server

server-rs/src/routes/actual_listings.rs

@@ -55,6 +55,16 @@ pub async fn get_actual_listings(
 ) -> Result<Json<ActualListingsResponse>, Response> {
     let state = shared.load_state();
     let offset = params.offset.unwrap_or(0);
+
+    // Gate the entire feature behind the per-user `can_see_listings` flag. The
+    // flag is off by default for everyone, so listings are invisible unless a
+    // superuser has explicitly granted access to this account.
+    if !user.0.as_ref().is_some_and(|u| u.can_see_listings) {
+        return Err(
+            ApiError::Forbidden("You do not have access to listings".to_string()).into_response(),
+        );
+    }
+
     let Some(actual_listings) = state.actual_listings.clone() else {
         return Ok(Json(ActualListingsResponse {
             listings: Vec::new(),

server-rs/src/routes/ai_filters.rs

@@ -359,9 +359,9 @@ pub fn build_system_prompt(
            or \"max\" (at most this value). Never set two filters on the same feature.\n\
          - Use EXACT feature names from the list — spelling, capitalisation, and punctuation must match.\n\
          - \"cheap\" / \"affordable\" = lower price range. \"expensive\" = higher price range.\n\
-         - \"low crime\" / \"safe\" = low values on the Serious crime and Minor crime features. \
-           Prefer the per-1k resident crime features for broad area safety; use specific crime \
-           features only when the user names a crime type.\n\
+         - \"low crime\" / \"safe\" = low values on the Serious crime (avg/yr) and Minor crime (avg/yr) \
+           features (incidents counted within 50m of the postcode). Prefer these aggregates for broad \
+           area safety; use specific crime features only when the user names a crime type.\n\
          - \"quiet\" = low Noise (dB). \"green\" / \"near parks\" = high Number of amenities (Park) within 2km \
            or low Distance to nearest park (km), depending on wording.\n\
          - \"good schools\" = Good+ school features. \"outstanding schools\" = Outstanding school features.\n\
@@ -505,8 +505,8 @@ pub fn build_system_prompt(
     parts.push(
         "\nUser: \"safe quiet area with good schools and parks\"\n\
          Output: {\"numeric_filters\": [\
-         {\"name\": \"Serious crime per 1k residents (avg/yr)\", \"bound\": \"max\", \"value\": 20}, \
-         {\"name\": \"Minor crime per 1k residents (avg/yr)\", \"bound\": \"max\", \"value\": 50}, \
+         {\"name\": \"Serious crime (avg/yr)\", \"bound\": \"max\", \"value\": 5}, \
+         {\"name\": \"Minor crime (avg/yr)\", \"bound\": \"max\", \"value\": 20}, \
          {\"name\": \"Noise (dB)\", \"bound\": \"max\", \"value\": 55}, \
          {\"name\": \"Good+ primary schools within 2km\", \"bound\": \"min\", \"value\": 2}, \
          {\"name\": \"Good+ secondary schools within 2km\", \"bound\": \"min\", \"value\": 1}, \

server-rs/src/routes/overlays.rs

@@ -13,6 +13,7 @@ pub enum OverlayTileFormat {
     VectorMvtGzip,
     RasterPng,
     RasterJpeg,
+    RasterWebp,
 }
 
 impl OverlayTileFormat {
@@ -21,6 +22,7 @@ impl OverlayTileFormat {
             Self::VectorMvtGzip => "application/x-protobuf",
             Self::RasterPng => "image/png",
             Self::RasterJpeg => "image/jpeg",
+            Self::RasterWebp => "image/webp",
         }
     }
 
@@ -30,15 +32,11 @@ impl OverlayTileFormat {
 }
 
 pub async fn get_overlay_tile(
-    reader: Option<Arc<TileReader>>,
+    reader: Arc<TileReader>,
     format: OverlayTileFormat,
     overlay_name: &'static str,
     Path((zoom, col, row)): Path<(u8, u32, u32)>,
 ) -> Response {
-    let Some(reader) = reader else {
-        return StatusCode::NOT_FOUND.into_response();
-    };
-
     let tile_coord = match TileCoord::new(zoom, col, row) {
         Ok(tile_coord) => tile_coord,
         Err(err) => {

server-rs/src/routes/places.rs

@@ -227,6 +227,7 @@ pub async fn get_places(
                 .postcodes
                 .iter()
                 .filter(|postcode| postcode_starts_with_compact(postcode, &compact_query))
+                .filter(|postcode| !property_data.rows_for_postcode(postcode).is_empty())
                 .take(limit)
                 .cloned()
                 .collect()

server-rs/src/routes/properties.rs

@@ -123,7 +123,15 @@ fn insert_feature_value(
         return;
     }
 
-    let value = state.data.get_feature(row, feat_idx);
+    // `get_feature` decodes the lossy u16-quantized value, which turns round
+    // sale prices into noise (e.g. £428,000 → £427,984). For the last sale we
+    // keep an unquantized copy, so serve that instead to match the exact
+    // `historical_prices` entries and the price-history chart.
+    let value = if feature_names[feat_idx] == "Last known price" {
+        state.data.last_known_price_raw(row)
+    } else {
+        state.data.get_feature(row, feat_idx)
+    };
     if value.is_finite() {
         features.insert(feature_names[feat_idx].clone(), value);
     }

server-rs/src/routes/stats.rs

@@ -257,10 +257,11 @@ pub fn compute_feature_stats(
 
 /// Compute property-weighted per-year crime means across the selection.
 ///
-/// Each matching property contributes its LSOA's per-year counts; this is the
-/// same property-weighted-LSOA-average shape used elsewhere in the right pane.
-/// LSOAs with no series for a given crime type contribute 0 for that type
-/// (matching how the existing `(avg/yr)` columns treat missing crime types).
+/// Each matching property contributes its postcode's per-year counts (incidents
+/// within 50m of that postcode); this is the same property-weighted-average
+/// shape used elsewhere in the right pane. Postcodes with no series for a given
+/// crime type contribute 0 for that type (matching how the `(avg/yr)` columns
+/// treat missing crime types).
 pub fn compute_crime_by_year(
     matching_rows: &[usize],
     data: &PropertyData,
@@ -273,19 +274,19 @@ pub fn compute_crime_by_year(
     }
 
     // For each crime type, accumulate per-year sums and the count of rows whose
-    // LSOA exists in the crime side table.
+    // postcode exists in the crime side table.
     let num_types = crime_by_year.crime_types.len();
     let mut per_type_year_sums: Vec<FxHashMap<i32, f64>> =
         (0..num_types).map(|_| FxHashMap::default()).collect();
     let mut per_type_row_counts: Vec<u32> = vec![0; num_types];
 
     for &row in matching_rows {
-        let lsoa = data.lsoa(row);
-        let Some(series_list) = crime_by_year.series_by_lsoa.get(lsoa) else {
+        let postcode = data.postcode(row);
+        let Some(series_list) = crime_by_year.series_by_postcode.get(postcode) else {
             continue;
         };
 
-        // For every type the LSOA reports, add its per-year counts.
+        // For every type the postcode reports, add its per-year counts.
         // For types it doesn't report, treat the row as contributing 0 — so we
         // bump the row count for *every* known type below.
         for series in series_list {