Added proportionality to age

app/app.py

@@ -111,7 +111,7 @@ with ui.layout_columns(col_widths=[8, 4]):
                 station_coord = (y,x)
 
                 # Fetch age bracket data from the Data_provider instance
-                age_data = initBackend.get_station_score(station_coord)['age_data']  # Assume this returns a dictionary or DataFrame
+                age_data = initBackend.get_station_score(station_coord, radius=input.rad())['age_data']  # Assume this returns a dictionary or DataFrame
                     
                 # Example structure: {'0-4 ára': 120, '5-9 ára': 140, ...}
                 age_brackets = list(age_data.keys())

app/data_processing/get_station_coverage.py

@@ -38,13 +38,17 @@ def get_station_coverage(
             intersection = geometry.intersection(station_buffer)
             intersection_area = intersection.area
 
-            # Calculate the percentage of the buffer covered by this area
+            # Calculate the percentage of the radius covered by this area
             coverage_percentage = (intersection_area / buffer_area) * 100
 
+            # Calculate the percentage of the area covered by the radius
+            small_zone_percentage = (intersection_area / geometry.area) * 100
+
             # Add the area and coverage percentage to the result
             covered_areas.append({
                 "id": area["id"],  # Include the area's ID or relevant metadata
-                "coverage_percentage": coverage_percentage
+                "coverage_percentage": coverage_percentage,
+                "small_zone_percentage": small_zone_percentage
             })
 
     return covered_areas

app/data_processing/point_scoring.py

@@ -39,14 +39,24 @@ def score_current(station_coord, df_features, cov_smsv, w_density, w_income, w_a
     income_score = 0
     density_score = 0
     age_score = 0
-    for smsv in cov_smsv:
-        
+    aggregated_age_distribution = {}
 
+    for smsv in cov_smsv:
         smsv_info = df_features[df_features["smallAreaId"] == smsv["id"]]
 
+        # Get age distribution for the year 2024
         age_dist = smsv_info["age_distribution"].iloc[0].get(2024, {})  # only interested in 2024 for current score
-        # print(data_2024)
-        age_score = get_age_score(age_dist) * w_age
+        
+        # Aggregate proportional age distribution
+        for age_group, population in age_dist.items():
+            proportion = population * (smsv["small_zone_percentage"]/100)
+            if age_group in aggregated_age_distribution:
+                aggregated_age_distribution[age_group] += proportion
+            else:
+                aggregated_age_distribution[age_group] = proportion
+
+        
+
 
         income_dist = smsv_info["income_distribution_per_year"].iloc[0].get(2024, {})  # only interested in 2024 for current score
         # print(data_2024)
@@ -54,9 +64,13 @@ def score_current(station_coord, df_features, cov_smsv, w_density, w_income, w_a
 
         density_score = smsv_info["density"].iloc[0] * w_density
         total_score += (age_score + income_score + density_score) * smsv["coverage_percentage"] # TODO: Area of the cricle * percent covered / total area of the small area
-    return {"total_score": total_score, "income_score": income_score, "age_score": age_score, "density_score": density_score, "age_data": age_dist}
 
-def get_age_score(age_distribution):
+    # Calculate age score
+    age_score = get_age_score(aggregated_age_distribution) * w_age
+    total_score += age_score
+    return {"total_score": total_score, "income_score": income_score, "age_score": age_score, "density_score": density_score, "age_data": aggregated_age_distribution}
+
+def get_age_score(proportional_age_distribution):
     """
     Calculate a score based on age distribution.
 
@@ -94,10 +108,10 @@ def get_age_score(age_distribution):
 }
 
     # Calculate the weighted sum of the age distribution
-    weighted_sum = sum(age_distribution.get(age, 0) * weight for age, weight in age_weights.items())
+    weighted_sum = sum(proportional_age_distribution.get(age, 0) * weight for age, weight in age_weights.items())
 
     # Normalize the score by the total population
-    total_population = sum(age_distribution.values())
+    total_population = sum(proportional_age_distribution.values())
     if total_population == 0:
         return 0