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analyses.R
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analyses.R
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###
### CÓDIGO DA POSTAGEM "Roubos de celular aumentam em Ribeirão Preto, SP"
### https://medium.com/@ghcarvalho/roubos-de-celulares-aumentam-em-ribeir%C3%A3o-preto-sp-d67c7d09a1e8
###
require(lubridate)
require(tidyverse)
require(stringr)
require(ggplot2)
require(ggmap)
require(leaflet)
require(geosphere)
require(dbscan)
require(fields)
arquivos_bos <- list.files("data")
arquivos_bos <- paste("data/", arquivos_bos, sep = "")
binder <- function(filename) {
x1 <- str_split(readLines(filename, skipNul = T, encoding = "latin1"), "\t")
x2 <- do.call("rbind.data.frame", x1[-1])
names(x2) <- x1[[1]]
x2
}
ocorrencias <- sapply(arquivos_bos, binder, simplify = FALSE)
ocorrencias <- do.call("rbind", ocorrencias)
rownames(ocorrencias) <- NULL
p_ocorrencias <- ocorrencias %>%
filter(CIDADE == "RIBEIRAO PRETO") %>%
filter(grepl("Roubo", RUBRICA)) %>%
filter(grepl("Transeunte", RUBRICA, ignore.case = TRUE)) %>%
mutate(DATAOCORRENCIA = dmy(DATAOCORRENCIA)) %>%
filter(year(DATAOCORRENCIA) >= 2010, month(DATAOCORRENCIA) %in% 1:9) %>%
group_by(Ano = year(DATAOCORRENCIA)) %>%
count() %>%
ggplot(aes(x = Ano, y = n)) +
stat_summary(fun.y = sum, geom="bar", size = 4) +
stat_summary(geom="text", aes(label=c(n[-length(n)], paste(n[length(n)], "*", sep = ""))), vjust = -0.5, family = "IBMPlexSans", size = 6) +
ggtitle(label = "Roubos de celular em Ribeirão Preto", subtitle = "Roubos à transeuntes entre 2010 e 2017") +
labs(x = NULL, y = "Número de roubos", caption = "Fonte: SSP-SP. Autor: Gustavo Carvalho. * dados até setembro de 2017") +
theme_plex() +
scale_x_discrete(limits = 2010:2017) +
theme(
axis.text = element_text(size = 14),
axis.text.x = element_text(vjust = -1),
axis.title = element_text(family="IBMPlexSans-Bold"),
plot.caption = element_text(size = 10)
) +
ylim(c(0, 1600))
ggsave(p_ocorrencias, file = "~/Desktop/p_ocorrencias.jpg", width = 11, height = 6)
ocorrencias %>%
filter(CIDADE == "RIBEIRAO PRETO") %>%
filter(grepl("Roubo", RUBRICA)) %>%
filter(grepl("Transeunte", RUBRICA, ignore.case = TRUE)) %>%
mutate(DATAOCORRENCIA = dmy(DATAOCORRENCIA)) %>%
distinct(NUMERO_BOLETIM, .keep_all = TRUE) %>%
filter(ANO_BO %in% 2010:2017) %>%
group_by(month = month(DATAOCORRENCIA, label = TRUE), year = year(DATAOCORRENCIA)) %>%
count() %>%
arrange(year, month) %>%
ggplot(aes(x = 1:NROW(.), y = n)) + geom_line() + theme_fivethirtyeight()
prophet_rp <- ocorrencias %>%
filter(CIDADE == "RIBEIRAO PRETO") %>%
filter(grepl("Roubo", RUBRICA)) %>%
filter(grepl("Transeunte", RUBRICA, ignore.case = TRUE)) %>%
mutate(DATAOCORRENCIA = dmy(DATAOCORRENCIA)) %>%
mutate(dia_semana = week(DATAOCORRENCIA)) %>%
filter(year(DATAOCORRENCIA) >= 2010) %>%
group_by(ds = DATAOCORRENCIA) %>%
summarise(y = n()) %>%
prophet()
future <- make_future_dataframe(prophet_rp, periods = 365)
forecast <- predict(prophet_rp, future)
plot(prophet_rp, forecast)
ribs <- ocorrencias %>%
filter(CIDADE == "RIBEIRAO PRETO") %>%
filter(grepl("Roubo", RUBRICA)) %>%
filter(grepl("Transeunte", RUBRICA, ignore.case = TRUE)) %>%
filter(NUMERO != 0) %>%
mutate(lat = as.numeric(gsub(",", ".", LATITUDE))) %>%
mutate(long = as.numeric(gsub(",", ".", LONGITUDE))) %>%
filter(!is.na(lat)) %>%
filter(lat != 0) %>%
filter(round(lat) %in% -22:-19) %>%
filter(round(long) %in% -49:-46) %>%
filter(NUMERO_BOLETIM_PRINCIPAL == "") %>%
mutate(data = dmy(DATAOCORRENCIA)) %>%
mutate(ano = year(data)) %>%
mutate(mes = month(data)) %>%
mutate(semana = week(data))
ribs <- ribs %>%
mutate(month = month(DATAOCORRENCIA, label = TRUE)) %>%
mutate(horario = factor(case_when(
PERIDOOCORRENCIA %in% c("A NOITE", "DE MADRUGADA") ~ "Noite ou madrugada",
PERIDOOCORRENCIA %in% c("PELA MANHÃ", "A TARDE") ~ "Dia",
TRUE ~ "Em hora incerta"
), levels = c("Dia", "Noite ou madrugada", "Em hora incerta")))
ribs <- ocorrencias %>%
mutate(data = dmy(DATAOCORRENCIA)) %>%
mutate(ano = year(data)) %>%
mutate(mes = month(data)) %>%
mutate(semana = week(data)) %>%
filter(ano %in% 2010:2017)
qmplot(long, lat, data = filter(ribs, ano == 2017, semana == 10), source = "google", maptype = "road", size = I(1), alpha = I(0.5))
qmplot(long, lat, data = ribs, geom = "blank",maptype = "road",source="google", darken = .3, legend = "topleft") +
stat_density_2d(aes(fill = ..level..), geom = "polygon", alpha = .2, color = NA) +
scale_fill_gradient2("Robbery\nPropensity", low = "white", mid = "yellow", high = "red", midpoint = 200)
pal <- colorFactor(c("#008837", "#7b3294", "grey"), ribs$horario)
leaflet(ribs) %>% addTiles() %>%
addCircleMarkers(~long, ~lat, radius = 3, stroke = FALSE, fillColor = pal(ribs$horario), color = "black", fillOpacity = 0.7) %>%
addLegend("bottomleft", pal=pal, values=ribs$horario, title="Período",
layerId="colorLegend", opacity = 1) %>%
addProviderTiles(providers$CartoDB, options = providerTileOptions(opacity = 1))
p_map <-
get_stamenmap(bbox = c(left = -47.860964, right = -47.756268, top = -21.097576, bottom = -21.226648),
color = "bw", maptype = "toner", zoom = 14
) %>%
ggmap(base_layer = ggplot(aes(x = long, y = lat), data = ribs)) +
geom_point(aes(colour = horario), alpha = 0.4, size = 2) +
xlab(NULL) +
ylab(NULL) +
ggtitle("Localização dos roubos de celular em Ribeirão Preto", subtitle = "Roubos à transeuntes entre 2010 e 2017") +
labs(caption = "Fonte: SSP-SP. Autor: Gustavo Carvalho (gustavo.bio@gmail.com)") +
theme_plex() +
theme(
legend.position = "bottom",
legend.text = element_text(size = 20),
legend.title = element_text(size = 20, family="IBMPlexSans-Bold"),
axis.text = element_blank(),
plot.title = element_text(size = 20),
plot.subtitle = element_text(size = 18),
plot.caption = element_text(size = 10)
) +
scale_color_manual(
values = c(
"Noite ou madrugada" = "darkblue",
"Dia" = "darkgreen",
"Em hora incerta" = "red"
), name = "Horário do roubo"
)
ggsave(p_map,
file = "~/Desktop/p_map.jpg",
width = 12,
height = 12)
p_map2 <-
get_stamenmap(bbox = c(left = -47.860964, right = -47.756268, top = -21.097576, bottom = -21.226648),
color = "bw", maptype = "toner", zoom = 15
) %>%
ggmap(base_layer = ggplot(aes(x = long, y = lat), data = ribs)) +
geom_point(alpha = 0.4, size = 2) +
xlab(NULL) +
ylab(NULL) +
ggtitle("Localização dos roubos de celular em Ribeirão Preto", subtitle = "Roubos à transeuntes entre 2010 e 2017") +
labs(caption = "Fonte: SSP-SP. Autor: Gustavo Carvalho (gustavo.bio@gmail.com).") +
stat_density_2d(aes(fill = ..level..), geom = 'polygon', alpha = 0.3) +
scale_fill_viridis(name = "Densidade de roubos") +
theme_plex() +
theme(
legend.position = "bottom",
legend.text = element_text(size = 20),
legend.title = element_text(size = 20, family="IBMPlexSans-Bold"),
axis.text = element_blank(),
plot.title = element_text(size = 20),
plot.subtitle = element_text(size = 18),
plot.caption = element_text(size = 10),
legend.key.width = unit(2, "cm")
)
ggsave(p_map2,
file = "~/Desktop/p_map2.jpg",
width = 12,
height = 12)
p_map3 <-
get_stamenmap(bbox = c(left = -47.860964, right = -47.756268, top = -21.097576, bottom = -21.226648),
color = "bw", maptype = "toner-lite", zoom = 14
) %>%
ggmap(base_layer = ggplot(aes(x = long, y = lat), data = filter(ribs, ano == 2017, month %in% c("Janeiro", "Fevereiro", "Março", "Abril", "Maio")))) +
geom_point(aes(colour = horario), alpha = 0.4, size = 2) +
xlab(NULL) +
ylab(NULL) +
ggtitle("Localização dos roubos de celular em Ribeirão Preto", subtitle = "Roubos à transeuntes de janeiro à maio de 2017") +
labs(caption = "Fonte: SSP-SP. Autor: Gustavo Carvalho (gustavo.bio@gmail.com)") +
theme_plex() +
theme(
legend.position = "bottom",
legend.text = element_text(size = 16),
legend.title = element_text(size = 16, family="IBMPlexSans-Bold"),
axis.text = element_blank(),
plot.title = element_text(size = 16),
plot.subtitle = element_text(size = 14),
plot.caption = element_text(size = 8)
) +
scale_color_manual(
values = c(
"Noite ou madrugada" = "darkblue",
"Dia" = "darkgreen",
"Em hora incerta" = "red"
), name = "Horário do roubo"
) +
facet_wrap(~month, ncol = 5)
ggsave(p_map3,
file = "~/Desktop/p_map3.jpg",
width = 12,
height = 5)
p_map_week <-
get_googlemap(
"Ribeirão Preto",
zoom = 13,
maptype = "roadmap",
language = "pt-BR",
color = "bw"
) %>%
ggmap(base_layer = ggplot(aes(x = long, y = lat), data = filter(ribs, mes == 4, ano == 2017, semana %in% 14:17))) +
geom_point(aes(colour = as.factor(semana)), alpha = 0.8, size = 3) +
xlab(NULL) +
ylab(NULL) +
ggtitle("Localização dos roubos de celular em Ribeirão Preto", subtitle = "Roubos à transeuntes entre 2010 e 2017") +
labs(caption = "Fonte: Secretaria de Segurança Pública de São Paulo") +
theme_plex() +
theme(
axis.text = element_blank(),
plot.title = element_text(size = 20),
plot.subtitle = element_text(size = 18),
legend.position = "none"
)
ggsave(p_map_week,
file = "~/Desktop/p_map_week.jpg",
width = 12,
height = 12)
ts_test <- ocorrencias %>%
filter(CIDADE == "RIBEIRAO PRETO") %>%
filter(grepl("Roubo", RUBRICA)) %>%
filter(grepl("Transeunte", RUBRICA, ignore.case = TRUE)) %>%
mutate(DATAOCORRENCIA = dmy(DATAOCORRENCIA)) %>%
filter(year(DATAOCORRENCIA) %in% 2010:2017) %>%
group_by(ano = year(DATAOCORRENCIA), semana = week(DATAOCORRENCIA)) %>%
count() %>%
ungroup()
semanas <- ocorrencias %>%
filter(CIDADE == "RIBEIRAO PRETO") %>%
filter(grepl("Roubo", RUBRICA)) %>%
filter(grepl("Transeunte", RUBRICA, ignore.case = TRUE)) %>%
mutate(DATAOCORRENCIA = dmy(DATAOCORRENCIA)) %>%
filter(year(DATAOCORRENCIA) %in% 2010:2016) %>%
group_by(semana = week(DATAOCORRENCIA)) %>%
count() %>%
ungroup()
semanas %>%
ggplot(aes(x = semana, y = n)) + geom_line() + geom_smooth(span = 0.2)
ts_test %>%
ggplot(aes(x = ano, y = n)) + geom_path()
ocorrencias %>%
filter(CIDADE == "RIBEIRAO PRETO") %>%
filter(grepl("Roubo", RUBRICA)) %>%
filter(grepl("Transeunte", RUBRICA, ignore.case = TRUE)) %>%
mutate(DATAOCORRENCIA = dmy(DATAOCORRENCIA)) %>%
group_by(mes = month(DATAOCORRENCIA), ano = year(DATAOCORRENCIA)) %>%
summarise(dias = length(unique(DATAOCORRENCIA)))
first_days <-
dias <- seq(ymd("2010/01/01"), ymd("2017/09/30"), by = "1 day")
dias <- data.frame(dia = dias, semana = week(dias), ano = as.numeric(format(dias, "%Y")))
dias <- dias[!duplicated(select(dias, semana, ano)), ]
dias$trend <- stl(ts_roubos, 31)$trend
ts_test <- ts_test %>% left_join(dias)
ts_test$trend <- stl(ts_roubos, "periodic")$time.series[, "trend"]
p_ts <- ts_test %>%
ggplot(aes(x = dia, y = n)) +
geom_line(alpha = 0.5) +
geom_line(aes(x = dia, y = trend), col = "red", size = 1) +
scale_x_date(breaks = seq(as.Date("2010/01/01"), as.Date("2017/12/31"), by = "1 year"),
labels = 2010:2017) + theme_plex() +
theme(
panel.grid.major.x = element_line(),
axis.title = element_text(family = "IBMPlexSans-Bold"),
axis.text = element_text(size = 14),
plot.caption = element_text(size = 10)
) +
xlab(NULL) +
ylab("Número de roubos por semana") +
labs(caption = "Fonte: SSP-SP. Autor: Gustavo Carvalho (gustavo.bio@gmail.com).") +
ggtitle("Variação semanal no número de roubos em Ribeirão Preto")
ggsave(p_ts, file = "~/Desktop/p_ts.jpg", width = 15, height = 5)
dias_1 <- unique(dmy(ribs$DATAOCORRENCIA))
p_semana <- ocorrencias %>%
filter(CIDADE == "RIBEIRAO PRETO") %>%
filter(grepl("Roubo", RUBRICA)) %>%
filter(grepl("Transeunte", RUBRICA, ignore.case = TRUE)) %>%
mutate(DATAOCORRENCIA = dmy(DATAOCORRENCIA)) %>%
filter(year(DATAOCORRENCIA) %in% 2010:2017) %>%
group_by(wday = wday(DATAOCORRENCIA, label = TRUE)) %>%
count() %>%
ggplot(aes(x = wday, y = n)) +
stat_summary(fun.y = sum, geom="bar", size = 4) +
stat_summary(geom="text", aes(label=n), vjust = -0.5, family = "IBMPlexSans") +
# ggtitle(label = "Roubos de celular em Ribeirão Preto", subtitle = "Roubos à transeuntes entre 2010 e 2017") +
labs(x = "Dia da semana", y = "Número de roubos") +
theme_plex() +
theme(
axis.text.x = element_text(vjust = -1),
axis.title = element_text(family="IBMPlexSans-Bold"),
plot.caption = element_text(size = 9, colour = "darkgrey")
) + ylim(c(0, 1550)) +
scale_x_discrete(labels = c("Dom", "Seg", "Ter", "Qua", "Qui", "Sex", "Sab"))
ggsave(p_semana, file = "~/Desktop/p_semana.jpg", width = 10, height = 7)
plot_yearly <- function (m,
uncertainty = TRUE,
yearly_start = 0)
{
days <-
seq(prophet:::set_date("2017-01-01"),
by = "d",
length.out = 365) +
as.difftime(yearly_start, units = "days")
df.y <- prophet:::seasonality_plot_df(m, days)
seas <- prophet:::predict_seasonal_components(m, df.y)
seas$ds <- df.y$ds
gg.yearly <-
ggplot2::ggplot(seas, ggplot2::aes(x = as.Date(ds), y = yearly,
group = 1)) + ggplot2::geom_line(color = "red", na.rm = TRUE, size = 1) +
ggplot2::labs(x = "Dia do ano") + ggplot2::scale_x_date(breaks = dmy("01/01/2017") + months(0:11), labels = c("Jan", "Fev", "Mar", "Abr", "Mai", "Jun", "Jul", "Ago", "Set", "Out", "Nov", "Dez")) +
theme_plex() +
theme(
panel.grid.major.x = element_line(),
panel.grid.minor.x = element_line(linetype = "dotted", colour = "lightgrey"),
axis.text.x = element_text(angle = 0),
axis.title.y = element_text(family="IBMPlexSans-Bold"),
axis.text = element_text(size = 14),
plot.caption = element_text(size = 10)
) +
xlab(NULL) +
ylab("Variação do número de roubos") +
ggtitle("Variação anual do número de roubos", subtitle = "Dados combinados entre 2010 e 2017")
labs(caption = "Fonte: SSP-SP. Autor: Gustavo Carvalho (gustavo.bio@gmail.com).")
if (uncertainty) {
gg.yearly <-
gg.yearly + ggplot2::geom_ribbon(
ggplot2::aes(ymin = yearly_lower,
ymax = yearly_upper),
alpha = 0.2,
fill = "#0072B2",
na.rm = TRUE
)
}
return(gg.yearly)
}
p_yearly <- plot_yearly(prophet_rp)
ggsave(p_yearly, file = "~/Desktop/p_yearly.jpg", width = 15, height = 5)
p_mes <- ocorrencias %>%
filter(CIDADE == "RIBEIRAO PRETO") %>%
filter(grepl("Roubo", RUBRICA)) %>%
filter(grepl("Transeunte", RUBRICA, ignore.case = TRUE)) %>%
mutate(DATAOCORRENCIA = dmy(DATAOCORRENCIA)) %>%
filter(year(DATAOCORRENCIA) %in% 2010:2017) %>%
group_by(month = month(DATAOCORRENCIA, label = TRUE)) %>%
count() %>%
ggplot(aes(x = month, y = n)) +
stat_summary(fun.y = sum, geom="bar", size = 4) +
stat_summary(geom="text", aes(label=n), vjust = -0.5, family = "IBMPlexSans") +
# ggtitle(label = "Roubos de celular em Ribeirão Preto", subtitle = "Roubos à transeuntes entre 2010 e 2017") +
labs(x = "Mês", y = "Número de roubos") +
theme_plex() +
theme(
axis.text.x = element_text(vjust = -1),
axis.title = element_text(family="IBMPlexSans-Bold"),
plot.caption = element_text(size = 9, colour = "darkgrey")
) + scale_x_discrete(labels = c("Jan", "Fev", "Mar", "Abr", "Mai", "Jun", "Jul", "Ago", "Set", "Out", "Nov", "Dez"))
ggsave(p_mes, file = "~/Desktop/p_mes.jpg", width = 10, height = 7)
ggplot(ts_test, aes(x = rolling_week, y = rollmean(n, 52))) +
geom_line(col = "red")
ts_roubos <- ts(ts_test$n, frequency = 365.25/7, start = 2010)
decompose_roubos <- decompose(ts_roubos, "additive")
adjust_roubos <- ts_roubos - decompose_roubos$seasonal
plot(decompose_roubos$trend)
plot(adjust_roubos)
theme_plex <- function(base_size = 14,
strip_text_size = 12,
strip_text_margin = 5,
subtitle_size = 13,
subtitle_margin = 10,
plot_title_size = 18,
plot_title_margin = 10,
...) {
ret <- ggplot2::theme_minimal(base_family = "IBMPlexSans",
base_size = base_size, ...)
ret$strip.text <- ggplot2::element_text(hjust = 0, size=strip_text_size,
margin=margin(b=strip_text_margin),
family="IBMPlexSans-Medium")
ret$plot.subtitle <- ggplot2::element_text(hjust = 0, size=subtitle_size,
margin=margin(b=subtitle_margin),
family="IBMPlexSans")
ret$plot.title <- ggplot2::element_text(hjust = 0, size = plot_title_size,
margin=margin(b=plot_title_margin),
family="IBMPlexSans-Bold")
ret$panel.grid.minor = ggplot2::element_blank()
ret$panel.grid.major.x = ggplot2::element_blank()
ret$plot.margin = ggplot2::unit(c(1,1, 1, 1), "lines")
ret
}
ribs_week <- ocorrencias %>%
filter(CIDADE == "RIBEIRAO PRETO") %>%
filter(grepl("Roubo", RUBRICA)) %>%
filter(grepl("Transeunte", RUBRICA, ignore.case = TRUE)) %>%
mutate(date = dmy(DATAOCORRENCIA)) %>%
filter(year(date) %in% 2010:2017) %>%
filter(NUMERO_BOLETIM_PRINCIPAL == "") %>%
group_by(date) %>%
count() %>% ungroup()
test2 <- filter(station_data_summary, station_id == 83669, year(date) >= 2010) %>%
filter(prec > 5) %>%
right_join(ribs_week)
ggplot(test2, aes(x = prec, y = n)) + geom_point() + geom_smooth(method = "lm") + theme_plex()
test3 <- ribs %>%
filter(ano == 2016) %>%
mutate(x = long, y = lat) %>%
filter(horario == "Noite ou madrugada")
threshold.in.km <- 0.5
coors <- test3[,c("long", "lat")]
#distance matrix
dist.in.km.matrix <- rdist.earth(coors,miles = F,R=6371)
#clustering
fit <- hclust(as.dist(dist.in.km.matrix), method = "single")
clusters <- cutree(fit,h = threshold.in.km)
plot(test3$long, test3$lat, col = clusters, pch = 20)
ggplot(ribs, aes(long, lat)) +
stat_density_2d(aes(fill = ..level..), geom = 'polygon', alpha = 0.3) +
scale_fill_viridis(name = "density") +
geom_point(shape = '.')