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KFW_matching_and_cross-section_analysis.R
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KFW_matching_and_cross-section_analysis.R
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#clear variables and values
rm(list=ls())
#set the working directory to where the files are stored - !CHANGE THIS TO YOUR OWN DIRECTORY!
setwd("/Users/rbtrichler/Documents/AidData/Git Repos/kfw2_amazon_conflict")
#setwd("/home/aiddata/Desktop/Github/kfw2_amazon_conflict/")
#setwd("C:/Users/jflak/OneDrive/GitHub/kfw2_amazon_conflict/")
#essential spatial view packages (load and project shapefiles etc...)
library(rgdal)
library(sp)
#tools for spatial objects
library(rgeos)
library(maptools)
#tools for data manipulation
library(reshape2)
#library that handles matching
library(MatchIt)
#library that has old sci functions (like timeRangeTrend)
library(SCI)
shp_file <- "Processed_Data/shpfilecross.shp"
dta_shp = readShapePoly(shp_file)
#Eliminate non-PPTAL indigenous lands
dta_shp@data$proj_check <- 0
dta_shp@data$proj_check[is.na(dta_shp@data$reu_id)] <- 1
proj_shp <- dta_shp[dta_shp@data$proj_check !=1,]
dta_shp <- proj_shp
####Creating Pre, Post, etc. Variables####
#Make Pre-Level Values (2003)
dta_shp$prelevel_pmean <- dta_shp$MeanP_2003
dta_shp$prelevel_pmin <- dta_shp$MinP_2003
dta_shp$prelevel_pmax <- dta_shp$MaxP_2003
dta_shp$prelevel_tmean <- dta_shp$MeanT_2003
dta_shp$prelevel_tmin <- dta_shp$MinT_2003
dta_shp$prelevel_tmax <- dta_shp$MaxT_2003
dta_shp$prelevel_ndvimean <- dta_shp$MeanL_2003
dta_shp$prelevel_ndvimax <- dta_shp$MaxL_2003
dta_shp$prelevel_ntl <- dta_shp$ntl_2003
dta_shp$prelevel_iviolence <- dta_shp$ifreq2003
dta_shp$prelevel_iviolence[is.na(dta_shp$ifreq2003)] <- 0
dta_shp$prelevel_lviolence <- dta_shp$lfreq2003
dta_shp$prelevel_lviolence[is.na(dta_shp$lfreq2003)] <- 0
#fills in 0s for NAs in lfreq_tota
dta_shp$lfreq_tota[is.na(dta_shp$lfreq_tota)] <- 0
#Make Pre-Trend Values (1982-2003)
dta_shp$pretrend_pmean <- timeRangeTrend(dta_shp,"MeanP_[0-9][0-9][0-9][0-9]",1982,2003,"id")
dta_shp$pretrend_pmin <- timeRangeTrend(dta_shp,"MinP_[0-9][0-9][0-9][0-9]",1982,2003,"id")
dta_shp$pretrend_pmax <- timeRangeTrend(dta_shp,"MaxP_[0-9][0-9][0-9][0-9]",1982,2003,"id")
dta_shp$pretrend_tmean <- timeRangeTrend(dta_shp,"MeanT_[0-9][0-9][0-9][0-9]",1982,2003,"id")
dta_shp$pretrend_tmin <- timeRangeTrend(dta_shp,"MinT_[0-9][0-9][0-9][0-9]",1982,2003,"id")
dta_shp$pretrend_tmax <- timeRangeTrend(dta_shp,"MaxT_[0-9][0-9][0-9][0-9]",1982,2003,"id")
dta_shp$pretrend_ndvimean <- timeRangeTrend(dta_shp,"MeanL_[0-9][0-9][0-9][0-9]",1982,2003,"id")
dta_shp$pretrend_ndvimax <- timeRangeTrend(dta_shp,"MaxL_[0-9][0-9][0-9][0-9]",1982,2003,"id")
#This is the nighttime lights pretrend. Note that it only runs 1992-2003, not 1982-2003
dta_shp$pretrend_ntl <- timeRangeTrend(dta_shp,"ntl_[0-9][0-9][0-9][0-9]",1992,2003,"id")
#Make Post-Trend Values
dta_shp$posttrend_pmean <- timeRangeTrend(dta_shp,"MeanP_[0-9][0-9][0-9][0-9]",2003,2014,"id")
dta_shp$posttrend_pmin <- timeRangeTrend(dta_shp,"MinP_[0-9][0-9][0-9][0-9]",2003,2014,"id")
dta_shp$posttrend_pmax <- timeRangeTrend(dta_shp,"MaxP_[0-9][0-9][0-9][0-9]",2003,2014,"id")
dta_shp$posttrend_tmean <- timeRangeTrend(dta_shp,"MeanT_[0-9][0-9][0-9][0-9]",2003,2014,"id")
dta_shp$posttrend_tmin <- timeRangeTrend(dta_shp,"MinT_[0-9][0-9][0-9][0-9]",2003,2014,"id")
dta_shp$posttrend_tmax <- timeRangeTrend(dta_shp,"MaxT_[0-9][0-9][0-9][0-9]",2003,2014,"id")
dta_shp$posttrend_ndvimean <- timeRangeTrend(dta_shp,"MeanL_[0-9][0-9][0-9][0-9]",2003,2014,"id")
dta_shp$posttrend_ndvimax <- timeRangeTrend(dta_shp,"MaxL_[0-9][0-9][0-9][0-9]",2003,2014,"id")
#no ntl data for 2014 yet, so posttrend only goes through 2013
dta_shp$posttrend_ntl <- timeRangeTrend(dta_shp,"ntl_[0-9][0-9][0-9][0-9]",2003,2013,"id")
#Make Pop pretrend value
dta_shp$pretrend_pop <- dta_shp$Pop_2000_y - dta_shp$Pop_1990
#Make a binary for ever demarcated vs. never demarcated
dta_shp@data["DemBin"] <- 0
dta_shp@data$NA_check <- 0
dta_shp@data$NA_check[is.na(dta_shp@data$demend_y)] <- 1
dta_shp@data$DemBin[dta_shp@data$NA_check != 1] <- 1
# demtable <- table(dta_shp@data$DemBin)
# View(demtable)
#Make a binary for treated (demarcated 2004-2008)
dta_shp@data["Treat"] <- 0
dta_shp@data$NA_list <- 1
dta_shp@data$NA_list[!is.na(dta_shp@data$demend_y) & (dta_shp@data$demend_y > 2003 & dta_shp@data$demend_y < 2009)] <- 0
dta_shp@data$Treat[dta_shp@data$NA_list == 0] <- 1
#Eliminate lands that were demarcated in years other than 2004-2008
dta_shp@data <- subset(dta_shp@data, Treat == 1 | DemBin == 0)
#aVars <- c("Treat", "terrai_are", "prelevel_pmean", "prelevel_pmin", "prelevel_pmax", "prelevel_tmean",
# "prelevel_tmin", "prelevel_tmax", "prelevel_ndvimean", "prelevel_ndvimax", "prelevel_iviolence", "prelevel_lviolence",
# "pretrend_pmean", "pretrend_pmin", "pretrend_pmax", "pretrend_tmean", "pretrend_tmin", "pretrend_tmax",
# "pretrend_ndvimean", "pretrend_ndvimax", "pretrend_ntl", "pretrend_pop", "Slope", "Elevation", "Riv_Dist", "Road_dist",
# "posttrend_pmean", "posttrend_pmin", "posttrend_pmax", "posttrend_tmean", "posttrend_tmin", "posttrend_tmax",
# "posttrend_ndvimean", "posttrend_ndvimax", "posttrend_ntl", "id")
aVars <- c("Treat", "terrai_are", "prelevel_pmean", "prelevel_pmin", "prelevel_pmax", "prelevel_tmean",
"prelevel_tmin", "prelevel_tmax", "prelevel_ndvimean", "prelevel_ndvimax", "prelevel_ntl", "prelevel_iviolence", "prelevel_lviolence",
"pretrend_pmean", "pretrend_pmin", "pretrend_pmax", "pretrend_tmean", "pretrend_tmin", "pretrend_tmax",
"pretrend_ndvimean", "pretrend_ndvimax", "pretrend_ntl", "pretrend_pop", "Slope", "Elevation", "Riv_Dist", "Road_dist", "Pop_2000_y", "id")
#cuts the dataset down to only complete cases (matchit won't work if there are NAs)
# dta_shp1 <- dta_shp[complete.cases(dta_shp@data[aVars]),]
# dta_shp<-dta_shp1
#matchit.results <- matchit(Treat ~ terrai_are + prelevel_pmean + prelevel_pmin + prelevel_pmax + prelevel_tmean +
# prelevel_tmin + prelevel_tmax + prelevel_ndvimean + prelevel_ndvimax + prelevel_iviolence + prelevel_lviolence +
# pretrend_pmean + pretrend_pmin + pretrend_pmax + pretrend_tmean + pretrend_tmin + pretrend_tmax +
# pretrend_ndvimean + pretrend_ndvimax + pretrend_ntl + pretrend_pop + Slope + Elevation + Riv_Dist + Road_dist +
# posttrend_pmean + posttrend_pmin + posttrend_pmax + posttrend_tmean + posttrend_tmin + posttrend_tmax +
# posttrend_ndvimean + posttrend_ndvimax + posttrend_ntl,
# data = dta_shp@data[aVars],
# method = "nearest", distance="logit")
####MatchIt####
matchit.results <- matchit(Treat ~ terrai_are + prelevel_pmean + prelevel_pmin + prelevel_pmax + prelevel_tmean +
prelevel_tmin + prelevel_tmax + prelevel_ndvimean + prelevel_ndvimax + prelevel_ntl + prelevel_iviolence + prelevel_lviolence +
pretrend_pmean + pretrend_pmin + pretrend_pmax + pretrend_tmean + pretrend_tmin + pretrend_tmax +
pretrend_ndvimean + pretrend_ndvimax + pretrend_ntl + pretrend_pop + Slope + Elevation + Riv_Dist + Road_dist + Pop_2000_y,
data = dta_shp@data[aVars],
method = "nearest", distance="logit")
#prints the matchit results
print(summary(matchit.results))
#makes a new dataframe with the matched pair ids, to identify each pair
df_pairs <- as.data.frame(matchit.results$match.matrix)
df_pairs$treated_obs <- as.numeric(rownames(df_pairs))
rownames(df_pairs) <- NULL
colnames(df_pairs)[1] <- "untreated_obs"
df_pairs$untreated_obs <- as.numeric(as.character(df_pairs$untreated_obs))
df_pairs$pair_id <- as.numeric(rownames(df_pairs))
df_pairs <- data.frame(id = c(df_pairs$untreated_obs, df_pairs$treated_obs), pair_id = c(df_pairs$pair_id, df_pairs$pair_id))
View(df_pairs)
#df_test <- data.frame(x = c(df_pairs$untreated_obs, df_pairs$treated_obs), y = c(df_pairs$pair_id, df_pairs$pair_id))
#View(df_test)
#subsets the data to only the matched data
modelData <- match.data(matchit.results)
dta_shp_subset <- dta_shp
dta_shp_subset@data$id_present <- (dta_shp_subset$id %in% modelData$id)
dta_shp_subset@data <- dta_shp_subset@data[dta_shp_subset$id_present == TRUE,]
dta_shp@data <- dta_shp_subset@data
#adds id, lfreq_tota, and pair_id variables to modelData, as well as all the posttrends
modelData <- merge.default(modelData, dta_shp@data[c("posttrend_pmean", "posttrend_pmin", "posttrend_pmax", "posttrend_tmean", "posttrend_tmin", "posttrend_tmax",
"posttrend_ndvimean", "posttrend_ndvimax", "posttrend_ntl", "id", "lfreq_tota")], by = "id")
modelData$id <- modelData$id - 1
modelData <- merge.default(modelData, df_pairs, by = "id")
####Start of Models####
model_treat_only <- lm(lfreq_tota ~ Treat,
data = modelData)
print(summary(model_treat_only))
print('______________________________________________________________________________________', quote = FALSE)
model_treat_FE <- lm(lfreq_tota ~ Treat + factor(pair_id),
data = modelData)
print(summary(model_treat_FE))
print('______________________________________________________________________________________', quote = FALSE)
#This model includes all of the covariates including the violence prelevels,
#but I think that the violence prelevels should probably be taken out since they are almost all zeros
model_all_covars <- lm(lfreq_tota ~ Treat + terrai_are +
#prelevel_pmean + prelevel_pmin + prelevel_pmax + prelevel_tmean +
#prelevel_tmin + prelevel_tmax + prelevel_ndvimean + prelevel_ndvimax + prelevel_ntl + prelevel_iviolence+
prelevel_lviolence +
Slope + Elevation + Riv_Dist + Road_dist +
posttrend_pmean + posttrend_pmin + posttrend_pmax + posttrend_tmean + posttrend_tmin + posttrend_tmax +
posttrend_ndvimean + posttrend_ndvimax + posttrend_ntl + Pop_2000_y + factor(pair_id),
data = modelData)
print(summary(model_all_covars))
print('______________________________________________________________________________________', quote = FALSE)
####2 models that I added with different sets of variables####
#This model drops the violence prelevels because they have almost no information (they're almost all zeros),
#and drops the pair fixed effects because there are too many variables, giving an error: "ALL 46 residuals are 0: no residual degrees of freedom!"
model_some_covars <- lm(lfreq_tota ~ Treat + terrai_are + prelevel_pmean + prelevel_pmin + prelevel_pmax + prelevel_tmean +
prelevel_tmin + prelevel_tmax + prelevel_ndvimean + prelevel_ndvimax + prelevel_ntl +
Slope + Elevation + Riv_Dist + Road_dist +
posttrend_pmean + posttrend_pmin + posttrend_pmax + posttrend_tmean + posttrend_tmin + posttrend_tmax +
posttrend_ndvimean + posttrend_ndvimax + posttrend_ntl + Pop_2000_y,
data = modelData)
print(summary(model_some_covars))
print('______________________________________________________________________________________', quote = FALSE)
#This model drops variables with low significance - The p-value for Treat is still greater than the
#p-value for treat from model_treat_FE
model_cherrypicked <- lm(lfreq_tota ~ Treat + prelevel_pmean + prelevel_pmin + prelevel_pmax + prelevel_tmean +
prelevel_tmin + prelevel_tmax + prelevel_ndvimean +
Riv_Dist +
posttrend_ndvimean + posttrend_ndvimax + posttrend_ntl + Pop_2000_y + factor(pair_id),
data = modelData)
print(summary(model_cherrypicked))
print('______________________________________________________________________________________', quote = FALSE)
#Views the data
View(as.data.frame(dta_shp)[1:100])
View(as.data.frame(dta_shp)[101:200])
View(as.data.frame(dta_shp)[201:300])
View(as.data.frame(dta_shp)[301:400])
View(as.data.frame(dta_shp)[401:500])
View(as.data.frame(dta_shp)[501:504])