0  0  1  0  0  1  0  0  1  0  0  0  0  1  0
[20061]  1  1  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1
[20078]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1
[20095]  1  1  1  1  1  1  0  0  0  0  1  0  0  0  1  1  1
[20112]  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0
[20129]  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0
[20146]  1  1  0 -1 -1 -1 -1  1  1  0  0  0  0  0  0  0  0
[20163]  0  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1
[20180]  1  1  1  0  1  1  1  1  1  1  1  1  0  0  0  0  0
[20197]  1  1  1  1  1  0  0  0  0  0  0  1  1  0  0  0  0
[20214]  0  0  0  0  0  1  1  1  1  1  1  1  0  0  1  0  1
[20231]  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0
[20248]  0  0  0  0  1  1  0  0  0  0  1  1  0  0  0  1  0
[20265]  0  0  0  0  1  0  1  0  0  1  1  1  1  1  1  1  1
[20282]  0  0  1  1  1  1  0  0  0  0  0  0  0  0  1  1  1
[20299]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[20316]  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  0
[20333]  1  0  0  0  0  0  0  1  1  1  1  1  1  1  1  0 -1
[20350] -1  1  1  1  1  1  0  1  1  0  1  0  1  0  0  0  0
[20367]  0  0  0  0  1  0  1  0  1  1  1  1  1  1  0  1  1
[20384]  1  1  1  1  1  1  1  1  1  0  0  1  0  0  1  1  1
[20401]  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1
[20418]  1  0  1  1  1  1  1  1  1  1  1  1  0  0  1  1  1
[20435]  1  1  0  1  1  1  0  0  0  0  0  0  0  0  0  1  1
[20452]  0  0  1  1  0  1  0  1  1  1  0  0  0  1  1  1  1
[20469]  1  1  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1
[20486]  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0
[20503]  0  0  1  1  1  0  0  0  0  0  0  0  0  1  1  1  1
[20520] -1 -1 -1 -1 -1 -1 -1  1  1  0  1  1  1  0  0  0  0
[20537]  0  1  1  1  1  1  1  1  1  1  0  0  1  0  0  0  0
[20554]  0  0  0  0  0  0  1  1  1  1  1  1  1  1  0  1  1
[20571]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[20588]  1  1  1  1  1  1  1  1  1  1  1  0  0  1  0  0  1
[20605]  1  0  1  1  1  0  1  1  0  1  1  0  0  0  0  0  0
[20622]  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1
[20639]  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1  0  1
[20656]  1  0  0  1  1  0  1  1  0  1  1  1  1  1  1  0  0
[20673]  0  0  1  0  0  0  0  0  1  1  1  1  1  1  0  0  0
[20690]  0  0  1  1  0  1  0  0  0  0  0  0  0  1  1  1  1
[20707]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[20724]  1  1  0  1  0  0  0  1  1  0  0  0  1  1  1  1  1
[20741]  1  1  1  1  1  0  1  1  1  1  1  1  1  1  0  1  1
[20758]  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1  0  0
[20775]  0  0  0  0  0  1  1  1  1  1  1  0 -1 -1 -1 -1  1
[20792]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[20809]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[20826]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[20843]  1  1  1  1  0  0  1  1  1  1  1  0  1  1  1  1  1
[20860]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[20877]  1  1  1  1  1  1  1  1  1  0  1  1  0  1  1  0  0
[20894]  1  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1
[20911]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[20928]  1  1  1  0  0  0  1  1  1  1  1  1  1  1  1  1  1
[20945]  1  1  1  1  1 -1 -1 -1 -1 -1 -1  1  1  1  1  1  1
[20962] -1 -1 -1 -1 -1  1  1  0  1  1  0  0  0  0  0  0  1
[20979]  1  1  1  0  0  0  0  0  0  0  1  0  0  0  1  0  0
[20996]  0  0  0  0  1  1  0  0  0  0  0  0  0  0  0  1  1
[21013]  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0
[21030]  0  0  0  1  1  1  1  1  1  1  1  1  0  1  1  0  0
[21047]  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0
[21064]  0  0  1  1  0  1  1  1  1  0  1  0  0  1  1  1  1
[21081]  1  1  0  1  1  1  1  1  0  0  0  0  0  0  0  0  0
[21098]  0  1  1  1  1  1  1  1  1 -1 -1 -1  1  0  0  1  1
[21115]  1  1  1  1  0  0  1  0  0  0  0  0  0  0  0  0  0
[21132]  1  0  0  0  1  1  1  0  1  1  0  1  0  0  0  0  0
[21149]  0  0  0  0  0  1  0  1  0  0  0  0  0  0  0  0  1
[21166]  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0
[21183]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  0
[21200]  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0
[21217]  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  0  0
[21234]  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1
[21251]  1  0  1  1  1  1  0  0  0  0  1  1  0  1  0  0  0
[21268]  0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  0  1
[21285]  0  1  1  1  1  1  1  1  1 -1 -1 -1  1  0  0  0  0
[21302]  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1
[21319]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[21336]  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  1
[21353]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[21370]  1  1  1  0  1  1  0  1  1  1  1  1  1  1  1  1  1
[21387]  1  1  1  1  1  1  0  1  0  1  1  1  0  0  0  0  0
[21404]  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[21421]  1  1  1  0  0  0  0  0  1  0  1  0  0  0  0  0  0
[21438]  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1
[21455]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[21472]  1  1  0  0  0 -1 -1 -1 -1 -1 -1 -1  1  1  0  1  1
[21489]  1  1  0  1  1  1  1  1  1  1  1  1  1  0  0  1  1
[21506]  1  1  0  0  1  0  0  0  0  0  0  1  1  1  1  1  1
[21523]  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1
[21540]  1  1  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1
[21557]  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  1  0
[21574]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1
[21591]  1  1  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[21608]  1  1  1  1  1  1  1  1  1  0  0  0  1  1  0  1  1
[21625]  0  0  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0
[21642]  0  1  0  0  1  1  0  1  0  1  0  0  0  0  0  0  0
[21659]  0  1  1  1 -1 -1 -1 -1 -1 -1 -1 -1  1  1  1  1  1
[21676]  1  1  1  0  0  0  1  0  1  1  1  1  1  1  1  1  0
[21693]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[21710]  1  1  0  1  1  1  1  1  1  1  1  0  1  1  1  0  1
[21727]  1  1  0  1  1  1  1  1  0  1  1  1  1  1  1  1  1
[21744]  1  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  0
[21761]  0  1  1  1  1  0  1  0  0  1  1  1  1  1  1  1  1
[21778]  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1  1  1
[21795]  1  1  1  0  1  0  1  0  1  0  1  0  0  0  0  0  0
[21812]  0  0  1  0  0  0  0  0  1  1  0  1  0  1  1  1  1
[21829]  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0
[21846]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[21863]  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1
[21880]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0
[21897]  0  0  0  0  0  1  1  1  1  1  1  1  1  0  0  0  1
[21914]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[21931] -1 -1 -1 -1  1  1  0  1  1  1  1  1  1  1  1  1  1
[21948]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[21965]  1  1  1  1  1  1  0  1  1  1  0  0  0  0  1  1  1
[21982]  1  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1
[21999]  1  1  1  1  1  1  0  0  0  0  0  0  0  1  1  0  0
[22016]  1  1  0  0  0  1  0  1  0  1  1  1  0  0  1  0  1
[22033]  0  1  1  1  1  1  1  0  1  1  0  0  1  0  0  0  0
[22050]  0  0  0  0  0  0  1  1  1  1  1  1  1  0  0  0  1
[22067]  1  0  1  1  0  1  1  1  0  1  1  1  1  1  1  1  1
[22084]  1  1  1  0  0  1  0  1  1  1 -1 -1 -1 -1 -1 -1  1
[22101]  1  1  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1
[22118]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[22135]  1  1  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1
[22152]  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1  1  1
[22169]  1  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  0
[22186]  1  1  1  0  0  1  1  1  1  0  0  0  0  1  1  1  1
[22203]  1  1  1  1  1  1  1  1  0  0  0  0  0  0 -1 -1 -1
[22220] -1  1  1  0  1  0  0  0  0  0  0  0  1  1  1  1  1
[22237]  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[22254]  1  1  0  1  1  1  1  1  0  1  1  1  0  0  0  0  0
[22271]  0  0  0  0  0  1  1  1  1  1  1  0  1  1  1  1  1
[22288]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[22305]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[22322]  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  1
[22339]  1  1  1  1  1  1  1  1  1  1  1  1  0  1  1  0  0
[22356]  1  1  1  1  1  1  1  1  1  0  1  0  0  0  0  0  0
[22373]  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[22390]  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0
[22407]  0  1  1  1 -1 -1 -1 -1 -1 -1 -1 -1  1  0  0  0  1
[22424]  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
[22441]  1  1  1  1  1  1  1  1  1  1 -1  1  0  1  0  0  0
[22458]  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1
[22475]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[22492]  1  1  1  1  1  0  1  1  1  1  0  1  0  0  0  1  1
[22509]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[22526]  1  1  0  1  1  1  1  0  1  1  1  1  1  1  1  1  1
[22543] -1 -1 -1 -1 -1 -1 -1 -1  1  1  0  0  0  1  1  0  0
[22560]  0  0  1  1  0  1  1  1  1  1  1  1  1  1  0  0  1
[22577]  0  0  0  0  0  0  0  1  0  1 -1 -1 -1 -1 -1 -1 -1
[22594] -1  1  0  1  0  0  0  1  0  0  0  1  1  1  0  0  0
[22611]  0  0  0  0  0  0  1  1  0  0  1  1  0  0  0  0  0
[22628]  1  1  0  0  0  0  0  0  0  1  0  1  1  1  0  0  0
[22645]  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1
[22662]  1  1  1  1  0  1  1  1  1  1  1  1  0  0  0  0  1
[22679]  0  0  1  1  1  1  1  1  1  1  1  1  0  0  0  1  1
[22696]  0  0  0  0  1  0  1  0  0  1  0  0  0  0  0  0  0
[22713]  0  0  0  1  0  0  0  0  0  0  0  0  1  0  1  1  1
[22730]  0  0  0  0  0  0  1  0  1  1  1  1  1  1  1  1  0
[22747]  0  1  1  1  0  1  1  0  0  1  0  0  0  1  1  1  1
[22764]  1  1  1  1  1  1  1  1  1  1  0  0  0  0 -1 -1 -1
[22781] -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0
[22798]  0  0  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1
[22815]  1  1  1  1  1  0  0  0  0  0  0  1  1  1  1  1  1
[22832]  1  1  1  1  1  1  0  0  0  0 -1 -1 -1 -1 -1 -1  1
[22849]  0  0  0  0  0  0 -1 -1 -1 -1  1  1  0  1  0  1  0
[22866]  1  0  1  0  1  1  1  0  1  1  1  1  1  1  1  1  0
[22883]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  0  1
[22900]  1  0  1  1  1  1  0  1  1  1  0  0  0  0  1  1  1
[22917]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  0  0  0
[22934]  0  0  1  0  0  1  0  1 -1 -1 -1 -1 -1  1  1  1  0
[22951]  0  0  0  0  0  0  0  1  1  1  1  1  0  0  0  0  0
[22968]  0  1  1  1  0  0  0  0  0  0  0  0  1  1  1  1  1
[22985]  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
[23002]  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0
[23019]  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1
[23036]  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0
[23053]  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  0
[23070]  0  0  0  0  0  0  0  0  0  1  0  0  1  0  0  0  0
[23087]  0  0  0  1  0  0  1  0  0  0  0  0  0  0  1  1  1
[23104]  1  1  1  1  1  1  1  1  1 -1 -1 -1 -1 -1 -1 -1 -1
[23121] -1 -1  1  1  0  0  1  1  1  1  0  1  0  1  1  1  1
[23138]  1  1  1  1  1  1  1  1  1  0  1  1  1  1  1  0  0
[23155]  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1
[23172]  1  1  1  1  1  0  1  1  1  1  1  1  1  1  1  1  1
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[23206]  0  0  0  0  0  1  1  1  1  1  1  1  0  0  0  0  1
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[43912]  1  1  1  1  1  1  1  1  1  1  1  0  1  1  1  1  0
[43929]  1  0  0  0  0  0  1  1  1  1  1  1  0  0  1  0  0
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[43980]  0  0 -1 -1 -1 -1 -1 -1 -1 -1  1  0  0  0  0  0  0
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[44541]  0  0  0  0  0  0 -1 -1 -1 -1  1  1  1  1  1  1  0
[44558]  0  0  0  0  1  1  0  0  0  0  0  0  0  0  0  1  1
[44575]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[44592]  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1  1  1
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[44745]  0  0  0  0  1  0  0  1  0  0  1  1  0  0  0  1  1
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[44813] -1 -1  1  1  1  1  1  1  1  1  1  1  1  1 -1 -1 -1
[44830] -1 -1 -1 -1 -1 -1 -1  1  1  1  1  1  1  1  0  0  0
[44847]  0  1  0  0  1  0  0  0  0  0  0  0  1  0  0  0  0
[44864]  0  0  0  0  0  0  1  1  0  1  1  1  0  0  0  0  0
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[44915]  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0
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[44949]  1  1  1  1  0  0  0  0  0  1  1  1  1  1  0  0  1
[44966]  1  0  0  1  0  0  1  1  1  1  1  1  1  1  1  0  0
[44983]  1  1  1  1  1  0  1  0  1  0  0  1  1  1  1  1  1
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[45017]  1  1  0  0  0  0  0  1  1  0  1  0  0  0  0  0  0
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[45068]  1  1  1  1  1  1  0  0  0  0  0  1  1  0  0  0  0
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[45867]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0
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[46955] -1 -1 -1 -1 -1  1  1  1  1  1  1  1  1  1  1  0  1
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[91801]  0  0  1  1  1  0  0  0  1  1  1  1  1  1  1  1  1
[91818]  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1
[91835]  1  1  1  1  1  0  0  1  1  1  1  1  1  0  1  1  0
[91852]  0  1  0  0  0  0  0  0  0  0  0  0  1  1  0  0  0
[91869]  1  0  0  0  0  0  1  1  1  1  1  1  1  1  1  0  0
[91886]  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1
[91903]  0  1  1  0  0  1  1  1  1  1  1  0  1  1  0  0  1
[91920]  1  1  1  1  1  0  1  0  0  0  1  1  1  0  0  0  0
[91937]  0  0  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
[91954]  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1
[91971]  1  0  0  0  1  1  0  0  0  0  0  0  0  0  1  1  1
[91988]  1  1  1  1  1  1  0  0  1  0  1  1  0  1  1  1  1
[92005]  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1
[92022]  1  1  1  1  1  0  0  1  1  1  1  0  1  0  0  0  0
[92039]  0  1  1  1  1  1  1  0  1  1  0  0  1  1  1  1  1
[92056]  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0
[92073]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1
[92090]  1  1  1  0  0  1  0  0  1  0  1  1  0  0  0  0  1
[92107]  0  0  0  0  0  1  0  1  0  0  1  1  1  1  1  1  1
[92124]  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
[92141]  1  0  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1
[92158]  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1
[92175]  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1
[92192]  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1
[92209]  1  1  1  1  1  0  0  1  1  1  1  1  1  0  1  1  0
[92226]  0  1  1  1  1  1  1  0  0  0  0  0  1  1  0  1  1
[92243]  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0
[92260]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1
[92277]  1  1  1  0  0  1  0  0  0  0  0  1  0  0  0  0  1
[92294]  1  1  1  1  1  1  0  1  0  0  1  1  0  1  1  1  1
[92311]  0  0  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
[92328]  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1
[92345]  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1
[92362]  0  1  1  1  1  0  0  0  1  1  1  1  0  0  1  0  0
[92379]  0  0  1  1  0  0  0  0  0  0  0  0  0  1  0  0  0
[92396]  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  0
[92413]  0  1  1  1  1  1  1  1  1  1  0  0  1  0  0  0  0
[92430]  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
[92447]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1
[92464]  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1
[92481]  1  0  1  1  1  0  1  0  0  0  1  1  1  1  1  1  1
[92498]  0  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
[92515]  1  1  1  1  1  0  1  0  0  1  1  1  1  1  1  0  0
[92532]  1  0  0  1  1  0  0  1  1  0  0  0  0  0  1  1  1
[92549]  1  1  1  1  0  1  0  0  1  1  1  1  1  1  1  1  1
[92566]  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  0  1
[92583]  0  0  0  0  0  0  0  1  0  1  1  1  1  0  1  1  0
[92600]  0  1  1  1  1  1  0  0  1  0  0  0  1  1  1  1  1
[92617]  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0
[92634]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1
[92651]  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1
[92668]  1  1  1  1  1  1  1  0  0  0  1  1  1  1  1  1  1
[92685]  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
[92702]  1  1  1  1  0  0  0  0  0  1  1  1  1  1  1  1  1
[92719]  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  0  1
[92736]  0  0  0  0  0  0  0  0  0  1  1  1  0  1  1  1  1
[92753]  0  0  1  0  0  1  0  0  0  0  0  0  0  1  1  1  1
[92770]  1  1  1  1  1  0  0  0  1  1  1  1  0  0  0  1  0
[92787]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[92804]  1  1  1  1  0  0  0  0  0  0  0  0  0  1  0  0  0
[92821]  1  1  1  1  1  1  1  0  1  0  0  1  1  1  1  1  1
[92838]  1  1  1  0  0  1  1  1  1  1  1  1  1  0  0  0  1
[92855]  1  1  1  1  1  1  1  1  0  0  0  0  0  1  1  0  0
[92872]  1  0  0  0  1  1  1  1  0  1  1  1  0  0  0  1  1
[92889]  1  1  1  1  1  0  1  0  0  1  1  0  1  0  0  1  0
[92906]  0  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1
[92923]  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1
[92940]  0  0  0  1  1  1  1  1  1  1  1  0  0  1  1  0  1
[92957]  0  1  0  0  0  0  0  1  1  1  1  1  1  0  1  1  0
[92974]  0  1  1  1  1  1  1  1  1 -1  0  0  1  1  1  1  1
[92991]  1  1  1  1  0  0  1  1  1  0  1  0  0  0  0  0  0
[93008]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1
[93025]  1  1  1  0  0  1  1  1  1  1  1  0  0  0  0  0  1
[93042]  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1
[93059]  1  1  0  0  1  1  1  0  0  0  0  0  0  0  0  1  1
[93076]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1
[93093]  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1
[93110]  1  1  1  1  1  1  0  0  1  1  1  1  1  0  1  1  1
[93127]  0  0  1  1  1  1  0  0  1  1  1  0  0  1  1  1  1
[93144]  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  0  0
[93161]  0  1  1  1  1  1  1  1  1  0  0  0  1  1  1  1  1
[93178]  1  1  1  1  0  0  1  1  1  1  1  0  0  0  0  0  0
[93195]  1  1  1  1  1  0  0  0  0  0  0  1  1  1  1  1  1
[93212]  0  1  1  0  0  1  1  1  1  0  0  0  0  0  0  0  1
[93229]  1  1  1  1  1  1  1  1  0  0  1  0  0  0  0  0  0
[93246]  0  0  0  0  1  1  1  1  1  1  1  0  0  0  0  1  1
[93263]  1  1  1  1  1  1  0  0  0  1  1  1  1  1  1  0  1
[93280]  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  0  0
[93297]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1
[93314]  0  0  1  1  1  1  0  0  0  0  0  0  0  1  1  1  1
[93331]  1  1  0  0  1  0  0  1  1  1  1  1  1  1  1  1  0
[93348]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[93365]  1  1  1  1  0  0  1  1  1  1 -1 -1 -1 -1 -1  0  0
[93382]  1  1  1  0  1  1  1  1  1  0  0  1  1  1  0  0  0
[93399]  0  0  0  0  0  1  1  1  1  1  1  1  1  1  0  0  1
[93416]  1  1  1  1  1  1  1  1  0  0  1  1  1  1  0  0  0
[93433]  0  0  0  0  1  1  1  0  0  0  0  0  0  0  0  1  0
[93450]  0  1  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1
[93467]  1  0  0  0  1  1  1  1  1  0  0  1  0  0  1  1  1
[93484]  1  1  1  1  1  0  0  0  1  1  0  0  0  0  0  0  0
[93501]  0  0  1  1  1  1  1  1  0  1  0  0  0  0  0  0  0
[93518]  0  1  1  1  1  0  0  0  0  0  0  0  0  0  0  1  0
[93535]  0  1  1  1  1  1  1  1  0  0  0  0  1  0  0  0  0
[93552]  0  0  1  1  0  0  1  1  1  1  0  0  0  1  1  0  0
[93569]  1  1  1  1  1  1  0  1  1  0  0  1  1  1  1  1  1
[93586]  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1
[93603]  1  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1
[93620]  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
[93637]  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0
[93654]  0  0  0  1  1  1  1  1  1  0  1  1  0  0  1  1  1
[93671]  1  1  1  1  1  1  0  0  1  1  1  1  0  0  0  0  1
[93688]  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  0
[93705]  0  1  1  0  0  0  0  1  1  1  1  1  1  1  1  1  0
[93722]  0  0  0  0  0  0  0  0  0  1  0  0  1  0  1  0  0
[93739]  0  0  0  0  0  0  1  1  1  1  1  1  1  1  0  0  0
[93756]  1  0  0  0  1  1  1  1  0  0  0  1  1  1  1  1  1
[93773]  1  1  1  0  0  1  0  0  1  1  0  1  1  1  0  0  1
[93790]  1  1  1  1  1  1  1  1  0  0  1  1  0  0  0  0  0
[93807]  0  0  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
[93824]  1  1  1  1 -1 -1 -1  0  0  1  1  1  1  1  1  1  1
[93841]  1  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1
[93858]  1  1  1  0  1  1  0  0  1  1  1  1  1  1  1  1  1
[93875]  0  0  1  1  1  1  0  0  0  0  0  0  0  1  1  1  1
[93892]  0  0  0  1  0  0  0  1  1  1  1  1  1  1  1  1  0
[93909]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[93926]  1  1  1  1  0  0  1  1  1  0  0  1  0  1  1  0  0
[93943]  1  1  1  1  1  1  1  1  1  0  0  1  1  0  0  1  1
[93960]  0  0  0  0  0  1  1  1  1  0  1  1  0  1  0  0  1
[93977]  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1
[93994]  1  1  0  0  1  1  1  1  1  1  0  0  0  0  0  1  1
[94011]  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1
[94028]  1  0  0  1  1  1  1  1  0  0  0  0  0  0  1  0  1
[94045]  1  0  0  1  1  1  0  0  1  1  1  0  0  0  0  0  0
[94062]  0  0  1  0  1  1  1  1  1  1  1  0  0  1  1  1  1
[94079]  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0
[94096]  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1
[94113]  1  1  1  1  0  0  1  1  1  1  0  0  0  0  0  0  0
[94130]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1
[94147]  0  1  1  0  0  1  1  0  1  0  0  0  0  0  0  0  1
[94164]  1  1  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1
[94181]  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
[94198]  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1 -1 -1
[94215] -1  0  0  1  0  1  0  0  0  0  0  0  0  0  1  1  1
[94232]  0  0  1  1  1  1  0  0  1  1  1  1  1  1  0  1  1
[94249]  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1
[94266]  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0
[94283]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[94300]  1  1  1  1  0  0  1  0  0  1  1  1  1  1  1  0  0
[94317]  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1
[94334]  1  1  1  0  0  1  0  0  0  0  0  0  0  0  0  0  1
[94351]  1  1  1  1  1 -1 -1 -1  0  0  1  1  0  0  0  0  0
[94368]  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  1  1
[94385]  1  1  1  1  1  1  1  0  0  1  0  0  0  0  0  0  0
[94402]  0  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1
[94419]  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1
[94436]  0  0  1  1  0  1  1  0  0  0  0  0  0  1  1  1  1
[94453]  1  1  1  0  1  0  0  1  1  1  1  1  1  1  1  1  0
[94470]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  0  0
[94487]  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  0  0
[94504]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  0
[94521]  0  1  1  0  0  1  0  1  1  1  1  0  1  1  0  0  1
[94538]  1  1  1  1  1  0  0  1  0  0  1  1  1  1  1  1  1
[94555]  1  1  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1
[94572]  1  1  1  0  0  0  0  0  0  1  1  1  1  1  1  0  1
[94589]  1  0  0  1  1 -1 -1 -1 -1 -1 -1 -1  0  0  1  1  1
[94606]  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1
[94623]  0  0  1  0  1  0  0  0  0  0  0  0  0  1  1  1  1
[94640]  1  1  1  1  1  0  0  1  1  1  1  1  1  0  1  1  0
[94657]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[94674]  1  1  1  1  0  0  1  0  0  0  0  0  0  1  1  0  0
[94691]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  0  1
[94708]  0  1  1  0  0  1  1  0  0  0  0  0  1  1  0  0  1
[94725]  1  1  0  0  1  1  1  1  0  0  1  1  1  1  1  1  1
[94742]  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  0  1
[94759]  1  1  1  0  1  1  1  0  0  1  1  1  1  1  1  1  1
[94776]  1  0  0  1  1  1  1  1  1  1  1 -1  0  0  1  1  1
[94793]  1  1  1  1  1  0  0  0  1  1  1  1  0  0  0  1  1
[94810]  0  0  1  1  1  1  1  1  1  0  1  0  0  1  1  1  1
[94827]  0  0  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0
[94844]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[94861]  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0
[94878]  1  1  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1
[94895]  0  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1
[94912]  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1
[94929]  1  1  0  0  1  1  1  1  1  0  1  1  1  0  0  1  1
[94946]  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1
[94963]  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  0
[94980]  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1
[94997]  0  0  1  1  1  1  1  1  0  1  0  0  0  1  1  1  1
[95014]  1  1  1  0  0  0  0  1  1  1  1  1  1  1  1  1  0
[95031]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[95048]  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0
[95065]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1
[95082]  1  1  1  0  0  1  0  0  0  0  1  0  0  0  0  0  1
[95099]  1  1  1  1  1  1  1  1  0  0  1  1  0  0  0  0  0
[95116]  0  0  0  0  1  1  1  1  1  1  0  0  0  0  0  1  1
[95133]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1
[95150] -1  0  0  1  1  1  1  1  1  1  0  0  0  0  1  1  1
[95167]  1  1  1  1  1  1  0  0  1  0  0  1  1  1  1  1  1
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[95201]  1  1  0  1  1  0  0  1  1  1  1  1  1  1  1  0  0
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[98601]  1  1  1  1  0  0  1  1  1  1  0  1  1  0  0  0  0
[98618]  1  1  0  0  0  0  0  1  0  0  0  1  1  1  1  1  1
[98635]  1  1  1  0  0  1  0  0  0  0  0  1  0  1  0  0  1
[98652]  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1
[98669]  1  1  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1
[98686]  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1
[98703]  1  0  0  1  1  1  1  1  1  0  1  1  0  0  1  1  1
[98720]  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1
[98737]  0  0  1  0  0  1  1  1  1  0  1  0  0  1  1  1  1
[98754]  1  1  1  1  1  0  0  1  1  1  1  0  1  0  1  1  0
[98771]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[98788]  1  0  1  0  0  0  1  0  1  1  0  1  0  0  1  0  0
[98805]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1
[98822]  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1
[98839]  1  0  1  1  1  1  1  1  0  0  1  1  1  1  0  1  0
[98856]  0  0  0  0  1  1  1  1  0  0  0  0  0  0  0  1  1
[98873]  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1
[98890] -1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1
[98907]  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1
[98924]  0  0  1  0  0  0  0  0  1  1  0  0  0  0  1  1  1
[98941]  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0
[98958]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[98975]  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0
[98992]  1  1  1  1  1  1  1  1  1  0  0  1  1  0  1  1  0
[99009]  0  0  0  0  0  1  1  1  1  1  1  1  1  1  0  0  1
[99026]  1  1  1  1  1  1  1  0  0  0  1  1  1  1  1  1  1
[99043]  1  1  0  0  1  1  1  0  0  0  0  0  0  0  0  1  1
[99060]  1  1  1  1  1  1  1  0  0  1  1  0  1  0  1  0  0
[99077]  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1
[99094]  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  0
[99111]  0  0  0  0  0  0  0  0  0  1  1  0  0  1  0  0  0
[99128]  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  0
[99145]  0  1  1  1  1  1  0  0  0  0  0  0  1  1  1  1  1
[99162]  1  1  0  0  0  0  1  1  1  1  1  1  1  1  1  0  0
[99179]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  0  0  1
[99196]  0  1  1  0  0  1  1  1  1  1  1  0  1  1  0  0  1
[99213]  0  1  1  0  0  0  0  0  0  0  1  1  1  1  1  1  1
[99230]  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
[99247]  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0
[99264]  0  0  0  1  1  0  0  0  0  0  0  0  0  0  1  1  1
[99281]  1  1  1  1  1  1  0  0  1  1  0  1  0  0  0  0  0
[99298]  0  0  1  0  0  0  0  0  0  0  0  0  0  1  1  1  1
[99315]  1  1  0  0  0  0  0  1  1  1  1  1  1  1  0  1  0
[99332]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[99349]  1  1  1  1  0  0  1  0  0  1  0  0  1  0  1  0  0
[99366]  1  1  1  1  0  0  0  0  0  0  0  1  0  1  0  0  0
[99383]  0  0  0  0  0  0  1  1  1  1  1  1  1  1  0  0  1
[99400]  1  0  1  1  1  1  0  1  0  0  1  1  1  1  0 -1 -1
[99417] -1 -1  0  0  1  1  1  1  0  0  0  1  1  0  0  1  1
[99434]  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1
[99451]  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1
[99468]  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1  1
[99485]  0  0  1  1  0  0  0  0  0  1  0  0  0  1  0  0  1
[99502]  0  1  1  1  1  0  0  1  1  1  1 -1 -1 -1 -1 -1  0
[99519]  0  1  1  1  1  0  0  0  0  0  0  0  1  1  1  1  1
[99536]  1  1  1  1  0  0  1  1  1  0  0  0  0  0  0  0  0
[99553]  1  1  1  0  0  0  0  0  0  0  0  0  0  0  1  1  0
[99570]  0  0  0  0  0  1  1  1  0  1  1  0  1  1  0  0  1
[99587]  1  0  0  0  0  1  1  0  0  0  1  1  1  1  1  1  1
[99604]  1  1  0  0  1  0  0  0  0  0  0  0  0  0  0  1  0
[99621]  0  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  1
[99638]  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  0  1
[99655]  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1
[99672]  0  0  1  1  1  1  1  1  1  1  0  0  0  1  1  1  1
[99689]  1  1  1  1  1  0  0  1  1  1  0  1  1  0  0  0  0
[99706]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[99723]  0  0  0  0  0  0  1  1  1  1  0  0  0  0  0  0  0
[99740]  1  1  1  1  1  1  0  0  0  0  0  1  1  1  1  1  1
[99757]  1  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1
[99774]  0  1  1  0  0  0  1  1  0  0  1  1  1  1  1  1  1
[99791]  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  0
[99808]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1
[99825]  1  0  0  1  0  1  0  1  1  0  0  0  0  0  1  1  0
[99842]  0  1  1  0  1  0  0  0  1  1  1  1  1  1  1  1  1
[99859]  0  0  1  1  1  1  1  1  1  0  0  0  0  1  1  0  0
[99876]  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  0
[99893]  0  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1
[99910]  1  0  1  1  0  0  1  1  1  1  1  1  1  1  0  0  0
[99927]  1  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1
[99944]  1  1  1  0  0  1  1  1  1  0  1  1  0  1  0  0  1
[99961]  1  1  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1
[99978]  1  1  0  0  1  1  1  1  1  1  1  1  1  0  0  1  1
[99995]  1  1  1  1  1
 [ reached getOption("max.print") -- omitted 644591 entries ]

Show code

df_rdd3 <- df %>%
  dplyr::filter(YearMeasured == 1) %>%
    dplyr::filter(Wave == 2016 | Wave == 2017 | Wave == 2018| Wave == 2019) %>%
  dplyr::select(
    TSCORE,
    WSCORE,
    Terrorism.Anxiety,
    KESSLER6sum,
    KESSLER6,
    BELONG,
    PWI,
    SWB.SoC01,
    YearMeasured,
    WSCORE
  ) %>%
  droplevels() %>%
   dplyr::mutate(Pre_Post_attack = as.factor(ifelse(
    TSCORE >= 3545, "Post_Attack", "Pre_Attack"
  ))) %>%
  dplyr::mutate(Pre_Post_attack = forcats::fct_relevel(Pre_Post_attack, c("Pre_Attack", "Post_Attack"))) %>%
  dplyr::mutate(Timeline = make_date(year = 2009, month = 6, day = 30)+ TSCORE)

# long term k6 periodicity


# TERRORISM ANXIETY
df_rdd4 <- df %>%
  dplyr::filter(YearMeasured == 1) %>%
    dplyr::filter(Wave != 20099) %>%
  dplyr::select(
    TSCORE,
    WSCORE,
    Terrorism.Anxiety,
    KESSLER6sum,
    KESSLER6,
    BELONG,
    PWI,
    SWB.SoC01,
    YearMeasured,
    WSCORE
  ) %>%
  droplevels() %>%
   dplyr::mutate(Pre_Post_attack = as.factor(ifelse(
    TSCORE >= 3545, "Post_Attack", "Pre_Attack"
  ))) %>%
  dplyr::mutate(Pre_Post_attack = forcats::fct_relevel(Pre_Post_attack, c("Pre_Attack", "Post_Attack"))) %>%
  dplyr::mutate(Timeline = make_date(year = 2009, month = 6, day = 30)+ TSCORE)



#library(table1)  # make pretty tables quickly
#table1::table1(~Muslim|Wave, data = km, overall=F)# there are 31 muslims in 2017

Show code

## RDD FULL SAMPLE


np <-
  ggplot(data = df_rdd,
         aes(
           x = Timeline,
           y = KESSLER6,
           group = Pre_Post_attack,
           colour = Pre_Post_attack
         )) + scale_fill_viridis_c(option = "plasma")
change_distress <- np +
  geom_point(alpha = .03) +
 # stat_smooth(method =  stats::loess) +
   stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +
  labs(title = "Regression discontinuity attack shows little change in distress")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Kessler 6: scale 0-4")  +
  xlab("Timeline: 2018-OCT-16 to 2019-AUG-12")

cd <-
  change_distress + ggtitle("Regression Discontinuity Analysis of Kessler6 Distress Pre/Post Attacks")
cd

Show code



## long trend

npk <-
  ggplot(data = df_rdd4,
         aes(
           x = Timeline,
           y = KESSLER6
         )) + scale_fill_viridis_c(option = "plasma")
change_distressk <- npk +
  geom_point(alpha = .03) +
 # stat_smooth(method =  stats::loess) +
   stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +
  labs(title = "Long term trend k6 distress")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Kessler 6: scale 0-4")  +
  xlab("Timeline: 2018-OCT-16 to 2019-AUG-12")

cdk <-
  change_distressk + ggtitle("Long term trend k6 distress")
cdk

Show code






#### Terr Anx





np2 <-
  ggplot(data = df_rdd2,
         aes(
           x = Timeline,
           y = Terrorism.Anxiety #,
        #   group = Pre_Post_attack,
         #  colour = Pre_Post_attack
         )) + scale_fill_viridis_c(option = "plasma")
change_distress2 <- np2 +
  geom_point(alpha = .03) +
 # stat_smooth(method =  stats::loess) +
   stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +
  labs(title = "Regression discontinuity attack shows little change in distress")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Kessler 6: scale 0-4")  +
  xlab("Timeline: 2018-JUL-01 to 2019-AUG-12")

cd2 <-
  change_distress2 + ggtitle("Regression Discontinuity Analysis of Kessler6 Distress Pre/Post Attacks")
cd2

Show code






np3 <-
  ggplot(
    data = df_rdd3,
    aes(
      x = Timeline,
      y = Terrorism.Anxiety 
    )
  ) + scale_fill_viridis_c(option = "plasma")
change_ta3 <- np3 +
  geom_point(alpha = .03) +
  # stat_smooth(method =  stats::loess) +
  stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +  theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Kessler 6: scale 0-4")  +
  xlab("Timeline:2016-2020")

cd3 <-
  change_ta3 + ggtitle("Terrorism Anxiety 2016-2020")
cd3

Show code




np3k <-
  ggplot(
    data = df_rdd3,
    aes(
      x = Timeline,
      y = KESSLER6 ,
      group = Pre_Post_attack,
      colour = Pre_Post_attack
    )
  ) + scale_fill_viridis_c(option = "plasma")
change_ta3l<- np3k +
  geom_point(alpha = .03) +
  # stat_smooth(method =  stats::loess) +
  stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Kessler 6: scale 0-4")  +
  xlab("Timeline:2016-2020")

np3k <-
  change_ta3l + ggtitle("K6 Anxiety 2016-2020")
np3k

Show code


# library(splines)
# out<-glm(
#   KESSLER6 ~ bs(days_0),
#   data = df_rdd)
#
# summary(out)
# plot(
#   ggeffects::ggpredict(out, "days_0"),
#   add.data = TRUE,
#   dot.alpha = .01
# )

## RDD FULL SAMPLE
np2 <-
  ggplot(
    data = df_rdd,
    aes(
      x = Timeline,
      y = Terrorism.Anxiety,
      group = Pre_Post_attack,
      colour = Pre_Post_attack
    )
  ) + scale_fill_viridis_c(option = "plasma")
change_terror <- np2 +
  geom_point(alpha = .03) +
 # stat_smooth(method =  stats::loess) +
  stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +
  labs(title = "Regression discontinuity attack shows greater change in terrorism anxiety")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Terrorism Anxiety: scale 1-7") +
  xlab("Timeline: 2018-OCT-16 to 2019-AUG-12")

ct <-
  change_terror +  ggtitle("Regression Discontinuity Analysis of Terrorism Anxiety Pre/Post Attacks")
ct

Show code

# library(splines)
# out2<-glm(
#   Terrorism.Anxiety ~ bs(days_0),
#   data = df_rdd)
#
# summary(out2)
# plot(
#   ggeffects::ggpredict(out2, "days_0"),
#   add.data = TRUE,
#   dot.alpha = .01
# )



np3 <-
  ggplot(
    data = df_rdd,
    aes(
      x = Timeline,
      y = Neighbourhood_Community,
      group = Pre_Post_attack,
      colour = Pre_Post_attack
    )
  ) + scale_fill_viridis_c(option = "plasma")
change_belong <- np3 +
  geom_point(alpha = .03) +
 # stat_smooth(method =  stats::loess) +
   stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +
  labs(title = "Regression discontinuity attack shows greater change in community belonging")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Sense of Neighbourhood Community: scale 1-7") +
  xlab("Timeline: 2018-OCT-16 to 2019-AUG-12")

nc <-
  change_belong + ggtitle("Regression Discontinuity Analysis of Neighbourhood Community Pre/Post Attacks")
nc

Show code




np4 <-
  ggplot(data = df_rdd,
         aes(
           x = Timeline,
           y = PWI,
           group = Pre_Post_attack,
           colour = Pre_Post_attack
         )) + scale_fill_viridis_c(option = "plasma")
change_pwi <- np4 +
  geom_point(alpha = .03) +
 # stat_smooth(method =  stats::loess) +
   stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +
  labs(title = "Regression discontinuity attack shows greater change in Personal Wellbeing")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Personal Wellbeing: scale 1-7") +
  xlab("Timeline: 2018-OCT-16 to 2019-AUG-12")

pwb <-
  change_belong + ggtitle("Regression Discontinuity Analysis of Personal Wellbeing Pre/Post Attacks")

pwb

Show code



library(patchwork)

(ct  + cd) / (nc + pwb)  + plot_annotation(
  title = "Specificity of Wellbeing/Distress Response",
  subtitle = "Non-Muslims Distress Dimenisons Pre/Post Mosque Attacks,  N =  17228
smooth = GAM (mgcv)",
tag_levels = "a"
) +
  plot_layout(guides = 'collect')

Show code




table1::table1(
  ~ Age + Male + EthnicCats + Terrorism.Anxiety + KESSLER6 + Neighbourhood_Community + Personal_Wellbeing |
    Pre_Post_attack,
  data = df_rdd
)

	Pre_Attack (N=11679)	Post_Attack (N=5549)	Overall (N=17228)
Age
Mean (SD)	53.3 (13.7)	51.3 (13.4)	52.7 (13.6)
Median [Min, Max]	55.2 [19.0, 99.1]	52.7 [20.6, 94.1]	54.4 [19.0, 99.1]
Missing	1 (0.0%)	0 (0%)	1 (0.0%)
Male
Not_Male	7311 (62.6%)	3486 (62.8%)	10797 (62.7%)
Male	4338 (37.1%)	2048 (36.9%)	6386 (37.1%)
Missing	30 (0.3%)	15 (0.3%)	45 (0.3%)
EthnicCats
Euro	9570 (81.9%)	4448 (80.2%)	14018 (81.4%)
Maori	1322 (11.3%)	658 (11.9%)	1980 (11.5%)
Pacific	205 (1.8%)	113 (2.0%)	318 (1.8%)
Asian	413 (3.5%)	229 (4.1%)	642 (3.7%)
Missing	169 (1.4%)	101 (1.8%)	270 (1.6%)
Terrorism.Anxiety
Mean (SD)	2.56 (1.60)	3.11 (1.74)	2.73 (1.67)
Median [Min, Max]	2.00 [1.00, 7.00]	3.00 [1.00, 7.00]	2.00 [1.00, 7.00]
Missing	431 (3.7%)	240 (4.3%)	671 (3.9%)
KESSLER6
Mean (SD)	0.829 (0.658)	0.873 (0.656)	0.843 (0.658)
Median [Min, Max]	0.667 [0, 4.00]	0.667 [0, 4.00]	0.667 [0, 4.00]
Missing	47 (0.4%)	20 (0.4%)	67 (0.4%)
Neighbourhood_Community
Mean (SD)	4.35 (1.58)	4.32 (1.60)	4.34 (1.58)
Median [Min, Max]	4.00 [1.00, 7.00]	4.00 [1.00, 7.00]	4.00 [1.00, 7.00]
Missing	65 (0.6%)	25 (0.5%)	90 (0.5%)
Personal_Wellbeing
Mean (SD)	7.14 (1.64)	7.07 (1.64)	7.11 (1.64)
Median [Min, Max]	7.50 [0, 10.0]	7.25 [0, 10.0]	7.25 [0, 10.0]
Missing	12 (0.1%)	4 (0.1%)	16 (0.1%)

Show code


npN <-
  ggplot(data = df_rdd,
         aes(x = Timeline,
             y = KESSLER6)) + scale_fill_viridis_c(option = "plasma")
change_distressN <- npN +
  geom_point(alpha = .03) +
  stat_smooth(method =  stats::loess) +
  # stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +
  labs(title = "Regression discontinuity attack shows little change in distress")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Kessler 6: scale 0-4")  +
  xlab("Timeline: 2018-OCT-16 to 2019-AUG-12")

cdN <-
  change_distressN + ggtitle("Regression Discontinuity Analysis of Kessler6 Distress Pre/Post Attacks")
cdN

Show code

# library(splines)
# out<-glm(
#   KESSLER6 ~ bs(days_0),
#   data = df_rdd)
#
# summary(out)
# plot(
#   ggeffects::ggpredict(out, "days_0"),
#   add.data = TRUE,
#   dot.alpha = .01
# )

## RDD FULL SAMPLE
np2N <-
  ggplot(data = df_rdd,
         aes(x = Timeline,
             y = Terrorism.Anxiety)) + scale_fill_viridis_c(option = "plasma")
change_terrorN <- np2N +
  geom_point(alpha = .03) +
  stat_smooth(method =  stats::loess) +
  # stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +
  labs(title = "Regression discontinuity attack shows greater change in terrorism anxiety")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Terrorism Anxiety: scale 1-7") +
  xlab("Timeline: 2018-OCT-16 to 2019-AUG-12")

ctN <-
  change_terrorN +  ggtitle("Regression Discontinuity Analysis of Terrorism Anxiety Pre/Post Attacks")
ctN

Show code

# library(splines)
# out2<-glm(
#   Terrorism.Anxiety ~ bs(days_0),
#   data = df_rdd)
#
# summary(out2)
# plot(
#   ggeffects::ggpredict(out2, "days_0"),
#   add.data = TRUE,
#   dot.alpha = .01
# )



np3N <-
  ggplot(data = df_rdd,
         aes(x = Timeline,
             y = Neighbourhood_Community,)) + scale_fill_viridis_c(option = "plasma")
change_belongN <- np3N +
  geom_point(alpha = .03) +
  stat_smooth(method =  stats::loess) +
  # stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +
  labs(title = "Regression discontinuity attack shows greater change in community belonging")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Sense of Neighbourhood Community: scale 1-7") +
  xlab("Timeline: 2018-OCT-16 to 2019-AUG-12")

ncN <-
  change_belongN + ggtitle("Regression Discontinuity Analysis of Neighbourhood Community Pre/Post Attacks")
ncN

Show code




np4N <-
  ggplot(data = df_rdd,
         aes(x = Timeline,
             y = PWI)) + scale_fill_viridis_c(option = "plasma")
change_pwiN <- np4 +
  geom_point(alpha = .03) +
  stat_smooth(method =  stats::loess) +
  # stat_smooth(method = "gam") +
  theme_classic() +
  scale_color_viridis_d(option = "plasma") +
  labs(title = "Regression discontinuity attack shows greater change in Personal Wellbeing")  +   theme(
    legend.position = "right",
    legend.text = element_text(size = 6),
    legend.title = element_text(color = "Black", size = 8)
  ) +
  ylab("Personal Wellbeing: scale 1-7") +
  xlab("Timeline: 2018-OCT-16 to 2019-AUG-12")

pwbN <-
  change_belongN + ggtitle("Regression Discontinuity Analysis of Personal Wellbeing Pre/Post Attacks")

pwbN

Show code



library(patchwork)

(ctN  + cdN) / (ncN + pwbN)  + plot_annotation(
  title = "Specificity of Wellbeing/Distress Response",
  subtitle = "Non-Muslims Distress Dimenisons Pre/Post Mosque Attacks,  N =  17228
method = loess smooth",
tag_levels = "a"
) +
  plot_layout(guides = 'collect')

Show code




table1::table1(
  ~ Age + Male + EthnicCats + Terrorism.Anxiety + KESSLER6 + Neighbourhood_Community + Personal_Wellbeing |
    Pre_Post_attack,
  data = df_rdd
)

	Pre_Attack (N=11679)	Post_Attack (N=5549)	Overall (N=17228)
Age
Mean (SD)	53.3 (13.7)	51.3 (13.4)	52.7 (13.6)
Median [Min, Max]	55.2 [19.0, 99.1]	52.7 [20.6, 94.1]	54.4 [19.0, 99.1]
Missing	1 (0.0%)	0 (0%)	1 (0.0%)
Male
Not_Male	7311 (62.6%)	3486 (62.8%)	10797 (62.7%)
Male	4338 (37.1%)	2048 (36.9%)	6386 (37.1%)
Missing	30 (0.3%)	15 (0.3%)	45 (0.3%)
EthnicCats
Euro	9570 (81.9%)	4448 (80.2%)	14018 (81.4%)
Maori	1322 (11.3%)	658 (11.9%)	1980 (11.5%)
Pacific	205 (1.8%)	113 (2.0%)	318 (1.8%)
Asian	413 (3.5%)	229 (4.1%)	642 (3.7%)
Missing	169 (1.4%)	101 (1.8%)	270 (1.6%)
Terrorism.Anxiety
Mean (SD)	2.56 (1.60)	3.11 (1.74)	2.73 (1.67)
Median [Min, Max]	2.00 [1.00, 7.00]	3.00 [1.00, 7.00]	2.00 [1.00, 7.00]
Missing	431 (3.7%)	240 (4.3%)	671 (3.9%)
KESSLER6
Mean (SD)	0.829 (0.658)	0.873 (0.656)	0.843 (0.658)
Median [Min, Max]	0.667 [0, 4.00]	0.667 [0, 4.00]	0.667 [0, 4.00]
Missing	47 (0.4%)	20 (0.4%)	67 (0.4%)
Neighbourhood_Community
Mean (SD)	4.35 (1.58)	4.32 (1.60)	4.34 (1.58)
Median [Min, Max]	4.00 [1.00, 7.00]	4.00 [1.00, 7.00]	4.00 [1.00, 7.00]
Missing	65 (0.6%)	25 (0.5%)	90 (0.5%)
Personal_Wellbeing
Mean (SD)	7.14 (1.64)	7.07 (1.64)	7.11 (1.64)
Median [Min, Max]	7.50 [0, 10.0]	7.25 [0, 10.0]	7.25 [0, 10.0]
Missing	12 (0.1%)	4 (0.1%)	16 (0.1%)

Show code



# Weekly counts

table1::table1(~ Terrorism.Anxiety|factor(WSCORE), data = df_rdd, transpose = TRUE)

	Terrorism.Anxiety
489 (N=699)	: Mean (SD): 2.29 (1.47) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 5 (0.7%)
490 (N=1463)	: Mean (SD): 2.26 (1.47) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 15 (1.0%)
491 (N=549)	: Mean (SD): 2.41 (1.55) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 13 (2.4%)
492 (N=315)	: Mean (SD): 2.62 (1.62) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 9 (2.9%)
493 (N=284)	: Mean (SD): 2.73 (1.66) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 12 (4.2%)
494 (N=1120)	: Mean (SD): 2.64 (1.56) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 65 (5.8%)
495 (N=579)	: Mean (SD): 2.79 (1.67) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 25 (4.3%)
496 (N=712)	: Mean (SD): 2.47 (1.56) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 26 (3.7%)
497 (N=568)	: Mean (SD): 2.83 (1.68) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 21 (3.7%)
498 (N=302)	: Mean (SD): 2.54 (1.57) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 15 (5.0%)
499 (N=87)	: Mean (SD): 2.46 (1.62) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 3 (3.4%)
500 (N=109)	: Mean (SD): 2.51 (1.54) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 2 (1.8%)
501 (N=751)	: Mean (SD): 2.49 (1.63) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 16 (2.1%)
502 (N=546)	: Mean (SD): 2.52 (1.59) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 18 (3.3%)
503 (N=1130)	: Mean (SD): 2.77 (1.70) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 53 (4.7%)
504 (N=522)	: Mean (SD): 2.74 (1.67) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 28 (5.4%)
505 (N=580)	: Mean (SD): 2.55 (1.62) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 30 (5.2%)
506 (N=484)	: Mean (SD): 2.55 (1.58) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 29 (6.0%)
507 (N=297)	: Mean (SD): 2.68 (1.64) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 15 (5.1%)
508 (N=191)	: Mean (SD): 2.83 (1.64) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 15 (7.9%)
509 (N=157)	: Mean (SD): 2.33 (1.49) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 4 (2.5%)
510 (N=279)	: Mean (SD): 2.86 (1.80) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 14 (5.0%)
511 (N=152)	: Mean (SD): 3.31 (1.80) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 9 (5.9%)
512 (N=118)	: Mean (SD): 3.54 (1.89) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 4 (3.4%)
513 (N=85)	: Mean (SD): 3.34 (1.83) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 2 (2.4%)
514 (N=56)	: Mean (SD): 3.12 (1.75) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 4 (7.1%)
515 (N=34)	: Mean (SD): 2.94 (1.77) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 1 (2.9%)
516 (N=43)	: Mean (SD): 3.25 (1.71) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 3 (7.0%)
517 (N=27)	: Mean (SD): 3.38 (1.65) Median [Min, Max]: 4.00 [1.00, 6.00] Missing: 1 (3.7%)
518 (N=16)	: Mean (SD): 3.81 (1.28) Median [Min, Max]: 4.00 [1.00, 5.00] Missing: 0 (0%)
519 (N=25)	: Mean (SD): 3.56 (1.76) Median [Min, Max]: 4.00 [1.00, 7.00] Missing: 0 (0%)
520 (N=504)	: Mean (SD): 2.99 (1.77) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 9 (1.8%)
521 (N=931)	: Mean (SD): 3.11 (1.76) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 14 (1.5%)
522 (N=473)	: Mean (SD): 3.21 (1.79) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 22 (4.7%)
523 (N=212)	: Mean (SD): 3.05 (1.68) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 11 (5.2%)
524 (N=164)	: Mean (SD): 2.96 (1.64) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 7 (4.3%)
525 (N=133)	: Mean (SD): 3.10 (1.75) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 8 (6.0%)
526 (N=119)	: Mean (SD): 2.97 (1.60) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 7 (5.9%)
527 (N=675)	: Mean (SD): 3.02 (1.76) Median [Min, Max]: 2.50 [1.00, 7.00] Missing: 41 (6.1%)
528 (N=787)	: Mean (SD): 3.11 (1.75) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 37 (4.7%)
529 (N=394)	: Mean (SD): 2.96 (1.64) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 25 (6.3%)
530 (N=310)	: Mean (SD): 3.19 (1.66) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 16 (5.2%)
531 (N=192)	: Mean (SD): 3.15 (1.67) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 11 (5.7%)
532 (N=54)	: Mean (SD): 3.25 (1.64) Median [Min, Max]: 3.00 [1.00, 6.00] Missing: 6 (11.1%)
Overall (N=17228)	: Mean (SD): 2.73 (1.67) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 671 (3.9%)

Show code



table1::table1(~ Terrorism.Anxiety + Age + Male + EthnicCats|factor(WSCORE), data = df_rdd, 
               transpose = TRUE)

	Terrorism.Anxiety	Age	Male	EthnicCats
489 (N=699)	: Mean (SD): 2.29 (1.47) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 5 (0.7%)	: Mean (SD): 55.5 (15.3) Median [Min, Max]: 57.1 [19.9, 89.3] Missing: 0 (0%)	: Not_Male: 405 (57.9%) Male: 290 (41.5%) Missing: 4 (0.6%)	: Euro: 595 (85.1%) Maori: 58 (8.3%) Pacific: 14 (2.0%) Asian: 24 (3.4%) Missing: 8 (1.1%)
490 (N=1463)	: Mean (SD): 2.26 (1.47) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 15 (1.0%)	: Mean (SD): 55.5 (13.4) Median [Min, Max]: 56.8 [19.6, 92.5] Missing: 0 (0%)	: Not_Male: 892 (61.0%) Male: 569 (38.9%) Missing: 2 (0.1%)	: Euro: 1269 (86.7%) Maori: 123 (8.4%) Pacific: 19 (1.3%) Asian: 29 (2.0%) Missing: 23 (1.6%)
491 (N=549)	: Mean (SD): 2.41 (1.55) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 13 (2.4%)	: Mean (SD): 54.1 (14.3) Median [Min, Max]: 56.0 [19.0, 90.9] Missing: 0 (0%)	: Not_Male: 328 (59.7%) Male: 220 (40.1%) Missing: 1 (0.2%)	: Euro: 458 (83.4%) Maori: 54 (9.8%) Pacific: 11 (2.0%) Asian: 22 (4.0%) Missing: 4 (0.7%)
492 (N=315)	: Mean (SD): 2.62 (1.62) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 9 (2.9%)	: Mean (SD): 55.5 (14.8) Median [Min, Max]: 56.8 [19.6, 99.1] Missing: 0 (0%)	: Not_Male: 190 (60.3%) Male: 125 (39.7%) Missing: 0 (0%)	: Euro: 263 (83.5%) Maori: 33 (10.5%) Pacific: 7 (2.2%) Asian: 10 (3.2%) Missing: 2 (0.6%)
493 (N=284)	: Mean (SD): 2.73 (1.66) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 12 (4.2%)	: Mean (SD): 54.1 (15.1) Median [Min, Max]: 55.3 [19.6, 94.1] Missing: 0 (0%)	: Not_Male: 191 (67.3%) Male: 92 (32.4%) Missing: 1 (0.4%)	: Euro: 230 (81.0%) Maori: 33 (11.6%) Pacific: 8 (2.8%) Asian: 10 (3.5%) Missing: 3 (1.1%)
494 (N=1120)	: Mean (SD): 2.64 (1.56) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 65 (5.8%)	: Mean (SD): 56.2 (13.8) Median [Min, Max]: 57.1 [20.4, 91.9] Missing: 0 (0%)	: Not_Male: 714 (63.8%) Male: 403 (36.0%) Missing: 3 (0.3%)	: Euro: 929 (82.9%) Maori: 138 (12.3%) Pacific: 12 (1.1%) Asian: 26 (2.3%) Missing: 15 (1.3%)
495 (N=579)	: Mean (SD): 2.79 (1.67) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 25 (4.3%)	: Mean (SD): 55.4 (13.2) Median [Min, Max]: 57.2 [19.7, 90.7] Missing: 0 (0%)	: Not_Male: 394 (68.0%) Male: 184 (31.8%) Missing: 1 (0.2%)	: Euro: 490 (84.6%) Maori: 58 (10.0%) Pacific: 5 (0.9%) Asian: 22 (3.8%) Missing: 4 (0.7%)
496 (N=712)	: Mean (SD): 2.47 (1.56) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 26 (3.7%)	: Mean (SD): 52.1 (14.5) Median [Min, Max]: 53.3 [20.0, 90.3] Missing: 0 (0%)	: Not_Male: 447 (62.8%) Male: 260 (36.5%) Missing: 5 (0.7%)	: Euro: 600 (84.3%) Maori: 71 (10.0%) Pacific: 10 (1.4%) Asian: 21 (2.9%) Missing: 10 (1.4%)
497 (N=568)	: Mean (SD): 2.83 (1.68) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 21 (3.7%)	: Mean (SD): 52.8 (12.7) Median [Min, Max]: 53.9 [21.0, 84.9] Missing: 0 (0%)	: Not_Male: 337 (59.3%) Male: 227 (40.0%) Missing: 4 (0.7%)	: Euro: 466 (82.0%) Maori: 65 (11.4%) Pacific: 8 (1.4%) Asian: 20 (3.5%) Missing: 9 (1.6%)
498 (N=302)	: Mean (SD): 2.54 (1.57) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 15 (5.0%)	: Mean (SD): 50.8 (13.4) Median [Min, Max]: 51.0 [20.7, 86.6] Missing: 0 (0%)	: Not_Male: 175 (57.9%) Male: 126 (41.7%) Missing: 1 (0.3%)	: Euro: 237 (78.5%) Maori: 47 (15.6%) Pacific: 5 (1.7%) Asian: 9 (3.0%) Missing: 4 (1.3%)
499 (N=87)	: Mean (SD): 2.46 (1.62) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 3 (3.4%)	: Mean (SD): 47.9 (15.0) Median [Min, Max]: 49.1 [19.8, 87.5] Missing: 0 (0%)	: Not_Male: 43 (49.4%) Male: 44 (50.6%) Missing: 0 (0%)	: Euro: 65 (74.7%) Maori: 8 (9.2%) Pacific: 2 (2.3%) Asian: 11 (12.6%) Missing: 1 (1.1%)
500 (N=109)	: Mean (SD): 2.51 (1.54) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 2 (1.8%)	: Mean (SD): 48.6 (13.2) Median [Min, Max]: 50.8 [22.7, 74.3] Missing: 0 (0%)	: Not_Male: 67 (61.5%) Male: 42 (38.5%) Missing: 0 (0%)	: Euro: 84 (77.1%) Maori: 15 (13.8%) Pacific: 1 (0.9%) Asian: 8 (7.3%) Missing: 1 (0.9%)
501 (N=751)	: Mean (SD): 2.49 (1.63) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 16 (2.1%)	: Mean (SD): 49.0 (13.1) Median [Min, Max]: 50.6 [19.6, 88.7] Missing: 1 (0.1%)	: Not_Male: 493 (65.6%) Male: 257 (34.2%) Missing: 1 (0.1%)	: Euro: 605 (80.6%) Maori: 87 (11.6%) Pacific: 16 (2.1%) Asian: 33 (4.4%) Missing: 10 (1.3%)
502 (N=546)	: Mean (SD): 2.52 (1.59) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 18 (3.3%)	: Mean (SD): 51.4 (13.3) Median [Min, Max]: 54.1 [20.2, 91.0] Missing: 0 (0%)	: Not_Male: 330 (60.4%) Male: 216 (39.6%) Missing: 0 (0%)	: Euro: 426 (78.0%) Maori: 78 (14.3%) Pacific: 13 (2.4%) Asian: 20 (3.7%) Missing: 9 (1.6%)
503 (N=1130)	: Mean (SD): 2.77 (1.70) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 53 (4.7%)	: Mean (SD): 52.3 (12.3) Median [Min, Max]: 54.7 [19.1, 89.8] Missing: 0 (0%)	: Not_Male: 726 (64.2%) Male: 402 (35.6%) Missing: 2 (0.2%)	: Euro: 885 (78.3%) Maori: 155 (13.7%) Pacific: 27 (2.4%) Asian: 42 (3.7%) Missing: 21 (1.9%)
504 (N=522)	: Mean (SD): 2.74 (1.67) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 28 (5.4%)	: Mean (SD): 52.9 (12.5) Median [Min, Max]: 55.2 [20.3, 89.6] Missing: 0 (0%)	: Not_Male: 323 (61.9%) Male: 199 (38.1%) Missing: 0 (0%)	: Euro: 412 (78.9%) Maori: 66 (12.6%) Pacific: 13 (2.5%) Asian: 24 (4.6%) Missing: 7 (1.3%)
505 (N=580)	: Mean (SD): 2.55 (1.62) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 30 (5.2%)	: Mean (SD): 50.7 (13.1) Median [Min, Max]: 52.8 [20.3, 87.2] Missing: 0 (0%)	: Not_Male: 380 (65.5%) Male: 199 (34.3%) Missing: 1 (0.2%)	: Euro: 474 (81.7%) Maori: 54 (9.3%) Pacific: 10 (1.7%) Asian: 33 (5.7%) Missing: 9 (1.6%)
506 (N=484)	: Mean (SD): 2.55 (1.58) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 29 (6.0%)	: Mean (SD): 52.2 (12.9) Median [Min, Max]: 54.0 [20.5, 90.0] Missing: 0 (0%)	: Not_Male: 316 (65.3%) Male: 166 (34.3%) Missing: 2 (0.4%)	: Euro: 383 (79.1%) Maori: 60 (12.4%) Pacific: 8 (1.7%) Asian: 17 (3.5%) Missing: 16 (3.3%)
507 (N=297)	: Mean (SD): 2.68 (1.64) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 15 (5.1%)	: Mean (SD): 53.3 (12.9) Median [Min, Max]: 55.7 [20.4, 97.9] Missing: 0 (0%)	: Not_Male: 198 (66.7%) Male: 99 (33.3%) Missing: 0 (0%)	: Euro: 231 (77.8%) Maori: 41 (13.8%) Pacific: 9 (3.0%) Asian: 11 (3.7%) Missing: 5 (1.7%)
508 (N=191)	: Mean (SD): 2.83 (1.64) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 15 (7.9%)	: Mean (SD): 53.0 (14.2) Median [Min, Max]: 54.7 [23.0, 95.5] Missing: 0 (0%)	: Not_Male: 115 (60.2%) Male: 75 (39.3%) Missing: 1 (0.5%)	: Euro: 150 (78.5%) Maori: 25 (13.1%) Pacific: 3 (1.6%) Asian: 9 (4.7%) Missing: 4 (2.1%)
509 (N=157)	: Mean (SD): 2.33 (1.49) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 4 (2.5%)	: Mean (SD): 51.1 (13.2) Median [Min, Max]: 52.0 [21.9, 79.3] Missing: 0 (0%)	: Not_Male: 109 (69.4%) Male: 48 (30.6%) Missing: 0 (0%)	: Euro: 124 (79.0%) Maori: 24 (15.3%) Pacific: 1 (0.6%) Asian: 6 (3.8%) Missing: 2 (1.3%)
510 (N=279)	: Mean (SD): 2.86 (1.80) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 14 (5.0%)	: Mean (SD): 53.7 (13.6) Median [Min, Max]: 54.6 [21.7, 89.0] Missing: 0 (0%)	: Not_Male: 167 (59.9%) Male: 111 (39.8%) Missing: 1 (0.4%)	: Euro: 231 (82.8%) Maori: 34 (12.2%) Pacific: 4 (1.4%) Asian: 7 (2.5%) Missing: 3 (1.1%)
511 (N=152)	: Mean (SD): 3.31 (1.80) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 9 (5.9%)	: Mean (SD): 52.5 (14.3) Median [Min, Max]: 54.8 [20.6, 87.6] Missing: 0 (0%)	: Not_Male: 93 (61.2%) Male: 59 (38.8%) Missing: 0 (0%)	: Euro: 124 (81.6%) Maori: 19 (12.5%) Pacific: 1 (0.7%) Asian: 6 (3.9%) Missing: 2 (1.3%)
512 (N=118)	: Mean (SD): 3.54 (1.89) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 4 (3.4%)	: Mean (SD): 53.4 (13.4) Median [Min, Max]: 56.3 [24.7, 90.7] Missing: 0 (0%)	: Not_Male: 76 (64.4%) Male: 42 (35.6%) Missing: 0 (0%)	: Euro: 95 (80.5%) Maori: 15 (12.7%) Pacific: 2 (1.7%) Asian: 4 (3.4%) Missing: 2 (1.7%)
513 (N=85)	: Mean (SD): 3.34 (1.83) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 2 (2.4%)	: Mean (SD): 50.1 (14.2) Median [Min, Max]: 51.8 [20.7, 82.5] Missing: 0 (0%)	: Not_Male: 57 (67.1%) Male: 27 (31.8%) Missing: 1 (1.2%)	: Euro: 74 (87.1%) Maori: 6 (7.1%) Pacific: 1 (1.2%) Asian: 3 (3.5%) Missing: 1 (1.2%)
514 (N=56)	: Mean (SD): 3.12 (1.75) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 4 (7.1%)	: Mean (SD): 50.0 (13.7) Median [Min, Max]: 52.2 [24.1, 82.3] Missing: 0 (0%)	: Not_Male: 34 (60.7%) Male: 22 (39.3%) Missing: 0 (0%)	: Euro: 47 (83.9%) Maori: 6 (10.7%) Pacific: 0 (0%) Asian: 2 (3.6%) Missing: 1 (1.8%)
515 (N=34)	: Mean (SD): 2.94 (1.77) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 1 (2.9%)	: Mean (SD): 49.5 (13.7) Median [Min, Max]: 49.2 [21.2, 74.5] Missing: 0 (0%)	: Not_Male: 24 (70.6%) Male: 10 (29.4%) Missing: 0 (0%)	: Euro: 24 (70.6%) Maori: 6 (17.6%) Pacific: 2 (5.9%) Asian: 0 (0%) Missing: 2 (5.9%)
516 (N=43)	: Mean (SD): 3.25 (1.71) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 3 (7.0%)	: Mean (SD): 50.6 (14.5) Median [Min, Max]: 50.3 [20.6, 77.3] Missing: 0 (0%)	: Not_Male: 29 (67.4%) Male: 14 (32.6%) Missing: 0 (0%)	: Euro: 34 (79.1%) Maori: 4 (9.3%) Pacific: 0 (0%) Asian: 4 (9.3%) Missing: 1 (2.3%)
517 (N=27)	: Mean (SD): 3.38 (1.65) Median [Min, Max]: 4.00 [1.00, 6.00] Missing: 1 (3.7%)	: Mean (SD): 54.8 (13.9) Median [Min, Max]: 54.1 [25.3, 82.0] Missing: 0 (0%)	: Not_Male: 19 (70.4%) Male: 7 (25.9%) Missing: 1 (3.7%)	: Euro: 24 (88.9%) Maori: 3 (11.1%) Pacific: 0 (0%) Asian: 0 (0%) Missing: 0 (0%)
518 (N=16)	: Mean (SD): 3.81 (1.28) Median [Min, Max]: 4.00 [1.00, 5.00] Missing: 0 (0%)	: Mean (SD): 51.9 (10.3) Median [Min, Max]: 50.5 [36.4, 77.9] Missing: 0 (0%)	: Not_Male: 12 (75.0%) Male: 4 (25.0%) Missing: 0 (0%)	: Euro: 13 (81.3%) Maori: 3 (18.8%) Pacific: 0 (0%) Asian: 0 (0%) Missing: 0 (0%)
519 (N=25)	: Mean (SD): 3.56 (1.76) Median [Min, Max]: 4.00 [1.00, 7.00] Missing: 0 (0%)	: Mean (SD): 51.3 (13.2) Median [Min, Max]: 55.1 [26.6, 77.9] Missing: 0 (0%)	: Not_Male: 15 (60.0%) Male: 10 (40.0%) Missing: 0 (0%)	: Euro: 19 (76.0%) Maori: 1 (4.0%) Pacific: 1 (4.0%) Asian: 2 (8.0%) Missing: 2 (8.0%)
520 (N=504)	: Mean (SD): 2.99 (1.77) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 9 (1.8%)	: Mean (SD): 47.3 (13.7) Median [Min, Max]: 47.6 [20.6, 90.7] Missing: 0 (0%)	: Not_Male: 342 (67.9%) Male: 160 (31.7%) Missing: 2 (0.4%)	: Euro: 406 (80.6%) Maori: 53 (10.5%) Pacific: 13 (2.6%) Asian: 24 (4.8%) Missing: 8 (1.6%)
521 (N=931)	: Mean (SD): 3.11 (1.76) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 14 (1.5%)	: Mean (SD): 50.0 (13.3) Median [Min, Max]: 51.4 [20.6, 92.6] Missing: 0 (0%)	: Not_Male: 589 (63.3%) Male: 338 (36.3%) Missing: 4 (0.4%)	: Euro: 752 (80.8%) Maori: 112 (12.0%) Pacific: 16 (1.7%) Asian: 34 (3.7%) Missing: 17 (1.8%)
522 (N=473)	: Mean (SD): 3.21 (1.79) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 22 (4.7%)	: Mean (SD): 52.3 (14.3) Median [Min, Max]: 53.6 [21.6, 88.9] Missing: 0 (0%)	: Not_Male: 289 (61.1%) Male: 182 (38.5%) Missing: 2 (0.4%)	: Euro: 388 (82.0%) Maori: 55 (11.6%) Pacific: 11 (2.3%) Asian: 12 (2.5%) Missing: 7 (1.5%)
523 (N=212)	: Mean (SD): 3.05 (1.68) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 11 (5.2%)	: Mean (SD): 54.0 (14.1) Median [Min, Max]: 55.5 [21.8, 92.9] Missing: 0 (0%)	: Not_Male: 141 (66.5%) Male: 70 (33.0%) Missing: 1 (0.5%)	: Euro: 160 (75.5%) Maori: 33 (15.6%) Pacific: 3 (1.4%) Asian: 11 (5.2%) Missing: 5 (2.4%)
524 (N=164)	: Mean (SD): 2.96 (1.64) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 7 (4.3%)	: Mean (SD): 50.1 (13.3) Median [Min, Max]: 51.6 [20.9, 78.5] Missing: 0 (0%)	: Not_Male: 89 (54.3%) Male: 75 (45.7%) Missing: 0 (0%)	: Euro: 129 (78.7%) Maori: 15 (9.1%) Pacific: 8 (4.9%) Asian: 10 (6.1%) Missing: 2 (1.2%)
525 (N=133)	: Mean (SD): 3.10 (1.75) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 8 (6.0%)	: Mean (SD): 52.1 (14.0) Median [Min, Max]: 55.2 [20.6, 94.1] Missing: 0 (0%)	: Not_Male: 71 (53.4%) Male: 61 (45.9%) Missing: 1 (0.8%)	: Euro: 108 (81.2%) Maori: 16 (12.0%) Pacific: 2 (1.5%) Asian: 6 (4.5%) Missing: 1 (0.8%)
526 (N=119)	: Mean (SD): 2.97 (1.60) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 7 (5.9%)	: Mean (SD): 51.0 (14.0) Median [Min, Max]: 52.0 [20.8, 85.4] Missing: 0 (0%)	: Not_Male: 77 (64.7%) Male: 42 (35.3%) Missing: 0 (0%)	: Euro: 96 (80.7%) Maori: 17 (14.3%) Pacific: 0 (0%) Asian: 5 (4.2%) Missing: 1 (0.8%)
527 (N=675)	: Mean (SD): 3.02 (1.76) Median [Min, Max]: 2.50 [1.00, 7.00] Missing: 41 (6.1%)	: Mean (SD): 52.1 (13.3) Median [Min, Max]: 54.0 [20.7, 91.4] Missing: 0 (0%)	: Not_Male: 432 (64.0%) Male: 242 (35.9%) Missing: 1 (0.1%)	: Euro: 511 (75.7%) Maori: 91 (13.5%) Pacific: 20 (3.0%) Asian: 35 (5.2%) Missing: 18 (2.7%)
528 (N=787)	: Mean (SD): 3.11 (1.75) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 37 (4.7%)	: Mean (SD): 52.7 (12.7) Median [Min, Max]: 53.8 [21.1, 92.1] Missing: 0 (0%)	: Not_Male: 480 (61.0%) Male: 306 (38.9%) Missing: 1 (0.1%)	: Euro: 628 (79.8%) Maori: 96 (12.2%) Pacific: 18 (2.3%) Asian: 33 (4.2%) Missing: 12 (1.5%)
529 (N=394)	: Mean (SD): 2.96 (1.64) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 25 (6.3%)	: Mean (SD): 52.3 (12.6) Median [Min, Max]: 53.8 [22.5, 93.5] Missing: 0 (0%)	: Not_Male: 246 (62.4%) Male: 148 (37.6%) Missing: 0 (0%)	: Euro: 317 (80.5%) Maori: 48 (12.2%) Pacific: 3 (0.8%) Asian: 20 (5.1%) Missing: 6 (1.5%)
530 (N=310)	: Mean (SD): 3.19 (1.66) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 16 (5.2%)	: Mean (SD): 51.4 (12.5) Median [Min, Max]: 53.1 [21.0, 83.7] Missing: 0 (0%)	: Not_Male: 182 (58.7%) Male: 128 (41.3%) Missing: 0 (0%)	: Euro: 254 (81.9%) Maori: 30 (9.7%) Pacific: 7 (2.3%) Asian: 12 (3.9%) Missing: 7 (2.3%)
531 (N=192)	: Mean (SD): 3.15 (1.67) Median [Min, Max]: 3.00 [1.00, 7.00] Missing: 11 (5.7%)	: Mean (SD): 50.6 (12.4) Median [Min, Max]: 51.0 [24.2, 86.5] Missing: 0 (0%)	: Not_Male: 128 (66.7%) Male: 64 (33.3%) Missing: 0 (0%)	: Euro: 167 (87.0%) Maori: 16 (8.3%) Pacific: 2 (1.0%) Asian: 5 (2.6%) Missing: 2 (1.0%)
532 (N=54)	: Mean (SD): 3.25 (1.64) Median [Min, Max]: 3.00 [1.00, 6.00] Missing: 6 (11.1%)	: Mean (SD): 54.2 (12.8) Median [Min, Max]: 57.7 [21.2, 76.8] Missing: 0 (0%)	: Not_Male: 32 (59.3%) Male: 21 (38.9%) Missing: 1 (1.9%)	: Euro: 41 (75.9%) Maori: 8 (14.8%) Pacific: 2 (3.7%) Asian: 0 (0%) Missing: 3 (5.6%)
Overall (N=17228)	: Mean (SD): 2.73 (1.67) Median [Min, Max]: 2.00 [1.00, 7.00] Missing: 671 (3.9%)	: Mean (SD): 52.7 (13.6) Median [Min, Max]: 54.4 [19.0, 99.1] Missing: 1 (0.0%)	: Not_Male: 10797 (62.7%) Male: 6386 (37.1%) Missing: 45 (0.3%)	: Euro: 14018 (81.4%) Maori: 1980 (11.5%) Pacific: 318 (1.8%) Asian: 642 (3.7%) Missing: 270 (1.6%)

Show code


library(naniar)

df_rdd_n <- df_rdd %>%
  dplyr::select(Terrorism.Anxiety, KESSLER6, Neighbourhood_Community,Personal_Wellbeing)

vis_miss(df_rdd_n)

2021. https://www.psych.auckland.ac.nz/en/about/new-zealand-attitudes-and-values-study.html.

Greaves, Lara M, Aarif Rasheed, Stephanie D’Souza, Nichola Shackleton, Luke D Oldfield, Chris G Sibley, Barry Milne, and Joseph Bulbulia. 2020. “Comparative Study of Attitudes to Religious Groups in New Zealand Reveals Muslim-Specific Prejudice.” Kōtuitui: New Zealand Journal of Social Sciences Online 15 (2): 260–79.

Hawi, Diala, Danny Osborne, Joseph Bulbulia, and Chris G Sibley. 2019. “Terrorism Anxiety and Attitudes Toward Muslims.” New Zealand Journal of Psychology (Online) 48 (1): 80–89.

Highland, Benjamin R, Geoffrey Troughton, John Shaver, Justin L Barrett, Chris G Sibley, and Joseph Bulbulia. 2019. “Attitudes to Religion Predict Warmth for Muslims in New Zealand.” New Zealand Journal of Psychology (Online) 48 (1): 122–32.

OECD. 2011. “Society at a Glance: OECD Social Indicators.” OECD.

Shaver, John H, Chris G Sibley, Danny Osborne, and Joseph Bulbulia. 2017. “News Exposure Predicts Anti-Muslim Prejudice.” PloS One 12 (3): e0174606.

Shaver, John H, Geoffrey Troughton, Chris G Sibley, and Joseph A Bulbulia. 2016. “Religion and the Unmaking of Prejudice Toward Muslims: Evidence from a Large National Sample.” PLoS One 11 (3): e0150209.

Sibley, Chris G, M Usman Afzali, Nicole Satherley, Anastasia Ejova, Samantha Stronge, Kumar Yogeeswaran, Michael Grimshaw, et al. 2020. “Prejudice Toward Muslims in New Zealand: Insights from the New Zealand Attitudes and Values Study.” New Zealand Journal of Psychology 49 (1).

Yogeeswaran, Kumar, M Usman Afzali, Nadia P Andrews, Elizabeth A Chivers, Meng-Jie Wang, Thierry Devos, and Chris G Sibley. 2019. “Exploring New Zealand National Identity and Its Importance for Attitudes Toward Muslims and Support for Diversity.” New Zealand Journal of Psychology (Online) 48 (1): 29–35.

The New Zealand Attitudes and Values Study (NZAVS) is a nationally representative panel study that Prof Chris Sibley started in 2009 (2021), which has measured over 65,000 New Zealanders (see NZAVS ↩︎

How did the Christchurch Mosque Attacks Affect Attitudes to Muslims in New Zealand?

Talk

Summary

Introduction

Findings

Importance of findings

Appendix 1: Design

Appendix 2: Background

Sources of anti-Muslim prejudice

Appendix 3: Prejudice trends from 2012-2020 reveal a longstanding “Muslim Acceptance Gap”

Appendix 4: Political conservatives also grew more accepting of Muslims after the 2019 Mosque attacks.

Appendix 5: Regression Discontinuity Analysis

References

Corrections

Reuse

Citation