diff --git a/examples/geospatial-python-urban-analysis-with-postgis/data/countries/ne_10m_admin_0_countries.README.html b/examples/geospatial-python-urban-analysis-with-postgis/data/countries/ne_10m_admin_0_countries.README.html
index 07fde25..715a75d 100644
--- a/examples/geospatial-python-urban-analysis-with-postgis/data/countries/ne_10m_admin_0_countries.README.html
+++ b/examples/geospatial-python-urban-analysis-with-postgis/data/countries/ne_10m_admin_0_countries.README.html
@@ -99,7 +99,7 @@
 <div id="page">
 <div id="header">
 	<div id="headerimg">		
-        <h1><a href="https://www.naturalearthdata.com/"><img src="https://www.naturalearthdata.com/wp-content/themes/NEV/images/nev_logo.png" alt="Natural Earth title="Natural Earth" /></a></h1> 
+        <h1><a href="https://www.naturalearthdata.com/"><img src="https://www.naturalearthdata.com/wp-content/themes/NEV/images/nev_logo.png" alt="Natural Earth" title="Natural Earth" /></a></h1> 
         <div class="description">Free vector and raster map data at 1:10m, 1:50m, and 1:110m scales</div> 
         <div class="header_search"><form method="get" id="searchform" action="https://www.naturalearthdata.com/">
 <label class="hidden" for="s">Search for:</label>
diff --git a/examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py b/examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py
index 82d9541..11fbf98 100644
--- a/examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py
+++ b/examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py
@@ -4,6 +4,8 @@
 
 def spatial_join(bus_stops: gpd.GeoDataFrame, comunas: gpd.GeoDataFrame):
     """Join bus stops with districts based on spatial location."""
+    if bus_stops.crs != comunas.crs:
+        raise ValueError(f"CRS mismatch: bus_stops CRS is {bus_stops.crs}, comunas CRS is {comunas.crs}")
     return gpd.sjoin(bus_stops, comunas, how="inner", predicate="within")
 
 def count_stops_per_district(bus_stops_in_district: gpd.GeoDataFrame):