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add_distance_to_injury.R
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add_distance_to_injury.R
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rm(list=ls())
#setwd('~/overflow_dropbox/mh-injury/')
######################################################################
library(data.table)
library(stringr)
library(dplyr)
library(stats)
library(tidyr)
library(splines)
#library(biglm)
model_modes <- c('pedestrian','cyclist','car/taxi','motorcycle')
mh_path <- getwd()
overflow_path <- paste0(mh_path,"/rds_storage/")
if(file.exists(paste0(overflow_path,'codes_for_stats19.Rds'))&&file.exists(paste0(overflow_path,'city_regions.Rds'))){
codes_for_stats19 <- readRDS(paste0(overflow_path,'codes_for_stats19.Rds'))
city_regions <- readRDS(paste0(overflow_path,'city_regions.Rds'))
}else{
source('get_area_codes.R')
}
######################################################################
## add distance
if(file.exists(paste0(overflow_path,'processed_injuries_8.Rds'))){
injury_table <- readRDS(paste0(overflow_path,'processed_injuries_8.Rds'))
}else{
######################################################################
## add index to injuries
library(readxl)
demography <- readxl::read_xlsx('../mh-execute/inputs/scenarios/190330_sp_ind_codebook.xlsx',sheet=2,col_names=F)
demogindex_to_numerical <- unlist(demography[,3])
demography[,3] <- 1:nrow(demography)
demo_indices <- unlist(demography[,3])
age_table <- readxl::read_xlsx('../mh-execute/inputs/scenarios/190330_sp_ind_codebook.xlsx',sheet=1,col_names=F)
age_category <- unlist(age_table[,1])
age_lower_bounds <- as.numeric(sapply(age_category,function(x)strsplit(x,' to ')[[1]][1]))
if(file.exists(paste0(overflow_path,'processed_injuries_7.Rds'))){
injury_table <- readRDS(paste0(overflow_path,'processed_injuries_7.Rds'))
}else{
######################################################################
## injury data
if(file.exists(paste0(overflow_path,'processed_injuries_6_tabulated_injuries.Rds'))){
injury_table <- readRDS(paste0(overflow_path,'processed_injuries_6_tabulated_injuries.Rds'))
}else{
source('build_injury_data_from_stats19.R')
}
age_match <- demography
colnames(age_match) <- c('cas_male','cas_age_cat','cas_index')
age_match$cas_male <- as.numeric(age_match$cas_male=='male')
for(i in 1:2) injury_table[[1]][[i]] <- left_join(injury_table[[1]][[i]],age_match,by=c('cas_male','cas_age_cat'))
colnames(age_match) <- c('strike_male','strike_age_cat','strike_index')
for(i in 1:2) injury_table[[i]][[1]] <- left_join(injury_table[[i]][[1]],age_match,by=c('strike_male','strike_age_cat'))
roads <- unique(injury_table[[1]][[1]]$road)
for(i in 1:2) for(j in 1:2)
injury_table[[i]][[j]]$road_index <- match(injury_table[[i]][[j]]$road,roads)
saveRDS(injury_table,paste0(overflow_path,'processed_injuries_7.Rds'),version=2)
}
######################################################################
for(i in 1:2) for(j in 1:2) injury_table[[i]][[j]] <- copy(setDT(injury_table[[i]][[j]]))
## distance data
# scale by year
##!! needs to match a sheet called scalingweight_cityreg which on 28/12/2019 is sheet number 4
scale_by_year_raw <- readxl::read_xls('../mh-scenarios/1_InputData/2_CityRegion_scaling/RTS_NTS_distances_cityreg.xls',sheet='scalingweight_cityreg',col_names=T)
## reference year is 2015
reference_year <- 2015
reference_col <- which(sapply(colnames(scale_by_year_raw),function(x)grepl(reference_year,x)))
col_names <- strsplit(colnames(scale_by_year_raw)[reference_col],reference_year)[[1]][1]
colnames(scale_by_year_raw) <- sapply(colnames(scale_by_year_raw),function(x)gsub(col_names,'',x))
scale_by_year <- scale_by_year_raw
years <- unique(injury_table[[2]][[2]]$year)
for(yr in years)
scale_by_year[[as.character(yr)]] <- scale_by_year_raw[[as.character(yr)]]/scale_by_year_raw[[as.character(reference_year)]]
scale_by_year$modename[scale_by_year$modename=='cartaxi'] <- 'car/taxi'
scale_by_year$modename[scale_by_year$modename=='cycle'] <- 'cyclist'
scale_by_year$modename[scale_by_year$modename=='van'] <- 'lgv'
# road by mode
roads <- unique(injury_table[[1]][[1]]$road)
mode_road_city_dist <- read.csv(paste0(overflow_path,'mode_road_city_year.csv'),stringsAsFactors = F)
colnames(mode_road_city_dist)[1:3] <- c('road','mode','year')
mode_road_city_dist$mode[mode_road_city_dist$mode=='lgv'] <- 'light goods'
mode_road_city_dist$mode[mode_road_city_dist$mode=='hgv'] <- 'heavy goods'
all_scens <- list.dirs(path = "../mh-execute/inputs/scenarios", full.names = FALSE, recursive = FALSE)
all_scens <- paste0(all_scens, "_")
scenarios <- c('base_', all_scens)
all_distances <- list()
for(scen in 1:length(scenarios))
all_distances[[scen]] <- readRDS(paste0(overflow_path,scenarios[scen],'injury_distances.Rds'))
names(all_distances) <- scenarios
for(scen in 1:length(scenarios)){
scen_pref <- scenarios[scen]
strike_col <- paste0(scen_pref,'strike_distance')
cas_col <- paste0(scen_pref,'cas_distance')
strike_sum_col <- paste0(scen_pref,'strike_distance_sum')
cas_sum_col <- paste0(scen_pref,'cas_distance_sum')
distance_for_strike <- all_distances[[scen]]$distance_for_strike
distance_for_cas <- all_distances[[scen]]$distance_for_cas
all_distances[[scen]] <- 0
# initialise distance
for(j in 1:2){
injury_table[[1]][[j]][[cas_col]] <- 0
injury_table[[j]][[1]][[strike_col]] <- 0
injury_table[[2]][[j]][[cas_col]] <- 1
injury_table[[j]][[2]][[strike_col]] <- 1
injury_table[[1]][[j]][[cas_sum_col]] <- 0
injury_table[[j]][[1]][[strike_sum_col]] <- 0
injury_table[[2]][[j]][[cas_sum_col]] <- 1
injury_table[[j]][[2]][[strike_sum_col]] <- 1
}
distance_for_cas$mode_name[distance_for_cas$mode_name=='cardrive'] <- 'car/taxi'
distance_for_cas$mode_name[distance_for_cas$mode_name=='cycle'] <- 'cyclist'
distance_for_cas$mode_name[distance_for_cas$mode_name=='walk'] <- 'pedestrian'
distance_for_cas$mode_name[distance_for_cas$mode_name=='mbikedrive'] <- 'motorcycle'
distance_for_cas$demogindex <- match(distance_for_cas$demogindex,demogindex_to_numerical)
colnames(distance_for_cas)[c(1,2)] <- c('cas_mode','cas_index')#,"rural_B","rural_A","urban_B","urban_A")
distance_for_strike$mode_name[distance_for_strike$mode_name=='cardrive'] <- 'car/taxi'
distance_for_strike$mode_name[distance_for_strike$mode_name=='cycle'] <- 'cyclist'
distance_for_strike$mode_name[distance_for_strike$mode_name=='walk'] <- 'pedestrian'
distance_for_strike$mode_name[distance_for_strike$mode_name=='mbikedrive'] <- 'motorcycle'
distance_for_strike$demogindex <- match(distance_for_strike$demogindex,demogindex_to_numerical)
colnames(distance_for_strike)[c(1,2)] <- c('strike_mode','strike_index')#,"rural_B","rural_A","urban_B","urban_A")
for(city_name in names(codes_for_stats19)){
##!! scale by year - scale_by_year
## primary cas
la_distances <- subset(distance_for_cas,city_region==city_name)
city_distances <- la_distances[,lapply(.SD,sum),.SDcols=roads,by=c('cas_index','cas_mode')]
melted_city_distances <- melt(city_distances,id.vars=c('cas_mode','cas_index'),variable.name='road',value.name=cas_col)
melted_city_distances <- subset(melted_city_distances,cas_mode%in%c("pedestrian","cyclist","car/taxi","motorcycle"))
melted_city_distances[,region := city_name]
melted_city_distances[,paste0(cas_sum_col):=sum(get(..cas_col)),by=c('cas_mode','region','road'),.SDcols=cas_col]
for(j in 1:2){
injury_table[[1]][[j]][melted_city_distances,on=c('cas_mode','cas_index','road','region'),c(paste0(cas_col),paste0(cas_sum_col)):=list(get(paste0('i.',cas_col)),get(paste0('i.',cas_sum_col)))]
for(mode_name in unique(injury_table[[1]][[j]]$cas_mode))
for(yr in unique(injury_table[[1]][[j]]$year)){
values <- injury_table[[1]][[j]]$year==yr&injury_table[[1]][[j]]$cas_mode==mode_name&injury_table[[1]][[j]]$region==city_name
if(mode_name%in%unique(scale_by_year$modename)){
injury_table[[1]][[j]][[cas_col]][values] <-
injury_table[[1]][[j]][[cas_col]][values] * as.numeric(scale_by_year[scale_by_year$home_cityregion==city_name&scale_by_year$modename==mode_name,colnames(scale_by_year)==yr])
injury_table[[1]][[j]][[cas_sum_col]][values] <-
injury_table[[1]][[j]][[cas_sum_col]][values] * as.numeric(scale_by_year[scale_by_year$home_cityregion==city_name&scale_by_year$modename==mode_name,colnames(scale_by_year)==yr])
}
}
}
## whw strike
la_distances <- subset(distance_for_strike,city_region==city_name)
city_distances <- la_distances[,lapply(.SD,sum),.SDcols=roads,by=c('strike_index','strike_mode')]
melted_city_distances <- melt(city_distances,id.vars=c('strike_mode','strike_index'),variable.name='road',value.name=strike_col)
melted_city_distances <- subset(melted_city_distances,strike_mode%in%c("pedestrian","cyclist","car/taxi","motorcycle"))
melted_city_distances[,region := city_name]
melted_city_distances[,paste0(strike_sum_col):=sum(get(..strike_col)),by=c('strike_mode','region','road'),.SDcols=strike_col]
for(j in 1:2){
injury_table[[j]][[1]][melted_city_distances,on=c('strike_mode','strike_index','road','region'),c(paste0(strike_col),paste0(strike_sum_col)):=list(get(paste0('i.',strike_col)),get(paste0('i.',strike_sum_col)))]
for(mode_name in unique(injury_table[[j]][[1]]$strike_mode))
for(yr in unique(injury_table[[j]][[1]]$year)){
values <- injury_table[[j]][[1]]$year==yr&injury_table[[j]][[1]]$strike_mode==mode_name&injury_table[[j]][[1]]$region==city_name
if(mode_name%in%unique(scale_by_year$modename)){
injury_table[[j]][[1]][[strike_col]][values] <-
injury_table[[j]][[1]][[strike_col]][values] * as.numeric(scale_by_year[scale_by_year$home_cityregion==city_name&scale_by_year$modename==mode_name,colnames(scale_by_year)==yr])
injury_table[[j]][[1]][[strike_sum_col]][values] <-
injury_table[[j]][[1]][[strike_sum_col]][values] * as.numeric(scale_by_year[scale_by_year$home_cityregion==city_name&scale_by_year$modename==mode_name,colnames(scale_by_year)==yr])
}
}
}
# road by year, city
##!! bus cas distance should be sum over distance_for_cas e.g.
#for(road_type in roads)
# mode_road_city_dist_ordered[mode_road_city_dist$mode=='bus'&mode_road_city_dist$city==city_name,which(colnames(mode_road_city_dist_ordered)==road_type)] <- sum(subset(distance_for_cas,cas_mode=='bus')[[road_type]])
for(j in 1:2){
city_index <- injury_table[[2]][[j]]$region==city_name
for(cas_mode in c('bus','heavy goods','light goods'))
for(yr in years){
print(paste(city_name, cas_mode, yr))
indices <- which(injury_table[[2]][[j]]$cas_mode==cas_mode&city_index&injury_table[[2]][[j]]$year==yr)
injury_table[[2]][[j]][[cas_col]][indices] <- c(mode_road_city_dist[mode_road_city_dist$year==yr&mode_road_city_dist$mode==cas_mode,colnames(mode_road_city_dist)==city_name])[injury_table[[2]][[j]]$road_index[indices]]
}
injury_table[[2]][[j]][[cas_sum_col]] <- injury_table[[2]][[j]][[cas_col]]
city_index <- injury_table[[j]][[2]]$region==city_name
for(strike_mode in c('bus','heavy goods','light goods'))
for(yr in years){
indices <- which(injury_table[[j]][[2]]$strike_mode==strike_mode&city_index&injury_table[[j]][[2]]$year==yr)
injury_table[[j]][[2]][[strike_col]][indices] <- c(mode_road_city_dist[mode_road_city_dist$year==yr&mode_road_city_dist$mode==strike_mode,colnames(mode_road_city_dist)==city_name])[injury_table[[j]][[2]]$road_index[indices]]
}
injury_table[[j]][[2]][[strike_sum_col]] <- injury_table[[j]][[2]][[strike_col]]
}
}
#for(i in 1:2) for(j in 1:2) injury_table[[i]][[j]] <- subset(injury_table[[i]][[j]],get(cas_col)>0&get(strike_col)>0)
}
ages <- unique(injury_table[[1]][[1]]$cas_age_cat)
ages <- ages[!is.na(ages)]
lower_age <- as.numeric(sapply(ages,function(x)strsplit(x,' to ')[[1]][1]))
upper_age <- as.numeric(sapply(ages,function(x)strsplit(x,' to ')[[1]][2]))
mid_age <- (lower_age+upper_age)/2
mid_age[length(mid_age)] <- 97
mid_ages <- c(mid_age,mid_age)
for(j in 1:2) injury_table[[1]][[j]]$cas_age <- mid_ages[injury_table[[1]][[j]]$cas_index]
for(j in 1:2) injury_table[[j]][[1]]$strike_age <- mid_ages[injury_table[[j]][[1]]$strike_index]
keepnames <- c("year","cas_male","cas_severity","cas_mode","strike_mode","road","region","strike_male","count","cas_age","strike_age","rate",'cas_index','strike_index',
paste0(scenarios,'cas_distance_sum'),paste0(scenarios,'strike_distance_sum'),paste0(scenarios,'cas_distance'),paste0(scenarios,'strike_distance'))
for(i in 1:2)
for(j in 1:2)
injury_table[[i]][[j]] <- setDT(injury_table[[i]][[j]])[,colnames(injury_table[[i]][[j]])%in%keepnames,with=F]
saveRDS(injury_table,paste0(overflow_path,'processed_injuries_8.Rds'),version=2)
}
## save for prediction and compute model #############################################
pred_year <- 2015
# get city data
city_table <- injury_table
for(i in 1:2)
for(j in 1:2)
city_table[[i]][[j]] <- injury_table[[i]][[j]][injury_table[[i]][[j]]$year==pred_year,]
## model ######################################################################
trim_glm_object <- function(obj){
obj$y <- c()
obj$model <- c()
obj$R <- c()
obj$qr$qr <- c()
obj$residuals <- c()
obj$fitted.values <- c()
obj$effects <- c()
#obj$linear.predictors <- c()
obj$weights <- c()
obj$prior.weights <- c()
obj$data <- c()
obj$family$variance = c()
obj$family$dev.resids = c()
obj$family$aic = c()
obj$family$validmu = c()
obj$family$simulate = c()
#attr(obj$terms,".Environment") = c()
attr(obj$formula,".Environment") = c()
obj
}
for(i in 1:2) for(j in 1:2){
colnames(injury_table[[i]][[j]]) <- sapply(colnames(injury_table[[i]][[j]]),function(x) gsub('base_','',x))
injury_table[[i]][[j]] <- subset(injury_table[[i]][[j]],cas_distance>0&strike_distance>0)
}
test_model <- F
if(test_model){
new_data <- injury_table[[2]][[2]]
new_data$cas_distance <- new_data$cas_distance*1.5
new_data$strike_distance <- new_data$strike_distance*0.75
sums <- seq(0.5,1.5,by=0.1)
inter <- c()
base_predictions <- predictions <- list()
for(i in 1:length(sums)){
coeff <- sums[i]
form <- paste0('count~offset(',coeff,'*log(cas_distance_sum)+',coeff,'*log(strike_distance_sum))')
mod <- glm(as.formula(form),offset=-log(rate),family=poisson(link=log),data=injury_table[[2]][[2]])
inter[i] <- coef(mod)[1]
base_predictions[[i]] <- fitted.values(mod)
predictions[[i]] <- predict(mod,newdata = new_data,type='response')
}
form <- paste0('count~offset(',1,'*log(cas_distance)+',1,'*log(strike_distance))')
mod <- glm(as.formula(form),offset=-log(rate),family=poisson(link=log),data=injury_table[[2]][[2]])
raw_pred <- predict(mod,type='response')
approx_predictions <- list()
for(i in 1:length(sums)){
coeff <- sums[i]
approx_predictions[[i]] <- raw_pred*(new_data$cas_distance/injury_table[[2]][[2]]$cas_distance)^coeff*(new_data$strike_distance/injury_table[[2]][[2]]$strike_distance)^coeff
}
mod1 <- trim_glm_object(mod)
predict(mod1)
}
basic_formula <- 'count~year+cas_severity+cas_mode*strike_mode+road+road:(cas_mode+strike_mode)+region+cas_mode:cas_severity+strike_mode:cas_severity'
## original model: for distance and exponents not uncertain ###############################################
#formula_one <- 'count~ns(year,df=2)+cas_severity+cas_mode+strike_mode+road+region+offset(log(cas_distance)+log(strike_distance))'
CAS_EXPONENT <- 0.9
STR_EXPONENT <- 0.9
model_settings <- list(DISTANCE_SCALAR_CAR_TAXI=1,DISTANCE_SCALAR_CYCLING=1,DISTANCE_SCALAR_MOTORCYCLE=1,DISTANCE_SCALAR_WALKING=1,CAS_EXPONENT=CAS_EXPONENT,STR_EXPONENT=STR_EXPONENT)
saveRDS(model_settings,'../mh-execute/inputs/injury/model_settings.Rds')
formula_one <- paste0(basic_formula,'+offset(log(cas_distance)+log(strike_distance)+(CAS_EXPONENT-1)*log(cas_distance_sum)+(STR_EXPONENT-1)*log(strike_distance_sum))')
##!! decide offset, splines, interactions
mod <- list()
for(i in 1:2) {
mod[[i]] <- list()
for(j in 1:2) {
injury_table[[i]][[j]][order(year),]
form <- formula_one
if(i==1){
form <- paste0(form,'+cas_male*ns(cas_age,df=4)+cas_mode:(ns(cas_age,df=4)+cas_male)')
#injury_table[[i]][[j]] <- injury_table[[i]][[j]][order(cas_male,cas_age),]
}
if(j==1){
form <- paste0(form,'+strike_male*ns(strike_age,df=4)+strike_mode:(strike_male+ns(strike_age,df=4))')
#injury_table[[i]][[j]] <- injury_table[[i]][[j]][order(strike_male,strike_age),]
}
print(form)
##for model build, set rate=1
injury_table[[i]][[j]]$rate <- 1
mod[[i]][[j]] <- glm(as.formula(form),family=poisson(link=log),data=injury_table[[i]][[j]])
print(AIC(mod[[i]][[j]]))
trimmed_mod <- trim_glm_object(mod[[i]][[j]])
trimmed_mod$offset <- c()
trimmed_mod$linear.predictors <- c()
nonzero <- city_table[[i]][[j]]$base_cas_distance > 0 & city_table[[i]][[j]]$base_strike_distance > 0
pred_values <- predict(trimmed_mod,newdata=subset(injury_table[[i]][[j]],year==pred_year),type='response')
city_table[[i]][[j]]$base_pred <- 0
city_table[[i]][[j]]$base_pred[nonzero] <- pred_values
saveRDS(trimmed_mod,paste0('../mh-execute/inputs/injury/city_region',i,j,'.Rds'),version=2)
}
}
saveRDS(city_table,'../mh-execute/inputs/injury/processed_injuries_9.Rds',version=2)
common_coefs <- intersect(names(mod[[1]][[1]]$coefficients),names(mod[[2]][[2]]$coefficients))
do.call(cbind,lapply(mod,function(x)sapply(x,function(y)y$coefficients[names(y$coefficients)%in%common_coefs])))
cas_coefs <- names(mod[[1]][[1]]$coefficients)[!names(mod[[1]][[1]]$coefficients)%in%names(mod[[2]][[1]]$coefficients)]
sapply(mod[[1]],function(y)y$coefficients[names(y$coefficients)%in%cas_coefs])
str_coefs <- names(mod[[1]][[1]]$coefficients)[!names(mod[[1]][[1]]$coefficients)%in%names(mod[[1]][[2]]$coefficients)]
sapply(list(mod[[1]][[1]],mod[[2]][[1]]),function(y)y$coefficients[names(y$coefficients)%in%str_coefs])