Spatial Risk Map Module for H5N1

This module provides functionality to create spatial risk maps for H5N1 outbreaks at the zip code level.

Overview

The RiskMap class calculates composite risk scores for zip codes based on multiple risk factors relevant to H5N1 transmission:

• Population Density: Higher population density increases contact rates
• Bird/Poultry Density: Primary reservoir for H5N1
• Water Proximity: Wild bird habitats (migration routes)
• Healthcare Capacity: Ability to respond to outbreaks
• Socioeconomic Vulnerability: Factors affecting outbreak response

Installation

Install required dependencies:

pip install -r requirements.txt

Quick Start

Basic Usage

from risk_map import RiskMap
import pandas as pd

# Load your zip code data
data = pd.read_csv('data/raw/zip_codes.csv')

# Initialize RiskMap
risk_map = RiskMap(zip_code_data=data)

# Calculate risk scores
risk_scores = risk_map.calculate_risk_scores()

# Get high-risk zip codes
high_risk = risk_map.get_high_risk_zips(top_n=10)

# Export results
risk_map.export_risk_data('output/risk_scores.csv', format='csv')

With Custom Risk Weights

# Customize risk factor weights
custom_weights = {
    'population_density': 0.25,
    'bird_density': 0.45,  # Higher weight for avian flu
    'water_proximity': 0.20,
    'healthcare_capacity': 0.05,
    'vulnerability_index': 0.05
}

risk_map = RiskMap(
    zip_code_data=data,
    risk_weights=custom_weights
)

Loading Geospatial Data

# Load from GeoJSON
risk_map = RiskMap()
gdf = risk_map.load_zip_code_data('data/raw/nyc_zip_codes.geojson')

# Calculate risk scores
risk_scores = risk_map.calculate_risk_scores()

# Create visualizations
risk_map.visualize_risk_map(output_path='output/risk_map.png')
risk_map.create_interactive_map(output_path='output/risk_map.html')

Data Requirements

Your zip code data should include the following columns:

Required Columns

• zip_code: Zip code identifier
• population: Population count
• area_km2: Area in square kilometers (or geometry for automatic calculation)

Optional Columns

• bird_density: Density of birds/poultry
• water_proximity: Proximity to water bodies (0-1 scale)
• healthcare_capacity: Healthcare capacity index (0-1 scale)
• vulnerability_index: Socioeconomic vulnerability index (0-1 scale)
• geometry: GeoSeries geometry (for spatial visualizations)

Risk Score Calculation

Risk scores are calculated as a weighted sum of normalized risk factors:

risk_score = Σ (normalized_factor_i × weight_i)

Each factor is normalized to a 0-1 scale using min-max normalization. Risk scores range from 0 (lowest risk) to 1 (highest risk).

Risk categories:

• Low: 0.0 - 0.25
• Medium: 0.25 - 0.5
• High: 0.5 - 0.75
• Very High: 0.75 - 1.0

Methods

calculate_risk_scores()

Calculates composite risk scores for all zip codes.

get_high_risk_zips(threshold, top_n)

Returns zip codes with risk scores above a threshold or top N zip codes.

visualize_risk_map(output_path, ...)

Creates a static choropleth map visualization (requires geometry data).

create_interactive_map(output_path, ...)

Creates an interactive Folium map (requires geometry data).

export_risk_data(output_path, format)

Exports risk scores to CSV, GeoJSON, or Shapefile format.

Example Workflow

1. Prepare Data: Collect zip code data with population, area, and risk factors
2. Load Data: Use load_zip_code_data() or pass DataFrame to RiskMap()
3. Calculate Risk: Call calculate_risk_scores()
4. Analyze: Use get_high_risk_zips() to identify priority areas
5. Visualize: Create maps for reporting and communication
6. Export: Save results for further analysis or integration

Integration with NYC Data

For NYC-specific implementation, you'll need:

1. Zip Code Boundaries:

   • NYC Open Data: https://data.cityofnewyork.us/Business/Zip-Code-Boundaries/i8iw-xf4u
   • Or US Census Bureau TIGER/Line files

2. Population Data:

   • US Census Bureau ACS data
   • NYC Department of City Planning

3. Bird/Poultry Data:

   • USDA poultry facility locations
   • Wild bird migration data
   • Waterfowl habitat maps

4. Healthcare Capacity:

   • Hospital locations and bed counts
   • ICU capacity data

5. Socioeconomic Data:

   • CDC Social Vulnerability Index
   • NYC Department of Health data

Future Enhancements

• Temporal risk mapping (risk over time/stages of outbreak)
• Integration with epidemiological models
• Real-time data updates
• Scenario modeling with different parameter sets
• API endpoints for rapid deployment

References

• H5N1 epidemiological parameters from literature review
• Spatial risk assessment methodologies
• NYC public health data sources