Assignment 2: Spatial Analysis and Visualization

Healthcare Access and Equity in Pennsylvania

Author

Your Name Here

Published

October 1, 2025

Assignment Overview

Learning Objectives: - Apply spatial operations to answer policy-relevant research questions - Integrate census demographic data with spatial analysis - Create publication-quality visualizations and maps - Work with spatial data from multiple sources - Communicate findings effectively for policy audiences

Part 1: Healthcare Access for Vulnerable Populations

Research Question

Which Pennsylvania counties have the highest proportion of vulnerable populations (elderly + low-income) living far from hospitals?

Your analysis should identify counties that should be priorities for healthcare investment and policy intervention.

Required Analysis Steps

Complete the following analysis, documenting each step with code and brief explanations:

Step 1: Data Collection (5 points)

Load the required spatial data: - Pennsylvania county boundaries - Pennsylvania hospitals (from lecture data) - Pennsylvania census tracts

Your Task:

library(tidyverse)
library(sf)
library(tidycensus)
library(tigris)


# Load spatial data




# Check that all data loaded correctly

Questions to answer: - How many hospitals are in your dataset? - How many census tracts? - What coordinate reference system is each dataset in?

Step 2: Get Demographic Data

Use tidycensus to download tract-level demographic data for Pennsylvania.

Required variables: - Total population - Median household income - Population 65 years and over (you may need to sum multiple age categories)

Your Task:

# Get demographic data from ACS




# Join to tract boundaries

Questions to answer: - What year of ACS data are you using? - How many tracts have missing income data? - What is the median income across all PA census tracts?

Step 3: Define Vulnerable Populations

Identify census tracts with vulnerable populations based on TWO criteria: 1. Low median household income (choose an appropriate threshold) 2. Significant elderly population (choose an appropriate threshold)

Your Task:

# Filter for vulnerable tracts based on your criteria

Questions to answer:

  • What income threshold did you choose and why?
  • What elderly population threshold did you choose and why?
  • How many tracts meet your vulnerability criteria?
  • What percentage of PA census tracts are considered vulnerable by your definition?

Step 4: Calculate Distance to Hospitals

For each vulnerable tract, calculate the distance to the nearest hospital.

Your Task:

# Transform to appropriate projected CRS


# Calculate distance from each tract centroid to nearest hospital

Requirements:

  • Use an appropriate projected coordinate system for Pennsylvania
  • Calculate distances in miles
  • Explain why you chose your projection

Questions to answer:

  • What is the average distance to the nearest hospital for vulnerable tracts?
  • What is the maximum distance?
  • How many vulnerable tracts are more than 15 miles from the nearest hospital?

Step 5: Identify Underserved Areas

Define “underserved” as vulnerable tracts that are more than 15 miles from the nearest hospital.

Your Task:

# Create underserved variable

Questions to answer:

  • How many tracts are underserved?
  • What percentage of vulnerable tracts are underserved?
  • Does this surprise you? Why or why not?

Step 6: Aggregate to County Level

Use spatial joins and aggregation to calculate county-level statistics about vulnerable populations and hospital access.

Your Task:

# Spatial join tracts to counties


# Aggregate statistics by county

Required county-level statistics:

  • Number of vulnerable tracts
  • Number of underserved tracts
  • Percentage of vulnerable tracts that are underserved
  • Average distance to nearest hospital for vulnerable tracts
  • Total vulnerable population

Questions to answer:

  • Which 5 counties have the highest percentage of underserved vulnerable tracts?
  • Which counties have the most vulnerable people living far from hospitals?
  • Are there any patterns in where underserved counties are located?

Step 7: Create Summary Table

Create a professional table showing the top 10 priority counties for healthcare investment.

Your Task:

# Create and format priority counties table

Requirements:

  • Use knitr::kable() or similar for formatting
  • Include descriptive column names
  • Format numbers appropriately (commas for population, percentages, etc.)
  • Add an informative caption
  • Sort by priority (you decide the metric)

Part 2: Comprehensive Visualization

Using the skills from Week 3 (Data Visualization), create publication-quality maps and charts.

Map 1: County-Level Choropleth

Create a choropleth map showing healthcare access challenges at the county level.

Your Task:

# Create county-level access map

Requirements:

  • Fill counties by percentage of vulnerable tracts that are underserved
  • Include hospital locations as points
  • Use an appropriate color scheme
  • Include clear title, subtitle, and caption
  • Use theme_void() or similar clean theme
  • Add a legend with formatted labels

Map 2: Detailed Vulnerability Map

Create a map highlighting underserved vulnerable tracts.

Your Task:

# Create detailed tract-level map

Requirements:

  • Show underserved vulnerable tracts in a contrasting color
  • Include county boundaries for context
  • Show hospital locations
  • Use appropriate visual hierarchy (what should stand out?)
  • Include informative title and subtitle

Chart: Distribution Analysis

Create a visualization showing the distribution of distances to hospitals for vulnerable populations.

Your Task:

# Create distribution visualization

Suggested chart types:

  • Histogram or density plot of distances
  • Box plot comparing distances across regions
  • Bar chart of underserved tracts by county
  • Scatter plot of distance vs. vulnerable population size

Requirements:

  • Clear axes labels with units
  • Appropriate title
  • Professional formatting
  • Brief interpretation (1-2 sentences as a caption or in text)

Part 3: Bring Your Own Data Analysis

Choose your own additional spatial dataset and conduct a supplementary analysis.

Challenge Options

Choose ONE of the following challenge exercises, or propose your own research question using OpenDataPhilly data (https://opendataphilly.org/datasets/).

Note these are just loose suggestions to spark ideas - follow or make your own as the data permits and as your ideas evolve. This analysis should include bringing in your own dataset, ensuring the projection/CRS of your layers align and are appropriate for the analysis (not lat/long or geodetic coordinate systems). The analysis portion should include some combination of spatial and attribute operations to answer a relatively straightforward question

Education & Youth Services

Option A: Educational Desert Analysis

  • Data: Schools, Libraries, Recreation Centers, Census tracts (child population)
  • Question: “Which neighborhoods lack adequate educational infrastructure for children?”
  • Operations: Buffer schools/libraries (0.5 mile walking distance), identify coverage gaps, overlay with child population density
  • Policy relevance: School district planning, library placement, after-school program siting

Option B: School Safety Zones

  • Data: Schools, Crime Incidents, Bike Network
  • Question: “Are school zones safe for walking/biking, or are they crime hotspots?”
  • Operations: Buffer schools (1000ft safety zone), spatial join with crime incidents, assess bike infrastructure coverage
  • Policy relevance: Safe Routes to School programs, crossing guard placement

Environmental Justice

Option C: Green Space Equity

  • Data: Parks, Street Trees, Census tracts (race/income demographics)
  • Question: “Do low-income and minority neighborhoods have equitable access to green space?”
  • Operations: Buffer parks (10-minute walk = 0.5 mile), calculate tree canopy or park acreage per capita, compare by demographics
  • Policy relevance: Climate resilience, environmental justice, urban forestry investment

Public Safety & Justice

Option D: Crime & Community Resources

  • Data: Crime Incidents, Recreation Centers, Libraries, Street Lights
  • Question: “Are high-crime areas underserved by community resources?”
  • Operations: Aggregate crime counts to census tracts or neighborhoods, count community resources per area, spatial correlation analysis
  • Policy relevance: Community investment, violence prevention strategies

Infrastructure & Services

Option E: Polling Place Accessibility

  • Data: Polling Places, SEPTA stops, Census tracts (elderly population, disability rates)
  • Question: “Are polling places accessible for elderly and disabled voters?”
  • Operations: Buffer polling places and transit stops, identify vulnerable populations, find areas lacking access
  • Policy relevance: Voting rights, election infrastructure, ADA compliance

Health & Wellness

Option F: Recreation & Population Health

  • Data: Recreation Centers, Playgrounds, Parks, Census tracts (demographics)
  • Question: “Is lack of recreation access associated with vulnerable populations?”
  • Operations: Calculate recreation facilities per capita by neighborhood, buffer facilities for walking access, overlay with demographic indicators
  • Policy relevance: Public health investment, recreation programming, obesity prevention

Emergency Services

Option G: EMS Response Coverage

  • Data: Fire Stations, EMS stations, Population density, High-rise buildings
  • Question: “Are population-dense areas adequately covered by emergency services?”
  • Operations: Create service area buffers (5-minute drive = ~2 miles), assess population coverage, identify gaps in high-density areas
  • Policy relevance: Emergency preparedness, station siting decisions

Arts & Culture

Option H: Cultural Asset Distribution

  • Data: Public Art, Museums, Historic sites/markers, Neighborhoods
  • Question: “Do all neighborhoods have equitable access to cultural amenities?”
  • Operations: Count cultural assets per neighborhood, normalize by population, compare distribution across demographic groups
  • Policy relevance: Cultural equity, tourism, quality of life, neighborhood identity

Data Sources

OpenDataPhilly: https://opendataphilly.org/datasets/

  • Most datasets available as GeoJSON, Shapefile, or CSV with coordinates
  • Always check the Metadata for a data dictionary of the fields.

Additional Sources:

  • Pennsylvania Open Data: https://data.pa.gov/
  • Census Bureau (via tidycensus): Demographics, economic indicators, commute patterns
  • TIGER/Line (via tigris): Geographic boundaries

Your Analysis

Your Task:

  1. Find and load additional data
    • Document your data source
    • Check and standardize the CRS
    • Provide basic summary statistics
# Load your additional dataset

Questions to answer: - What dataset did you choose and why? - What is the data source and date? - How many features does it contain? - What CRS is it in? Did you need to transform it?

  1. Pose a research question

Write a clear research statement that your analysis will answer.

Examples: - “Do vulnerable tracts have adequate public transit access to hospitals?” - “Are EMS stations appropriately located near vulnerable populations?” - “Do areas with low vehicle access have worse hospital access?”

  1. Conduct spatial analysis

Use at least TWO spatial operations to answer your research question.

Required operations (choose 2+): - Buffers - Spatial joins - Spatial filtering with predicates - Distance calculations - Intersections or unions - Point-in-polygon aggregation

Your Task:

# Your spatial analysis

Analysis requirements: - Clear code comments explaining each step - Appropriate CRS transformations - Summary statistics or counts - At least one map showing your findings - Brief interpretation of results (3-5 sentences)

Your interpretation:

[Write your findings here]

Finally - A few comments about your incorporation of feedback!

Take a few moments to clean up your markdown document and then write a line or two or three about how you may have incorporated feedback that you recieved after your first assignment.