Unite buffers

Author

Affiliation

Vagish Hemmige

Montefiore Medical Center/ Albert Einstein College of Medicine

This script combines individual transplant center buffers into unified, organ-level catchment areas.

Earlier steps created buffers (either distance-based or 60-minute travel-time isochrones) around each transplant center.
Here, those individual polygons are merged into a single combined service area for each organ, distance, and year.

Source code

The full R script is available at:

• R/unite_buffers.R

This R script file is itself reliant on the following helper files:

• R/setup.R
• R/functions.R

Why “Unite” Buffers?

When buffers are created around multiple centers, they often:

• Overlap geographically
  
• Cover adjacent regions
  
• Share common areas

If we simply summed tract populations within each individual buffer, overlapping areas would be double-counted.

To prevent this, we:

• Combine (union) all individual center buffers
• Create a single, continuous polygon representing the total catchment area
• Ensure that each geographic area is counted only once

---

Objects Created

Four parallel objects are constructed:

• Transplant_center_all_buffer_united
• Transplant_centers_active_buffer_united
• Transplant_centers_HIV_buffer_united
• Transplant_centers_HOPE_buffer_united

Each object is indexed by:

1. Organ
   
2. Distance
   
3. Year (when applicable)

---

How the Union Works

For each:

• Organ
• Distance
• (And year, when relevant)

The script:

1. Takes the set of individual buffer polygons
2. Applies st_union(geometry)
   
3. Collapses them into one merged geometry
4. Converts the result back into an sf object

The resulting dataset contains a single row labeled:

catchment_area = “All Centers”

This label clarifies that the polygon represents the combined service area of all centers in that category.

---

Structure of the Result

After processing, the structure is:

• Transplant_center_all_buffer_united[[organ]][[distance]]
• Transplant_centers_active_buffer_united[[organ]][[distance]][[year]]
• Transplant_centers_HIV_buffer_united[[organ]][[distance]][[year]]
• Transplant_centers_HOPE_buffer_united[[organ]][[distance]][[year]]

Each object is a single merged polygon representing the total geographic coverage of that center group.

---

Why This Step Matters

Uniting buffers allows accurate calculation of:

• Total population within service areas
• Total number of people living with HIV within reach
• Changes in geographic access between years
• Comparisons across center categories (Active vs HIV vs HOPE)

Without uniting overlapping buffers, population totals would be inflated and geographic access overstated.

---

#Unite for total calculations

Transplant_center_all_buffer_united<-list()
Transplant_centers_active_buffer_united<-list()
Transplant_centers_HIV_buffer_united<-list()
Transplant_centers_HOPE_buffer_united<-list()

for (organ_loop in organ_list){
  for (distance_loop in distance_list){
    
    Transplant_center_all_buffer_united[[organ_loop]][[distance_loop]]<-
      Transplant_center_all_buffer[[organ_loop]][[distance_loop]]%>%
      reframe(
        catchment_area = "All Centers", 
        geometry = st_union(geometry)
      )%>%
      st_as_sf()
    
    
    for (year_loop in year_list){
      
      
      Transplant_centers_active_buffer_united[[organ_loop]][[distance_loop]][[year_loop]]<-
        Transplant_centers_active_buffer[[organ_loop]][[distance_loop]][[year_loop]]%>%
        reframe(
          catchment_area = "All Centers", 
          geometry = st_union(geometry)
        )%>%
        st_as_sf()
      
      Transplant_centers_HIV_buffer_united[[organ_loop]][[distance_loop]][[year_loop]]<-
        Transplant_centers_HIV_buffer[[organ_loop]][[distance_loop]][[year_loop]]%>%
        reframe(
          catchment_area = "All Centers", 
          geometry = st_union(geometry)
        )%>%
        st_as_sf()
      
      
      
      
      Transplant_centers_HOPE_buffer_united[[organ_loop]][[distance_loop]][[year_loop]]<-
        Transplant_centers_HOPE_buffer[[organ_loop]][[distance_loop]][[year_loop]]%>%
        reframe(
          catchment_area = "All Centers", 
          geometry = st_union(geometry)
        )%>%
        st_as_sf()
      
      
      
      
    }
    
  }
}

Other portions of the analysis

• Setup: Defines global paths, data sources, cohort inclusion criteria, and analysis-wide constants.
• Functions: Reusable helper functions for cohort construction, matching, costing, and modeling.
• Tables: Summary tables and regression outputs generated from the final models.
• Figures:Visualizations of costs, risks, and model-based estimates.
• About: methods, assumptions, and disclosures