diff --git a/spatial-data-science/scripts/build/lib/heatri/__init__.py b/spatial-data-science/scripts/build/lib/heatri/__init__.py
deleted file mode 100644
index e784f62..0000000
--- a/spatial-data-science/scripts/build/lib/heatri/__init__.py
+++ /dev/null
@@ -1,58 +0,0 @@
-
-from .heat_risk_index import hri_main
-from arcgis.geometry import Envelope
-from arcpy import Extent as PyExtent, SpatialReference as PySpatialReference
-import json
-import os
-
-
-
-def calculate_heat_risk_index_using_extent(extent: Envelope) -> str:
-    """
-    Calculates the heat risk index for a defined geographic region.
-
-    :return: The local path to the calculated heat risk index spatial bin features.
-    """
-
-    landsat_surf_temp = "https://landsat2.arcgis.com/arcgis/rest/services/Landsat/MS/ImageServer"
-    land_cover = "https://tiledimageservices.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/European_Space_Agency_WorldCover_2021_Land_Cover_WGS84_7/ImageServer"
-    zensus_2022 = "https://services2.arcgis.com/jUpNdisbWqRpMo35/arcgis/rest/services/Zensus2022_grid_final/FeatureServer/0"
-
-    if not 'spatialReference' in extent:
-        raise ValueError('The extent has no spatial reference!')
-    
-    extent_spatial_reference = extent['spatialReference']
-    if not 'wkid' in extent_spatial_reference:
-        raise ValueError('The extent has no wkid!')
-    
-    wkid = extent_spatial_reference['wkid']
-    spatial_reference = PySpatialReference(wkid)
-    extent = PyExtent(XMin=extent['xmin'], YMin=extent['ymin'], XMax=extent['xmax'], YMax=extent['ymax'], spatial_reference=spatial_reference)
-    hri_spatial_bins = hri_main(landsat_surf_temp, land_cover, zensus_2022, extent, spatial_reference)
-    return hri_spatial_bins
-
-def calculate_heat_risk_index(x_min: float, y_min: float, x_max: float, y_max: float, wkid: int) -> str:
-    """
-    Calculates the heat risk index for a defined geographic region.
-
-    :return: The local path to the calculated heat risk index spatial bin features.
-    """
-
-    landsat_surf_temp = "https://landsat2.arcgis.com/arcgis/rest/services/Landsat/MS/ImageServer"
-    land_cover = "https://tiledimageservices.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/European_Space_Agency_WorldCover_2021_Land_Cover_WGS84_7/ImageServer"
-    zensus_2022 = "https://services2.arcgis.com/jUpNdisbWqRpMo35/arcgis/rest/services/Zensus2022_grid_final/FeatureServer/0"
-
-    spatial_reference = PySpatialReference(wkid)
-    extent = PyExtent(XMin=x_min, YMin=y_min, XMax=x_max, YMax=y_max, spatial_reference=spatial_reference)
-    hri_spatial_bins = hri_main(landsat_surf_temp, land_cover, zensus_2022, extent, spatial_reference)
-    return hri_spatial_bins
-
-def generate_renderer() -> dict:
-    """
-    Generates the heat index renderer.
-
-    :return: The class breaks renderer as dict.
-    """
-
-    with open(os.path.join(os.path.dirname(__file__), 'data', 'hri-renderer.json'), mode='r', encoding='utf8') as in_stream:
-        return json.load(in_stream)
\ No newline at end of file
diff --git a/spatial-data-science/scripts/build/lib/heatri/data/hri-renderer.json b/spatial-data-science/scripts/build/lib/heatri/data/hri-renderer.json
deleted file mode 100644
index c2693a4..0000000
--- a/spatial-data-science/scripts/build/lib/heatri/data/hri-renderer.json
+++ /dev/null
@@ -1,1034 +0,0 @@
-{"type": "classBreaks",
- "visualVariables": [],
- "rotationType": "arithmetic",
- "minValue": 0,
- "field": "HRI",
- "defaultLabel": "Other",
- "classBreakInfos": [{"classMaxValue": 2,
-   "symbol": {"type": "esriSFS",
-    "style": "esriSFSSolid",
-    "color": [13, 8, 135, 128]}},
-  {"classMaxValue": 2.038215498830758,
-   "symbol": {"type": "esriSFS",
-    "style": "esriSFSSolid",
-    "color": [16, 7, 136, 128]}},
-  {"classMaxValue": 2.076430997661516,
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-   "symbol": {"type": "esriSFS",
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-    "color": [25, 6, 140, 128]}},
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\ No newline at end of file
diff --git a/spatial-data-science/scripts/build/lib/heatri/heat_risk_index.py b/spatial-data-science/scripts/build/lib/heatri/heat_risk_index.py
deleted file mode 100644
index c3262e5..0000000
--- a/spatial-data-science/scripts/build/lib/heatri/heat_risk_index.py
+++ /dev/null
@@ -1,212 +0,0 @@
-import arcpy
-from arcpy.da import SearchCursor
-from arcpy.sa import *
-from arcpy import Extent
-from arcpy import SpatialReference
-import pandas as pd
-
-
-
-def initialize_arcpy():
-    """ Initialize arcpy settings and check necessary extensions. """
-    arcpy.env.overwriteOutput = True
-    arcpy.CheckOutExtension("3D")
-    arcpy.CheckOutExtension("spatial")
-    arcpy.CheckOutExtension("ImageAnalyst")
-
-def generate_tessellation(output_feature_class, extent, size, spatial_ref):
-    """ Generate a tessellation grid. """
-    arcpy.management.GenerateTessellation(
-        Output_Feature_Class=output_feature_class,
-        Extent=extent,
-        Size=size,
-        Spatial_Reference=spatial_ref
-    )
-
-def spatial_join(target_features, join_features, out_feature_class):
-    """ Perform a spatial join between two feature sets. """
-    arcpy.analysis.SpatialJoin(
-        target_features=target_features,
-        join_features=join_features,
-        out_feature_class=out_feature_class,
-        match_option="LARGEST_OVERLAP"
-    )
-
-def zonal_statistics(in_zone_data, zone_field, in_value_raster, out_table, statistics_type="MAXIMUM"):
-    """ Calculate zonal statistics as a table. """
-    arcpy.sa.ZonalStatisticsAsTable(
-        in_zone_data,
-        zone_field,
-        in_value_raster,
-        out_table,
-        "DATA",
-        statistics_type
-    )
-
-def copy_raster(in_raster, out_rasterdataset, raster_format="TIFF"):
-    """ Copy a raster to a new dataset. """
-    arcpy.management.CopyRaster(in_raster, out_rasterdataset, format=raster_format)
-
-def reclassify_raster(in_raster, remap, out_raster):
-    """ Reclassify a raster based on value ranges. """
-    reclass_raster = arcpy.sa.Reclassify(in_raster, "Value", remap)
-    reclass_raster.save(out_raster)
-
-def join_field(in_data, in_field, join_table, join_field, fields):
-    """ Join fields from one table to another. """
-    arcpy.management.JoinField(
-        in_data=in_data,
-        in_field=in_field,
-        join_table=join_table,
-        join_field=join_field,
-        fields=fields
-    )
-
-def calculate_field(in_table, field, expression, field_type="FLOAT"):
-    """ Calculate a new field based on an expression. """
-    arcpy.management.CalculateField(
-        in_table=in_table,
-        field=field,
-        expression=expression,
-        field_type=field_type
-    )
-
-def standardize_field(in_table, fields, method="MIN-MAX", min_value=1, max_value=5):
-    """ Standardize fields using the given method. """
-    arcpy.management.StandardizeField(
-        in_table=in_table,
-        fields=fields,
-        method=method,
-        min_value=min_value,
-        max_value=max_value
-    )
-
-#def delete_features(in_features):
-#    """ Delete features from a feature class or layer. """
-#    arcpy.management.DeleteFeatures(in_features=in_features)
-
-def hri_main(landsat_surf_temp, land_cover, zensus_2022, extent, spatial_reference, workspace=None):
-    """ Main function to execute the HRI analysis. """
-    initialize_arcpy()
-
-    if workspace is None: 
-        arcpy.env.workspace = arcpy.env.scratchGDB
-    else: 
-        arcpy.env.workspace = workspace
-  
-    # Generate tessellation
-    tessellation_output = "HRI_Hexagone"
-    generate_tessellation(
-        output_feature_class=tessellation_output, 
-        extent=extent,
-        size="1500 SquareMeters",
-        spatial_ref=spatial_reference
-    )
-   
-    # Spatial join
-    spatial_join_output = "HRI_Spatial_Bins"
-    spatial_join(tessellation_output, zensus_2022, spatial_join_output)
-    surf_temp_layer = "Landsat Surface Temperature"
-    image_server_result = arcpy.management.MakeImageServerLayer(landsat_surf_temp, surf_temp_layer, processing_template="Band 10 Surface Temperature in Celsius", template=extent)
-    landsat_layer = image_server_result.getOutput(0)
-    definition_query = "((Best < 2000000) OR (Name LIKE 'Ov%')) AND (CloudCover < 0.05) AND (Month = 7 OR Month = 8)"
-    landsat_layer.definitionQuery = definition_query
-
-    landsat_images = []
-    with SearchCursor(landsat_layer, ["OBJECTID","AcquisitionDate"], where_clause=definition_query, spatial_filter=extent) as cursor:
-        for row in cursor:
-            landsat_images.append({"OBJECTID": row[0], "AcquisitionDate": row[1]})
-    
-    landsat_images_df = pd.DataFrame(landsat_images)
-    landsat_images_sorted_df = landsat_images_df.sort_values(by="AcquisitionDate", ascending=False)
-    if landsat_images_sorted_df.empty:
-        raise ValueError("No valid Landsat Image found!")
-    
-    objectid = landsat_images_sorted_df["OBJECTID"][0]
-    landsat_layer.definitionQuery = f"OBJECTID={objectid}"
-
-    landsat_surf_temp_output = "landsat_surf_temp"
-    with arcpy.EnvManager(extent=extent):
-        arcpy.management.CopyRaster(
-            in_raster=landsat_layer,
-            out_rasterdataset=landsat_surf_temp_output,
-            config_keyword="",
-            background_value=None,
-            nodata_value="",
-            onebit_to_eightbit="NONE",
-            colormap_to_RGB="NONE",
-            pixel_type="8_BIT_SIGNED",
-            scale_pixel_value="NONE",
-            RGB_to_Colormap="NONE",
-            format="TIFF",
-            transform="NONE",
-            process_as_multidimensional="CURRENT_SLICE",
-            build_multidimensional_transpose="NO_TRANSPOSE"
-        )
-
-    # Zonal statistics for surface temperature as table
-    surf_temp_table_output = "surf_temp_max_1"
-    zonal_statistics(spatial_join_output, "GRID_ID", landsat_surf_temp_output, surf_temp_table_output)
-
-    # Copy raster for tree canopy
-    tree_canopy_output = "tree_canopy"
-    with arcpy.EnvManager(extent=extent):
-        copy_raster(land_cover, tree_canopy_output)
-
-    # Reclassify tree canopy raster
-    tree_canopy_reclassify_output = "reclass_tree_canopy"
-    reclassify_raster(tree_canopy_output, "10 1;20 0;30 0;40 0;50 0;60 0;70 0;80 0;90 0;95 0;100 0", tree_canopy_reclassify_output)
-
-    # Zonal statistics for reclassified tree canopy
-    tree_canopy_table_output = "tree_canopy_count"
-    zonal_statistics(spatial_join_output, "GRID_ID", tree_canopy_reclassify_output, tree_canopy_table_output, statistics_type="SUM")
-
-    # Join fields for tree canopy statistics
-    join_field(spatial_join_output, "GRID_ID", tree_canopy_table_output, "GRID_ID", ["SUM"])
-
-    # Calculate percentage tree cover and lacking
-    calculate_field(tree_canopy_table_output, "PCT_Tree_Cover", "(!SUM! / !COUNT!) * 100")
-    calculate_field(tree_canopy_table_output, "PCT_Lacking", "100 - !PCT_Tree_Cover!")
-
-    # Join the fields for surface temperature and tree canopy data
-    join_field(spatial_join_output, "GRID_ID", surf_temp_table_output, "GRID_ID", ["MAX"])
-    join_field(spatial_join_output, "GRID_ID", tree_canopy_table_output, "GRID_ID", ["PCT_Tree_Cover", "PCT_Lacking"])
-
-    # Copy raster for built-up area
-    built_up_area_output = "built_up_area"
-    with arcpy.EnvManager(extent=extent):
-        copy_raster(land_cover, built_up_area_output)
-
-    # Reclassify built-up area raster
-    built_up_area_reclassify_output = "reclass_built_up_area"
-    reclassify_raster(built_up_area_output, "10 0;20 0;30 0;40 0;50 1;60 0;70 0;80 0;90 0;95 0;100 0", built_up_area_reclassify_output)
-
-    # Zonal statistics for reclassified built-up area
-    built_up_area_table_output = "built_up_area_count"
-    zonal_statistics(spatial_join_output, "GRID_ID", built_up_area_reclassify_output, built_up_area_table_output, statistics_type="SUM")
-
-    # Join the built-up area statistics
-    join_field(spatial_join_output, "GRID_ID", built_up_area_table_output, "GRID_ID", ["SUM"])
-    calculate_field(built_up_area_table_output, "PCT_built_up_area", "(!SUM! / !COUNT!) * 100")
-    join_field(spatial_join_output, "GRID_ID", built_up_area_table_output, "GRID_ID", ["PCT_built_up_area"])
-
-    # Final HRI calculation using standardized values
-    standardize_field(
-        spatial_join_output,
-        [["Einwohner", "Einwohner_MIN_MAX"], ["MAX", "TEMP_MAX_MIN_MAX"], ["PCT_Lacking", "PCT_Lacking_MIN_MAX"]]
-    )
-    field_expression = "!TEMP_MAX_MIN_MAX! + !PCT_Lacking_MIN_MAX! + !Einwohner_MIN_MAX!"
-    #calculate_field(spatial_join_output, "HRI", "Sum($feature.TEMP_MAX_MIN_MAX, $feature.PCT_Lacking_MIN_MAX, $feature.Einwohner_MIN_MAX)", field_type="FLOAT")
-    calculate_field(spatial_join_output, "HRI", field_expression, field_type="FLOAT")
-    return f"{arcpy.env.workspace}/{spatial_join_output}"
-
-    # Select layer by location
-  #  arcpy.management.SelectLayerByLocation(
-  #      in_layer=[spatial_join_output],
-  #      overlap_type="WITHIN",
-  #      select_features="Ortsteile_Bonn",
-  #      invert_spatial_relationship="INVERT"
-  #  )
-
-    # Delete features not in Bonn
-   # delete_features(spatial_join_output)