diff --git a/ImpactInformationProfiles.xlsx b/ImpactInformationProfiles.xlsx
index db10276..2609284 100644
Binary files a/ImpactInformationProfiles.xlsx and b/ImpactInformationProfiles.xlsx differ
diff --git a/RCode/Analysis/SEN.R b/RCode/Analysis/SEN.R
new file mode 100644
index 0000000..ab8e5ea
--- /dev/null
+++ b/RCode/Analysis/SEN.R
@@ -0,0 +1,268 @@
+options(scipen = 999)
+
+iso3<-"SEN"
+
+impies<-readRDS("./CleanedData/MostlyImpactData/AllHaz_impies_20231019_nozeros.RData")
+# Load taxonomies
+taxies<-openxlsx::read.xlsx("./ImpactInformationProfiles.xlsx")
+# Load the SEN data specifically
+SEN<-impies%>%filter(ISO3=="SEN" & Year>2000 & !is.na(haz_Ab))
+# left join imp_det variable to the dataframe
+SEN%<>%left_join(data.frame(
+  imp_det=taxies%>%filter(list_name=="imp_det")%>%pull(name),
+  imp_det_lab=taxies%>%filter(list_name=="imp_det")%>%pull(label)
+))
+# left join imp_type variable to the dataframe
+SEN%<>%left_join(data.frame(
+  imp_type=taxies%>%filter(list_name=="imp_type")%>%pull(name),
+  imp_type_lab=taxies%>%filter(list_name=="imp_type")%>%pull(label)
+))
+# Merge the two so that we can see the full impact descriptions
+SEN%<>%mutate(Impact=paste0(imp_det_lab," ",imp_type_lab))
+
+SEN$Impact[SEN$Impact=="General Aid Contributions NA"]<-"IFRC Funding Allocated (DREF & EA)"
+# Plot the yearly counts by data base
+SEN%>%group_by(Year,imp_src_db)%>%reframe(Count=n())%>%
+  arrange(Year)%>%group_by(imp_src_db)%>%mutate(Cum=cumsum(Count))%>%
+  ggplot()+geom_point(aes(Year,Cum,colour=imp_src_db))#+scale_y_log10()
+table(SEN$imp_cats)
+SEN%>%filter(imp_cats=="impcatphyinf")%>%
+  ggplot()+geom_bar(aes(factor(Impact),fill=imp_src_db))+facet_wrap(~imp_src_db)+
+  theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust=1))
+
+unique(impies$imp_spat_ID[impies$imp_src_db=="Desinventar"])
+
+table(SEN$imp_src_db)
+
+q<-SEN%>%group_by(Year,imp_src_db,haz_Ab)%>%reframe(Count=n())%>%
+  arrange(Year)%>%group_by(imp_src_db,haz_Ab)%>%mutate(Cum=cumsum(Count))%>%
+  ggplot()+geom_point(aes(Year,Cum,colour=haz_Ab))+
+  xlab("Year")+ylab("Cumulative Number")+
+  theme(plot.title = element_text(hjust = 0.5,face="bold",size=18))+
+  ggtitle("Cumulative Number of Hazard Events")+labs(colour="Hazard")+
+  facet_wrap(~imp_src_db);q
+ggsave("./Plots/SEN/SEN_NoFL_Des_perYear.png",q,width=12,height=8)
+
+q<-SEN%>%filter(Impact%in%c("Buildings Damaged","People (All Demographics) Deaths",
+                            "People (All Demographics) Total Affected",
+                            "IFRC Funding Allocated (DREF & EA)"))%>%
+  mutate(Date=as.Date(ev_sdate))%>%
+  ggplot()+geom_point(aes(Date,imp_value,colour=haz_Ab))+
+  facet_wrap(~Impact,scales = "free_y")+labs(colour="Hazard")+
+  theme(plot.title = element_text(hjust = 0.5,face="bold",size=18))+
+  ggtitle("Estimated Impact")+labs(colour="Hazard")+
+  ylab("Impact");q
+ggsave("./Plots/SEN/SEN_HazTimeline_perImpact.png",q,width=12,height=4)
+
+#@@@@@@@@@@ ADM 1 @@@@@@@@@@#
+# Desinventar
+i<-1
+maps<-readRDS("./tmp_maps_SEN.RData")
+loccy<-str_split(maps$filename[i+1],"/",simplify = T); loccy<-loccy[length(loccy)]
+dADM<-sf::st_read(paste0("./RawData/MostlyImpactData/Desinventar/",str_to_lower(iso3),"/",loccy),quiet=T)
+plot(dADM)
+dADM%<>%dplyr::select(ID)
+
+dADM$Allrecords<-sapply(dADM$ID,function(codie){
+  sum(grepl(codie,SEN$imp_spat_ID,ignore.case = T))
+})
+
+# Plot it out!
+q<-dADM%>%ggplot()+
+  geom_sf(aes(fill=Allrecords), linewidth=0.1) + #, inherit.aes = FALSE) +
+  coord_sf(datum = NULL) + #ylim(c(-80,80)) +
+  # coord_sf(datum = crs_mappy, xlim = c(-100,-40), ylim=c(-30,30), expand = FALSE) + #ylim(c(-80,80)) + 
+  ggthemes::theme_map() +
+  theme(legend.background = element_rect(fill="white",
+                                         linetype="solid", 
+                                         colour ="black"),
+        plot.title = element_text(hjust = 0.5,face="bold",size=18),
+        legend.position = c(0.1, 0.1))+
+  ggtitle("No. Impact Records (Desinventar)")+
+  scale_fill_gradient(name = "Impact Records",guide="legend", trans = "log10",
+                      low="magenta4",high="magenta",n.breaks=6);q
+
+
+
+dADM$deaths<-sapply(dADM$ID,function(codie){
+  sum(SEN$imp_value[grepl(codie,SEN$imp_spat_ID,ignore.case = T) & 
+                      SEN$imp_det=="impdetallpeop" & SEN$imp_type=="imptypdeat"])
+})
+
+# Plot it out!
+q<-dADM%>%ggplot()+
+  geom_sf(aes(fill=deaths), linewidth=0.1) + #, inherit.aes = FALSE) +
+  coord_sf(datum = NULL) + #ylim(c(-80,80)) +
+  # coord_sf(datum = crs_mappy, xlim = c(-100,-40), ylim=c(-30,30), expand = FALSE) + #ylim(c(-80,80)) + 
+  ggthemes::theme_map() +
+  theme(legend.background = element_rect(fill="white",
+                                         linetype="solid", 
+                                         colour ="black"),
+        plot.title = element_text(hjust = 0.5,face="bold",size=18),
+        legend.position = c(0.1, 0.1))+
+  ggtitle("Total Deaths (Desinventar)")+
+  scale_fill_gradient(name = "Deaths",guide="legend", trans = "log10",
+                      low="magenta4",high="magenta",n.breaks=6);q
+
+
+dADM$builds<-sapply(dADM$ID,function(codie){
+  sum(SEN$imp_value[grepl(codie,SEN$imp_spat_ID,ignore.case = T) & 
+                      SEN$imp_det=="impdetbuild"])
+})
+
+# Plot it out!
+q<-dADM%>%ggplot()+
+  geom_sf(aes(fill=builds), linewidth=0.1) + #, inherit.aes = FALSE) +
+  coord_sf(datum = NULL) + #ylim(c(-80,80)) +
+  # coord_sf(datum = crs_mappy, xlim = c(-100,-40), ylim=c(-30,30), expand = FALSE) + #ylim(c(-80,80)) + 
+  ggthemes::theme_map() +
+  theme(legend.background = element_rect(fill="white",
+                                         linetype="solid", 
+                                         colour ="black"),
+        plot.title = element_text(hjust = 0.5,face="bold",size=18),
+        legend.position = c(0.1, 0.1))+
+  ggtitle("Total Buildings Destroyed/Damaged (Desinventar)")+
+  scale_fill_gradient(name = "Buildings",guide="legend", #trans = "log10",
+                      low="magenta4",high="magenta",n.breaks=6);q
+
+
+
+dADM$FLdeaths<-sapply(dADM$ID,function(codie){
+  sum(SEN$imp_value[grepl(codie,SEN$imp_spat_ID,ignore.case = T) & 
+                      SEN$imp_det=="impdetallpeop" & SEN$imp_type=="imptypdeat" &
+                      SEN$haz_Ab=="FL"])
+})
+
+# Plot it out!
+q<-dADM%>%ggplot()+
+  geom_sf(aes(fill=FLdeaths), linewidth=0.1) + #, inherit.aes = FALSE) +
+  coord_sf(datum = NULL) + #ylim(c(-80,80)) +
+  # coord_sf(datum = crs_mappy, xlim = c(-100,-40), ylim=c(-30,30), expand = FALSE) + #ylim(c(-80,80)) + 
+  ggthemes::theme_map() +
+  theme(legend.background = element_rect(fill="white",
+                                         linetype="solid", 
+                                         colour ="black"),
+        plot.title = element_text(hjust = 0.5,face="bold",size=18),
+        legend.position = c(0.1, 0.1))+
+  ggtitle("Total Flood Deaths (Desinventar)")+
+  scale_fill_gradient(name = "Deaths",guide="legend", trans = "log10",
+                      low="magenta4",high="magenta",n.breaks=6);q
+
+
+dADM$FL<-sapply(dADM$ID,function(codie){
+  sum(grepl(codie,SEN$imp_spat_ID,ignore.case = T) & 
+                      SEN$haz_Ab=="FL")
+})
+
+# Plot it out!
+q<-dADM%>%ggplot()+
+  geom_sf(aes(fill=FL), linewidth=0.1) + #, inherit.aes = FALSE) +
+  coord_sf(datum = NULL) + #ylim(c(-80,80)) +
+  # coord_sf(datum = crs_mappy, xlim = c(-100,-40), ylim=c(-30,30), expand = FALSE) + #ylim(c(-80,80)) + 
+  ggthemes::theme_map() +
+  theme(legend.background = element_rect(fill="white",
+                                         linetype="solid", 
+                                         colour ="black"),
+        plot.title = element_text(hjust = 0.5,face="bold",size=18),
+        legend.position = c(0.1, 0.1))+
+  ggtitle("Number of Floods (Desinventar)")+
+  theme(legend.position =  c(.75, .75))+
+  scale_fill_gradient(name = "Flood Records",guide="legend",# trans = "log10",
+                      low="darkblue",high="lightblue",n.breaks=6);q
+ggsave("./Plots/SEN/SEN_NoFL_Des.png",q,width=12,height=10)
+
+
+dADM$WF<-sapply(dADM$ID,function(codie){
+  sum(grepl(codie,SEN$imp_spat_ID,ignore.case = T) & 
+        SEN$haz_Ab=="WF")
+})
+
+# Plot it out!
+q<-dADM%>%ggplot()+
+  geom_sf(aes(fill=WF), linewidth=0.1) + #, inherit.aes = FALSE) +
+  coord_sf(datum = NULL) + #ylim(c(-80,80)) +
+  # coord_sf(datum = crs_mappy, xlim = c(-100,-40), ylim=c(-30,30), expand = FALSE) + #ylim(c(-80,80)) + 
+  ggthemes::theme_map() +
+  theme(legend.background = element_rect(fill="white",
+                                         linetype="solid", 
+                                         colour ="black"),
+        plot.title = element_text(hjust = 0.5,face="bold",size=18),
+        legend.position = c(0.1, 0.1))+
+  ggtitle("Number of Wildfires (Desinventar)")+
+  theme(legend.position =  c(.75, .75))+
+  scale_fill_gradient(name = "Wildfire Records",guide="legend",trans = "log10",
+                      low="darkred",high="red1",n.breaks=6);q
+ggsave("./Plots/SEN/SEN_NoWF_Des.png",q,width=12,height=10)
+
+dADM$FLbuilds<-sapply(dADM$ID,function(codie){
+  sum(SEN$imp_value[grepl(codie,SEN$imp_spat_ID,ignore.case = T) & 
+                      SEN$imp_det=="impdetbuild" &
+                      SEN$haz_Ab=="FL"])
+})
+
+# Plot it out!
+q<-dADM%>%ggplot()+
+  geom_sf(aes(fill=FLbuilds), linewidth=0.1) + #, inherit.aes = FALSE) +
+  coord_sf(datum = NULL) + #ylim(c(-80,80)) +
+  # coord_sf(datum = crs_mappy, xlim = c(-100,-40), ylim=c(-30,30), expand = FALSE) + #ylim(c(-80,80)) + 
+  ggthemes::theme_map() +
+  theme(legend.background = element_rect(fill="white",
+                                         linetype="solid", 
+                                         colour ="black"),
+        plot.title = element_text(hjust = 0.5,face="bold",size=18),
+        legend.position = c(0.1, 0.1))+
+  ggtitle("Floods - Buildings Destroyed/Damaged (Desinventar)")+
+  scale_fill_gradient(name = "Buildings",guide="legend", trans = "log10",
+                      low="magenta4",high="magenta",n.breaks=6);q
+
+
+
+dADM$WFbuilds<-sapply(dADM$ID,function(codie){
+  sum(SEN$imp_value[grepl(codie,SEN$imp_spat_ID,ignore.case = T) & 
+                      SEN$imp_det=="impdetbuild" &
+                      SEN$haz_Ab=="WF"])
+})
+
+# Plot it out!
+q<-dADM%>%ggplot()+
+  geom_sf(aes(fill=WFbuilds), linewidth=0.1) + #, inherit.aes = FALSE) +
+  coord_sf(datum = NULL) + #ylim(c(-80,80)) +
+  # coord_sf(datum = crs_mappy, xlim = c(-100,-40), ylim=c(-30,30), expand = FALSE) + #ylim(c(-80,80)) + 
+  ggthemes::theme_map() +
+  theme(legend.background = element_rect(fill="white",
+                                         linetype="solid", 
+                                         colour ="black"),
+        plot.title = element_text(hjust = 0.5,face="bold",size=18),
+        legend.position = c(0.1, 0.1))+
+  ggtitle("Wildfires - Buildings Destroyed/Damaged (Desinventar)")+
+  scale_fill_gradient(name = "Buildings",guide="legend", trans = "log10",
+                      low="magenta4",high="magenta",n.breaks=6);q
+
+
+
+
+
+
+# EM-DAT 
+# eADM<-sf::st_read(paste0("./RawData/SocioPoliticalData/GAUL/gadm40_SEN_shp/gadm40_SEN_",i,".shp"))
+eADM<-sf::st_read(paste0("./RawData/SocioPoliticalData/GAUL/SEN_adm_shp_old/SEN_adm",i,".shp"))
+# eADM<-sf::st_read(paste0("./RawData/SocioPoliticalData/GAUL/gadm36_SEN_shp/gadm36_SEN_",i,".shp"))
+
+SEN_adm_shp_old
+
+codies<-SEN%>%filter(imp_src_db=="EM-DAT")%>%pull(imp_spat_ID)%>%str_split(";",simplify = T)%>%unlist()%>%c()%>%str_split(",",simplify = T)%>%c()%>%unique()  
+  
+  
+  
+  
+
+
+#@@@@@@@@@@ ADM 1 @@@@@@@@@@#
+i<-1
+loccy<-str_split(maps$filename[i+1],"/",simplify = T); loccy<-loccy[length(loccy)]
+dADM<-ADM<-sf::st_read(paste0("./RawData/MostlyImpactData/Desinventar/",str_to_lower(iso3),"/",loccy),quiet=T)
+# EM-DAT 
+eADM<-GetGAUL(iso3,lADM=i)  
+
+
+
diff --git a/RCode/Analysis/WDR_Analysis.R b/RCode/Analysis/WDR_Analysis.R
index 9274ead..6b571bb 100644
--- a/RCode/Analysis/WDR_Analysis.R
+++ b/RCode/Analysis/WDR_Analysis.R
@@ -811,12 +811,154 @@ ggsave("./Plots/Counts_db_w-LOESS.png",p,width=10,height=6)
 
 
 
-
-
-
-
-
-
+impies%<>%mutate(Region=left_join(impies,readxl::read_xlsx(filer)%>%
+                                    transmute(ISO3=`ISO Code`,continent=`UN Region`),
+                                  by="ISO3")$continent,
+                 Subregion=left_join(impies,readxl::read_xlsx(filer)%>%
+                                       transmute(ISO3=`ISO Code`,continent=`World Bank Regions`),
+                                     by="ISO3")$continent)
+# So that the months are plotted in english
+Sys.setlocale(category = "LC_TIME", locale="en_GB.UTF-8")
+
+month_labs<-c("January","February","March","April",
+              "May","June","July","August",
+              "September","October","November","December")
+# 
+p<-impies%>%filter(!is.na(Subregion) & Subregion!="Not Classified" & 
+                     (haz_type=="haztypehydromet" | haz_Ab=="WF") & imp_src_db=="EM-DAT")%>%
+  distinct(GCDB_ID,.keep_all = T)%>%group_by(Subregion)%>%
+  mutate(difftime=as.numeric((as.Date(ev_sdate)-as.Date(paste0(AsYear(ev_sdate),"-01-01")))/365))%>%
+  ggplot()+geom_density(aes(difftime,y=..scaled..,fill=Subregion),alpha=0.5)+
+  scale_x_continuous(breaks=0:11/12,labels=month_labs)+xlab("Month")+ylab("Proportion")+
+  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+        panel.background = element_rect(fill = "white",
+                                        colour = "white",
+                                        size = 0.5, linetype = "solid"),
+        panel.grid.major = element_line(size = 0.1, linetype = 'solid',
+                                        colour = "grey"),
+        plot.title = element_text(hjust = 0.5))+
+  facet_wrap(~Subregion,nrow=3)+
+  ggtitle("Proportion of Climate- & Weather-Related Events");p
+ggsave("./Plots/PercEvents_Month_Subregion.png",p,height = 8,width=12) 
+# 
+p<-impies%>%filter(!is.na(Subregion) & Subregion!="Not Classified" & 
+                     imp_src_db=="EM-DAT" & haz_Ab%in%c("FL"))%>%
+  distinct(GCDB_ID,.keep_all = T)%>%group_by(Subregion)%>%
+  mutate(difftime=as.numeric((as.Date(ev_sdate)-as.Date(paste0(AsYear(ev_sdate),"-01-01")))/365))%>%
+  ggplot()+geom_density(aes(difftime,y=..scaled..,fill=Subregion),alpha=0.5)+
+  scale_x_continuous(breaks=0:11/12,labels=month_labs)+xlab("Month")+ylab("Proportion")+
+  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+        panel.background = element_rect(fill = "white",
+                                        colour = "white",
+                                        size = 0.5, linetype = "solid"),
+        panel.grid.major = element_line(size = 0.1, linetype = 'solid',
+                                        colour = "grey"),
+        plot.title = element_text(hjust = 0.5))+
+  facet_wrap(~Subregion)+
+  ggtitle("Proportion of Flood Events");p
+ggsave("./Plots/PercFloods_Month_Subregion.png",p,height = 8,width=12) 
+# 
+p<-impies%>%filter(!is.na(Subregion) & Subregion!="Not Classified" & 
+                     (haz_type=="haztypehydromet" | haz_Ab=="WF") & 
+                     imp_det=="impdetallpeop" & imp_type=="imptypdeat")%>%
+  distinct(GCDB_ID,.keep_all = T)%>%group_by(Subregion)%>%
+  mutate(difftime=as.numeric((as.Date(ev_sdate)-as.Date(paste0(AsYear(ev_sdate),"-01-01")))/365),
+         Subregion=as.factor(Subregion))%>%filter(imp_src_db=="EM-DAT")%>%
+  ggplot()+geom_density(aes(difftime,y=..scaled..,weights=imp_value/sum(imp_value),fill=Subregion),alpha=0.5)+
+  scale_x_continuous(breaks=0:11/12,labels=month_labs)+xlab("Month")+ylab("Proportion")+
+  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+        panel.background = element_rect(fill = "white",
+                                        colour = "white",
+                                        size = 0.5, linetype = "solid"),
+        panel.grid.major = element_line(size = 0.1, linetype = 'solid',
+                                        colour = "grey"),
+        plot.title = element_text(hjust = 0.5))+
+  facet_wrap(~Subregion,nrow=3)+
+  ggtitle("Proportion of Climate- & Weather-Related Deaths");p
+ggsave("./Plots/PercDeaths_Month_Subregion.png",p,height = 8,width=12) 
+# 
+p<-impies%>%filter(!is.na(Subregion) & Subregion!="Not Classified" & 
+                     (haz_type=="haztypehydromet" | haz_Ab=="WF") & 
+                     imp_det=="impdetallpeop" & imp_type=="imptypdeat")%>%
+  distinct(GCDB_ID,.keep_all = T)%>%group_by(Subregion)%>%
+  mutate(difftime=as.numeric((as.Date(ev_sdate)-as.Date(paste0(AsYear(ev_sdate),"-01-01")))/365))%>%
+  filter(imp_src_db=="EM-DAT")%>%
+  ggplot()+geom_density(aes(difftime,y=..scaled..,weights=imp_value/sum(imp_value),fill=Subregion),alpha=0.5)+
+  scale_x_continuous(breaks=0:11/12,labels=month_labs)+xlab("Month")+ylab("Proportion")+
+  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+        panel.background = element_rect(fill = "white",
+                                        colour = "white",
+                                        size = 0.5, linetype = "solid"),
+        panel.grid.major = element_line(size = 0.1, linetype = 'solid',
+                                        colour = "grey"),
+        plot.title = element_text(hjust = 0.5))+
+  facet_wrap(~Subregion)+
+  ggtitle("Proportion of Flood Deaths");p
+ggsave("./Plots/PercFloodDeaths_Month_Subregion.png",p,height = 8,width=12) 
+
+
+p<-impies%>%filter(!is.na(Subregion) & Subregion!="Not Classified" & 
+                     (haz_type=="haztypehydromet" | haz_Ab=="WF") & 
+                     imp_det%in%c("impdetinfloccur","impdetloccur") & imp_type=="imptypcost")%>%
+  distinct(GCDB_ID,.keep_all = T)%>%group_by(Subregion)%>%
+  mutate(difftime=as.numeric((as.Date(ev_sdate)-as.Date(paste0(AsYear(ev_sdate),"-01-01")))/365))%>%
+  # filter(imp_src_db=="EM-DAT")%>%
+  ggplot()+geom_density(aes(difftime,y=..scaled..,weights=imp_value/sum(imp_value),
+                            fill=Subregion,linetype=imp_src_db,alpha=imp_src_db))+
+  scale_x_continuous(breaks=0:11/12,labels=month_labs)+xlab("Month")+ylab("Proportion")+
+  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+        panel.background = element_rect(fill = "white",
+                                        colour = "white",
+                                        size = 0.5, linetype = "solid"),
+        panel.grid.major = element_line(size = 0.1, linetype = 'solid',
+                                        colour = "grey"),
+        plot.title = element_text(hjust = 0.5))+
+  scale_alpha_discrete(range = c(0, 0.5))+
+  facet_wrap(~Subregion)+
+  ggtitle("Proportion of Cost");p
+ggsave("./Plots/PercCost_Month_Subregion_perDB.png",p,height = 8,width=12) 
+
+p<-impies%>%filter(!is.na(Subregion) & Subregion!="Not Classified" & 
+                     (haz_type=="haztypehydromet" | haz_Ab=="WF") & 
+                     imp_det=="impdetinfloccur" & imp_type=="imptypcost")%>%
+  distinct(GCDB_ID,.keep_all = T)%>%group_by(Subregion)%>%
+  mutate(difftime=as.numeric((as.Date(ev_sdate)-as.Date(paste0(AsYear(ev_sdate),"-01-01")))/365))%>%
+  # filter(imp_src_db=="EM-DAT")%>%
+  ggplot()+geom_density(aes(difftime,y=..scaled..,weights=imp_value/sum(imp_value),
+                            fill=Subregion),alpha=0.5)+
+  scale_x_continuous(breaks=0:11/12,labels=month_labs)+xlab("Month")+ylab("Proportion")+
+  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+        panel.background = element_rect(fill = "white",
+                                        colour = "white",
+                                        size = 0.5, linetype = "solid"),
+        panel.grid.major = element_line(size = 0.1, linetype = 'solid',
+                                        colour = "grey"),
+        plot.title = element_text(hjust = 0.5))+
+  scale_alpha_discrete(range = c(0, 0.5))+
+  facet_wrap(~Subregion)+
+  ggtitle("Proportion of Cost (EM-DAT)");p
+ggsave("./Plots/PercCost_Month_Subregion_EMDAT.png",p,height = 8,width=12) 
+
+
+tmp<-impies%>%mutate(Year=AsYear(ev_sdate), Date=as.Date(ev_sdate))%>%
+  filter(haz_Ab=="EQ" & imp_src_db=="EM-DAT" &
+         imp_det=="impdetallpeop" & imp_type=="imptypdeat" &
+         Year<2005)%>%distinct(GCDB_ID,.keep_all = T)%>%
+  arrange(Date)
+tmp%>%group_by(Date)%>%
+  mutate(Count=sum(tmp$Date>unique(Date)-365/2 & tmp$Date<unique(Date)+365/2))%>%
+  ggplot()+geom_point(aes(Date,Count))
+
+tmp
+
+
+
+
+tmp$dateDiff2<-c(0,as.numeric(tmp$Date[2:nrow(tmp)]-tmp$Date[1:(nrow(tmp)-1)]))
+
+tmp%>%mutate(Gradient=zoo::rollmean(1/dateDiff2,k=30, fill=NA, align='right'))%>%
+  filter(Gradient<1 & Year>1970)%>%
+  ggplot()+geom_point(aes(Date,Gradient))
 
 
 
diff --git a/RCode/GCDB_Objects/GCDB_table.R b/RCode/GCDB_Objects/GCDB_table.R
index 83c9b76..4bd05ac 100644
--- a/RCode/GCDB_Objects/GCDB_table.R
+++ b/RCode/GCDB_Objects/GCDB_table.R
@@ -66,7 +66,7 @@ col_tabGCDB<-c("event_ID"="character", # GCDB event ID
                "haz_link"="character", # Associated impactful-hazards to the specific hazard
                "haz_potlink"="character", # Potential other impactful-hazards that may be associated to the specific hazard
                "haz_maxvalue"="numeric", # Maximum intensity or magnitude of the hazard, e.g.  
-               "haz_units"="character", # Units of the max intensity/magnitude value estimate
+               "haz_maxunits"="character", # Units of the max intensity/magnitude value estimate
                "haz_est_type"="character", # Estimate type: primary, secondary, modelled
                "haz_src_db"="character", # Source database name of impact estimate or the curated estimate
                "haz_src_org"="character", # Source organisation of impact estimate or the curated estimate
@@ -84,21 +84,28 @@ col_tabGCDB<-c("event_ID"="character", # GCDB event ID
                "imp_spat_covcode"="character", # Spatial object type
                "imp_spat_res"="character", # Spatial resolution of impact estimate
                "imp_spat_resunits"="character", # Spatial resolution units of impact estimate (e.g. ADM level, raster grid)
+               "imp_spat_crs"="character",
                "imp_spat_srcorg"="character", # organisation of the spatial data
+               "imp_spat_srcdb"="character",
                "imp_spat_srcurl"="character", # URL of the impact estimate
                "imp_spat_colIDs"="character",
                "imp_spat_rowIDs"="character",
                "imp_spat_fileloc"="character",
-               
+               "imp_spat_fileread"="character",
+               "imp_spat_URL"="character",
                # Spatial info - hazard
                "haz_spat_ID"="character", # ID of the spatial object
                "haz_spat_covcode"="character", # Spatial object type
+               "haz_spat_fileread"="character",
                "haz_spat_res"="character", # Spatial resolution of impact estimate
                "haz_spat_resunits"="character", # Spatial resolution units of impact estimate (e.g. ADM level, raster grid)
+               "haz_spat_crs"="character",
                "haz_spat_srcorg"="character", # Source organisation from where the spatial object comes from
                "haz_spat_srcurl"="character", # URL of the impact estimate
-               "haz_spat_colIDs"="character",
-               "haz_spat_rowIDs"="character",
+               "haz_spat_srcdb"="character",
+               "haz_spat_URL"="character",
+               "haz_spat_colname"="character",
+               "haz_spat_rowname"="character",
                "haz_spat_fileloc"="character")
 # Required fields
 oblig_tabGCDB<-c("event_ID","imp_sub_ID","ev_ISO3s","imp_cat","imp_subcat","imp_det","imp_units",
@@ -142,11 +149,16 @@ GetGCDB_hazID<-function(impies){
   return(impies)
 }
 
-GetGCDB_spatID<-function(impies){
+GetGCDB_imp_spatID<-function(impies){
   impies%>%dplyr::select(imp_spat_srcorg,imp_spat_srcdb,imp_spat_covcode,imp_spat_res,imp_spat_resunits)%>%
     apply(1,function(x) paste0(x,collapse = "-"))
 }
 
+GetGCDB_haz_spatID<-function(hazzies){
+  hazzies%>%dplyr::select(haz_spat_srcorg,haz_spat_srcdb,haz_spat_covcode,haz_spat_res,haz_spat_resunits)%>%
+    apply(1,function(x) paste0(x,collapse = "-"))
+}
+
 AddEmptyColImp<-function(DF){
   for(i in which(!names(col_tabGCDB)%in%colnames(DF))){
     tmp<-NA
diff --git a/RCode/GCDB_Objects/MontandonSchema_JSON.R b/RCode/GCDB_Objects/MontandonSchema_JSON.R
index 0dd3aa5..6825dce 100644
--- a/RCode/GCDB_Objects/MontandonSchema_JSON.R
+++ b/RCode/GCDB_Objects/MontandonSchema_JSON.R
@@ -3,6 +3,19 @@ source("./RCode/Setup/GetPackages.R")
 # https://json-schema.org/learn/glossary
 # Get the RDLS JSON schema
 RDLS<-jsonlite::fromJSON("https://docs.riskdatalibrary.org/en/0__2__0/rdls_schema.json")
+
+
+
+
+
+
+stop("rename ext_IDs to something less overlappyyyyy")
+stop("change _fileread to _format, check _spat_fileloc and _spat_URL")
+
+
+
+
+
 # Get the taxonomy data
 taxies<-openxlsx::read.xlsx("./ImpactInformationProfiles.xlsx")
 # Country codes and associated regions
diff --git a/RCode/GCDB_Objects/MontyMethods.R b/RCode/GCDB_Objects/MontyMethods.R
index c523c99..cc36acf 100644
--- a/RCode/GCDB_Objects/MontyMethods.R
+++ b/RCode/GCDB_Objects/MontyMethods.R
@@ -1,3 +1,44 @@
+taxies<-openxlsx::read.xlsx("./ImpactInformationProfiles.xlsx")
+imp_class<-data.frame(
+  imp_det_code=taxies%>%filter(list_name=="imp_det")%>%pull(name),
+  imp_det_lab=taxies%>%filter(list_name=="imp_det")%>%pull(label),
+  imp_subcat_code=taxies%>%filter(list_name=="imp_det")%>%pull(link_group),
+  imp_subcat_lab=left_join(taxies[taxies$list_name=="imp_det",2:4],
+                           taxies[taxies$list_name=="imp_subcats",2:4],
+                           by=c("link_group"="name"))%>%pull(label.y),
+  imp_cat_code=taxies%>%filter(list_name=="imp_det")%>%pull(link_maingroup),
+  imp_cat_lab=left_join(left_join(taxies[taxies$list_name=="imp_det",2:4],
+                                  taxies[taxies$list_name=="imp_subcats",2:4],
+                                  by=c("link_group"="name")),
+                        taxies[taxies$list_name=="imp_cats",2:3],
+                        by=c("link_group.y"="name"))%>%pull(label)
+)
+haz_class<-data.frame(
+  haz_spec_code=taxies%>%filter(list_name=="hazardsubsubtypes")%>%pull(name),
+  haz_spec_lab=taxies%>%filter(list_name=="hazardsubsubtypes")%>%pull(label),
+  haz_cluster_code=taxies%>%filter(list_name=="hazardsubsubtypes")%>%pull(link_group),
+  haz_cluster_lab=left_join(taxies[taxies$list_name=="hazardsubsubtypes",2:4],
+                            taxies[taxies$list_name=="hazardsubtypes",2:4],
+                            by=c("link_group"="name"))%>%pull(label.y),
+  haz_type_code=taxies%>%filter(list_name=="hazardsubsubtypes")%>%pull(link_maingroup),
+  haz_type_lab=left_join(left_join(taxies[taxies$list_name=="hazardsubsubtypes",2:4],
+                                   taxies[taxies$list_name=="hazardsubtypes",2:4],
+                                   by=c("link_group"="name")),
+                         taxies[taxies$list_name=="hazardtypes",2:3],
+                         by=c("link_group.y"="name"))%>%pull(label)  
+)
+
+# ExtrLabel_Monty<-function(impies,HazNOTImp=F){
+#   if(HazNOTImp){
+#     
+#   } else {
+#     
+#   }
+#   
+#   return(impies)
+# }
+
+
 # A function to go from a series of data into the JSON-accepted DF-list style object
 Add_EvIDlink_Monty<-function(dframe){
   # Setup the other entries
@@ -7,6 +48,8 @@ Add_EvIDlink_Monty<-function(dframe){
   output$ext_IDs<-sapply(unique(dframe$event_ID),function(ID){
     # Find the corresponding indices for this entry
     indy<-dframe$event_ID==ID
+    # check for no external IDs
+    if(any(is.na(dframe$ext_ID[indy]))) return(list())
     # Highlight the external IDs that share the same Monty IDs 
     list(data.frame(ext_ID=dframe$ext_ID[indy],
                ext_ID_db=dframe$ext_ID_db[indy], 
@@ -28,7 +71,7 @@ Add_EvSpat_Monty<-function(dframe){
     dframe$ev_ISO3s[dframe$event_ID==ID]
   })
   # Gimme gimme gimme
-  return(output)
+  return(output%>%distinct())
 }
 
 Add_EvTemp_Monty<-function(dframe){
@@ -36,7 +79,7 @@ Add_EvTemp_Monty<-function(dframe){
   dframe%>%group_by(event_ID)%>%
     reframe(ev_sdate=as.character(min(as.Date(ev_sdate))),
             ev_fdate=as.character(max(as.Date(ev_fdate))))%>%
-    dplyr::select(-event_ID)%>%distinct()
+    distinct()%>%dplyr::select(-event_ID)
 }
 
 Add_HazTax_Monty<-function(dframe){
@@ -56,13 +99,15 @@ Add_HazTax_Monty<-function(dframe){
 }
 
 # A function to form the impact IDlinkage field
-Add_ImIDlink_Monty<-function(dframe){
+Add_ImpIDlink_Monty<-function(dframe){
   # Setup the other entries
   output<-dframe%>%dplyr::select(event_ID,imp_sub_ID)%>%distinct()
   # Generate all the elements of the dataset
   output$haz_sub_ID<-sapply(unique(dframe$imp_sub_ID),function(ID){
     # Find the corresponding indices for this entry
     indy<-dframe$imp_sub_ID==ID
+    # check for no haz_sub_imp values
+    if(any(is.na(dframe$haz_sub_ID[indy]))) return(list())
     # Highlight the external IDs that share the same Monty IDs 
     list(dframe$haz_sub_ID[indy])
   },simplify = T)
@@ -72,7 +117,7 @@ Add_ImIDlink_Monty<-function(dframe){
   return(output)
 }
 
-Add_ImSpatID_Monty<-function(dframe){
+Add_ImpSpatID_Monty<-function(dframe){
   
   output<-sapply(dframe$imp_spat_rowname,function(rnm){
   list(data.frame(
@@ -88,7 +133,7 @@ Add_ImSpatID_Monty<-function(dframe){
   
 }
 
-Add_ImSpatAll_Monty<-function(ID_linkage,spatial_info,source){
+Add_ImpSpatAll_Monty<-function(ID_linkage,spatial_info,source){
   # multiple-entry rows: imp_spat_rowname,imp_spat_colname,imp_ISO3s,imp_spat_res,imp_spat_fileread
   lapply(ID_linkage$imp_sub_ID,function(ID){
     # Set out only the entries that we need
@@ -111,12 +156,103 @@ Add_ImSpatAll_Monty<-function(ID_linkage,spatial_info,source){
 }
 
 
+Add_hazIDlink_Monty<-function(dframe){
+  # Setup the other entries
+  output<-dframe%>%dplyr::select(event_ID,haz_sub_ID)%>%distinct()
+  # Extract the external ID codes
+  output$haz_ext_IDs<-sapply(dframe$haz_sub_ID,function(ID){
+    # Find the corresponding indices for this entry
+    indy<-dframe$haz_sub_ID==ID
+    # Check for no IDs
+    if(all(is.na(dframe$ext_ID[indy]))) return(list())
+    # Highlight the external IDs that share the same Monty IDs 
+    list(data.frame(ext_ID=dframe$ext_ID[indy],
+                    ext_ID_db=dframe$ext_ID_db[indy], 
+                    ext_ID_org=dframe$ext_ID_org[indy])%>%distinct())
+  },simplify = T)
+  # Let's keep this neat
+  names(output$haz_ext_IDs)<-NULL
+  # Output that bea-u-t
+  return(output)
+}
+
+
+Add_hazSpatAll_Monty<-function(ID_linkage,spatial_info,source){
+  # multiple-entry rows: haz_spat_rowname,haz_spat_colname,haz_ISO3s,haz_spat_res,haz_spat_fileread
+  lapply(ID_linkage$haz_sub_ID,function(ID){
+    # Set out only the entries that we need
+    indy<-ID_linkage$haz_sub_ID==ID
+    # Extract the easier elements
+    minout<-ID_linkage%>%filter(indy)%>%
+      dplyr::select(haz_spat_ID,haz_spat_fileloc)%>%distinct()
+    # Add the column and row specifier elements
+    minout$haz_spat_colname<-list(ID_linkage$haz_spat_colname[indy])
+    minout$haz_spat_rowname<-list(ID_linkage$haz_spat_rowname[indy])
+    # Output
+    output<-list()
+    output$ID_linkage<-minout
+    output$spatial_info<-spatial_info%>%filter(indy)%>%dplyr::select(
+      haz_ISO3s,haz_spat_covcode,haz_spat_res,haz_spat_resunits,
+      haz_spat_fileread,haz_spat_crs)
+    output$source<-source%>%filter(indy)
+    return(output)
+  })
+}
+
 
 
 
 
 
 
+# Function to left_join the s@#t out of Monty
+OverlapMonty<-function(MontyA,MontyB){
+  # Check which MontyA events lie generally within the window of MontyB
+  iiis<-
+    as.Date(MontyA$event_Level$temporal$ev_sdate)>
+    min(as.Date(MontyB$event_Level$temporal$ev_sdate))-10 &
+    as.Date(MontyA$event_Level$temporal$ev_fdate)>
+    max(as.Date(MontyB$event_Level$temporal$ev_fdate))+10
+  # First reduce the crossover by hazard type (through the abbreviated hazard)
+  allMatch<-mclapply(which(iiis),function(i){
+    # First check for any overlapping hazards
+    haz_Ab<-unique(MontyA$event_Level$allhaz_class[[i]]$all_hazs_Ab)
+    # Find all indices worth pursuing
+    indy<-vapply(1:length(MontyB$event_Level$allhaz_class),function(j) {
+      any(haz_Ab%in%
+            unique(MontyB$event_Level$allhaz_class[[j]]$all_hazs_Ab))
+    },FUN.VALUE = logical(1))
+    # Now let's check by overlapping dates
+    indy<-indy & 
+      as.Date(MontyA$event_Level$temporal$ev_sdate[i]) > 
+      as.Date(MontyB$event_Level$temporal$ev_sdate)-10 &
+      as.Date(MontyA$event_Level$temporal$ev_fdate[i]) <
+      as.Date(MontyB$event_Level$temporal$ev_fdate)+10
+    # Now lets check for overlapping continents or countries
+    indy & 
+      vapply(1:length(MontyB$event_Level$spatial$ev_ISO3s),function(j) {
+        any(convIso3Continent_alt(MontyA$event_Level$spatial$ev_ISO3s[[i]]) %in% 
+        convIso3Continent_alt(MontyB$event_Level$spatial$ev_ISO3s[[j]])) |
+        any(MontyA$event_Level$spatial$ev_ISO3s[[i]] %in% 
+        MontyB$event_Level$spatial$ev_ISO3s[[j]])
+      },FUN.VALUE = logical(1)) %>%
+      which()
+  },mc.cores = 12)
+  
+  overlaps<-rep(F,length(iiis))
+  overlaps[iiis]<-vapply(1:length(allMatch),function(k) any(allMatch[[k]]),logical(1))
+  
+}
+
+
+MergeMonty<-function(MontyA,MontyB){
+  Monty<-MontyA
+  Monty$event_Level$ID_linkage<-rbind(MontyA$event_Level$ID_linkage,
+                                      MontyB$event_Level$ID_linkage)
+  
+}
+
+
 
 
 
diff --git a/RCode/MainlyHazardData/GetGDACS.R b/RCode/MainlyHazardData/GetGDACS.R
index 07d8f44..367b8b5 100755
--- a/RCode/MainlyHazardData/GetGDACS.R
+++ b/RCode/MainlyHazardData/GetGDACS.R
@@ -167,15 +167,15 @@ severitysplitter<-function(haz,txt){
   # } 
   if(haz=="EQ") {
     return(list(haz_maxvalue=txt$severity,
-                haz_units="unitsrichter"))
+                haz_maxunits="unitsrichter"))
   } else if(haz=="TC") {
     return(list(haz_maxvalue=txt$severity,
-                haz_units="unitskph"))
+                haz_maxunits="unitskph"))
   } else if(haz=="DR") {
     return(list(haz_maxvalue=txt$severity,
-                haz_units="unitskm2"))
+                haz_maxunits="unitskm2"))
   } else return(list(haz_maxvalue=NA_real_,
-                     haz_units=NA_character_))
+                     haz_maxunits=NA_character_))
 }
 
 GetIntMap<-function(hazard="EQ"){
@@ -227,6 +227,7 @@ FilterGDACS<-function(haz=NULL,syear=2016L,fyear=NULL,list_GDACS=NULL,red=F){
                                         episodeid=rep(tmp$properties$episodeid,len),
                                         link=rep(tmp$properties$url$details,len),
                                         imp_ISO3s=dfct$ISO3,
+                                        ev_ISO3s=dfct$ISO3,
                                         country=dfct$country,
                                         ev_sdate=rep(as.Date(as.POSIXct(tmp$properties$fromdate),format = "%Y%m%d"),len),
                                         ev_fdate=rep(as.Date(as.POSIXct(tmp$properties$todate),format = "%Y%m%d"),len),
@@ -237,7 +238,7 @@ FilterGDACS<-function(haz=NULL,syear=2016L,fyear=NULL,list_GDACS=NULL,red=F){
                                         haz_src_org=tmp$properties$source,
                                         ext_IDs=rep(tmp$properties$glide,len),
                                         ext_ID_dbs="GLIDE",
-                                        ext_ID_orgs="Asian Disaster Reduction Center (ADRC)",
+                                        ext_ID_orgs="ADRC",
                                         geom_type=rep(tmp$geometry$type,len),
                                         cent_lon=rep(tmp$geometry$coordinates[1],len),
                                         cent_lat=rep(tmp$geometry$coordinates[2],len),
@@ -266,34 +267,59 @@ GDACSHazards<-function(GDACS){
 convGDACS_GCDB<-function(GDACS){
   # Form the ID for the event
   GDACS$event_ID<-GetMonty_ID(GDACS)
+  # Make the dates the correct type
+  GDACS%<>%mutate_at(vars(ev_sdate,ev_fdate),as.character)
   # Date shifts
-  GDACS$imp_sdate<-GDACS$imp_unitdate<-GDACS$haz_sdate<-as.character(GDACS$ev_sdate)
+  GDACS$imp_sdate<-GDACS$imp_unitdate<-GDACS$haz_sdate<-GDACS$ev_sdate
   GDACS$imp_fdate<-GDACS$haz_fdate<-GDACS$ev_fdate
   # Add the continent, then remove the unnecesary layers
-  GDACS%<>%mutate(region=convIso3Continent(imp_ISO3s))%>%
+  GDACS%<>%mutate(region=convIso3Continent_alt(imp_ISO3s))%>%
     filter(!is.na(region))
   # Add alertscore as the impact value
-  GDACS$imp_value<-GDACS$alertscore
-  GDACS$imp_cat<-"impother"
-  GDACS$imp_subcat<-"imptyperisk"
-  GDACS$imp_det<-"impdetalert"
-  GDACS$imp_type<-"imptypalert"
-  GDACS$imp_units<-"unitsgdacsalert"
-  # This estimate is modelled
-  GDACS$imp_est_type<-"esttype_model"
-  GDACS$haz_est_type<-"esttype_second"
-  # Organisation
-  GDACS$imp_src_db<-"GDACS"
-  GDACS$imp_src_org<-"European Commission"
-  GDACS$imp_src_orgtype<-"orgtyperio"
+  GDACS%<>%mutate(
+    imp_value=GDACS$alertscore,
+    imp_cat="impother",
+    imp_subcat="imptyperisk",
+    imp_det="impdetalert",
+    imp_type="imptypalert",
+    imp_units="unitsgdacsalert",
+    # This estimate is modelled
+    imp_est_type="esttype_model",
+    haz_est_type="esttype_second",
+    # Organisation
+    imp_src_db="GDACS",
+    imp_src_org="EC-JRC",
+    imp_src_orgtype="orgtyperio",
+    imp_spat_covcode="spat_polygon",
+    imp_spat_res=0,
+    imp_spat_resunits="adminlevel",
+    imp_spat_fileread="spatfstanshp",
+    imp_spat_crs="EPSG:4326",
+    imp_spat_srcorg="IFRC",
+    imp_spat_srcdb="GO-Maps",
+    imp_spat_URL="https://go-user-library.ifrc.org/maps",
+    imp_spat_ID=NA_character_,
+    haz_ISO3s=imp_ISO3s,
+    haz_spat_covcode="spat_polygon",
+    haz_spat_res=NA_real_,
+    haz_spat_resunits="spatresother",
+    haz_spat_fileread="spatfstanshp",
+    haz_spat_fileloc=src_URL,
+    haz_spat_crs="EPSG:4326",
+    haz_spat_srcorg="EC-JRC",
+    haz_spat_srcdb="GDACS",
+    haz_spat_URL=src_URL)
+  
   colnames(GDACS)[colnames(GDACS)=="src_URL"]<-"haz_src_URL"
   colnames(GDACS)[colnames(GDACS)=="link"]<-"imp_src_URL"
   # Convert to the UNDRR-ISC hazard taxonomy
   GDACS%<>%GDACSHazards()
   # Create the impact and hazard sub-ID for the speciic level, not event level
   GDACS%<>%GetGCDB_impID()
+  GDACS$imp_spat_ID<-GetGCDB_imp_spatID(GDACS)
   # Now for hazards
   GDACS%<>%GetGCDB_hazID()
+  GDACS$haz_spat_ID<-GetGCDB_haz_spatID(GDACS)
   # Add the extra columns to make it officially a GCDB object
   GDACS%>%AddEmptyColImp()%>%distinct()
 }
@@ -305,6 +331,179 @@ GetGDACS_GCDB<-function(){
   GDACS%>%convGDACS_GCDB()
 }
 
+convGDACS_Monty<-function(GDACS){
+  # Get rid of repeated entries
+  GDACS%<>%distinct(imp_sub_ID,.keep_all = TRUE)
+  # Extract the Monty JSON schema template
+  gdacsMonty<-jsonlite::fromJSON("./Taxonomies/Montandon_JSON-Example.json")
+  #@@@@@ Impact-level data @@@@@#
+  # IDs
+  ID_linkage<-Add_ImpIDlink_Monty(
+    GDACS%>%
+      dplyr::select(event_ID,imp_sub_ID,haz_sub_ID)
+  )
+  # Sources for impact data
+  source<-GDACS%>%dplyr::select(imp_src_db,imp_src_URL,imp_src_org)
+  # impact estimates
+  impact_estimate<-GDACS%>%
+    dplyr::select(imp_det,imp_value,imp_type,imp_units,imp_est_type,imp_unitdate)
+  # Add temporal information
+  temporal<-GDACS%>%dplyr::select(imp_sdate,imp_fdate)
+  # Spatial data relevant to the impact estimates
+  # multiple-entry rows: imp_spat_rowname,imp_spat_colname,imp_ISO3s,imp_spat_res,imp_spat_fileread
+  spatial<-Add_ImpSpatAll_Monty(
+    ID_linkage=data.frame(
+      imp_sub_ID=GDACS$imp_sub_ID,
+      imp_spat_ID="GO-ADM0-World-shp",
+      imp_spat_fileloc="https://go-user-library.ifrc.org/maps",
+      imp_spat_colname="iso3",
+      imp_spat_rowname=GDACS$imp_ISO3s
+    ),
+    spatial_info=GDACS%>%dplyr::select(
+      imp_ISO3s,
+      imp_spat_covcode,
+      imp_spat_res,
+      imp_spat_resunits,
+      imp_spat_fileread,
+      imp_spat_crs
+    ),
+    source=GDACS%>%dplyr::select(
+      imp_spat_srcdb,
+      imp_spat_URL,
+      imp_spat_srcorg
+    )
+  )
+  
+  # Gather it all and store it in the template!
+  # (I know this is hideous, but I don't understand how JSON files can have lists that are also S3 data.frames)
+  gdacsMonty$impact_Data<-data.frame(imp_sub_ID=GDACS$imp_sub_ID)
+  gdacsMonty$impact_Data$ID_linkage=ID_linkage
+  gdacsMonty$impact_Data$source=source
+  gdacsMonty$impact_Data$impact_estimate=impact_estimate
+  gdacsMonty$impact_Data$temporal=temporal
+  gdacsMonty$impact_Data$spatial=spatial
+  gdacsMonty$impact_Data$imp_sub_ID<-NULL
+  
+  #@@@@@ Event-level data @@@@@#
+  # IDs
+  ID_linkage<-Add_EvIDlink_Monty(
+    GDACS%>%dplyr::select(event_ID, ev_name, ext_IDs,ext_ID_dbs,ext_ID_orgs)%>%
+      rename(ext_ID=ext_IDs,ext_ID_db=ext_ID_dbs,ext_ID_org=ext_ID_orgs)
+  )
+  # Spatial
+  spatial<-Add_EvSpat_Monty(
+    GDACS%>%dplyr::select(event_ID,imp_ISO3s,location)%>%
+      rename(ev_ISO3s=imp_ISO3s,gen_location=location)
+  )
+  # temporal
+  temporal<-Add_EvTemp_Monty(
+    GDACS%>%dplyr::select(event_ID,imp_sdate,imp_fdate,ev_sdate,ev_fdate)
+  )
+  # Hazards
+  allhaz_class<-Add_HazTax_Monty(
+    GDACS%>%dplyr::select(event_ID, haz_Ab, haz_spec)
+  )
+  # Gather it all and store it in the template!
+  gdacsMonty$event_Level<-data.frame(ev=ID_linkage$event_ID)
+  gdacsMonty$event_Level$ID_linkage<-ID_linkage
+  gdacsMonty$event_Level$temporal<-temporal
+  gdacsMonty$event_Level$spatial<-spatial
+  gdacsMonty$event_Level$allhaz_class<-allhaz_class
+  gdacsMonty$event_Level$ev<-NULL
+  
+  
+  #@@@@@ Hazard-level data @@@@@#
+  GDACS%<>%distinct(haz_sub_ID,.keep_all = T)
+  
+  # Nothing to put here as we haven't linked any hazard data yet
+  ID_linkage<-Add_hazIDlink_Monty(
+    GDACS%>%
+      dplyr::select(event_ID,haz_sub_ID,ext_IDs,ext_ID_dbs,ext_ID_orgs)%>%
+      rename(ext_ID=ext_IDs,ext_ID_db=ext_ID_dbs,ext_ID_org=ext_ID_orgs)
+  )
+  # Sources for impact data
+  source<-GDACS%>%dplyr::select(haz_src_db,haz_src_URL,haz_src_org)%>%mutate(haz_src_db="GDACS")
+  # hazard intensity estimates
+  hazard_detail<-GDACS%>%
+    dplyr::select(haz_maxvalue,haz_maxunits,haz_est_type)
+  hazard_detail$concur_haz<-lapply(1:nrow(hazard_detail),function(i) list())
+  # hazard taxonomy
+  hazard_taxonomy<-Add_HazTax_Monty(
+    GDACS%>%dplyr::select(haz_sub_ID, haz_Ab, haz_spec)%>%rename(event_ID=haz_sub_ID)
+  )
+  # Add temporal information
+  temporal<-GDACS%>%dplyr::select(haz_sdate,haz_fdate)
+  # Spatial data relevant to the impact estimates
+  # multiple-entry rows: imp_spat_rowname,imp_spat_colname,imp_ISO3s,imp_spat_res,imp_spat_fileread
+  GDACS$haz_spat_fileloc<-GDACS$haz_spat_URL
+  # Spatial instance
+  spatial<-Add_hazSpatAll_Monty(
+    ID_linkage=GDACS%>%dplyr::select(
+      haz_sub_ID,
+      haz_spat_ID,
+      haz_spat_fileloc,
+      haz_spat_colname,
+      haz_spat_rowname
+    ),
+    spatial_info=GDACS%>%dplyr::select(
+      haz_ISO3s,
+      haz_spat_covcode,
+      haz_spat_res,
+      haz_spat_resunits,
+      haz_spat_fileread,
+      haz_spat_crs
+    ),
+    source=GDACS%>%dplyr::select(
+      haz_spat_srcdb,
+      haz_spat_URL,
+      haz_spat_srcorg
+    )
+  )
+  
+  # Gather it all and store it in the template!
+  # (I know this is hideous, but I don't understand how JSON files can have lists that are also S3 data.frames)
+  gdacsMonty$hazard_Data<-data.frame(imp_sub_ID=GDACS$imp_sub_ID)
+  gdacsMonty$hazard_Data$ID_linkage=ID_linkage
+  gdacsMonty$hazard_Data$source=source
+  gdacsMonty$hazard_Data$hazard_detail=hazard_detail
+  gdacsMonty$hazard_Data$hazard_taxonomy=hazard_taxonomy
+  gdacsMonty$hazard_Data$temporal=temporal
+  gdacsMonty$hazard_Data$spatial=spatial
+  gdacsMonty$hazard_Data$imp_sub_ID<-NULL
+  
+  
+  #@@@@@ Source Data In Taxonomy Field @@@@@#
+  gdacsMonty$taxonomies$src_info<-data.frame(
+    src_org_code="EC-JRC",
+    src_org_lab="European Commission - Joint Research Center",
+    src_org_typecode="orgtyperio",
+    src_org_typelab="Regional Intergovernmental Organisation",
+    src_org_email="coordination@gdacs.org",
+    src_db_code="GDACS",
+    src_db_lab="Global Disaster Alert and Coordination System (GDACS)",
+    src_db_attr="mediator",
+    src_db_lic="unknown",
+    src_db_URL="www.gdacs.org",
+    src_addinfo=""
+  )
+  
+  # Write it out just for keep-sake
+  write(jsonlite::toJSON(gdacsMonty,pretty = T,auto_unbox=T),
+        "./CleanedData/MostlyHazardData/GDACS/GDACS_20231119.json")
+  
+  return(gdacsMonty)
+}
+
+
+
+
+
+
+
+
+
+
+
 GetGDACSalertscore<-function(dfGDACS=NULL,haz,bbox,sdater,fdater=NULL,isos=NULL){
   
   if(any(is.null(c(haz,sdater,bbox)))) stop("Please provide hazard type, start date and bounding box to extract GDACS alertscore")
diff --git a/RCode/MainlyImpactData/GetIFRCappeal.R b/RCode/MainlyImpactData/GetIFRCappeal.R
index 8444a3e..4339869 100644
--- a/RCode/MainlyImpactData/GetIFRCappeal.R
+++ b/RCode/MainlyImpactData/GetIFRCappeal.R
@@ -130,7 +130,7 @@ CleanGO_app<-function(appeal){
   
   # Create an impact-specific ID
   appeal%<>%GetGCDB_impID()
-  appeal$imp_spat_ID<-GetGCDB_spatID(appeal)
+  appeal$imp_spat_ID<-GetGCDB_imp_spatID(appeal)
   # Add missing columns & reorder the dataframe to fit imp_GCDB object
   # appeal%<>%AddEmptyColImp()
   
@@ -220,7 +220,7 @@ CleanGO_field<-function(fieldr){
   
   # Create an impact-specific ID
   fieldr%<>%GetGCDB_impID()
-  fieldr$imp_spat_ID<-GetGCDB_spatID(fieldr)
+  fieldr$imp_spat_ID<-GetGCDB_imp_spatID(fieldr)
   # Add missing columns & reorder the dataframe to fit imp_GCDB object
   fieldr%<>%AddEmptyColImp()
   
@@ -284,7 +284,7 @@ convGOApp_Monty<-function(appeal){
   appMonty<-jsonlite::fromJSON("./Taxonomies/Montandon_JSON-Example.json")
   #@@@@@ Impact-level data @@@@@#
   # IDs
-  ID_linkage<-Add_ImIDlink_Monty(
+  ID_linkage<-Add_ImpIDlink_Monty(
     appeal%>%mutate(haz_sub_ID=NA_character_)%>%
       dplyr::select(event_ID,imp_sub_ID,haz_sub_ID)
   )
@@ -299,7 +299,7 @@ convGOApp_Monty<-function(appeal){
   temporal<-appeal%>%dplyr::select(imp_sdate,imp_fdate)
   # Spatial data relevant to the impact estimates
   # multiple-entry rows: imp_spat_rowname,imp_spat_colname,imp_ISO3s,imp_spat_res,imp_spat_fileread
-  spatial<-Add_ImSpatAll_Monty(
+  spatial<-Add_ImpSpatAll_Monty(
     ID_linkage=data.frame(
       imp_sub_ID=appeal$imp_sub_ID,
       imp_spat_ID="GO-ADM0-World-shp",
diff --git a/RCode/Other/GetGoogleEarthEngineData.R b/RCode/Other/GetGoogleEarthEngineData.R
index cbffe56..8833fa0 100644
--- a/RCode/Other/GetGoogleEarthEngineData.R
+++ b/RCode/Other/GetGoogleEarthEngineData.R
@@ -8,7 +8,7 @@ SetupGEE<-function(){
     install.packages("rgee")
     
     library(rgee) 
-    ee_install()
+    ee_install(py_env = "/home/hamishwp/anaconda3/bin/python3")
     Sys.setenv("EARTHENGINE_GCLOUD" = gee_bin)
     py_install( "earthengine-api==0.1.277", "rgee")
     ee_check()
@@ -24,3 +24,19 @@ SetupGEE<-function(){
   return(T)
   
 }
+
+
+# library(reticulate)
+# # Check which version of python3 you're using with
+# python3 --version
+# # Let's say we use python3.11, then we run:
+# sudo apt-get install python3.11-venv
+# use_python("/usr/bin/python3")
+# # Then run
+# py_install( "earthengine-api==0.1.277", "rgee")
+# use_python("/home/hamishwp/anaconda3/bin/python3")
+# rgee::ee_install_set_pyenv(py_path = "/home/hamishwp/anaconda3/bin/python3", py_env="rgee")
+
+# Sys.which("python3")
+# library(reticulate)
+# reticulate::use_python("/home/hamishwp/.local/share/r-miniconda/envs/r-reticulate/bin/python")
\ No newline at end of file