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287 add local morans i (#305)
* Added Local Moran's I --------- Co-authored-by: lehtonenp <[email protected]>
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‎docs/exploratory_analyses/local_morans_i.md

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# Local Moran's I
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::: eis_toolkit.exploratory_analyses.local_morans_i

‎eis_toolkit/exploratory_analyses/local_morans_i.py

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import geopandas as gpd
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import libpysal
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import numpy as np
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from beartype import beartype
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from beartype.typing import Literal
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from esda.moran import Moran_Local
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from eis_toolkit import exceptions
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from eis_toolkit.exceptions import InvalidParameterValueException
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@beartype
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def _local_morans_i(
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gdf: gpd.GeoDataFrame, column: str, weight_type: Literal["queen", "knn"], k: int, permutations: int
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) -> gpd.GeoDataFrame:
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if weight_type == "queen":
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w = libpysal.weights.Queen.from_dataframe(gdf)
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elif weight_type == "knn":
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w = libpysal.weights.KNN.from_dataframe(gdf, k=k)
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else:
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raise InvalidParameterValueException("Invalid weight_type. Use 'queen' or 'knn'.")
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w.transform = "R"
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if len(gdf[column]) != len(w.weights):
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raise InvalidParameterValueException("Dimension mismatch between data and weights matrix.")
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moran_loc = Moran_Local(gdf[column], w, permutations=permutations)
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gdf[f"{column}_local_moran_I"] = moran_loc.Is
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gdf[f"{column}_local_moran_I_p_value"] = moran_loc.p_sim
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gdf[f"{column}_local_moran_I_p_value"].fillna(value=np.nan, inplace=True)
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return gdf
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@beartype
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def local_morans_i(
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gdf: gpd.GeoDataFrame,
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column: str,
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weight_type: Literal["queen", "knn"] = "queen",
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k: int = 4,
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permutations: int = 999,
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) -> gpd.GeoDataFrame:
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"""Execute Local Moran's I calculation for the data.
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Args:
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gdf: The geodataframe that contains the data to be examined with local morans I.
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column: The column to be used in the analysis.
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weight_type: The type of spatial weights matrix to be used. Defaults to "queen".
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k: Number of nearest neighbors for the KNN weights matrix. Defaults to 4.
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permutations: Number of permutations for significance testing. Defaults to 999.
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Returns:
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Geodataframe appended with two new columns: one with Local Moran's I
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statistic and one with p-value for the statistic.
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Raises:
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EmptyDataFrameException: The input geodataframe is empty.
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"""
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if gdf.shape[0] == 0:
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raise exceptions.EmptyDataFrameException("Geodataframe is empty.")
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if column not in gdf.columns:
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raise exceptions.InvalidParameterValueException(f"Column '{column}' not found in the GeoDataFrame.")
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if k < 1:
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raise exceptions.InvalidParameterValueException("k must be > 0.")
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if permutations < 100:
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raise exceptions.InvalidParameterValueException("permutations must be > 99.")
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calculations = _local_morans_i(gdf, column, weight_type, k, permutations)
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return calculations

‎notebooks/testing_local_morans_i.ipynb

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‎tests/exploratory_analyses/local_morans_i_test.py

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import geopandas as gpd
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import numpy as np
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import pytest
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from eis_toolkit import exceptions
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from eis_toolkit.exploratory_analyses.local_morans_i import local_morans_i
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def test_local_morans_i_queen_correctness():
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"""Test Local Moran's I Queen correctness."""
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permutations = 999
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column = "gdp_md_est"
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data = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
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gdf = gpd.GeoDataFrame(data)
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gdf20 = gdf.head(20)
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Is = [
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-0.0,
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0.07475982388811646,
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-0.0,
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0.32707308155951376,
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0.3270730815595137,
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0.02567026668185298,
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0.06875988330128255,
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0.014784750951796788,
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0.014784750951796785,
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0.04431219450688388,
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0.04431219450688389,
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0.07606167537417435,
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0.07674902657188765,
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0.07510349948697309,
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0.08027860346365057,
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0.08027860346365055,
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0.0766008275457467,
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0.0766008275457467,
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-0.017419349937576573,
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-0.0,
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]
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p_sims = [
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0.001,
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0.365,
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0.001,
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0.056,
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0.056,
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0.191,
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0.355,
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0.206,
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0.38,
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0.201,
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0.256,
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0.464,
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0.351,
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0.24,
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0.224,
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0.386,
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0.397,
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0.338,
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0.299,
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0.001,
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]
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result = local_morans_i(gdf=gdf20, column=column, weight_type="queen", permutations=permutations)
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np.testing.assert_allclose(result[f"{column}_local_moran_I"], Is, rtol=0.1, atol=0.1)
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np.testing.assert_allclose(result[f"{column}_local_moran_I_p_value"], p_sims, rtol=0.1, atol=0.1)
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def test_local_morans_i_knn_correctness():
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"""Test Local Moran's I KNN correctness."""
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k = 4
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permutations = 999
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column = "gdp_md_est"
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data = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
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gdf = gpd.GeoDataFrame(data)
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gdf20 = gdf.head(20)
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Is = [
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0.03686709680905326,
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0.07635702591252187,
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0.07990131850915279,
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0.06552029316560676,
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-0.8031053484182811,
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0.048272793989863144,
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0.051515005797283464,
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0.03846954477931574,
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0.010137393086155687,
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0.051762074257733624,
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0.05895594777225281,
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0.0768224164382028,
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0.07889650044641662,
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0.07492029251731681,
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0.07855252515119235,
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0.07851482805880286,
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-0.26650904930879504,
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-0.25076447340691294,
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-0.015081612933344679,
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-0.2666687928014803,
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]
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p_sims = [
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0.223,
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0.094,
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0.082,
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0.148,
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0.257,
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0.465,
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0.396,
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0.27,
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0.469,
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0.319,
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0.319,
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0.115,
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0.126,
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0.095,
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0.087,
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0.133,
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0.035,
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0.054,
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0.416,
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0.033,
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]
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result = local_morans_i(gdf20, column, "knn", k=k, permutations=permutations)
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np.testing.assert_allclose(result[f"{column}_local_moran_I"], Is, rtol=0.1, atol=0.1)
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np.testing.assert_allclose(result[f"{column}_local_moran_I_p_value"], p_sims, rtol=0.1, atol=0.1)
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def test_empty_geodataframe():
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"""Test Local Moran's I raises EmptyDataFrameException."""
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empty_gdf = gpd.GeoDataFrame()
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# Use pytest.raises to check the expected exception
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with pytest.raises(exceptions.EmptyDataFrameException):
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local_morans_i(empty_gdf, column="value", weight_type="queen", k=2, permutations=999)
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def test_geodataframe_missing_column():
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"""Test Local Moran's I raises InvalidParameterValueException for missing column."""
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gdf = gpd.GeoDataFrame({"test_col": [1, 2, 3]})
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with pytest.raises(exceptions.InvalidParameterValueException):
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local_morans_i(gdf, column="value", weight_type="queen", k=4, permutations=999)
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def test_invalid_k_value():
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"""Test Local Moran's I raises InvalidParameterValueException for k value under 1."""
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gdf = gpd.GeoDataFrame({"value": [1, 2, 3]})
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with pytest.raises(exceptions.InvalidParameterValueException):
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local_morans_i(gdf, column="value", weight_type="queen", k=0, permutations=999)
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def test_invalid_permutations_value():
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"""Test Local Moran's I raises InvalidParameterValueException for permutations value under 100."""
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gdf = gpd.GeoDataFrame({"value": [1, 2, 3]})
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with pytest.raises(exceptions.InvalidParameterValueException):
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local_morans_i(gdf, column="value", weight_type="queen", k=4, permutations=99)

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