Join tracts to united buffers

Author

Affiliation

Vagish Hemmige

Montefiore Medical Center/ Albert Einstein College of Medicine

Linking Census Tracts to United Catchment Areas

This section performs spatial joins between census tracts and the united (merged) transplant center buffers.

Unlike the earlier version—where tracts were joined to individual center buffers—this approach joins tracts to a single merged catchment polygon for each organ, distance, and year.

This prevents duplication and ensures accurate population totals.

Source code

The full R script is available at:

• R/join_tracts_to_united_buffers.R

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

• R/setup.R
• R/functions.R

Why Use United Buffers?

When joining tracts to individual center buffers:

• A tract can intersect multiple centers
• That tract would appear multiple times
• Population totals would be inflated unless deduplicated later

By first uniting buffers using st_union() and then joining:

• Each tract appears at most once
• Population counts reflect total geographic coverage
• Overlapping service areas are handled correctly

This is essential for valid total-access calculations.

---

Objects Created

Three nested list objects are generated:

• tracts_in_buffers_active_united
• tracts_in_buffers_HIV_united
• tracts_in_buffers_HOPE_united

Each object is indexed by:

1. Organ
   
2. Distance
   
3. Year

---

What the Spatial Join Does

For each:

• Organ
  
• Distance
  
• Year

The script performs:

st_join(..., join = st_intersects)

This means:

A census tract is included if it intersects the combined catchment area of that transplant center group.

Because the buffer has already been united:

• Each tract can match only once per category
• No duplication occurs due to overlapping center service areas

---

Resulting Structure

After the loops complete:

• tracts_in_buffers_active_united[[organ]][[distance]][[year]]
• tracts_in_buffers_HIV_united[[organ]][[distance]][[year]]
• tracts_in_buffers_HOPE_united[[organ]][[distance]][[year]]

Each object contains tract-level population and HIV case estimates for tracts located within the total geographic service area of that center category.

---

Why This Step Matters

This unified join enables accurate calculation of:

• Total population within reach of transplant services
  
• Total number of people living with HIV within those catchment areas
  
• Changes in geographic access across years
  
• Comparisons between Active, HIV R+, and HOPE centers

By eliminating overlap-driven duplication, this approach ensures that access estimates reflect true geographic coverage, not inflated counts.

---

#Join tracts to united buffers


tracts_in_buffers_active_united<-list()
tracts_in_buffers_HIV_united<-list()
tracts_in_buffers_HOPE_united<-list()

for (organ_loop in organ_list){
  for (distance_loop in distance_list){
    for (year_loop in year_list){
      
      message(paste("Currently on:", organ_loop, "", distance_loop, "for year:", year_loop))
      message("Calculating tracts for active...")
      
      #ST join to individual tracts and the united transplant center geography to avoid duplication
      tracts_in_buffers_active_united[[organ_loop]][[distance_loop]][[year_loop]] <- 
        st_join(Merged_tracts[[year_loop]], 
                Transplant_centers_active_buffer_united[[organ_loop]][[distance_loop]][[year_loop]], 
                join = st_intersects)
      
      message("Calculating tracts for HIV...")
      tracts_in_buffers_HIV_united[[organ_loop]][[distance_loop]][[year_loop]] <- 
        st_join(Merged_tracts[[year_loop]], 
                Transplant_centers_HIV_buffer_united[[organ_loop]][[distance_loop]][[year_loop]], 
                join = st_intersects)
      
      message("Calculating tracts for HOPE...")
      tracts_in_buffers_HOPE_united[[organ_loop]][[distance_loop]][[year_loop]] <- 
        st_join(Merged_tracts[[year_loop]], 
                Transplant_centers_HOPE_buffer_united[[organ_loop]][[distance_loop]][[year_loop]], 
                join = st_intersects)
    }}}

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