sppt 0.1.4

Spatial Point Pattern Test (SPPT) for Aggregated Data — bootstrap-based spatial distribution comparison

sppt — Spatial Point Pattern Test for Aggregated Data

A Python implementation of the Spatial Point Pattern Test (SPPT) for aggregated count data. Uses bootstrap resampling to compare spatial distributions between variables and calculates S-Index metrics to quantify spatial pattern overlap.

Based on the original R package sppt.aggregated.data by Martin A. Andresen. This Python port faithfully reimplements the statistical methods, algorithms, and outputs of the R version.

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Features

• Bootstrap resampling with sparse-matrix acceleration (scipy.sparse + numpy)
• S-Index & Robust S-Index for quantifying spatial pattern overlap
• Bivariate comparison (base vs. test variable) with directional change detection
• Percentage or count mode — compare spatial distributions or absolute values
• Fixed base option — bootstrap only the test variable when the base is known
• Automatic choropleth maps via matplotlib + geopandas
• Multiple export formats — Shapefile, GeoPackage, CSV, TXT, Pickle
• Google Colab compatible — works out of the box in cloud notebooks

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Installation

pip install sppt

For development:

git clone https://github.com/yunusserhat/sppt-python.git
cd sppt-python
pip install -e ".[dev]"

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Quick Start

import geopandas as gpd
from sppt import sppt

# Load spatial data
data = gpd.read_file("your_data.shp")

# Compare two variables across spatial units
result = sppt(
    data=data,
    group_col="DAUID",                # spatial unit identifier
    count_col=["Crime_2020", "Crime_2021"],  # [base, test]
    B=200,                            # bootstrap samples
    check_overlap=True,               # compute S-Index
    seed=42,                          # reproducibility
)

# Access results
print(result.s_index)          # e.g. 0.7380
print(result.robust_s_index)   # e.g. 0.7289
print(result.data.head())      # DataFrame with CI bounds + overlap columns

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How It Works

Algorithm

1. Expand aggregated counts to individual events (uncount)
2. Build a sparse one-hot matrix (n × G) for group membership
3. Draw B multinomial bootstrap samples
4. Aggregate via matrix multiply: group_counts = onehot.T @ W
5. Convert to percentages (optional) and extract quantile-based confidence intervals
6. Compare intervals between variables to detect significant spatial changes

S-Index Interpretation

S-Index	Meaning
1.0	Perfect overlap — no spatial pattern change
0.5	Half the areas show significant change
0.0	Complete spatial difference

The Robust S-Index excludes spatial units where all variables are zero.

SIndex_Bivariate (per spatial unit)

Value	Meaning
-1	Base > Test (decline)
0	No significant difference
+1	Test > Base (increase)

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Parameters

Parameter	Default	Description
data	—	GeoDataFrame or DataFrame with count data
group_col	"group"	Column identifying spatial units
count_col	—	Column name(s) with counts. Pass ["base", "test"] for bivariate
B	200	Number of bootstrap samples
seed	None	Random seed for reproducibility
conf_level	0.95	Confidence level for intervals
check_overlap	False	Compute overlap + S-Index statistics
fix_base	False	Skip bootstrapping the base (first) variable
use_percentages	True	Compare spatial distributions (%) vs. raw counts
create_maps	True	Generate choropleth map for bivariate case
export_maps	False	Save map to disk
export_dir	None	Directory for map export
map_dpi	300	Resolution for exported maps
export_results	False	Save results to disk
export_format	"shp"	Format: "shp", "gpkg", "csv", "txt", "pickle"
export_results_dir	None	Directory for results export

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Examples

Example 1: Vancouver Crime Data

import geopandas as gpd
from sppt import sppt

data = gpd.read_file("Vancouver_DAs_Crime_2021.shp")
data = data.to_crs(epsg=26910)

result = sppt(
    data=data,
    group_col="DAUID",
    count_col=["TFV", "TOV"],  # Total Family Violence vs Total Other Violence
    B=200,
    check_overlap=True,
    create_maps=True,
    seed=171717,
)

Output:

========================================
Spatial Pattern Overlap Statistics
Using: Percentages (spatial distribution)
========================================
S-Index:           0.7380
Robust S-Index:    0.7289
----------------------------------------
Total observations:                 1019
Observations with overlap:          752
Observations with non-zero counts:  985
========================================

Example 2: Fixed Base Variable

result = sppt(
    data=data,
    group_col="DAUID",
    count_col=["Census_Official", "Survey_Estimate"],
    B=200,
    fix_base=True,       # don't bootstrap the census data
    check_overlap=True,
    seed=42,
)

Example 3: Count Mode

result = sppt(
    data=data,
    group_col="DAUID",
    count_col=["Crime_2020", "Crime_2021"],
    B=200,
    use_percentages=False,  # compare absolute counts
    check_overlap=True,
    seed=42,
)

Example 4: Export Results

result = sppt(
    data=data,
    group_col="DAUID",
    count_col=["TFV", "TOV"],
    B=500,
    check_overlap=True,
    export_results=True,
    export_format="gpkg",           # GeoPackage
    export_results_dir="output/",
    export_maps=True,
    export_dir="output/maps/",
    map_dpi=600,                    # publication quality
    seed=171717,
)

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Interactive Notebooks

Notebook	Description	Colab
Quickstart	Basic usage with Vancouver crime data
Advanced Examples	All modes, export, publication maps

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Sample Data

The package includes the Vancouver Dissemination Areas Crime 2021 dataset (1,019 polygons) for testing:

from sppt import load_sample_data

data = load_sample_data()
print(data.columns)
# ['DAUID', 'DGUID', 'LANDAREA', 'PRUID', 'BNEC', 'BNER',
#  'MISCHIEF', 'TFV', 'THEFT', 'TOB', 'TOV', 'geometry']

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Output Columns

After running sppt(), your data gains these columns:

Column	Description
{var}_L	Lower bound of confidence interval
{var}_U	Upper bound of confidence interval
intervals_overlap	1 if CIs overlap, 0 otherwise
SIndex_Bivariate	-1 (base > test), 0 (overlap), 1 (test > base)

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Citation

If you use this package in your research, please cite both the Python package and the original R implementation:

@software{bicakci2026sppt,
  author  = {Bıçakçı, Yunus Serhat},
  title   = {sppt: Spatial Point Pattern Test for Aggregated Data (Python)},
  year    = {2026},
  url     = {https://github.com/yunusserhat/sppt-python},
  doi     = {10.5281/zenodo.18813433},
  note    = {Python implementation based on the R package by Martin A. Andresen}
}

@software{andresen2025sppt,
  author  = {Andresen, Martin A.},
  title   = {sppt.aggregated.data: Spatial Point Pattern Test for Aggregated Data (R)},
  year    = {2025},
  url     = {https://github.com/martin-a-andresen/sppt.aggregated.data}
}

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Acknowledgements

This package is a faithful Python reimplementation of the R package sppt.aggregated.data created by Martin A. Andresen. The statistical methodology, bootstrap algorithm, S-Index calculations, and output structure are directly based on his original work.

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License

MIT