spacv is a small Python 3 (3.6 and above) package for cross-validation of models that assess generalization performance to datasets with spatial dependence. spacv provides a familiar sklearn-like API to expose a suite of tools useful for points-based spatial prediction tasks. See the notebook spacv_guide.ipynb for usage.

• numpy
• matplotlib
• pandas
• geopandas
• shapely
• scikit-learn
• scipy

To install use pip:

$ pip install spacv 

Then build quick spatial cross-validation workflows with sklearn as:

import spacv import geopandas as gpd from sklearn.model_selection import cross_val_score from sklearn.svm import SVC df = gpd.read_file('data/baltim.geojson') XYs = df['geometry'] X = df[['NROOM', 'BMENT', 'NBATH', 'PRICE', 'LOTSZ', 'SQFT']] y = df['PATIO'] # Build fold indices as a generator skcv = spacv.SKCV(n_splits=4, buffer_radius=10).split(XYs) svc = SVC() cross_val_score(svc, # Model  X, # Features y, # Labels cv = skcv) # Fold indices