Add POIs and journey times to map

server/routes/hexagons.py

@@ -77,14 +77,28 @@ def query_hexagons_cached(
     # Filter by year range
     df = df.filter((pl.col("year") >= min_year) & (pl.col("year") <= max_year))
 
+    # Check which journey time columns exist
+    journey_cols = [
+        "median_journey_minutes",
+        "median_pt_easy_minutes",
+        "median_pt_quick_minutes",
+        "median_cycling_minutes",
+    ]
+    available_journey_cols = [c for c in journey_cols if c in df.columns]
+
     # Aggregate across years (weighted by count)
-    df = df.group_by("h3").agg(
+    agg_exprs = [
         pl.col("count").sum().alias("count"),
         (pl.col("avg_price") * pl.col("count")).sum().alias("weighted_price_sum"),
         pl.col("median_price").median().alias("median_price"),
         pl.col("min_price").min().alias("min_price"),
         pl.col("max_price").max().alias("max_price"),
-    )
+    ]
+    for jc in available_journey_cols:
+        # Journey time is same across years, just take first non-null
+        agg_exprs.append(pl.col(jc).first())
+
+    df = df.group_by("h3").agg(agg_exprs)
 
     # Calculate weighted average price
     df = df.with_columns(
@@ -97,16 +111,18 @@ def query_hexagons_cached(
     )
 
     # Build response efficiently using Polars
-    df = df.select(
-        [
-            pl.col("h3"),
-            pl.col("count"),
-            pl.col("avg_price").round(2),
-            pl.col("median_price").round(2),
-            pl.col("min_price"),
-            pl.col("max_price"),
-        ]
-    )
+    select_cols = [
+        pl.col("h3"),
+        pl.col("count"),
+        pl.col("avg_price").round(2),
+        pl.col("median_price").round(2),
+        pl.col("min_price"),
+        pl.col("max_price"),
+    ]
+    for jc in available_journey_cols:
+        select_cols.append(pl.col(jc).round(0))
+
+    df = df.select(select_cols)
 
     return df.to_dicts()