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shapefile_to_raster.md

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Loading Shapefiles into the Raster Substrate

DisSModel can load any vector file (Shapefile, GeoJSON, GeoPackage) and convert it directly into a RasterBackend, making it possible to run high-performance raster models on real geographic data without any intermediate GIS steps.

How it works

Shapefile → GeoDataFrame → rasterize → NumPy arrays → RasterBackend → raster model

The rasterization step (powered by rasterio.features.rasterize) is fast and happens once — after that, the model runs entirely in NumPy at full vectorized speed.

!!! note "Grid regularity" This workflow is most accurate when the input shapefile already contains a regular grid of equal-area polygons (e.g. 100×100m cells), which is the typical output of spatial homogenization tools. For irregular polygons (municipalities, watersheds), cell values are burned by centroid or by touch — inspect the result before running long simulations.

shapefile_to_raster_backend

from dissmodel.geo.raster.io import shapefile_to_raster_backend

b = shapefile_to_raster_backend(
    path       = "data/mangue_grid.shp",
    resolution = 100,               # 100m cells
    attrs      = ["uso", "alt", "solo"],
    crs        = "EPSG:31984",      # reproject if needed
)

print(b.shape)          # (rows, cols) derived from bounding box + resolution
print(b.get("uso").dtype)  # int32

Parameters

Parameter Type Description
path str or Path Path to the vector file
resolution float Cell size in CRS units (metres for metric CRS)
attrs list[str] or dict[str, default] Columns to rasterize
crs str, int, or None Target CRS — reprojects if needed
all_touched bool Burn all touched cells (default: centre only)
nodata int or float Fill value for uncovered cells (default: 0)

To set per-column defaults for uncovered cells, pass a dict:

b = shapefile_to_raster_backend(
    path       = "data/mangue_grid.shp",
    resolution = 100,
    attrs      = {"uso": 5, "alt": 0.0, "solo": 1},
    crs        = "EPSG:31984",
)

Full example — flood model from shapefile

from dissmodel.core import Environment
from dissmodel.geo.raster.io import shapefile_to_raster_backend
from dissmodel.visualization.raster_map import RasterMap

# from coastal_dynamics.raster import FloodRasterModel
# (or your own RasterModel subclass)
from myproject.flood import FloodRasterModel

# 1. load shapefile → RasterBackend
b = shapefile_to_raster_backend(
    path       = "data/mangue_grid.shp",
    resolution = 100,
    attrs      = {"uso": 5, "alt": 0.0, "solo": 1},
    crs        = "EPSG:31984",
)

print(f"Grid: {b.shape[0]} rows × {b.shape[1]} cols = {b.shape[0]*b.shape[1]:,} cells")

# 2. run the raster model
env = Environment(start_time=2012, end_time=2100)
FloodRasterModel(backend=b, taxa=0.011)
RasterMap(backend=b, band="uso", title="Land Use")
env.run()

Saving results back to GeoTIFF

from dissmodel.geo.raster.io import save_raster_backend

save_raster_backend(
    backend   = b,
    path      = "output/flood_result.tif",
    bands     = ["uso", "alt"],
    crs       = "EPSG:31984",
    transform = transform,   # rasterio Affine from original bounds
)

Comparison: vector vs raster workflow from the same shapefile

# ── vector workflow ───────────────────────────────────────────────────────────
import geopandas as gpd
from dissmodel.geo.vector.model import SpatialModel

gdf = gpd.read_file("data/mangue_grid.shp").to_crs("EPSG:31984")
# → GeoDataFrame with real geometries, Queen neighbourhood, ~2 min/step @ 94k cells

# ── raster workflow ───────────────────────────────────────────────────────────
from dissmodel.geo.raster.io import shapefile_to_raster_backend

b = shapefile_to_raster_backend(
    "data/mangue_grid.shp", resolution=100,
    attrs=["uso", "alt", "solo"], crs="EPSG:31984"
)
# → RasterBackend, same data, ~8 ms/step @ 94k cells  (≈ 4,500× faster)

The data source is the same shapefile. The only difference is the substrate.

API Reference

::: dissmodel.geo.raster.io.shapefile_to_raster_backend

::: dissmodel.geo.raster.io.save_raster_backend