rename other to supporting

scripts/bigquery_analytical_layer.py

@@ -14,9 +14,9 @@ Filtering Order:
    → Indikator TIDAK di SDG_ONLY_KEYWORDS           → 'MDGs' selalu
    → Indikator DI SDG_ONLY_KEYWORDS + year >= SDG_TRANSITION_YEAR → 'SDGs'
    → Indikator DI SDG_ONLY_KEYWORDS + year <  SDG_TRANSITION_YEAR → 'MDGs'
-   → SDG_TRANSITION_YEAR = 2016 (HARDCODE — tanggal resmi SDGs berlaku)
+   → SDG_TRANSITION_YEAR = 2015 (HARDCODE — tanggal resmi SDGs berlaku)
      BUKAN dari actual_start_year data, karena data anaemia/FIES bisa ada
-     sebelum 2016 namun tetap harus dilabeli MDGs pada tahun-tahun tersebut.
+     sebelum 2015 namun tetap harus dilabeli MDGs pada tahun-tahun tersebut.
 7. Verify no gaps (dari actual_start_year per indikator, bukan start_year global)
 8. Calculate norm_value_1_100 per indicator (min-max, direction-aware, global)
 9. Calculate YoY per indicator per country
@@ -24,7 +24,7 @@ Filtering Order:
 11. Save analytical table
 
 FRAMEWORK LOGIC:
-- SDG_TRANSITION_YEAR = 2016 (HARDCODE, bukan auto-detect dari data)
+- SDG_TRANSITION_YEAR = 2015 (HARDCODE, bukan auto-detect dari data)
 - Semua SDG-only indicators menggunakan SDG_TRANSITION_YEAR yang SAMA
   sehingga label berubah serentak di satu titik waktu
 - SDG-only + year <  SDG_TRANSITION_YEAR → 'MDGs' (data tetap ada, tidak dihapus)
@@ -32,8 +32,8 @@ FRAMEWORK LOGIC:
 - Non-SDG-only indicators                → 'MDGs' selalu (di semua tahun)
 
 ALASAN HARDCODE:
-- SDGs resmi diadopsi PBB pada 25 September 2015 dan mulai berlaku 1 Januari 2016
+- SDGs resmi diadopsi PBB pada 25 September 2015 dan mulai berlaku 1 Januari 2015
-- Indikator FIES dan anaemia punya data sebelum 2016 (dari MDGs era)
+- Indikator FIES dan anaemia punya data sebelum 2015 (dari MDGs era)
 - Jika sdg_transition_year di-auto-detect dari min(actual_start_year),
   maka akan = 2013 (karena data ada sejak 2013), sehingga semua tahun
   berlabel SDGs — yang secara historis tidak tepat.
@@ -66,27 +66,44 @@ from google.cloud import bigquery
 # SDG-ONLY INDICATOR KEYWORDS
 # =============================================================================
 # Hanya indikator yang MURNI BARU di era SDGs yang didaftarkan di sini.
-# Indikator di set ini → 'SDGs' mulai dari SDG_TRANSITION_YEAR (2016).
+# Indikator di set ini → 'SDGs' mulai dari SDG_TRANSITION_YEAR (2015).
 # Semua indikator lain (shared maupun tidak dikenal) → 'MDGs' di semua tahun.
 
 SDG_ONLY_KEYWORDS = frozenset([
-    # TARGET 2.1.1 
+    # TARGET 2.1.1 — Undernourishment
     "prevalence of undernourishment (percent) (3-year average)",
     "number of people undernourished (million) (3-year average)",
-    # TARGET 2.1.2 — FIES (SDGs only)
+
+    # TARGET 2.1.2 — Food Insecurity (FIES)
     "prevalence of severe food insecurity in the total population (percent) (3-year average)",
     "prevalence of severe food insecurity in the male adult population (percent) (3-year average)",
     "prevalence of severe food insecurity in the female adult population (percent) (3-year average)",
+
     "prevalence of moderate or severe food insecurity in the total population (percent) (3-year average)",
     "prevalence of moderate or severe food insecurity in the male adult population (percent) (3-year average)",
     "prevalence of moderate or severe food insecurity in the female adult population (percent) (3-year average)",
+
     "number of severely food insecure people (million) (3-year average)",
     "number of severely food insecure male adults (million) (3-year average)",
     "number of severely food insecure female adults (million) (3-year average)",
+
     "number of moderately or severely food insecure people (million) (3-year average)",
     "number of moderately or severely food insecure male adults (million) (3-year average)",
     "number of moderately or severely food insecure female adults (million) (3-year average)",
-    # TARGET 2.2.3 — Anaemia (SDGs only)
+
+    # TARGET 2.2.1 — Stunting
+    "percentage of children under 5 years of age who are stunted (modelled estimates) (percent)",
+    "number of children under 5 years of age who are stunted (modeled estimates) (million)",
+
+    # TARGET 2.2.2 — Wasting
+    "percentage of children under 5 years affected by wasting (percent)",
+    "number of children under 5 years affected by wasting (million)",
+
+    # TARGET 2.2.2 — Overweight (children)
+    "percentage of children under 5 years of age who are overweight (modelled estimates) (percent)",
+    "number of children under 5 years of age who are overweight (modeled estimates) (million)",
+
+    # TARGET 2.2.3 — Anaemia
     "prevalence of anemia among women of reproductive age (15-49 years) (percent)",
     "number of women of reproductive age (15-49 years) affected by anemia (million)",
 ])
@@ -94,11 +111,9 @@ SDG_ONLY_KEYWORDS = frozenset([
 # =============================================================================
 # SDG TRANSITION YEAR — HARDCODE
 # =============================================================================
-# SDGs resmi berlaku mulai 1 Januari 2016 (diadopsi PBB 25 September 2015).
+# SDGs resmi berlaku mulai 1 Januari 2015 (diadopsi PBB 25 September 2015).
-# Nilai ini TIDAK boleh dihitung dari data karena indikator FIES/anaemia
-# punya data historis sebelum 2016 yang harus tetap dilabeli 'MDGs'.
 
-SDG_TRANSITION_YEAR = 2016
+SDG_TRANSITION_YEAR = 2015
 
 # =============================================================================
 # THRESHOLD KONDISI (fixed absolute, skala 1-100)
@@ -139,11 +154,11 @@ class AnalyticalLayerLoader:
         yoy_change, yoy_pct
 
     FRAMEWORK LOGIC:
-    - SDG_TRANSITION_YEAR = 2016 (HARDCODE — tanggal resmi SDGs berlaku)
+    - SDG_TRANSITION_YEAR = 2015 (HARDCODE — tanggal resmi SDGs berlaku)
     - Indikator TIDAK di SDG_ONLY_KEYWORDS → 'MDGs' di SEMUA tahun
     - Indikator DI SDG_ONLY_KEYWORDS:
-        year <  SDG_TRANSITION_YEAR (2016) → 'MDGs'  (data tetap ada, tidak dihapus)
+        year <  SDG_TRANSITION_YEAR (2015) → 'MDGs'  (data tetap ada, tidak dihapus)
-        year >= SDG_TRANSITION_YEAR (2016) → 'SDGs'
+        year >= SDG_TRANSITION_YEAR (2015) → 'SDGs'
     """
 
     def __init__(self, client: bigquery.Client):
@@ -163,7 +178,7 @@ class AnalyticalLayerLoader:
         self.end_year      = None
         self.baseline_year = 2023
 
-        # SDG_TRANSITION_YEAR diambil dari konstanta modul (HARDCODE = 2016)
+        # SDG_TRANSITION_YEAR diambil dari konstanta modul (HARDCODE = 2015)
         self.sdg_transition_year = SDG_TRANSITION_YEAR
 
         self.pipeline_metadata = {
@@ -510,8 +525,8 @@ class AnalyticalLayerLoader:
         self.logger.info("=" * 80)
 
         # ----------------------------------------------------------------
-        # SDG_TRANSITION_YEAR = 2016 (HARDCODE)
+        # SDG_TRANSITION_YEAR = 2015 (HARDCODE)
-        # SDGs diadopsi PBB 25 September 2015, berlaku 1 Januari 2016.
+        # SDGs diadopsi PBB 25 September 2015, berlaku 1 Januari 2015.
         #
         # PENTING — TIDAK dihitung dari data:
         #   Jika auto-detect dari min(actual_start_year SDG-only indicators),
@@ -520,7 +535,7 @@ class AnalyticalLayerLoader:
         #   Ini secara historis salah karena SDGs belum berlaku di 2013-2015.
         # ----------------------------------------------------------------
         self.logger.info(f"\n  SDG_TRANSITION_YEAR : {self.sdg_transition_year} (HARDCODE)")
-        self.logger.info(f"  Alasan              : SDGs resmi berlaku 1 Januari 2016")
+        self.logger.info(f"  Alasan              : SDGs resmi berlaku 1 Januari 2015")
         self.logger.info(f"  Bukan auto-detect   : data FIES/anaemia ada sejak 2013,")
         self.logger.info(f"                        tapi tahun 2013-2015 harus tetap MDGs")
 
@@ -573,7 +588,7 @@ class AnalyticalLayerLoader:
         #
         # Hasilnya dalam 1 indikator SDG-only (misal anaemia, data mulai 2013):
         #   2013, 2014, 2015 → 'MDGs'  (data tetap ada)
-        #   2016, 2017, ...  → 'SDGs'
+        #   2015, 2017, ...  → 'SDGs'
         # ----------------------------------------------------------------
         self.df_clean['_is_sdg_only'] = self.df_clean['indicator_id'].isin(sdg_only_ids)
 
@@ -1002,7 +1017,7 @@ class AnalyticalLayerLoader:
                     'end_year'               : self.end_year,
                     'baseline_year'          : self.baseline_year,
                     'sdg_transition_year'    : self.sdg_transition_year,
-                    'sdg_transition_source'  : 'HARDCODE — SDGs resmi berlaku 1 Jan 2016',
+                    'sdg_transition_source'  : 'HARDCODE — SDGs resmi berlaku 1 Jan 2015',
                     'fixed_countries'        : len(self.selected_country_ids),
                     'norm_scale'             : '1-100 per indicator global minmax direction-aware',
                     'framework_logic'        : (
@@ -1048,7 +1063,7 @@ class AnalyticalLayerLoader:
         self.logger.info(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
         self.logger.info(
             f"Framework: SDG_TRANSITION_YEAR={SDG_TRANSITION_YEAR} (HARDCODE). "
-            "SDG-only + year >= 2016 → SDGs; sebelumnya MDGs. Non-SDG-only → MDGs selalu."
+            "SDG-only + year >= 2015 → SDGs; sebelumnya MDGs. Non-SDG-only → MDGs selalu."
         )
         self.logger.info("=" * 80)
 
@@ -1102,7 +1117,7 @@ if __name__ == "__main__":
     print(f"Condition threshold: bad<{THRESHOLD_BAD}, good>{THRESHOLD_GOOD}")
     print(
         f"Framework: SDG_TRANSITION_YEAR={SDG_TRANSITION_YEAR} (HARDCODE). "
-        "SDG-only + year >= 2016 → SDGs; sebelumnya MDGs. Non-SDG-only → MDGs selalu."
+        "SDG-only + year >= 2015 → SDGs; sebelumnya MDGs. Non-SDG-only → MDGs selalu."
     )
     print("=" * 80)
 

scripts/bigquery_cleaned_layer.py

@@ -176,16 +176,16 @@ def standardize_country_names_asean(df: pd.DataFrame, country_column: str = 'cou
 def assign_pillar(indicator_name: str) -> str:
     """
     Assign pillar berdasarkan keyword indikator.
-    Return values: 'Availability', 'Access', 'Utilization', 'Stability', 'Other'
+    Return values: 'Availability', 'Access', 'Utilization', 'Stability', 'Supporting'
     All <= 20 chars (varchar(20) constraint).
     """
     if pd.isna(indicator_name):
-        return 'Other'
+        return 'Supporting'
     ind = str(indicator_name).lower()
 
     for kw in ['requirement', 'coefficient', 'losses', 'fat supply']:
         if kw in ind:
-            return 'Other'
+            return 'Supporting'
 
     if any(kw in ind for kw in [
         'adequacy', 'protein supply', 'supply of protein',
@@ -215,7 +215,7 @@ def assign_pillar(indicator_name: str) -> str:
     ]):
         return 'Utilization'
 
-    return 'Other'
+    return 'Supporting'
 
 
 # =============================================================================

scripts/bigquery_dimensional_model.py

@@ -374,7 +374,7 @@ class DimensionalModelLoader:
                     ]):
                         return 'Infrastructure'
                     else:
-                        return 'Other'
+                        return 'Supporting'
                 dim_indicator['indicator_category'] = dim_indicator['indicator_name'].apply(
                     categorize_indicator
                 )
@@ -503,10 +503,10 @@ class DimensionalModelLoader:
         try:
             pillar_codes = {
                 'Availability': 'AVL', 'Access'  : 'ACC',
-                'Utilization' : 'UTL', 'Stability': 'STB', 'Other': 'OTH',
+                'Utilization' : 'UTL', 'Stability': 'STB', 'Supporting': 'SPT',
             }
             pillars_data = [
-                {'pillar_name': p, 'pillar_code': pillar_codes.get(p, 'OTH')}
+                {'pillar_name': p, 'pillar_code': pillar_codes.get(p, 'SPT')}
                 for p in self.df_clean['pillar'].unique()
             ]