popexposure 1.0.0.dev10

A package for finding the number of people residing near environmental hazards

popexposure: Functions to estimate the number of people living near environmental hazards

Overview

popexposure is an open-source Python package providing fast, memory-efficient, and consistent estimates of the number of people living near environmental hazards, enabling environmental scientists to assess population-level exposure to environmental hazards based on residential proximity. Methodological details can be found in McBrien et al (2025). Extensive documentation can be found on in our quick start tutorial.

Installation

The easiest way to install popexposure is via the latest pre-compiled binaries from PyPI with:

pip install popexposure

You can build popexposure from source as you would any other Python package with:

git clone https://github.com/heathermcb/popexposure
cd popexposure
python -m pip install .

Tutorials

A number of tutorials providing worked examples using popexposure can be found in our demos folder.

Quickstart

import glob
import pandas as pd
from popexposure.find_exposure import PopEstimator

# Instantiate estimator
pop_est = PopEstimator()

# List of years and corresponding hazard file paths
years = [2016, 2017, 2018]
hazard_paths = [
    "hazard_2016.geojson",
    "hazard_2017.geojson",
    "hazard_2018.geojson"
]
pop_path = "my_pop_raster.tif"
admin_units_path = "my_admin_units.geojson"

# Prepare admin units data
admin_units = pop_est.prep_data(admin_units_path, geo_type="admin_unit")

# Find total num ppl residing <= 10km of each hazard in each year
exposed_list = []

for year, hazard_path in zip(years, hazard_paths):
    # Prepare hazard data for this year
    hazards = pop_est.prep_data(hazard_path, geo_type="hazard")
    # Estimate exposed population
    exposed = pop_est.est_exposed_pop(
        pop_path=pop_path,
        hazard_specific=False,  # set to True if you want per-hazard results
        hazards=hazards,
        admin_units=admin_units # leave as None for estimates not by admin unit
    )
    exposed['year'] = year
    exposed_list.append(exposed)

exposed_df = pd.concat(exposed_list, axis=0)

# Save output
exposed_df.to_parquet("pop_exposed_to_hazards.parquet")

Available functions

Function	Overview	Inputs	Outputs
prep_data	Reads, cleans, and preprocesses geospatial hazard or admin unit data by removing empty or missing geometries, and buffering hazard data according to user-passed buffer distances	Path to hazard or administrative unit file (.geojson or .parquet), geo_type ("hazard" or "admin_unit")	Cleaned GeoDataFrame with valid geometries
est_exposed_pop	Estimates number of people living within hazard buffer(s) using a raster	Population raster path (.tif), hazard data, hazard_specific (bool), optional administrative units	DataFrame with exposed population counts by hazard/administrative unit
est_pop	Estimates total population in admin geographies using a raster	Population raster path (.tif), administrative unit data (GeoDataFrame)	DataFrame with total population per administrative unit

Getting help and contributing

If you have any questions, a feature request, or would like to report a bug, please open an issue. We also welcome any new contributions and ideas. If you want to add code, please submit a pull request and we will get back to you when we can. Thanks!

Citing this package

Please cite our paper McBrien et al (2025).

Authors

• Heather McBrien
• Joan A. Casey
• Lawrence Chillrud
• Nina M. Flores
• Lauren B. Wilner

References

Our package is a fancy wrapper for the package exactextract.