diff --git a/INTERFACE.yaml b/INTERFACE.yaml
index ee35e7c..b4b9ace 100644
--- a/INTERFACE.yaml
+++ b/INTERFACE.yaml
@@ -12,6 +12,8 @@ pathvars:
       description: "location of module results."
   results:
     shapes:
-      default: "<results>/shapes.parquet"
+      default: "<results>/{scenario}/shapes.parquet"
       description: |
-        Harmonised geoparquet dataset with all the configured countries, including non-contested exclusive economic zones.
+        Harmonised geoparquet dataset for a configured scenario, including land and maritime non-contested EEZs.
+wildcards:
+  scenario: Scenario name from the module configuration.
diff --git a/README.md b/README.md
index 96c2c40..03bbeb7 100644
--- a/README.md
+++ b/README.md
@@ -26,21 +26,30 @@ Data processing steps:
 </p>
 
 
-1. The configuration file is read to identify the datasets to use as well as the specific countries and regional aggregation (`subtype` in the configuration) to process.
+1. The configuration file is read to identify which requested geopolitical combination (`scenarios`) was requested. These can be any number of nations (`countries`), each coming from a distinct dataset (`source`) and with its own subnational aggregation (`subtype`).
     - Country landmass data: [eurostat NUTS](https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/territorial-units-statistics), [GADM](https://gadm.org/), [geoBoundaries](https://www.geoboundaries.org/), and [Overture Maps](https://overturemaps.org/) are supported.
     - Exclusive Economic Zone (EEZ) data: [MarineRegions.org](https://www.marineregions.org/).
 2. Individual country files are downloaded and harmonised to fit a standardised schema.
     - If identified, contested regions are removed at this stage.
     - Land is clipped using maritime Exclusive Economic Zones (EEZ).
     - Optionally, a Voronoi algorithm is run to separate EEZ areas to fit subnational regions.
-3. Each country file is combined and then clipped using its neighbours to minimise overlapping polygons.
+3. The country files requested in the scenario are combined and then clipped using their neighbours to minimise overlapping polygons.
 
 > [!TIP]
-> The `subtype` naming matches that of the source database. For example, NUTS uses 0, 1, 2 and 3 (NUTS0, NUTS1, NUTS2, etc.).
-> Use the references at the bottom of this page for more details.
+> Keep in mind the following
+>
+> - All downloaded data is kept locally for future reuse across scenarios to minimise stress on the data sources the module relies on.
+> - Data source availability can vary.
+> Always consult the data source website to identify if a country is available at the desired resolution.
 
 > [!CAUTION]
-> To increase the replicability of your workflow, we recommend using NUTS and geoBoundaries as sources whenever possible as they have more stable hosting methods than Overture Maps and GADM.
+> Be aware of the following known issues.
+>
+>- Overture Maps replicability: this data source [does not retain versions](https://github.com/orgs/OvertureMaps/discussions/422) for long.
+> If replicability is important to you, we suggest configuring other sources.
+>- Anti-meridian distortions: regions near the 180/-180 line (e.g., Fiji, Hawaii, New Zealand, Alaska) might be distorted during processing.
+> This is a known issue in the libraries we rely on (`geopandas`, `GDAL`) and something that is being actively [worked on at the moment](https://geopandas.org/en/v1.1.3/about/roadmap.html#s2-geometry-engine).
+> For now, we advice to use global projections (EPSG:3857, EPSG:8857) as a way to mitigate it.
 
 ## Configuration
 <!-- Please describe how to configure this module below -->
@@ -50,7 +59,7 @@ Please consult the configuration [README](./config/README.md) and the [configura
 ## Input / output structure
 <!-- Please describe input / output file placement below -->
 
-This module only has one output: a geoparquet file with your requested geo-boundary "shapes".
+This module only has one output: a geoparquet file with your requested geo-boundary "shapes" for each of the the configured `scenarios`.
 
 Please consult the [interface file](./INTERFACE.yaml) for more information.
 
diff --git a/config/china_example.yaml b/config/china_example.yaml
deleted file mode 100644
index fd19204..0000000
--- a/config/china_example.yaml
+++ /dev/null
@@ -1,9 +0,0 @@
-# Example of regional disaggregation of a large country
-# China has contested borders, which will be removed by the module
-crs:
-  projected: "epsg:3857"
-  geographic: "epsg:4326"
-countries:
-  "CHN":
-    subtype: "country"
-    source: "overture"
diff --git a/config/config.yaml b/config/config.yaml
index 31116a0..70bcd68 100644
--- a/config/config.yaml
+++ b/config/config.yaml
@@ -1,33 +1,215 @@
 # A minimal example of how to configure this module
+
+# Default global settings across scenarios
 voronoi_eez:
   enabled: True
   sample_spacing: 10000 # sample every 10 km
 crs:
   projected: "epsg:3857"
   geographic: "epsg:4326"
-countries:
-  DNK:
-    subtype: "1"
-    source: "nuts"
-    resolution: 01M
-    year: 2024
-  GBR:
-    subtype: "country"
-    source: "overture"
-  NLD:
-    subtype: 0
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  BEL:
-    subtype: 1
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  CHE:
-    subtype: "country"
-    source: "overture"
-  ESP:
-    subtype: "2"
-    source: "nuts"
-    resolution: 01M
-    year: 2024
-    extra_eez: 8364
+
+# At least one scenario.
+# We include four cases as examples.
+scenarios:
+  mixed_example:
+    # Assorted countries
+    countries:
+      DNK:
+        subtype: "1"
+        source: "nuts"
+        resolution: 01M
+        year: 2024
+      GBR:
+        subtype: "country"
+        source: "overture"
+      NLD:
+        subtype: 0
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      BEL:
+        subtype: 1
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      CHE:
+        subtype: "country"
+        source: "overture"
+      ESP:
+        subtype: "2"
+        source: "nuts"
+        resolution: 01M
+        year: 2024
+        extra_eez: 8364
+  china_national:
+    # Large country at national level
+    # China has contested borders, which will be removed by the module
+    voronoi_eez:
+      enabled: False
+      sample_spacing: 10000
+    countries:
+      "CHN":
+        subtype: "country"
+        source: "overture"
+  USA_states:
+    # Example of regional disaggregation of a large country crossing the antimeridean.
+    # USA has multiple marine zones, which can be appended as extras
+    countries:
+      "USA":
+        subtype: "1"
+        source: "gadm"
+        extra_eez: [8463, 8453]
+  europe_regions:
+    # A large continental example.
+    # Europe at regional resolution.
+    crs:
+      projected: "epsg:3035"
+      geographic: "epsg:4326"
+    countries:
+      "ALB":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "AUT":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "BEL":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "BGR":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "BIH":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "CHE":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "CYP":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "CZE":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "DEU":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "DNK":
+        subtype: "1"
+        source: "nuts"
+        resolution: 01M
+        year: 2024
+      "ESP":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+        extra_eez: 8364
+      "EST":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "FIN":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "FRA":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "GBR":
+        subtype: "region"
+        source: "overture"
+      "GGY":
+        subtype: "0"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "GRC":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "HRV":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "HUN":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "IMN":
+        subtype: "0"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "IRL":
+        subtype: "region"
+        source: "overture"
+      "ITA":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "LTU":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "LUX":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "LVA":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "MKD":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "MNE":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "NLD":
+        subtype: "region"
+        source: "overture"
+      "NOR":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "POL":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "PRT":
+        subtype: "1"
+        source: "nuts"
+        resolution: 01M
+        year: 2024
+        extra_eez: [8361, 8363]
+      "ROU":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "SRB":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "SVK":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "SVN":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "SWE":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
+      "XKX":
+        subtype: "1"
+        source: "geoboundaries"
+        release_type: "gbOpen"
diff --git a/config/europe_example.yaml b/config/europe_example.yaml
deleted file mode 100644
index bfa1eb5..0000000
--- a/config/europe_example.yaml
+++ /dev/null
@@ -1,157 +0,0 @@
-# A minimal example of how to configure this module
-voronoi_eez:
-  enabled: True
-  sample_spacing: 10000
-crs:
-  projected: "epsg:3035"
-  geographic: "epsg:4326"
-countries:
-  "ALB":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "AUT":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "BEL":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "BGR":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "BIH":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "CHE":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "CYP":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "CZE":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "DEU":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "DNK":
-    subtype: "1"
-    source: "nuts"
-    resolution: 01M
-    year: 2024
-  "ESP":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-    extra_eez: 8364
-  "EST":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "FIN":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "FRA":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "GBR":
-    subtype: "region"
-    source: "overture"
-  "GGY":
-    subtype: "0"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "GRC":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "HRV":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "HUN":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "IMN":
-    subtype: "0"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "IRL":
-    subtype: "region"
-    source: "overture"
-  "ITA":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "LTU":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "LUX":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "LVA":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "MKD":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "MNE":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "NLD":
-    subtype: "region"
-    source: "overture"
-  "NOR":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "POL":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "PRT":
-    subtype: "1"
-    source: "nuts"
-    resolution: 01M
-    year: 2024
-    extra_eez: [8361, 8363]
-  "ROU":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "SRB":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "SVK":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "SVN":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "SWE":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
-  "XKX":
-    subtype: "1"
-    source: "geoboundaries"
-    release_type: "gbOpen"
diff --git a/config/usa_example.yaml b/config/usa_example.yaml
deleted file mode 100644
index 2d7fe8d..0000000
--- a/config/usa_example.yaml
+++ /dev/null
@@ -1,13 +0,0 @@
-# Example of regional disaggregation of a large country
-# USA has multiple marine zones, which can be appended as extras
-voronoi_eez:
-  enabled: True
-  sample_spacing: 10000 # sample every 10 km
-crs:
-  projected: "epsg:3857"
-  geographic: "epsg:4326"
-countries:
-  "USA":
-    subtype: "1"
-    source: "gadm"
-    extra_eez: [8463, 8453]
diff --git a/pixi.lock b/pixi.lock
index e34830e..6dc220e 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -803,6 +803,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freexl-2.0.0-h9dce30a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/geos-3.14.0-h480dda7_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/geotiff-1.7.4-h1000f5c_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda
@@ -837,17 +838,15 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.3-h73754d4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_19.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.11.4-h6c36cd4_6.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.10.3-h05c3bbc_24.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-h9d11ab5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.78.1-h1d1128b_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libhwy-1.4.0-h10be129_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.4.1-hb03c661_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libjxl-0.11.2-h174a0a3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libkml-1.3.0-haa4a5bd_1023.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-6_h47877c9_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda
@@ -882,7 +881,6 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py312h8a5da7c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.9-py312he3d6523_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/minizip-4.2.1-hb71707f_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/muparser-2.3.5-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.5-py312h33ff503_1.conda
@@ -899,13 +897,14 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-19.0.1-py312h7900ff3_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-19.0.1-py312hc195796_2_cpu.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.46.4-py312h868fb18_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.11.1-py312h6e8b602_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.11.0-py312h02b19dd_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.7.2-py312he675c61_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.12.13-hd63d673_0_cpython.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-duckdb-1.2.0-py312h2ec8cdc_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pytokens-0.4.1-py312h5253ce2_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.3-py312h8a5da7c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/rasterio-1.4.4-py312h762fea3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2025.11.05-h5301d42_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-h853b02a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.30.0-py312h868fb18_0.conda
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   timestamp: 1755851888781
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/geotiff-1.7.4-hf862be1_4.conda
+  sha256: 139909705857801b2cf4ff738a70c035365248a35583ba6ecab7c981ac5356da
+  md5: 111fe25c7b56f8e8f10322b4d99abe69
+  depends:
+  - proj
+  - zlib
+  - libjpeg-turbo
+  - libtiff
+  - __osx >=11.0
+  - libcxx >=19
+  - libzlib >=1.3.1,<2.0a0
+  - libjpeg-turbo >=3.1.0,<4.0a0
+  - libtiff >=4.7.0,<4.8.0a0
+  - proj >=9.7.0,<9.8.0a0
+  license: MIT
+  license_family: MIT
+  size: 128471
+  timestamp: 1757965588361
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gflags-2.2.2-hf9b8971_1005.conda
   sha256: fd56ed8a1dab72ab90d8a8929b6f916a6d9220ca297ff077f8f04c5ed3408e20
   md5: 57a511a5905caa37540eb914dfcbf1fb
@@ -9075,13 +9166,14 @@ packages:
   license_family: BSD
   size: 159247
   timestamp: 1766331953491
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.11.4-h693e041_6.conda
-  sha256: b10496eac3bfc9509009ea9e9ffd2fd4aea4409d780b0fc2c763c63e1656b14d
-  md5: dbc028bb7adefb044f57a3d1ef7d5577
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.10.3-h694df76_24.conda
+  sha256: 30c224f677f1d2779b955434fef8c16b3b391e7461e18f4855053ffeb7a9d6f2
+  md5: 021a4f4c798ede069f985f6bbeb2bb88
   depends:
   - __osx >=11.0
   - blosc >=1.21.6,<2.0a0
   - geos >=3.14.0,<3.14.1.0a0
+  - geotiff >=1.7.4,<1.8.0a0
   - giflib >=5.2.2,<5.3.0a0
   - json-c >=0.18,<0.19.0a0
   - lerc >=4.0.0,<5.0a0
@@ -9092,29 +9184,28 @@ packages:
   - libexpat >=2.7.1,<3.0a0
   - libiconv >=1.18,<2.0a0
   - libjpeg-turbo >=3.1.0,<4.0a0
-  - libjxl >=0.11,<0.12.0a0
   - libkml >=1.3.0,<1.4.0a0
   - liblzma >=5.8.1,<6.0a0
   - libpng >=1.6.50,<1.7.0a0
   - libspatialite >=5.1.0,<5.2.0a0
   - libsqlite >=3.50.4,<4.0a0
+  - libtiff >=4.7.1,<4.8.0a0
   - libwebp-base >=1.6.0,<2.0a0
   - libxml2
   - libxml2-16 >=2.14.6
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.10.0,<1.11.0a0
-  - muparser >=2.3.5,<2.4.0a0
   - openssl >=3.5.4,<4.0a0
   - pcre2 >=10.46,<10.47.0a0
   - proj >=9.7.0,<9.8.0a0
   - xerces-c >=3.3.0,<3.4.0a0
   - zstd >=1.5.7,<1.6.0a0
   constrains:
-  - libgdal 3.11.4.*
+  - libgdal 3.10.3.*
   license: MIT
   license_family: MIT
-  size: 9273075
-  timestamp: 1761262580865
+  size: 8497903
+  timestamp: 1761693365540
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda
   sha256: 68a6c1384d209f8654112c4c57c68c540540dd8e09e17dd1facf6cf3467798b5
   md5: 11e09edf0dde4c288508501fe621bab4
@@ -9228,15 +9319,6 @@ packages:
   license_family: APACHE
   size: 4820402
   timestamp: 1774012715207
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libhwy-1.4.0-ha332bbd_0.conda
-  sha256: 4fcad3cbec60da940312e883b7866816517acc5f9baecfe9a778de57327a1b1b
-  md5: 7394850583ca88325244b68b532c7a39
-  depends:
-  - __osx >=11.0
-  - libcxx >=19
-  license: Apache-2.0 OR BSD-3-Clause
-  size: 609931
-  timestamp: 1776990524407
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libiconv-1.18-h23cfdf5_2.conda
   sha256: de0336e800b2af9a40bdd694b03870ac4a848161b35c8a2325704f123f185f03
   md5: 4d5a7445f0b25b6a3ddbb56e790f5251
@@ -9274,19 +9356,6 @@ packages:
   license: IJG AND BSD-3-Clause AND Zlib
   size: 555681
   timestamp: 1775962975624
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libjxl-0.11.2-h934fa54_1.conda
-  sha256: 948cf1370abb58e06a7c9554838c68672ef1d78e01c3fc4e62ccfc3072579645
-  md5: 05bead8980f5ae6a070117dacec38b5b
-  depends:
-  - libcxx >=19
-  - __osx >=11.0
-  - libhwy >=1.4.0,<1.5.0a0
-  - libbrotlienc >=1.2.0,<1.3.0a0
-  - libbrotlidec >=1.2.0,<1.3.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  size: 1032419
-  timestamp: 1777065264956
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libkml-1.3.0-hb833057_1023.conda
   sha256: bfc6c0b1f30de524e3270eed2171d87e6b96552ff90cf2a5eb34c00d6bcb3dfa
   md5: 3d8eeedb8e541d1cb593e8204fcc397d
@@ -9957,17 +10026,6 @@ packages:
   license_family: Apache
   size: 91725
   timestamp: 1762504404391
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/muparser-2.3.5-h11e0b38_0.conda
-  sha256: 5533e7e3d4b0819b4426f8a1b3f680e6b9c922cdae2b7fabcd0e8c59df22772a
-  md5: 1cdbe54881794ee356d3cba7e3ed6668
-  depends:
-  - __osx >=11.0
-  - libcxx >=18
-  - llvm-openmp >=18.1.8
-  license: MIT
-  license_family: MIT
-  size: 154087
-  timestamp: 1747117056226
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/mypy-1.19.1-py313hd3e6d80_0.conda
   sha256: 7f52c1ede45433ae03b1ea4a45e1e6ed3fdf4f6e9e54d9ac05718205f49856e7
   md5: dd6f5c085908e1945002e50d60c5c4d8
@@ -10425,13 +10483,13 @@ packages:
   license_family: MIT
   size: 1720475
   timestamp: 1778084300413
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.11.1-py313hd8ca31c_1.conda
-  sha256: a028079ee0884dd778a94840733f864e7336c12ca2182b46a33592242b3a59cc
-  md5: d617721ce482cbba8c40f4a9872a2c4f
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.11.0-py313h4ad91d6_0.conda
+  sha256: 8fbeefdb9f67cd188c38cb50a9c1ffa360fd4c987666060e2cf7773eb38c54f6
+  md5: dafffca1d5d08b8966815eed08aece8a
   depends:
   - __osx >=11.0
-  - libcxx >=19
-  - libgdal-core >=3.11.3,<3.12.0a0
+  - libcxx >=18
+  - libgdal-core >=3.10.3,<3.11.0a0
   - numpy
   - packaging
   - python >=3.13,<3.14.0a0
@@ -10439,8 +10497,8 @@ packages:
   - python_abi 3.13.* *_cp313
   license: MIT
   license_family: MIT
-  size: 570550
-  timestamp: 1756824596862
+  size: 601750
+  timestamp: 1746734883266
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyproj-3.7.2-py313h6de5794_3.conda
   sha256: 7a65aa08a4dd6060289a73d461a379ee8e6c183f1b6dd78e0c01332acec8f651
   md5: 1f2ae983e8f36a664dbe220b8d1f7e97
@@ -10589,6 +10647,31 @@ packages:
   license: LicenseRef-Qhull
   size: 516376
   timestamp: 1720814307311
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/rasterio-1.4.4-py313hb3bd904_0.conda
+  sha256: fbbaf8378343197c30aea6d796847ac792e5f38e263383d6360d49122a33d611
+  md5: 699df60947da770aa55348cccfa4f082
+  depends:
+  - __osx >=11.0
+  - affine
+  - attrs
+  - certifi
+  - click >=4,!=8.2.*
+  - click-plugins
+  - cligj >=0.5
+  - libcxx >=19
+  - libgdal-core <3.11
+  - libgdal-core >=3.10.3,<3.11.0a0
+  - numpy >=1.23,<3
+  - proj >=9.7.1,<9.8.0a0
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  - setuptools >=0.9.8
+  - snuggs >=1.4.1
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 7141339
+  timestamp: 1765553472902
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2025.11.05-ha480c28_1.conda
   sha256: 5bab972e8f2bff1b5b3574ffec8ecb89f7937578bd107584ed3fde507ff132f9
   md5: a1ff22f664b0affa3de712749ccfbf04
@@ -11584,6 +11667,28 @@ packages:
   license: LGPL-2.1-only
   size: 1728594
   timestamp: 1755852570331
+- conda: https://conda.anaconda.org/conda-forge/win-64/geotiff-1.7.4-h73469f5_4.conda
+  sha256: 5f01f23fc4447b130238668a40d1c7baa916f3ad175e5772c8482fc1f8c9e2e7
+  md5: 2a62961eeffe28d84c166600e4bf6e25
+  depends:
+  - proj
+  - zlib
+  - libjpeg-turbo
+  - libtiff
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - libzlib >=1.3.1,<2.0a0
+  - libtiff >=4.7.0,<4.8.0a0
+  - proj >=9.7.0,<9.8.0a0
+  - libjpeg-turbo >=3.1.0,<4.0a0
+  license: MIT
+  license_family: MIT
+  size: 137535
+  timestamp: 1757965585058
 - conda: https://conda.anaconda.org/conda-forge/win-64/getopt-win32-0.1-h6a83c73_3.conda
   sha256: d04c4a6c11daa72c4a0242602e1d00c03291ef66ca2d7cd0e171088411d57710
   md5: 49c36fcad2e9af6b91e91f2ce5be8ebd
@@ -12190,12 +12295,13 @@ packages:
   license_family: BSD
   size: 166711
   timestamp: 1766331770351
-- conda: https://conda.anaconda.org/conda-forge/win-64/libgdal-core-3.11.4-h8a8bf46_6.conda
-  sha256: 2602a1e0503dc2083524d8428d89ccc66e73ed09e706334c62e1fd85c9375eb2
-  md5: fd3141a9ef5384b6ecad819ffe429378
+- conda: https://conda.anaconda.org/conda-forge/win-64/libgdal-core-3.10.3-h2f24e33_24.conda
+  sha256: e23a12478480266f7ac6bffbc22082f534c23e251dfec2923edfff9f60947b70
+  md5: 0489c36d03b99d3f71d9d0b4742f1620
   depends:
   - blosc >=1.21.6,<2.0a0
   - geos >=3.14.0,<3.14.1.0a0
+  - geotiff >=1.7.4,<1.8.0a0
   - lerc >=4.0.0,<5.0a0
   - libarchive >=3.8.2,<3.9.0a0
   - libcurl >=8.16.0,<9.0a0
@@ -12203,18 +12309,17 @@ packages:
   - libexpat >=2.7.1,<3.0a0
   - libiconv >=1.18,<2.0a0
   - libjpeg-turbo >=3.1.0,<4.0a0
-  - libjxl >=0.11,<0.12.0a0
   - libkml >=1.3.0,<1.4.0a0
   - liblzma >=5.8.1,<6.0a0
   - libpng >=1.6.50,<1.7.0a0
   - libspatialite >=5.1.0,<5.2.0a0
   - libsqlite >=3.50.4,<4.0a0
+  - libtiff >=4.7.1,<4.8.0a0
   - libwebp-base >=1.6.0,<2.0a0
   - libxml2
   - libxml2-16 >=2.14.6
   - libzlib >=1.3.1,<2.0a0
   - lz4-c >=1.10.0,<1.11.0a0
-  - muparser >=2.3.5,<2.4.0a0
   - openssl >=3.5.4,<4.0a0
   - pcre2 >=10.46,<10.47.0a0
   - proj >=9.7.0,<9.8.0a0
@@ -12224,11 +12329,11 @@ packages:
   - xerces-c >=3.3.0,<3.4.0a0
   - zstd >=1.5.7,<1.6.0a0
   constrains:
-  - libgdal 3.11.4.*
+  - libgdal 3.10.3.*
   license: MIT
   license_family: MIT
-  size: 9195264
-  timestamp: 1761261707518
+  size: 8467648
+  timestamp: 1761692955596
 - conda: https://conda.anaconda.org/conda-forge/win-64/libglib-2.86.3-h0c9aed9_0.conda
   sha256: 84b74fc81fff745f3d21a26c317ace44269a563a42ead3500034c27e407e1021
   md5: c2d5b6b790ef21abac0b5331094ccb56
@@ -12351,16 +12456,6 @@ packages:
   license_family: BSD
   size: 2411241
   timestamp: 1765104337762
-- conda: https://conda.anaconda.org/conda-forge/win-64/libhwy-1.4.0-h172a326_0.conda
-  sha256: 4b45bf59ee46d3c746272c27651da9ce709fda4eee8536c7424acea60d0e2ad0
-  md5: aeca1cb6665f19e560c1fbd20b5bcf34
-  depends:
-  - ucrt >=10.0.20348.0
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
-  license: Apache-2.0 OR BSD-3-Clause
-  size: 562583
-  timestamp: 1776989522919
 - conda: https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-hc1393d2_2.conda
   sha256: 0dcdb1a5f01863ac4e8ba006a8b0dc1a02d2221ec3319b5915a1863254d7efa7
   md5: 64571d1dd6cdcfa25d0664a5950fdaa2
@@ -12403,20 +12498,6 @@ packages:
   license: IJG AND BSD-3-Clause AND Zlib
   size: 842806
   timestamp: 1775962811457
-- conda: https://conda.anaconda.org/conda-forge/win-64/libjxl-0.11.2-h932607e_1.conda
-  sha256: 4715e22c602526c85da09f73865676add67e0995a944b821fbff84547a9db533
-  md5: 327bce3eb1ef1875c7145e915d25bcd3
-  depends:
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
-  - ucrt >=10.0.20348.0
-  - libhwy >=1.4.0,<1.5.0a0
-  - libbrotlienc >=1.2.0,<1.3.0a0
-  - libbrotlidec >=1.2.0,<1.3.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  size: 1194926
-  timestamp: 1777065171989
 - conda: https://conda.anaconda.org/conda-forge/win-64/libkml-1.3.0-h68a222c_1023.conda
   sha256: 6b2c6506dc7d7bb3bc4128d575e4ae6c2cf18d3f9828a4be331e0b5e49edf108
   md5: b44e0d9eb84c6c086d809787aab0a1da
@@ -13073,17 +13154,6 @@ packages:
   license_family: Apache
   size: 88214
   timestamp: 1762504204957
-- conda: https://conda.anaconda.org/conda-forge/win-64/muparser-2.3.5-he0c23c2_0.conda
-  sha256: 57f78d8cd9a282d03cd7a7ffb1f42d570e1bbfb42d606e99de5c16e089067185
-  md5: 013aabb169d59009bdf7d70319360e9b
-  depends:
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
-  license: MIT
-  license_family: MIT
-  size: 148557
-  timestamp: 1747117340968
 - conda: https://conda.anaconda.org/conda-forge/win-64/mypy-1.19.1-py313h5ea7bf4_0.conda
   sha256: cd7b84c381f42944de2deabe98e3f79f39f9fe5eeb9d5a9d071b3476e686a3ac
   md5: 1a933205527bdbc6ada5dab36347c993
@@ -13535,22 +13605,22 @@ packages:
   license_family: MIT
   size: 1888029
   timestamp: 1778084253466
-- conda: https://conda.anaconda.org/conda-forge/win-64/pyogrio-0.11.1-py313h0dbd5a6_1.conda
-  sha256: 0c37a6adf7f04180911bf46e676ca8ee0eefb5b3be76e872fd6854b953467e15
-  md5: 7d1eaf4ed949aeb268394cf2857e20b5
+- conda: https://conda.anaconda.org/conda-forge/win-64/pyogrio-0.11.0-py313h75b81ac_0.conda
+  sha256: a3294855e5682b79a5921d3749fa45286249f2c22782885c14903e6bae8cb507
+  md5: 7a3d31c8db0ac99557cb2faf51fc42b5
   depends:
-  - libgdal-core >=3.11.3,<3.12.0a0
+  - libgdal-core >=3.10.3,<3.11.0a0
   - numpy
   - packaging
   - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
   - ucrt >=10.0.20348.0
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   license: MIT
   license_family: MIT
-  size: 817932
-  timestamp: 1756824550436
+  size: 834342
+  timestamp: 1746735042949
 - conda: https://conda.anaconda.org/conda-forge/win-64/pyproj-3.7.2-py313hbf73894_3.conda
   sha256: fa38aae6747307dd46747052988b54e2cd340c98b3b969065f52460d9e66b50c
   md5: 779b40a8eb5e2aa5ffc5eddd3b136fb7
@@ -13735,6 +13805,31 @@ packages:
   license: LicenseRef-Qhull
   size: 1377020
   timestamp: 1720814433486
+- conda: https://conda.anaconda.org/conda-forge/win-64/rasterio-1.4.4-py313ha5c5119_0.conda
+  sha256: 6891f73e7272ce530d36a5e01ade4c93b5e62133439a0b21bda9b431298f6a1a
+  md5: c094475643548d60959967b5be3638bb
+  depends:
+  - affine
+  - attrs
+  - certifi
+  - click >=4,!=8.2.*
+  - click-plugins
+  - cligj >=0.5
+  - libgdal-core <3.11
+  - libgdal-core >=3.10.3,<3.11.0a0
+  - numpy >=1.23,<3
+  - proj >=9.7.1,<9.8.0a0
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - setuptools >=0.9.8
+  - snuggs >=1.4.1
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 7468329
+  timestamp: 1765553619782
 - conda: https://conda.anaconda.org/conda-forge/win-64/re2-2025.11.05-ha104f34_1.conda
   sha256: 345b1ed8288d81510101f886aaf547e3294370e5dab340c4c3fcb0b25e5d99e0
   md5: 6807f05dcf3f1736ad6cc9525b8b8725
diff --git a/pixi.toml b/pixi.toml
index fc18afa..c423cd1 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -59,7 +59,7 @@ channels = ["conda-forge"]
 [feature.shape.dependencies]
 boto3 = "1.40.46.*"
 duckdb = "1.2.0.*"
-geopandas = "1.0.1.*"
+geopandas = "1.1.3.*"
 ipdb = "0.13.13.*"
 pandera-geopandas = "0.24.*"
 pandera-io = "0.24.*"
@@ -70,6 +70,8 @@ numpy = "2.3.5.*"
 pandas = "3.0.2.*"
 pytz = "2026.2.*"
 requests = "2.33.1.*"
+antimeridian = "0.4.7.*"
+rasterio = "1.4.4.*"
 
 [environments]
 shape = { features = ["shape"], no-default-feature = true }
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..fef1d4d
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,11 @@
+"""Shared pytest fixtures."""
+
+from pathlib import Path
+
+import pytest
+
+
+@pytest.fixture(scope="module")
+def module_path():
+    """Parent directory of the project."""
+    return Path(__file__).parent.parent
diff --git a/tests/integration/Snakefile b/tests/integration/Snakefile
index ad71d73..564b3e4 100644
--- a/tests/integration/Snakefile
+++ b/tests/integration/Snakefile
@@ -9,7 +9,7 @@ configfile: workflow.source_path("./test_config.yaml")
 module module_geo_boundaries:
     pathvars:
         # Redirect specific module results (outputs)
-        shapes="results/outputs/shapes.parquet",
+        shapes="results/outputs/{scenario}/shapes.parquet",
         # Redirect module intermediate files
         logs="resources/module/logs",
         resources="resources/module/resources",
@@ -28,6 +28,6 @@ use rule * from module_geo_boundaries as geo_boundaries_module_*
 rule all:
     default_target: True
     input:
-        "results/outputs/shapes.parquet",
+        "results/outputs/integration/shapes.parquet",
     message:
         "A generic test case for this module."
diff --git a/tests/integration/test_config.yaml b/tests/integration/test_config.yaml
index ea3ad94..25d8bd0 100644
--- a/tests/integration/test_config.yaml
+++ b/tests/integration/test_config.yaml
@@ -2,16 +2,18 @@ module_geo_boundaries:
   crs:
     projected: "epsg:3857"
     geographic: "epsg:4326"
-  countries:
-    "MNE":
-      subtype: "0"
-      source: "geoboundaries"
-      release_type: "gbOpen"
-    "SVK":
-      subtype: "country"
-      source: "overture"
-    "MKD":
-      subtype: "0"
-      source: "nuts"
-      resolution: 03M
-      year: 2024
+  scenarios:
+    integration:
+      countries:
+        "MNE":
+          subtype: "0"
+          source: "geoboundaries"
+          release_type: "gbOpen"
+        "SVK":
+          subtype: "country"
+          source: "overture"
+        "MKD":
+          subtype: "0"
+          source: "nuts"
+          resolution: 03M
+          year: 2024
diff --git a/tests/integration_test.py b/tests/integration_test.py
index 009e87f..7210a3a 100644
--- a/tests/integration_test.py
+++ b/tests/integration_test.py
@@ -11,12 +11,6 @@
 from clio_tools.data_module import ModuleInterface
 
 
-@pytest.fixture(scope="module")
-def module_path():
-    """Parent directory of the project."""
-    return Path(__file__).parent.parent
-
-
 def test_interface_file(module_path):
     """The interfacing file should be correct."""
     assert ModuleInterface.from_yaml(module_path / "INTERFACE.yaml")
diff --git a/tests/local_test.py b/tests/local_test.py
index 8c1fe56..d8b5ce3 100644
--- a/tests/local_test.py
+++ b/tests/local_test.py
@@ -4,7 +4,7 @@
 You'll likely run out of memory.
 
 Instead, run a relevant case:
-pytest tests/local_test.py::test_config_example["europe_example"]
+pytest tests/local_test.py::test_config_example
 """
 
 import subprocess
@@ -13,29 +13,22 @@
 import pytest
 
 
-@pytest.fixture(scope="module")
-def module_path():
-    """Parent directory of the project."""
-    return Path(__file__).parent.parent
-
-
 @pytest.mark.parametrize(
-    "scenario", ["config", "china_example", "europe_example", "usa_example"]
+    "scenario", ["mixed_example", "china_national", "USA_states", "europe_regions"]
 )
-def test_config_example(module_path, scenario):
+def test_scenario(module_path: Path, scenario: str):
     """Example files should result in a successful run."""
-    result_file = "results/shapes.parquet"
-    config_file = Path(module_path / f"config/{scenario}.yaml")
-    smk_command = f"snakemake --cores 4 --replace-workflow-config --configfile={config_file} {result_file}"
+    result_file = f"results/{scenario}/shapes.parquet"
+    smk_command = f"snakemake --cores 4 {result_file}"
     subprocess.run(smk_command + " --forceall", shell=True, check=True, cwd=module_path)
     subprocess.run(
-        smk_command + " --report results/report.html",
+        smk_command + f" --report results/{scenario}/report.html",
         shell=True,
         check=True,
         cwd=module_path,
     )
     subprocess.run(
-        smk_command + " --rulegraph | dot -Tpng > results/rulegraph.png",
+        smk_command + f" --rulegraph | dot -Tpng > results/{scenario}/rulegraph.png",
         shell=True,
         check=True,
         cwd=module_path,
diff --git a/workflow/Snakefile b/workflow/Snakefile
index a546796..6ff7a30 100644
--- a/workflow/Snakefile
+++ b/workflow/Snakefile
@@ -10,7 +10,12 @@ min_version("9.19")
 # This allows users to re-wire how the module is used in their workflow with ease.
 pathvars:
     # Module results
-    shapes="<results>/shapes.parquet",
+    shapes="<results>/{scenario}/shapes.parquet",
+
+
+wildcard_constraints:
+    country="[A-Z]{3}",
+    eez_key="[0-9_]+",
 
 
 # Load the example configuration. This will be overridden by users.
@@ -33,6 +38,7 @@ include: "rules/build.smk"
 
 
 # Add all files to be delivered alongside the workflow here
+workflow.source_path("scripts/_geo.py")
 workflow.source_path("scripts/_schemas.py")
 workflow.source_path("scripts/_utils.py")
 
diff --git a/workflow/envs/shape.linux-64.pin.txt b/workflow/envs/shape.linux-64.pin.txt
index a434d88..cdcaf4b 100644
--- a/workflow/envs/shape.linux-64.pin.txt
+++ b/workflow/envs/shape.linux-64.pin.txt
@@ -23,20 +23,24 @@ https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda#4a13
 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda#18335a698559cdbcd86150a48bf54ba6
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda#d2ffd7602c02f2b316fd921d39876885
 https://conda.anaconda.org/conda-forge/linux-64/python-3.12.13-hd63d673_0_cpython.conda#7eccb41177e15cc672e1babe9056018e
-https://conda.anaconda.org/conda-forge/noarch/pytz-2026.2-pyhcf101f3_0.conda#03cb60f505ad3ada0a95277af5faeb1a
-https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda#8e194e7b992f99a5015edbd4ebd38efd
-https://conda.anaconda.org/conda-forge/noarch/pycountry-24.6.1-pyhd8ed1ab_0.conda#62ed8c560f1b5b8d74ed11e68e9ae223
+https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda#3687cc0b82a8b4c17e1f0eb7e47163d5
 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.12-8_cp312.conda#c3efd25ac4d74b1584d2f7a57195ddf1
 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_19.conda#5794b3bdc38177caf969dabd3af08549
-https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda#98b6c9dc80eb87b2519b97bcf7e578dd
-https://conda.anaconda.org/conda-forge/linux-64/libabseil-20260107.1-cxx17_h7b12aa8_0.conda#6f7b4302263347698fd24565fbf11310
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-https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.2-h25fd6f3_2.conda#c2a01a08fc991620a74b32420e97868a
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_19.conda#85072b0ad177c966294f129b7c04a2d5
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+https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.32-pthreads_h94d23a6_0.conda#89d61bc91d3f39fda0ca10fcd3c68594
+https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-6_h4a7cf45_openblas.conda#6d6d225559bfa6e2f3c90ee9c03d4e2e
+https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-6_h47877c9_openblas.conda#881d801569b201c2e753f03c84b85e15
+https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-6_h0358290_openblas.conda#36ae340a916635b97ac8a0655ace2a35
+https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.5-py312h33ff503_1.conda#03baecffb72fa96fe234fd505908065f
+https://conda.anaconda.org/conda-forge/noarch/snuggs-1.4.7-pyhd8ed1ab_2.conda#9aa358575bbd4be126eaa5e0039f835c
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+https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9
+https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.4.1-hb03c661_0.conda#6178c6f2fb254558238ef4e6c56fb782
+https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda#6c77a605a7a689d17d4819c0f8ac9a00
+https://conda.anaconda.org/conda-forge/linux-64/lerc-4.1.0-hdb68285_0.conda#a752488c68f2e7c456bcbd8f16eec275
+https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.1-h9d88235_1.conda#cd5a90476766d53e901500df9215e927
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 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda#920bb03579f15389b9e512095ad995b7
@@ -45,13 +49,54 @@ https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949
 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda#b38117a3c920364aff79f870c984b4a3
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+https://conda.anaconda.org/conda-forge/linux-64/proj-9.7.1-he0df7b0_3.conda#031e33ae075b336c0ce92b14efa886c5
+https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda#c80d8a3b84358cb967fa81e7075fbc8a
+https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.3.0-hd9031aa_1.conda#66a1db55ecdb7377d2b91f54cd56eafa
+https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.46-h1321c63_0.conda#7fa07cb0fb1b625a089ccc01218ee5b1
+https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda#9de5350a85c4a20c685259b889aa6393
+https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda#915f5995e94f60e9a4826e0b0920ee88
+https://conda.anaconda.org/conda-forge/linux-64/libxml2-16-2.15.3-hca6bf5a_0.conda#e79d2c2f24b027aa8d5ab1b1ba3061e7
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-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_19.conda#e5ce228e579726c07255dbf90dc62101
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@@ -59,10 +104,6 @@ https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.2.0-hb03c661_1.co
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diff --git a/workflow/envs/shape.osx-arm64.pin.txt b/workflow/envs/shape.osx-arm64.pin.txt
index eff4525..b2dc58f 100644
--- a/workflow/envs/shape.osx-arm64.pin.txt
+++ b/workflow/envs/shape.osx-arm64.pin.txt
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diff --git a/workflow/envs/shape.win-64.pin.txt b/workflow/envs/shape.win-64.pin.txt
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 https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-2.39.0-h01c467a_1.conda#453d3a0347fe049b922a2a851c1c0110
 https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.44.35208-h38c0c73_34.conda#f276d1de4553e8fca1dfb6988551ebb4
 https://conda.anaconda.org/conda-forge/win-64/libcrc32c-1.1.2-h0e60522_0.tar.bz2#cd4cc2d0c610c8cb5419ccc979f2d6ce
@@ -82,19 +129,6 @@ https://conda.anaconda.org/conda-forge/noarch/pandera-base-0.24.0-pyhd8ed1ab_2.c
 https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2026.2-pyhd8ed1ab_0.conda#f6ad7450fc21e00ecc23812baed6d2e4
 https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda#3339e3b65d58accf4ca4fb8748ab16b3
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8
-https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-hc1393d2_2.conda#64571d1dd6cdcfa25d0664a5950fdaa2
-https://conda.anaconda.org/conda-forge/win-64/libxml2-16-2.15.3-h692994f_0.conda#f7d6fcda29570e20851b78d92ea2154e
-https://conda.anaconda.org/conda-forge/win-64/libxml2-2.15.3-hbc0d294_0.conda#e3b5acbb857a12f5d59e8d174bc536c0
-https://conda.anaconda.org/conda-forge/win-64/libwinpthread-12.0.0.r4.gg4f2fc60ca-h57928b3_10.conda#8a86073cf3b343b87d03f41790d8b4e5
-https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.12.2-default_h4379cf1_1000.conda#3b576f6860f838f950c570f4433b086e
-https://conda.anaconda.org/conda-forge/win-64/tbb-2023.0.0-ha3553a1_1.conda#34aa94d586fe95fa121966c0d4e73cf4
-https://conda.anaconda.org/conda-forge/win-64/onemkl-license-2025.3.1-h57928b3_13.conda#eded4ef8a94852a2e3e4c779b3b6fdda
-https://conda.anaconda.org/conda-forge/win-64/llvm-openmp-22.1.4-h4fa8253_0.conda#761757ab617e8bfef18cc422dd02bbad
-https://conda.anaconda.org/conda-forge/win-64/mkl-2025.3.1-hac47afa_13.conda#c058cdc8796d5eb260a1aef2cbb7fbbf
-https://conda.anaconda.org/conda-forge/win-64/libblas-3.11.0-6_hf2e6a31_mkl.conda#95543eec964b4a4a7ca3c4c9be481aa1
-https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-6_hf9ab0e9_mkl.conda#7e9cdaf6f302142bc363bbab3b5e7074
-https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-6_h2a3cdd5_mkl.conda#9e4bf521c07f4d423cba9296b7927e3c
-https://conda.anaconda.org/conda-forge/win-64/numpy-2.3.5-py313hce7ae62_1.conda#78749843445581c6dcc0cb80d146982d
 https://conda.anaconda.org/conda-forge/win-64/pandas-3.0.2-py313h26f5e95_0.conda#4e6201ece5bfb083570145791feab397
 https://conda.anaconda.org/conda-forge/noarch/pandera-0.24.0-hd8ed1ab_2.conda#a12a21a89519f0cc224e44da86f5be2c
 https://conda.anaconda.org/conda-forge/noarch/validators-0.35.0-pyhd8ed1ab_0.conda#3449ef730c7d483adde81993994092b9
@@ -103,8 +137,6 @@ https://conda.anaconda.org/conda-forge/noarch/pygments-2.20.0-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda#592132998493b3ff25fd7479396e8351
 https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-4.2.0-pyhd8ed1ab_0.conda#6d03368f2b2b0a5fb6839df53b2eb5e0
 https://conda.anaconda.org/conda-forge/noarch/rich-15.0.0-pyhcf101f3_0.conda#0242025a3c804966bf71aa04eee82f66
-https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7
-https://conda.anaconda.org/conda-forge/noarch/click-8.3.3-pyh6dadd2b_0.conda#8f03c7a39b01ebc57b647f7b48bbeed9
 https://conda.anaconda.org/conda-forge/noarch/annotated-doc-0.0.4-pyhcf101f3_0.conda#52be5139047efadaeeb19c6a5103f92a
 https://conda.anaconda.org/conda-forge/noarch/typer-0.25.1-pyhcf101f3_0.conda#ef114c2eb2ff19f6bf616c81f4710841
 https://conda.anaconda.org/conda-forge/noarch/tabulate-0.10.0-pyhcf101f3_0.conda#3b887b7b3468b0f494b4fad40178b043
@@ -121,14 +153,12 @@ https://conda.anaconda.org/conda-forge/win-64/backports.zstd-1.4.0-py313h2a31948
 https://conda.anaconda.org/conda-forge/noarch/urllib3-2.7.0-pyhd8ed1ab_0.conda#cbb88288f74dbe6ada1c6c7d0a97223e
 https://conda.anaconda.org/conda-forge/noarch/idna-3.13-pyhcf101f3_0.conda#fb7130c190f9b4ec91219840a05ba3ac
 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda#a9167b9571f3baa9d448faa2139d1089
-https://conda.anaconda.org/conda-forge/noarch/certifi-2026.4.22-pyhd8ed1ab_0.conda#929471569c93acefb30282a22060dcd5
 https://conda.anaconda.org/conda-forge/noarch/requests-2.33.1-pyhcf101f3_1.conda#9659f587a8ceacc21864260acd02fc67
 https://conda.anaconda.org/conda-forge/noarch/text-unidecode-1.3-pyhd8ed1ab_2.conda#23b4ba5619c4752976eb7ba1f5acb7e8
 https://conda.anaconda.org/conda-forge/noarch/python-slugify-8.0.4-pyhd8ed1ab_1.conda#a4059bc12930bddeb41aef71537ffaed
 https://conda.anaconda.org/conda-forge/noarch/petl-1.7.17-pyhd8ed1ab_0.conda#4c2498dcda0d58cf25466e82f7287b32
 https://conda.anaconda.org/conda-forge/noarch/marko-2.2.2-pyhd8ed1ab_0.conda#3c3e9339e46fffba5920be28d9233860
 https://conda.anaconda.org/conda-forge/win-64/rpds-py-0.30.0-py313hfbe8231_0.conda#58ae648b12cfa6df3923b5fd219931cb
-https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda#c6b0543676ecb1fb2d7643941fe375f2
 https://conda.anaconda.org/conda-forge/noarch/referencing-0.37.0-pyhcf101f3_0.conda#870293df500ca7e18bedefa5838a22ab
 https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda#439cd0f567d697b20a8f45cb70a1005a
 https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda#ada41c863af263cc4c5fcbaff7c3e4dc
@@ -142,38 +172,11 @@ https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.9.6-pyhcf101f3_0.co
 https://conda.anaconda.org/conda-forge/noarch/pathspec-1.1.1-pyhd8ed1ab_0.conda#11adc78451c998c0fd162584abfa3559
 https://conda.anaconda.org/conda-forge/noarch/black-26.3.1-pyh866005b_0.conda#c7e43448266209d766a229cada982884
 https://conda.anaconda.org/conda-forge/noarch/pandera-io-0.24.0-hd8ed1ab_2.conda#d456360daad56c802e27504979ef1e19
-https://conda.anaconda.org/conda-forge/win-64/geos-3.14.0-hdade9fe_0.conda#605225b71402d12f4bf0324b0cc1db97
 https://conda.anaconda.org/conda-forge/win-64/shapely-2.1.1-py313hae85795_2.conda#8fad5990956ba5a8bbbdb966e7d017b7
 https://conda.anaconda.org/conda-forge/noarch/xyzservices-2026.3.0-pyhd8ed1ab_0.conda#4487b9c371d0161d54b5c7bbd890c0fc
-https://conda.anaconda.org/conda-forge/win-64/sqlite-3.53.1-hdb435a2_0.conda#5833ae18b609bf0790e0bfe6f95b3351
-https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.4.1-hfd05255_0.conda#25a127bad5470852b30b239f030ec95b
-https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.25-h51727cc_0.conda#e77030e67343e28b084fabd7db0ce43e
-https://conda.anaconda.org/conda-forge/win-64/lerc-4.1.0-hd936e49_0.conda#54b231d595bc1ff9bff668dd443ee012
-https://conda.anaconda.org/conda-forge/win-64/libtiff-4.7.1-h8f73337_1.conda#549845d5133100142452812feb9ba2e8
-https://conda.anaconda.org/conda-forge/win-64/proj-9.7.1-hd30e2cd_3.conda#f2b0478a02d35bac5b872d4d63b96be3
 https://conda.anaconda.org/conda-forge/win-64/pyproj-3.7.2-py313hbf73894_3.conda#779b40a8eb5e2aa5ffc5eddd3b136fb7
-https://conda.anaconda.org/conda-forge/win-64/xerces-c-3.3.0-hac47afa_1.conda#d1097e01041cfed41c81f1e3d1f52572
-https://conda.anaconda.org/conda-forge/win-64/pcre2-10.46-h3402e2f_0.conda#889053e920d15353c2665fa6310d7a7a
-https://conda.anaconda.org/conda-forge/win-64/muparser-2.3.5-he0c23c2_0.conda#013aabb169d59009bdf7d70319360e9b
-https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.6.0-h4d5522a_0.conda#f9bbae5e2537e3b06e0f7310ba76c893
-https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.2-hfd05255_2.conda#5187ecf958be3c39110fe691cbd6873e
-https://conda.anaconda.org/conda-forge/win-64/libxml2-devel-2.15.3-hbc0d294_0.conda#62b6eff67ddb7d8657e92fc3d1382a2d
-https://conda.anaconda.org/conda-forge/win-64/librttopo-1.1.0-h5ff11c1_19.conda#41949b02b89c2f2e08c5bd457295f237
-https://conda.anaconda.org/conda-forge/win-64/minizip-4.2.1-h0ffbb96_0.conda#5d55038c4d640e5df06de1cfb4e8d741
-https://conda.anaconda.org/conda-forge/win-64/freexl-2.0.0-hf297d47_2.conda#d6a8059de245e53478b581742b53f71d
-https://conda.anaconda.org/conda-forge/win-64/libspatialite-5.1.0-gpl_h3bf7137_118.conda#a00265bc2dea705fdae1c629f69e65d9
-https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.58-h7351971_0.conda#52f1280563f3b48b5f75414cd2d15dd1
-https://conda.anaconda.org/conda-forge/win-64/uriparser-0.9.8-h5a68840_0.conda#28b4cf9065681f43cc567410edf8243d
-https://conda.anaconda.org/conda-forge/win-64/libkml-1.3.0-h68a222c_1023.conda#b44e0d9eb84c6c086d809787aab0a1da
-https://conda.anaconda.org/conda-forge/win-64/libhwy-1.4.0-h172a326_0.conda#aeca1cb6665f19e560c1fbd20b5bcf34
-https://conda.anaconda.org/conda-forge/win-64/libjxl-0.11.2-h932607e_1.conda#327bce3eb1ef1875c7145e915d25bcd3
-https://conda.anaconda.org/conda-forge/win-64/lzo-2.10-h6a83c73_1002.conda#c5cb4159f0eea65663b31dd1e49bbb71
-https://conda.anaconda.org/conda-forge/win-64/libarchive-3.8.7-gpl_he24518a_100.conda#e8575b817fa0ed38f4f31415f1b4accf
-https://conda.anaconda.org/conda-forge/win-64/blosc-1.21.6-hfd34d9b_1.conda#357d7be4146d5fec543bfaa96a8a40de
-https://conda.anaconda.org/conda-forge/win-64/libgdal-core-3.11.4-h8a8bf46_6.conda#fd3141a9ef5384b6ecad819ffe429378
-https://conda.anaconda.org/conda-forge/win-64/pyogrio-0.11.1-py313h0dbd5a6_1.conda#7d1eaf4ed949aeb268394cf2857e20b5
+https://conda.anaconda.org/conda-forge/win-64/pyogrio-0.11.0-py313h75b81ac_0.conda#7a3d31c8db0ac99557cb2faf51fc42b5
 https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda#854fbdff64b572b5c0b470f334d34c11
-https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda#3687cc0b82a8b4c17e1f0eb7e47163d5
 https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.3-h0261ad2_1.conda#46a21c0a4e65f1a135251fc7c8663f83
 https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.4-h0e57b4f_0.conda#e723ab7cc2794c954e1b22fde51c16e4
 https://conda.anaconda.org/conda-forge/win-64/libgomp-15.2.0-h8ee18e1_19.conda#f1147651e3fdd585e2f442c0c2fc8f2d
@@ -202,10 +205,10 @@ https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda#61
 https://conda.anaconda.org/conda-forge/win-64/scikit-learn-1.8.0-np2py313h4ce4a18_1.conda#1a636c8e6f5b92fca019972db0ed348e
 https://conda.anaconda.org/conda-forge/noarch/networkx-3.6.1-pyhcf101f3_0.conda#a2c1eeadae7a309daed9d62c96012a2b
 https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.10.0-pyhd8ed1ab_1.conda#cc293b4cad9909bf66ca117ea90d4631
-https://conda.anaconda.org/conda-forge/noarch/geopandas-base-1.0.1-pyha770c72_3.conda#e8343d1b635bf09dafdd362d7357f395
+https://conda.anaconda.org/conda-forge/noarch/geopandas-base-1.1.3-pyha770c72_0.conda#18789a85c307970ae1786dfc6dfd234f
 https://conda.anaconda.org/conda-forge/noarch/branca-0.8.2-pyhd8ed1ab_0.conda#1fcdf88e7a8c296d3df8409bf0690db4
 https://conda.anaconda.org/conda-forge/noarch/folium-0.20.0-pyhd8ed1ab_0.conda#a6997a7dcd6673c0692c61dfeaea14ab
-https://conda.anaconda.org/conda-forge/noarch/geopandas-1.0.1-pyhd8ed1ab_3.conda#1baca589eb35814a392eaad6d152447e
+https://conda.anaconda.org/conda-forge/noarch/geopandas-1.1.3-pyhd8ed1ab_0.conda#4eb8b870142ca06d2a1d2c74662eac7d
 https://conda.anaconda.org/conda-forge/noarch/pandera-geopandas-0.24.0-hd8ed1ab_2.conda#fb21509f073465506ea994dc4667bf66
 https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhcf101f3_3.conda#d0fc809fa4c4d85e959ce4ab6e1de800
 https://conda.anaconda.org/conda-forge/noarch/traitlets-5.15.0-pyhcf101f3_0.conda#4bada6a6d908a27262af8ebddf4f7492
@@ -229,3 +232,4 @@ https://conda.anaconda.org/conda-forge/noarch/jmespath-1.1.0-pyhcf101f3_1.conda#
 https://conda.anaconda.org/conda-forge/noarch/botocore-1.40.76-pyhd8ed1ab_0.conda#4babebd5361bc44503358a519a81a465
 https://conda.anaconda.org/conda-forge/noarch/s3transfer-0.14.0-pyhd8ed1ab_0.conda#4e3089ce93822a25408c480dd53561b6
 https://conda.anaconda.org/conda-forge/noarch/boto3-1.40.46-pyhd8ed1ab_0.conda#a93057bba32eef7b217733734642bfea
+https://conda.anaconda.org/conda-forge/noarch/antimeridian-0.4.7-pyhd8ed1ab_0.conda#56e9ab4ca5ec150ad52ca420f2cec646
diff --git a/workflow/envs/shape.yaml b/workflow/envs/shape.yaml
index 9857891..21e5f99 100644
--- a/workflow/envs/shape.yaml
+++ b/workflow/envs/shape.yaml
@@ -5,7 +5,7 @@ channels:
 dependencies:
 - boto3 1.40.46.*
 - duckdb 1.2.0.*
-- geopandas 1.0.1.*
+- geopandas 1.1.3.*
 - ipdb 0.13.13.*
 - pandera-geopandas 0.24.*
 - pandera-io 0.24.*
@@ -16,3 +16,5 @@ dependencies:
 - pandas 3.0.2.*
 - pytz 2026.2.*
 - requests 2.33.1.*
+- antimeridian 0.4.7.*
+- rasterio 1.4.4.*
diff --git a/workflow/internal/config.schema.yaml b/workflow/internal/config.schema.yaml
index 07d49f8..8dfa47f 100644
--- a/workflow/internal/config.schema.yaml
+++ b/workflow/internal/config.schema.yaml
@@ -10,6 +10,108 @@ $defs:
       - type: integer
         minimum: 1
 
+  partialCrsSettings:
+    description: CRS codes (i.e., 'epsg:3035', 'EPSG:4326', 8857).
+    type: object
+    additionalProperties: false
+    properties:
+      projected:
+        description: "Projected CRS code (for area and length calculation)."
+        allOf:
+          - $ref: "#/$defs/crsCode"
+      geographic:
+        description: "Geographic CRS code (for latitude / longitude positioning)."
+        allOf:
+          - $ref: "#/$defs/crsCode"
+
+  crsSettings:
+    allOf:
+      - $ref: "#/$defs/partialCrsSettings"
+    required:
+      - projected
+      - geographic
+
+  partialVoronoiEEZSettings:
+    description: >
+      Optional configuration for the Voronoi EEZ splitting algorithm.
+    type: object
+    additionalProperties: false
+    properties:
+      enabled:
+        description: Activation of the splitting algorithm.
+        type: boolean
+        default: false
+      sample_spacing:
+        description: >
+          Maximum distance between sample points for shorelines.
+          Lower for finer splits at the cost of slower processing.
+          Matches the unit of the projected CRS.
+        type: integer
+        default: 10000
+        minimum: 1
+      min_samples_per_shoreline:
+        description: >
+          Minimum number of sampled points per touching shoreline component.
+          This helps small islands influence the split even when their coastlines
+          are shorter than `sample_spacing`.
+        type: integer
+        default: 8
+        minimum: 1
+      max_samples_per_maritime:
+        description: >
+          Approximate cap for sampled shoreline points per maritime shape.
+          If exceeded, the algorithm raises the effective sample spacing and
+          skips raw shoreline vertices to keep processing bounded.
+        type: integer
+        default: 50000
+        minimum: 1
+      include_shoreline_vertices:
+        description: >
+          Include existing shoreline vertices as Voronoi generators in addition
+          to adaptive midpoint samples.
+        type: boolean
+        default: true
+      shoreline_search_radius:
+        description: >
+          Distance around maritime polygons used to find the marine-facing
+          country exterior boundary. This handles land and EEZ datasets whose
+          coastlines do not exactly coincide. Matches the unit of the projected
+          CRS.
+        type: integer
+        default: 10000
+        minimum: 0
+      uncovered_area_tolerance:
+        description: >
+          Maximum area loss permitted per EEZ polygon after splitting. An error
+          will be raised if exceeded. Matches the unit of the projected CRS.
+        type: number
+        default: 1
+        minimum: 0
+
+  scenario:
+    type: object
+    required:
+      - countries
+    additionalProperties: false
+    properties:
+      crs:
+        $ref: "#/$defs/partialCrsSettings"
+      voronoi_eez:
+        $ref: "#/$defs/partialVoronoiEEZSettings"
+      countries:
+        $ref: "#/$defs/countries"
+
+  countries:
+    description: >
+      Dictionary of countries with each country code as the key.
+      Country code must be in ISO alpha-3 format (e.g., MEX, CHN, DEU).
+    type: object
+    patternProperties:
+      "^[A-Z]{3}$": # Keys must be exactly three uppercase letters
+        $ref: "#/$defs/country"
+    minProperties: 1
+    additionalProperties: false
+
   countryBase:
     type: object
     properties:
@@ -111,35 +213,15 @@ $defs:
 
 description: "Schema for user-provided configuration files."
 type: object
+required:
+  - scenarios
+  - crs
+additionalProperties: false
 properties:
   crs:
-    description: CRS codes (i.e., 'epsg:3035', 'EPSG:4326', 8857).
-    type: object
-    properties:
-      projected:
-        description: "Projected CRS code (for area and length calculation)."
-        allOf:
-          - $ref: "#/$defs/crsCode"
-      geographic:
-        description: "Geographic CRS code (for latitude / longitude positioning)."
-        allOf:
-          - $ref: "#/$defs/crsCode"
-    required:
-      - projected
-      - geographic
-    additionalProperties: false
-
-  countries:
-    description: >
-      Dictionary of countries with each country code as the key.
-      Country code must be in ISO alpha-3 format (e.g., MEX, CHN, DEU).
-    type: object
-    patternProperties:
-      "^[A-Z]{3}$": # Keys must be exactly three uppercase letters
-        $ref: "#/$defs/country"
-    minProperties: 1
-    additionalProperties: false
-
+    $ref: "#/$defs/crsSettings"
+  voronoi_eez:
+    $ref: "#/$defs/partialVoronoiEEZSettings"
   overture_release:
     description: |
       Overture data release to use. Two options are possible:
@@ -148,66 +230,13 @@ properties:
     type: string
     default: "latest"
     pattern: "^(?:latest|(?:\\d{4})-(?:0[1-9]|1[0-2])-(?:0[1-9]|[12]\\d|3[01])\\.\\d+)$"
-  voronoi_eez:
+  scenarios:
     description: >
-      Optional configuration for the Voronoi EEZ splitting algorithm.
+      Shape-building scenarios to run.
+      Downloaded source files will be reused between them if possible.
     type: object
-    required:
-      - enabled
-      - sample_spacing
-    properties:
-      enabled:
-        description: Activation of the splitting algorithm.
-        type: boolean
-        default: false
-      sample_spacing:
-        description: >
-          Maximum distance between sample points for shorelines.
-          Lower for finer splits at the cost of slower processing.
-          Matches the unit of the projected CRS.
-        type: integer
-        default: 10000
-        minimum: 1
-      min_samples_per_shoreline:
-        description: >
-          Minimum number of sampled points per touching shoreline component.
-          This helps small islands influence the split even when their coastlines
-          are shorter than `sample_spacing`.
-        type: integer
-        default: 8
-        minimum: 1
-      max_samples_per_maritime:
-        description: >
-          Approximate cap for sampled shoreline points per maritime shape.
-          If exceeded, the algorithm raises the effective sample spacing and
-          skips raw shoreline vertices to keep processing bounded.
-        type: integer
-        default: 50000
-        minimum: 1
-      include_shoreline_vertices:
-        description: >
-          Include existing shoreline vertices as Voronoi generators in addition
-          to adaptive midpoint samples.
-        type: boolean
-        default: true
-      shoreline_search_radius:
-        description: >
-          Distance around maritime polygons used to find the marine-facing
-          country exterior boundary. This handles land and EEZ datasets whose
-          coastlines do not exactly coincide. Matches the unit of the projected
-          CRS.
-        type: integer
-        default: 10000
-        minimum: 0
-      uncovered_area_tolerance:
-        description: >
-          Maximum area loss permitted per EEZ polygon after splitting. An error
-          will be raised if exceeded. Matches the unit of the projected CRS.
-        type: number
-        default: 1
-        minimum: 0
-
-required:
-  - countries
-  - crs
-additionalProperties: false
+    patternProperties:
+      "^[A-Za-z0-9_-]+$":
+        $ref: "#/$defs/scenario"
+    minProperties: 1
+    additionalProperties: false
diff --git a/workflow/report/build_combined_area.rst b/workflow/report/build_combined_area.rst
index bcbf5bf..8272f54 100644
--- a/workflow/report/build_combined_area.rst
+++ b/workflow/report/build_combined_area.rst
@@ -1,5 +1,7 @@
 Resulting polygonal data of country boundaries and marine exclusive economic zones (EEZ).
 
+Scenario: {{ snakemake.wildcards.scenario }}
+
 Countries: {{ snakemake.params.countries }}
 
 Source datasets: {{ snakemake.params.sources }}
diff --git a/workflow/report/build_country.rst b/workflow/report/build_country.rst
index c1b0e33..88d1e0d 100644
--- a/workflow/report/build_country.rst
+++ b/workflow/report/build_country.rst
@@ -1,4 +1,4 @@
-Built combined file for {{ snakemake.wildcards.country }}.
+Built combined file for {{ snakemake.wildcards.scenario}}-{{ snakemake.wildcards.country }}.
 
 * If present, contested EEZ regions are removed.
 * Optionally, EEZ regions are broken down using a Voronoi algorithm.
diff --git a/workflow/report/download_harmonised_eez.rst b/workflow/report/download_harmonised_eez.rst
index 2a70269..1651d2b 100644
--- a/workflow/report/download_harmonised_eez.rst
+++ b/workflow/report/download_harmonised_eez.rst
@@ -1,3 +1,3 @@
-Downloaded and harmonised EEZ for {{ snakemake.wildcards.country }}.
+Downloaded and harmonised EEZ for {{ snakemake.wildcards.eez }}.
 
 Source: Flanders Marine Institute: MarineRegions.org. Available online at `www.marineregions.org <https://www.marineregions.org>`_.
diff --git a/workflow/rules/_utils.smk b/workflow/rules/_utils.smk
index e951c1b..6db3347 100644
--- a/workflow/rules/_utils.smk
+++ b/workflow/rules/_utils.smk
@@ -1,18 +1,54 @@
 """Utility functions for snakemake rule handling."""
 
 
-def get_country_file(country: str):
-    """Build unique file names to avoid overwriting source files."""
-    source = config["countries"][country]["source"]
-    subtype = config["countries"][country]["subtype"]
+def get_country_file(scenario: str, country: str):
+    """Build unique file names to avoid overwriting source files.
+
+    Scenarios do not affect the downloaded source files.
+    """
+    country_settings: dict = config["scenarios"][scenario]["countries"][country]
+
+    source = country_settings["source"]
+    subtype = country_settings["subtype"]
 
     filename = f"{source}/harmonise/{country}_{subtype}"
     if source == "nuts":
-        resolution = config["countries"][country]["resolution"]
-        year = config["countries"][country]["year"]
+        resolution = country_settings["resolution"]
+        year = country_settings["year"]
         filename += f"_{year}_{resolution}"
     elif source == "geoboundaries":
-        release = config["countries"][country]["release_type"]
+        release = country_settings["release_type"]
         filename += f"_{release}"
 
     return filename
+
+
+def get_crs_config(scenario: str) -> dict:
+    """Get CRS configuration in order of priority."""
+    return config["crs"] | config["scenarios"][scenario].get("crs", {})
+
+
+def get_voronoi_eez_config(scenario: str) -> dict:
+    """Get Voronoi configuration in order of priority."""
+    scenario_override = config["scenarios"][scenario].get("voronoi_eez", {})
+    user_overrides = config.get("voronoi_eez", {}) | scenario_override
+    return internal["voronoi_eez"] | user_overrides
+
+
+def get_eez_file(scenario: str, country: str) -> str:
+    """Build an EEZ filename reusable by scenarios with matching EEZ requests."""
+    extra_eez = config["scenarios"][scenario]["countries"][country].get("extra_eez", [])
+    if not isinstance(extra_eez, list):
+        extra_eez = [extra_eez]
+
+    extra_eez = sorted([int(i) for i in extra_eez])
+    if extra_eez:
+        file_path = f"combined/{country}_{'_'.join([str(i) for i in extra_eez])}"
+    else:
+        file_path = f"single/{country}"
+    return file_path
+
+
+def get_extra_eez_from_key(eez_key: str) -> list[int]:
+    """Recover additional EEZ identifiers from an EEZ output wildcard."""
+    return [int(i) for i in eez_key.split("_")]
diff --git a/workflow/rules/build.smk b/workflow/rules/build.smk
index 3fb64f8..28e8944 100644
--- a/workflow/rules/build.smk
+++ b/workflow/rules/build.smk
@@ -1,54 +1,86 @@
 """Rules used to construct the final dataset."""
 
 
+rule build_eez:
+    input:
+        country="<resources>/automatic/eez/single/{country}.parquet",
+        extra=lambda wc: [
+            f"<resources>/automatic/eez/single/{mrgid}.parquet"
+            for mrgid in get_extra_eez_from_key(wc.eez_key)
+        ],
+    output:
+        path="<resources>/automatic/eez/combined/{country}_{eez_key}.parquet",
+    log:
+        "<logs>/eez/build/{country}_{eez_key}.log",
+    conda:
+        "../envs/shape.yaml"
+    message:
+        "{wildcards.country}: build EEZ dataset {wildcards.eez_key}."
+    script:
+        "../scripts/build_eez.py"
+
+
 rule build_country:
     input:
-        land=lambda wc: f"<resources>/automatic/{get_country_file(wc.country)}.parquet",
-        maritime="<resources>/automatic/eez/{country}.parquet",
+        land=lambda wc: (
+            f"<resources>/automatic/{get_country_file(wc.scenario, wc.country)}.parquet"
+        ),
+        maritime=lambda wc: (
+            f"<resources>/automatic/eez/{get_eez_file(wc.scenario, wc.country)}.parquet"
+        ),
     output:
-        country="<resources>/automatic/country/{country}.parquet",
+        country="<resources>/automatic/scenarios/{scenario}/{country}.parquet",
         plot=report(
-            "<resources>/automatic/country/{country}.png",
+            "<resources>/automatic/scenarios/{scenario}/{country}.png",
             caption="../report/build_country.rst",
             category="Module Geo-Boundaries",
             subcategory="Country area",
         ),
     log:
-        "<logs>/{country}/build_country.log",
+        "<logs>/scenarios/{scenario}/build_country/{country}.log",
     conda:
         "../envs/shape.yaml"
     params:
-        crs=config["crs"],
-        voronoi=internal["voronoi_eez"] | config.get("voronoi_eez", {}),
+        crs=lambda wc: get_crs_config(wc.scenario),
+        voronoi=lambda wc: get_voronoi_eez_config(wc.scenario),
     message:
-        "{wildcards.country}: build combined land and marine polygons."
+        "{wildcards.scenario}-{wildcards.country}: building combined single-country dataset."
     script:
         "../scripts/build_country.py"
 
 
 rule build_combined_area:
     input:
-        countries=[
-            f"<resources>/automatic/country/{country}.parquet"
-            for country in config["countries"]
+        countries=lambda wc: [
+            f"<resources>/automatic/scenarios/{wc.scenario}/{country}.parquet"
+            for country in config["scenarios"][wc.scenario]["countries"]
         ],
     output:
         combined="<shapes>",
         plot=report(
-            "<results>/shapes.png",
+            "<results>/{scenario}/shapes.png",
             caption="../report/build_combined_area.rst",
             category="Module Geo-Boundaries",
             subcategory="Combined area",
         ),
     log:
-        "<logs>/build_combined_area.log",
+        "<logs>/scenarios/{scenario}/build_combined_area.log",
     conda:
         "../envs/shape.yaml"
     params:
-        crs=config["crs"],
-        countries=sorted([i for i in config["countries"]]),
-        sources=sorted(set([i["source"] for i in config["countries"].values()])),
+        crs=lambda wc: get_crs_config(wc.scenario),
+        countries=lambda wc: sorted(
+            [i for i in config["scenarios"][wc.scenario]["countries"]]
+        ),
+        sources=lambda wc: sorted(
+            set(
+                [
+                    i["source"]
+                    for i in config["scenarios"][wc.scenario]["countries"].values()
+                ]
+            )
+        ),
     message:
-        "Combine land and marine polygons."
+        "{wildcards.scenario}: building combined dataset with all countries."
     script:
         "../scripts/build_combined_area.py"
diff --git a/workflow/rules/harmonise.smk b/workflow/rules/harmonise.smk
index 2723a1a..e59987f 100644
--- a/workflow/rules/harmonise.smk
+++ b/workflow/rules/harmonise.smk
@@ -41,9 +41,6 @@ rule harmonise_gadm:
         "<logs>/gadm/harmonise/{country}_{subtype}.log",
     conda:
         "../envs/shape.yaml"
-    params:
-        country_id=lambda wc: str(wc.country),
-        subtype=lambda wc: str(wc.subtype),
     message:
         "Harmonising '{wildcards.country}_{wildcards.subtype}' GADM dataset."
     script:
@@ -67,22 +64,21 @@ rule harmonise_nuts:
 
 rule download_harmonised_eez:
     output:
-        path="<resources>/automatic/eez/{country}.parquet",
+        path="<resources>/automatic/eez/single/{eez}.parquet",
         plot=report(
-            "<resources>/automatic/eez/{country}.png",
+            "<resources>/automatic/eez/single/{eez}.png",
             caption="../report/download_harmonised_eez.rst",
             category="Module Geo-Boundaries",
             subcategory="EEZ area",
         ),
     log:
-        "<logs>/eez/download_harmonised/{country}.log",
+        "<logs>/eez/download_harmonised/{eez}.log",
     localrule: True
     conda:
         "../envs/shape.yaml"
     params:
-        extra_eez=lambda wc: config["countries"][wc.country].get("extra_eez", []),
         timeouts=internal["timeouts"],
     message:
-        "Download and harmonise '{wildcards.country}' EEZ dataset."
+        "Download and harmonise EEZ dataset '{wildcards.eez}'."
     script:
         "../scripts/download_harmonised_eez.py"
diff --git a/workflow/scripts/_geo.py b/workflow/scripts/_geo.py
new file mode 100644
index 0000000..0e48a82
--- /dev/null
+++ b/workflow/scripts/_geo.py
@@ -0,0 +1,131 @@
+"""Reusable geometry helpers."""
+
+from collections.abc import Iterator
+from itertools import pairwise
+from warnings import warn
+
+import antimeridian
+import geopandas as gpd
+from pyproj import CRS
+from rasterio import warp
+from shapely import MultiPolygon, Polygon
+from shapely.geometry import GeometryCollection, mapping, shape
+from shapely.geometry.base import BaseGeometry
+
+CRS_MARINE_REGIONS = "EPSG:4326"
+
+
+def check_crs_config(crs: dict[str, int | str]) -> dict[str, CRS]:
+    """Check the crs configuration settings."""
+    result = {k: CRS.from_user_input(v) for k, v in crs.items()}
+    if not result["projected"].is_projected:
+        raise ValueError(f"CRS must be projected. Got {crs['projected']!r}.")
+    if not result["geographic"].is_geographic:
+        raise ValueError(f"CRS must be geographic. Got {crs['geographic']!r}.")
+    return result
+
+
+def _iter_polygons(geom: BaseGeometry | None) -> Iterator[Polygon]:
+    """Yield polygon parts from possibly mixed geometry."""
+    if geom is None or geom.is_empty:
+        return
+
+    if isinstance(geom, Polygon):
+        yield geom
+    elif isinstance(geom, (MultiPolygon, GeometryCollection)):
+        for part in geom.geoms:
+            yield from _iter_polygons(part)
+
+
+def extract_polygonal_geometry(
+    geom: BaseGeometry | None,
+) -> Polygon | MultiPolygon | None:
+    """Return only Polygon/MultiPolygon components from a geometry."""
+    polygons = list(_iter_polygons(geom))
+    result = None
+    if polygons:
+        result = polygons[0] if len(polygons) == 1 else MultiPolygon(polygons)
+    return result
+
+
+def _rasterio_to_crs(gdf: gpd.GeoDataFrame, to_crs: CRS) -> gpd.GeoDataFrame:
+    """CRS conversion using rasterio's more powerful toolset.
+
+    Compared to geopandas this should adequately handle antimeridian "splitting".
+    Order: geopandas -> GeoJSON-like dict -> rasterio -> geopandas
+    """
+    input_geoms = [mapping(geom) for geom in gdf.geometry]
+    transformed = warp.transform_geom(
+        src_crs=gdf.crs, dst_crs=to_crs, geom=input_geoms, antimeridian_cutting=True
+    )
+    output_geoms = [shape(geom) for geom in transformed]
+    return gdf.set_geometry(gpd.GeoSeries(output_geoms, index=gdf.index, crs=to_crs))
+
+
+def to_projected_crs(gdf: gpd.GeoDataFrame, crs: int | str | CRS) -> gpd.GeoDataFrame:
+    """CRS conversion using rasterio's more powerful toolset.
+
+    Compared to geopandas this adequately handles handles antimeridian "splitting".
+    """
+    to_crs = CRS.from_user_input(crs)
+    if not to_crs.is_projected:
+        raise ValueError(f"This function only converts to geographic CRS. Got {crs!r}.")
+    return _rasterio_to_crs(gdf, to_crs)
+
+
+def _crosses_antimeridian(poly: Polygon) -> bool:
+    rings = [poly.exterior, *poly.interiors]
+
+    crosses = any(
+        180 < abs(x2 - x1) < 360
+        for ring in rings
+        for (x1, _), (x2, _) in pairwise(ring.coords)
+    )
+
+    return crosses
+
+
+def _fix_antimeridian_geometry(geom: BaseGeometry) -> BaseGeometry:
+    fixed_geom = geom
+
+    if geom.geom_type == "Polygon":
+        if _crosses_antimeridian(geom):
+            fixed_geom = antimeridian.fix_polygon(geom)
+
+    elif geom.geom_type == "MultiPolygon":
+        parts = []
+
+        for poly in geom.geoms:
+            fixed_poly = poly
+
+            if _crosses_antimeridian(poly):
+                fixed_poly = antimeridian.fix_polygon(poly)
+
+            if fixed_poly.geom_type == "MultiPolygon":
+                parts.extend(fixed_poly.geoms)
+            else:
+                parts.append(fixed_poly)
+
+        fixed_geom = MultiPolygon(parts)
+
+    return fixed_geom
+
+
+def to_geographic_crs(gdf: gpd.GeoDataFrame, crs: int | str | CRS) -> gpd.GeoDataFrame:
+    """Convert to a geographic CRS fixing antimeridian-crossings."""
+    target_crs = CRS.from_user_input(crs)
+    if not target_crs.is_geographic:
+        raise ValueError(f"This function only converts to geographic CRS. Got {crs!r}.")
+
+    # Map to 4326 to ensure we match antimeridian algorithm requirements
+    fixed = _rasterio_to_crs(gdf, CRS.from_user_input("EPSG:4326"))
+    fixed.geometry = fixed.geometry.map(_fix_antimeridian_geometry)
+
+    if not fixed.crs.equals(target_crs):
+        warn(
+            f"Target CRS {crs!r} does not match {fixed.crs!r}. "
+            "This might behave oddly near the antimeridian."
+        )
+        fixed = fixed.to_crs(target_crs)
+
+    return fixed
diff --git a/workflow/scripts/_schemas.py b/workflow/scripts/_schemas.py
index af705f5..1e878c3 100644
--- a/workflow/scripts/_schemas.py
+++ b/workflow/scripts/_schemas.py
@@ -1,7 +1,11 @@
-import pandera.pandas as pa
+"""Reusable schemas."""
+
+import geopandas as gpd
+from _geo import extract_polygonal_geometry
+from pandera import pandas as pa
 from pandera.typing.geopandas import GeoSeries
 from pandera.typing.pandas import Series
-from shapely.validation import make_valid
+from shapely.geometry import MultiPolygon, Polygon
 
 SUPPORTED_DATASETS = ["gadm", "overture", "marineregions", "nuts", "geoboundaries"]
 
@@ -31,18 +35,24 @@ class Config:
     "Human-readable name in the parent dataset."
 
     @pa.dataframe_parser
-    def fix_geometries(cls, df):
-        """Attempt to correct empty or malformed geometries."""
-        mask = df["geometry"].apply(lambda g: (g is not None) and (not g.is_empty))
-        df = df.loc[mask]
-        df["geometry"] = df["geometry"].apply(
-            lambda g: g if g.is_valid else make_valid(g)
-        )
-        return df
+    def fix_geometries(cls, gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:  # type: ignore[misc]
+        """Attempt to correct empty, malformed, or non-polygonal geometries."""
+        mask = gdf["geometry"].notna() & ~gdf["geometry"].is_empty
+        gdf = gdf.loc[mask].copy()
+
+        invalid = ~gdf.geometry.is_valid
+        gdf.loc[invalid, "geometry"] = gdf.loc[invalid, "geometry"].make_valid()
+
+        gdf["geometry"] = gdf["geometry"].apply(extract_polygonal_geometry)
+        return gdf.loc[gdf["geometry"].notna()]
 
     @pa.check("geometry", element_wise=True)
-    def check_geometries(cls, geom):
-        return (geom is not None) and (not geom.is_empty) and geom.is_valid
+    def check_geometries(cls, geom) -> bool:
+        return (
+            isinstance(geom, (Polygon, MultiPolygon))
+            and not geom.is_empty
+            and geom.is_valid
+        )
 
 
 class EEZSchema(ShapesSchema):
diff --git a/workflow/scripts/_utils.py b/workflow/scripts/_utils.py
index 266d441..5cbf4f4 100644
--- a/workflow/scripts/_utils.py
+++ b/workflow/scripts/_utils.py
@@ -33,28 +33,14 @@ def request_timeout(self) -> tuple[int, int]:
         return (self.connect_seconds, self.read_seconds)
 
 
-def check_crs_config(crs: dict[str, int | str]) -> dict[str, CRS]:
-    """Check the crs configuration settings."""
-    result = {k: CRS.from_user_input(v) for k, v in crs.items()}
-    if not result["projected"].is_projected:
-        raise ValueError(f"CRS must be projected. Got {crs['projected']!r}.")
-    if not result["geographic"].is_geographic:
-        raise ValueError(f"CRS must be geographic. Got {crs['geographic']!r}.")
-    return result
-
-
 def plot_shapes(shapes: gpd.GeoDataFrame, crs: str | int | CRS) -> tuple[Figure, Axes]:
     """Generate a nice figure of dataframes that fit the module's schema."""
-    gdf = shapes.copy().to_crs(crs)
+    # NOTE: the use of geopandas' to_crs is purposeful.
+    # It's likely what users of the module will rely on later.
+    gdf = shapes.to_crs(crs)
     colors = {"land": "olive", "maritime": "tab:blue"}
     fig, ax = plt.subplots(layout="constrained")
-    ax = gdf.plot(
-        ax=ax,
-        column="shape_class",
-        color=gdf["shape_class"].map(colors),
-        legend=False,
-        zorder=-1,
-    )
+    ax = gdf.plot(ax=ax, color=gdf["shape_class"].map(colors), legend=False, zorder=-1)
     gdf.boundary.plot(ax=ax, color="black", lw=0.5, zorder=1)
     ax.set(xticks=[], yticks=[], xlabel="", ylabel="")
     return fig, ax
diff --git a/workflow/scripts/build_combined_area.py b/workflow/scripts/build_combined_area.py
index ce63718..6a99b9c 100644
--- a/workflow/scripts/build_combined_area.py
+++ b/workflow/scripts/build_combined_area.py
@@ -4,6 +4,7 @@
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any
 
+import _geo
 import _schemas
 import _utils
 import geopandas as gpd
@@ -21,7 +22,7 @@ def remove_overlaps(gdf: gpd.GeoDataFrame, crs: dict[str, CRS]) -> gpd.GeoDataFr
     The first row wins. Any area shared with an earlier kept geometry is removed
     from the later geometry.
     """
-    projected = gdf.to_crs(crs["projected"]).copy()
+    projected = _geo.to_projected_crs(gdf, crs["projected"])
 
     left_idx, right_idx = projected.sindex.query(
         projected.geometry, predicate="intersects"
@@ -57,22 +58,24 @@ def remove_overlaps(gdf: gpd.GeoDataFrame, crs: dict[str, CRS]) -> gpd.GeoDataFr
 
     projected["geometry"] = geoms
     projected = projected.loc[projected.geometry.notna() & ~projected.geometry.is_empty]
-
-    result = projected.to_crs(crs["geographic"])
-    result.geometry = result.geometry.buffer(0)
+    result = _geo.to_geographic_crs(projected, crs["geographic"])
 
     return result
 
 
 def main() -> None:
     """Main snakemake process."""
-    crs = _utils.check_crs_config(snakemake.params.crs)
-
-    country_list = [
-        gpd.read_parquet(i).to_crs(crs["geographic"]) for i in snakemake.input.countries
-    ]
-    combined = _schemas.ShapesSchema.validate(
-        pd.concat(country_list, ignore_index=True)
+    crs = _geo.check_crs_config(snakemake.params.crs)
+
+    # Load and ensure inputs are healthy
+    country_list = [gpd.read_parquet(i) for i in snakemake.input.countries]
+    crs_mismatch = [not crs["geographic"].equals(i.crs) for i in country_list]
+    if any(crs_mismatch):
+        raise ValueError(f"Received datasets with invalid CRS: {sum(crs_mismatch)!r} .")
+    combined = gpd.GeoDataFrame(
+        pd.concat(country_list, ignore_index=True),
+        crs=crs["geographic"],
+        geometry="geometry",
     )
 
     combined = remove_overlaps(combined, crs)
diff --git a/workflow/scripts/build_country.py b/workflow/scripts/build_country.py
index 47e6e7e..25a7dd0 100644
--- a/workflow/scripts/build_country.py
+++ b/workflow/scripts/build_country.py
@@ -6,6 +6,7 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any
 
+import _geo
 import _schemas
 import _utils
 import geopandas as gpd
@@ -197,11 +198,7 @@ def _make_shoreline_seed_records(
 
 
 def _split_one_maritime(
-    maritime_row,
-    land: gpd.GeoDataFrame,
-    *,
-    crs: int | str,
-    voronoi_config: VoronoiConfig,
+    maritime_row, land: gpd.GeoDataFrame, *, crs: CRS, voronoi_config: VoronoiConfig
 ) -> tuple[gpd.GeoDataFrame, gpd.GeoDataFrame]:
     """Split a single maritime shape to fit the coastline."""
     if land.empty:
@@ -268,6 +265,8 @@ def _split_one_maritime(
             .dissolve(by="assigned_shape_id", as_index=False)
             .rename(columns={"assigned_shape_id": "_assigned_shape_id"})
         )
+        # FIXME: check if rerunning `warp` is necessary to fix things near the antimeridian
+        pieces = _geo.to_projected_crs(pieces, crs)
         uncovered_area = maritime_geometry.difference(pieces.geometry.union_all()).area
         if uncovered_area > voronoi_config.uncovered_area_tolerance:
             raise RuntimeError(
@@ -297,11 +296,9 @@ def split_maritime_by_shoreline_voronoi(
     if not voronoi_config.enabled:
         return shapes, cells
 
-    shapes = shapes.copy().to_crs(crs["projected"])
-
-    land = shapes.loc[shapes["shape_class"].eq("land")]
-    maritime = shapes.loc[shapes["shape_class"].eq("maritime")]
-
+    shapes_proj = _geo.to_projected_crs(shapes, crs["projected"])
+    land = shapes_proj.loc[shapes_proj["shape_class"].eq("land")].copy()
+    maritime = shapes_proj.loc[shapes_proj["shape_class"].eq("maritime")].copy()
     if maritime.empty:
         raise ValueError("Requested voronoi without maritime shapes.")
 
@@ -310,67 +307,76 @@ def split_maritime_by_shoreline_voronoi(
             maritime_row,
             land.loc[land["country_id"].eq(maritime_row.country_id)],
             voronoi_config=voronoi_config,
-            crs=shapes.crs,
+            crs=crs["projected"],
         )
         for maritime_row in maritime.itertuples(index=False)
     ]
 
     split_maritime = [pieces for pieces, _ in split_results]
-    result = pd.concat([land, *split_maritime], ignore_index=True)
+    result = gpd.GeoDataFrame(
+        pd.concat([land, *split_maritime], ignore_index=True),
+        geometry="geometry",
+        crs=crs["projected"],
+    )
 
     cell_frames = [i for _, i in split_results if not i.empty]
     if cell_frames:
         cells = gpd.GeoDataFrame(
             pd.concat(cell_frames, ignore_index=True),
             geometry="geometry",
-            crs=shapes.crs,
+            crs=crs["projected"],
         )
-
-    result = result.to_crs(crs["geographic"])
-    result.geometry = result.geometry.buffer(0)
+    result.geometry = result.geometry.make_valid()
+    result = _geo.to_geographic_crs(result, crs["geographic"])
     return result, cells
 
 
-def combine_shapes(
-    land: gpd.GeoDataFrame, maritime: gpd.GeoDataFrame, geo_crs: CRS
+def build_country(
+    land: gpd.GeoDataFrame, maritime: gpd.GeoDataFrame, crs: dict[str, CRS]
 ) -> gpd.GeoDataFrame:
-    """Combine land and marine shapes."""
-    combined = land.copy().to_crs(geo_crs)
-    if not maritime.empty:
-        # remove contested zones and clip to give priority to maritime polygons
-        eez = maritime.copy().to_crs(geo_crs)
-        eez = eez[eez["contested"].eq(False)].drop(columns="contested")
-        combined.geometry = combined.geometry.difference(eez.geometry.union_all())
-        combined = pd.concat([combined, eez], ignore_index=True)
-
-    # Resolve floating point mismatches that occur during CRS conversion
-    combined.geometry = combined.geometry.buffer(0)
-    return combined
+    """Build country dataset, with maritime regions if necessary."""
+    if maritime.empty:
+        return _geo.to_geographic_crs(land, crs["geographic"])
+
+    # Reproject
+    p_land = _geo.to_projected_crs(land, crs["projected"])
+    p_marine = _geo.to_projected_crs(maritime, crs["projected"])
+    # Remove contested zones
+    p_marine = p_marine[p_marine["contested"].eq(False)].drop(columns="contested")
+    # Give priority to EEZs
+    p_land.geometry = p_land.geometry.difference(p_marine.geometry.union_all())
+    combined = gpd.GeoDataFrame(
+        pd.concat([p_land, p_marine], ignore_index=True), crs=crs["projected"]
+    )
+    combined.geometry = combined.geometry.make_valid()
+    return _geo.to_geographic_crs(combined, crs["geographic"])
 
 
 def plot_voronoi_cells(ax: _utils.Axes, cells: gpd.GeoDataFrame, crs: CRS) -> None:
     """Show voronoi tessellation."""
-    projected_cells = cells.copy().to_crs(crs)
+    projected_cells = cells.to_crs(crs)
     projected_cells.boundary.plot(ax=ax, lw=0.2, color="lightgrey", zorder=0)
 
 
 def main() -> None:
     """Main snakemake process."""
-    crs = _utils.check_crs_config(snakemake.params.crs)
-
+    crs = _geo.check_crs_config(snakemake.params.crs)
     country = snakemake.wildcards.country
+
+    # Load and ensure request matches expectations
     land = _schemas.ShapesSchema.validate(gpd.read_parquet(snakemake.input.land))
     maritime = _schemas.EEZSchema.validate(gpd.read_parquet(snakemake.input.maritime))
-
     country_ids = set(land["country_id"]) | set(maritime["country_id"])
-    if set(country_ids) - set([country]):
+    if set(country_ids) - {country}:
         raise ValueError(
-            f"Country processing mismatch for {country!r}. Found {country_ids!r}."
+            f"Country mismatch: expected {country!r}, found {country_ids!r}."
         )
 
-    shapes = combine_shapes(land, maritime, crs["geographic"])
+    # Build default country dataset
+    shapes = build_country(land, maritime, crs)
     shapes = _schemas.ShapesSchema.validate(shapes)
 
+    # If requested, run Voronoi splitting
     voronoi_config = VoronoiConfig(**snakemake.params.voronoi)
     cells = None
     if not maritime.empty and voronoi_config.enabled:
@@ -379,11 +385,11 @@ def main() -> None:
         )
         shapes = _schemas.ShapesSchema.validate(shapes)
 
+    # Save and show a pretty plot
     shapes.to_parquet(snakemake.output.country)
     fig, ax = _utils.plot_shapes(shapes, crs["projected"])
     if cells is not None and not cells.empty:
         plot_voronoi_cells(ax, cells, crs["projected"])
-
     fig.savefig(snakemake.output.plot, dpi=200, bbox_inches="tight")
 
 
diff --git a/workflow/scripts/build_eez.py b/workflow/scripts/build_eez.py
new file mode 100644
index 0000000..819cfee
--- /dev/null
+++ b/workflow/scripts/build_eez.py
@@ -0,0 +1,48 @@
+"""Build one configured EEZ dataset from cached MarineRegions downloads."""
+
+import sys
+from typing import TYPE_CHECKING, Any
+
+import _schemas
+import geopandas as gpd
+import pandas as pd
+from _geo import CRS_MARINE_REGIONS
+
+if TYPE_CHECKING:
+    snakemake: Any
+
+
+def build_eez(country: str, paths: list[str]) -> gpd.GeoDataFrame:
+    """Combine a country EEZ file with optional extra MarineRegions ID files."""
+    frames = [
+        _schemas.EEZSchema.validate(gpd.read_parquet(path)).to_crs(CRS_MARINE_REGIONS)
+        for path in paths
+    ]
+    frames = [frame for frame in frames if not frame.empty]
+
+    if frames:
+        combined = gpd.GeoDataFrame(pd.concat(frames, ignore_index=True))
+        combined["country_id"] = country
+        combined["shape_id"] = combined["parent_id"].apply(
+            lambda parent_id: "_".join([country, "marineregions", str(parent_id)])
+        )
+    else:
+        combined = gpd.GeoDataFrame(
+            columns=_schemas.EEZSchema.to_schema().columns,
+            geometry="geometry",
+            crs=CRS_MARINE_REGIONS,
+        )
+
+    return _schemas.EEZSchema.validate(combined)
+
+
+def main() -> None:
+    """Main snakemake process."""
+    input_paths = [snakemake.input.country, *snakemake.input.extra]
+    gdf = build_eez(snakemake.wildcards.country, input_paths)
+    gdf.to_parquet(snakemake.output.path)
+
+
+if __name__ == "__main__":
+    sys.stderr = open(snakemake.log[0], "w")
+    main()
diff --git a/workflow/scripts/download_harmonised_eez.py b/workflow/scripts/download_harmonised_eez.py
index 3a52578..8a4014d 100644
--- a/workflow/scripts/download_harmonised_eez.py
+++ b/workflow/scripts/download_harmonised_eez.py
@@ -1,10 +1,9 @@
 """Download data from the Marineregions database.
 
 https://www.marineregions.org/
-Constructs EEZ datasets per country
+
 - In cases where no EEZ is available for a country an empty dataframe will be saved.
-- Optionally, additional EEZs may be downloaded if specified in `extra_eez`.
-    - These should result in an error if the code is invalid.
+- Individual MarineRegions IDs can be downloaded separately and combined later.
 """
 
 import sys
@@ -13,8 +12,8 @@
 import _schemas
 import _utils
 import geopandas as gpd
-import pandas as pd
 import requests
+from _geo import CRS_MARINE_REGIONS
 from matplotlib import pyplot as plt
 
 if TYPE_CHECKING:
@@ -22,7 +21,6 @@
 
 WFS_BASE = "https://geo.vliz.be/geoserver/MarineRegions/wfs"
 WFS_VERSION = "2.0.0"
-CRS_MARINE_REGIONS = "EPSG:4326"
 
 
 def _raise_for_geoserver_exception(response: requests.Response) -> None:
@@ -179,45 +177,44 @@ def plot(gdf: gpd.GeoDataFrame, country: str):
     return fig, ax
 
 
-def download_eezs(
-    country: str, extra_eez: list[str], timeouts: _utils.DownloadTimeouts
+def download_eez(
+    cql_filter: str,
+    country_id: str,
+    timeouts: _utils.DownloadTimeouts,
+    *,
+    allow_empty: bool,
 ) -> gpd.GeoDataFrame:
-    """Download EEZ data as requested.
+    """Download and harmonise one EEZ query.
 
-    If no EEZ exists for a country, the dataframe will be empty.
+    If no EEZ exists for a country query, the dataframe will be empty.
+    MarineRegions ID queries are expected to return exactly one dataset.
     """
-    eez_gdfs: list[gpd.GeoDataFrame] = []
-
-    iso3_eez = get_eez_by_cql(f"iso_ter1='{country}'", timeouts)
-    if iso3_eez is not None:
-        eez_gdfs.append(iso3_eez)
-
-    for mrgid in extra_eez:
-        extra_gdf = get_eez_by_cql(f"mrgid={int(mrgid)}", timeouts)
-        if extra_gdf is None:
-            raise RuntimeError(f"Configured extra_eez {mrgid!r} returned no features")
-        eez_gdfs.append(extra_gdf)
-
-    combined_gdf = None
-    if eez_gdfs:
-        combined_gdf = pd.concat(eez_gdfs, ignore_index=True)
+    gdf = get_eez_by_cql(cql_filter, timeouts)
+    if gdf is None and not allow_empty:
+        raise RuntimeError(f"Configured EEZ query {cql_filter!r} returned no features")
 
-    return transform_to_schema(combined_gdf, country)
+    return transform_to_schema(gdf, country_id)
 
 
 def main() -> None:
     """Main snakemake process."""
-    country = snakemake.wildcards.country
-    extra_eez = snakemake.params.extra_eez
-
     timeouts = _utils.DownloadTimeouts(**snakemake.params.timeouts)
-    if not isinstance(extra_eez, list):
-        extra_eez = [extra_eez]
+    eez = snakemake.wildcards.eez
+
+    if eez.isdigit():
+        label = f"mrgid {eez}"
+        gdf = download_eez(
+            f"mrgid={int(eez)}", "extra_eez", timeouts, allow_empty=False
+        )
+    elif len(eez) == 3 and eez.isalpha() and eez.isupper():
+        label = eez
+        gdf = download_eez(f"iso_ter1='{eez}'", eez, timeouts, allow_empty=True)
+    else:
+        raise ValueError(f"Unsupported EEZ identifier: {eez!r}")
 
-    gdf = download_eezs(country, extra_eez, timeouts)
     gdf.to_parquet(snakemake.output.path)
 
-    fig, _ = plot(gdf, country)
+    fig, _ = plot(gdf, label)
     fig.savefig(snakemake.output.plot, bbox_inches="tight", dpi=200)
 
 
diff --git a/workflow/scripts/harmonise_gadm.py b/workflow/scripts/harmonise_gadm.py
index dfa34b0..c263941 100644
--- a/workflow/scripts/harmonise_gadm.py
+++ b/workflow/scripts/harmonise_gadm.py
@@ -46,7 +46,7 @@ def standardise_country_gadm(
 if __name__ == "__main__":
     standardise_country_gadm(
         input_path=snakemake.input.raw,
-        country_id=snakemake.params.country_id,
-        subtype=snakemake.params.subtype,
+        country_id=snakemake.wildcards.country,
+        subtype=snakemake.wildcards.subtype,
         output_path=snakemake.output.standardised,
     )