First steps in adding a second model based off the BRD dataset (not G…

…ED), this second model is not functional yet

Countries_BRD.csv

@@ -0,0 +1,30 @@
+BRD,homicides
+Laos,Lao PDR
+UnitedKingdom,United Kingdom
+Serbia(Yugoslavia),Serbia
+FYROM,North Macedonia
+SierraLeone,Sierra Leone
+Bosnia-Herzegovina,Bosnia and Herzegovina
+Syria,Syrian Arab Republic
+Egypt,"Egypt, Arab Rep."
+Kyrgyzstan,Kyrgyz Republic
+Myanmar(Burma),Myanmar
+TrinidadandTobago,Trinidad and Tobago
+SolomonIslands,Solomon Islands
+Turkey,Turkiye
+SouthSudan,South Sudan
+IvoryCoast,Cote d'Ivoire
+PapuaNewGuinea,Papua New Guinea
+SriLanka,Sri Lanka
+Russia(SovietUnion),Russian Federation
+Venezuela,"Venezuela, RB"
+CentralAfricanRepublic,Central African Republic
+BurkinaFaso,Burkina Faso
+Congo,"Congo, Rep."
+DRCongo(Zaire),"Congo, Dem. Rep."
+UnitedStatesofAmerica,United States
+Iran,"Iran, Islamic Rep."
+Yemen(NorthYemen),"Yemen, Rep."
+SaudiArabia,Saudi Arabia
+ElSalvador,El Salvador
+Cambodia(Kampuchea),Cambodia

main.py

@@ -5,9 +5,9 @@
 loc_homicides = "homicides.csv"
 loc_ged = "GED_cleaned.csv"
 loc_conflict_all = "ucdp-prio-acd-221.csv"
-loc_concordance = "Country_names.csv"
-loc_population = "CV_population.csv"
 
+loc_population = "CV_population.csv"
+loc_brd = "BRD.csv"
 
 def avg_years(values):
     try:
@@ -23,7 +23,8 @@ def avg_years(values):
         print([type(x) for x in values])
         sys.exit("Error in averaging the homicide rates for the following series:")
 
-def prep():
+def prepGED():
+    loc_concordance = "Countries_GED.csv"
     # Read datasets
     homicides = pd.read_csv(loc_homicides)
     homicides.drop(columns=homicides.columns[0], axis=1, inplace=True)
@@ -125,7 +126,7 @@ def prep():
         cc_ivdv.loc[i, "CV_pop"] = df_population.loc[df_population["Country Name"] == country, str(year)].values[0]
 
     # Output "dirty" dataset
-    cc_ivdv.to_csv("output_dirty.csv")
+    cc_ivdv.to_csv("output_GED_dirty.csv")
 
     # Determine SD for important variables, set outliers to none (beyond 2SD)
     for variable in ["HR_rel_change", "CV_global_homicides"]:
@@ -140,14 +141,164 @@ def prep():
                    inplace=True)
 
     # Output of csv for analysis
-    cc_ivdv.to_csv("output.csv")
+    cc_ivdv.to_csv("output_GED.csv")
+
+    return 0
+
+
+def prepBRD():
+    loc_concordance = "Countries_BRD.csv"
+    # Read datasets
+    df_brd = pd.read_csv(loc_brd)[["conflict_id", "year", "battle_location", "bd_best"]]
+    homicides = pd.read_csv(loc_homicides)
+    homicides.drop(columns=homicides.columns[0], axis=1, inplace=True)
+    conflict_all = pd.read_csv(loc_conflict_all)
+    conflict_new = conflict_all[["conflict_id", "start_date2", "ep_end", "ep_end_date", "year"]]
+    df_population = pd.read_csv(loc_population, sep=';', header=1)
+
+    # Create country dict
+    country_df = pd.read_csv(loc_concordance)
+    country_dict = dict(zip(list(country_df["BRD"]), list(country_df["homicides"])))
+
+    # Create country set
+    BRD_countries = []
+    for i in df_brd.index:
+        BRD_countries += [x for x in df_brd.loc[i, "battle_location"].replace(' ', '').split(',')]
+    BRD_countries = set(BRD_countries)
+    for c in BRD_countries:
+        if c in country_dict.values():
+            BRD_countries.remove(c)
+            BRD_countries.add(BRD_countries[c])
+
+    # Create dummy variables per country
+    for c in BRD_countries:
+        df_brd[c] = 0
+
+    # Assign values to the dummy variables
+    for i in df_brd.index:
+        for c in df_brd.loc[i, "battle_location"].replace(' ', '').split(','):
+            if c in BRD_countries:
+                df_brd.loc[i, c] = 1
+            else:
+                df_brd.loc[i, country_dict[c]] = 1
+    df_brd.drop(columns=["battle_location"], inplace=True)
+
+    # Mark the rows of conflicts that are over
+    df = conflict_new.loc[:, ["conflict_id", "start_date2", "ep_end"]].groupby(["conflict_id", "start_date2"]).sum()
+    df.reset_index(inplace=True)
+    df.rename(columns={"ep_end": "has_ended"}, inplace=True)
+    conflict_new = conflict_new.merge(df, left_on=["conflict_id", "start_date2"],
+                                      right_on=["conflict_id", "start_date2"])
+
+    # Remove presently ongoing conflicts
+    conflict_new = conflict_new[conflict_new["has_ended"] == 1]
+
+    # Fill end date for all conflict-years, add start and end year as well as duration
+    conflict_new["ep_end_date"].fillna(method="bfill", inplace=True)
+    conflict_new["start_year"] = conflict_new["start_date2"].str[:4].astype(int)
+    conflict_new["end_year"] = conflict_new["ep_end_date"].str[:4].astype(int)
+    conflict_new["duration"] = conflict_new["end_year"].astype(int) - conflict_new["start_year"].astype(int) + 1
+
+    # Add up deaths per CCY triad
+    ccy_merge = df_brd.loc[:, :].groupby(
+        by=set(df_brd.columns).remove("bd_best")).sum()
 
     return 0
 
+    ccy_merge.reset_index(inplace=True)
+
+    ccy_merge.rename(columns={"conflict_new_id": "conflict_id"}, inplace=True)
+    ccy_complete = ccy_merge.merge(conflict_new, left_on=["conflict_id", "year"], right_on=["conflict_id", "year"])
+
+    # Compact CCY into CC, removing the one line per year attribute
+    cc_iv = ccy_complete.loc[:, ["country", "conflict_id", "start_year", "end_year", "duration", "best"]].groupby(
+        by=["country", "conflict_id", "start_year", "end_year", "duration"]).sum().reset_index()
+    cc_iv["avg_deaths"] = cc_iv["best"] / cc_iv["duration"]
+
+    # Remove conflicts that started before 1965 or only just ended
+    cc_iv.drop(cc_iv[cc_iv.start_year < 1965].index, inplace=True)
+    cc_iv.drop(cc_iv[cc_iv.end_year > 2020].index, inplace=True)
+
+    # Harmonize country names
+    country_df = pd.read_csv(loc_concordance, sep=';')
+    country_dict = dict(zip(list(country_df["cc_iv"]), list(country_df["homicides"])))
+    cc_iv.replace({"country": country_dict}, inplace=True)
+
+    # Create DV columns
+    cc_iv["HR_before"] = None
+    cc_iv["HR_after"] = None
+
+    # Insert values for DV columns
+    for i in cc_iv.index:  # cc_iv.shape[0]
+        country = cc_iv.loc[i, "country"]
+        start_year = int(cc_iv.loc[i, "start_year"])
+        end_year = int(cc_iv.loc[i, "end_year"])
+
+        homicides_row = homicides.loc[homicides["Country Name"] == country, :]
+        rates_before = [homicides_row.loc[:, str(y)].values[0] for y in range(start_year - 5, start_year)]
+        rates_after = [homicides_row.loc[:, str(y)].values[0] for y in range(end_year + 1, min(end_year + 6, 2022))]
+
+        avg_before = avg_years(rates_before)
+        avg_after = avg_years(rates_after)
+
+        cc_iv.loc[i, "HR_before"] = avg_before
+        cc_iv.loc[i, "HR_after"] = avg_after
+
+    # Ensure numeric format
+    cc_iv["HR_before"] = cc_iv["HR_before"].astype(float)
+    cc_iv["HR_after"] = cc_iv["HR_after"].astype(float)
+
+    # Drop country-episodes with 0 deaths
+    cc_iv.drop(cc_iv[cc_iv.best == 0].index, inplace=True)
+
+    # Add DV as ration of HR_after and HR_before
+    cc_ivdv = cc_iv
+    for i in cc_ivdv.index:
+        if cc_ivdv.loc[i, "HR_after"] and cc_ivdv.loc[i, "HR_before"]:
+            cc_ivdv.loc[i, "HR_rel_change"] = cc_ivdv.loc[i, "HR_after"] / cc_ivdv.loc[i, "HR_before"]
+        else:
+            cc_ivdv.loc[i, "HR_rel_change"] = None
+
+    # Add CV: global homicide rate (starting 2000)
+    cc_ivdv["CV_global_homicides"] = None
+    homicides_world = homicides.loc[homicides["Country Name"] == "World", :]
+    for i in cc_ivdv.index:
+        year = cc_ivdv.loc[i, "end_year"]
+        homicides = homicides_world.loc[:, str(year)].values[0]
+        if isinstance(homicides, str):
+            homicides = float(homicides.replace(',', '.'))
+        cc_ivdv.loc[i, "CV_global_homicides"] = homicides
+
+    # Add CV: country population at conflict end
+    cc_ivdv["CV_pop"] = None
+    for i in cc_ivdv.index:
+        year = cc_ivdv.loc[i, "end_year"]
+        country = cc_ivdv.loc[i, "country"]
+        cc_ivdv.loc[i, "CV_pop"] = df_population.loc[df_population["Country Name"] == country, str(year)].values[0]
+
+    # Output "dirty" dataset
+    cc_ivdv.to_csv("output_GED_dirty.csv")
+
+    # Determine SD for important variables, set outliers to none (beyond 2SD)
+    for variable in ["HR_rel_change", "CV_global_homicides"]:
+        mean = cc_ivdv[variable].mean()
+        sd = cc_ivdv[variable].std()
+        for i in cc_ivdv.index:
+            if abs(cc_ivdv.loc[i, variable] - mean) > 2 * sd:
+                cc_ivdv.loc[i, variable] = None
+
+    # Drop rows that have missing values
+    cc_ivdv.dropna(subset=["avg_deaths", "HR_before", "HR_after", "HR_rel_change", "CV_pop", "CV_global_homicides"],
+                   inplace=True)
+
+    # Output of csv for analysis
+    cc_ivdv.to_csv("output_GED.csv")
+
+    return 0
 
 def analyse():
-    dirty_data = pd.read_csv("output_dirty.csv")
-    dataset = pd.read_csv("output.csv")
+    dirty_data = pd.read_csv("output_GED_dirty.csv")
+    dataset = pd.read_csv("output_GED.csv")
 
     dirty_data.describe().to_csv("dirty_descriptive_stats.csv")
     dataset.describe().to_csv("descriptive_stats.csv")
@@ -156,8 +307,8 @@ def analyse():
 
 
 def main():
-    prep()
-    analyse()
+    prepBRD()
+    # analyse()
     return 0