Introduction

Barriers are central to discussions surrounding the criminal justice system and system-involved individuals. A key topic faced is transportation. The “five A’s” describe the primary factors related to transportation that present issues for individuals: “affordability, accessibility, applicability, availability, and awareness”.

Transportation is a significant consideration for employment, healthcare, groceries/products, and access to government locations like courthouses. Massachusetts comprises of 7 types of trial courts, including Superior Court, District Court, Boston Municipal Court, Housing Court, Juvenile Court, Land Court, and Probate and Family Court. This research initially began as an inquiry into the accessibility of courthouses for system-involved individuals – attempting to identify barriers to compliance with court dates/appointments. It evolved into a state-wide examination of general public access to courthouses via the state’s public transit infrastructure.

Primary Goals:

Establish a comprehensive understanding of MA public transportation coverage across counties
Evaluate how courthouse locations and public transit options differ across counties

Overview of Data

Shape Files:

MA Counties
MBTA Bus Routes
MBTA Bus Stops
RTA Bus Routes
RTA Bus Stops
RTA Boundaries
Train Routes
Train Stops
Courthouse Locations

From the Massachusetts Bureau of Geographic Information through MassGIS and the Massachusetts Department of Transportation Open Data Portal through massDOT

Data Sets:

County Population
2021 Full Year Court Filings

From the United States Census Bureau through census.gov and MA Court Data, Metrics, and Reports through Mass.gov

Study Area

MA Public Transit Overview

Courthouse locations and bus/train routes (not included: bus/train stops)

RTA Coverage

The Regional Transit Authority (RTA) provides public transportation systems across Massachusetts. The RTA has 15 defined coverage areas.

Steps in ArcGIS Pro:

Add the county and RTA boundary shape files
Use the ‘calculate geometry’ feature to calculate the area in square miles of each county
Use the ‘erase’ analysis tool to remove county and RTA overlap
Calculate area of new layer, which is area not covered in each county
Export the table to Excel and import to R

# import data table
RTA_Coverage <- read_excel("RTA_coverage_data.xls")
RTA_Coverage <- select(RTA_Coverage, "COUNTY", "MiSQ", "MiSQExcl")
# Calculate percentage without coverage in each county
RTA_Coverage <- RTA_Coverage %>%
  group_by(COUNTY) %>% 
  mutate(Percentage=paste0(round(MiSQExcl/MiSQ*100, 1),"%"))

kable(RTA_Coverage, col.names = c("County", "Area (sq mi)", 
                                  "Area w/o RTA Coverage (sq mi)",
                                  "Percentage of County w/o RTA Coverage"), 
      align = c('c', 'c', 'c', 'c')) %>% 
  add_header_above(c("Percentage of Area per County not Covered by an RTA System" = 4)) %>% 
  kable_styling() %>% 
  scroll_box(height = "100%", width = "100%")

Percentage of Area per County not Covered by an RTA System
County	Area (sq mi)	Area w/o RTA Coverage (sq mi)	Percentage of County w/o RTA Coverage
BARNSTABLE	411.9740	0.1448718	0%
BERKSHIRE	946.3420	174.1378326	18.4%
BRISTOL	571.7110	0.9371677	0.2%
DUKES	106.4660	13.4747152	12.7%
ESSEX	513.2210	141.8912659	27.6%
FRANKLIN	724.4930	10.8300152	1.5%
HAMPDEN	634.1160	77.5864792	12.2%
MIDDLESEX	846.4100	163.2825623	19.3%
NANTUCKET	48.9112	0.0213758	0%
NORFOLK	409.4450	214.5567932	52.4%
PLYMOUTH	689.5540	129.9457397	18.8%
SUFFOLK	60.9833	2.9424100	4.8%
WORCESTER	1579.1600	86.1403275	5.5%

From this, we can see that Norfolk County is the least covered county by the RTA, with 52.4% of the county excluded from RTA’s defined boundaries.

Distribution of Courthouses Across MA

Next, I imported county populations and evaluated the proportion of courthouses in each county based on population and based on county area.

Closest Bus or Train Stop to Each Courthouse

Steps to calculate distance from closest stop to courthouse in ArcGIS Pro:

Use the ‘merge’ tool for RTA, MBTA, and Train stops, creating one feature
Use ‘near’ analysis tool to calculate geodesic distance from closest stop to courthouse
Export table to Excel and import in R

# Import data
Courthouse_Stops <- read_excel("../Samples/courthouse_stops.xls")
Courthouse_Stops <- select(Courthouse_Stops, "NAME", "CITY", "COUNTY", "NEAR_DIST")

# Convert from Meters to Miles
Courthouse_Stops$NEAR_DIST <- Courthouse_Stops$NEAR_DIST*0.00062137
# Calculate summary stats for entire state
round(summary(Courthouse_Stops$NEAR_DIST), digits = 2)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.01    0.04    0.07    0.21    0.14    2.69

Across the state, the average distance from the closest bus/train stop to courthouse is 0.21 miles, and the longest distance is 2.69 miles.

# Summary stats by county
Stops_Summary <- Courthouse_Stops %>% 
  group_by(COUNTY) %>% 
  summarize(min = min(NEAR_DIST),
            med = median(NEAR_DIST), 
            mean = mean(NEAR_DIST), 
            max = max(NEAR_DIST))

Stops_Summary <- data.frame(lapply(Stops_Summary,
                function(x) if(is.numeric(x)) round(x, 2) else x))

kable(Stops_Summary, col.names = c("County", "Min", "Median", "Mean", "Max"), align = c('c', 'c', 'c', 'c', 'c')) %>% add_header_above(c("Summary Statistics for Distance from Closest Stop to Courthouse by County" = 5)) %>% kable_styling() %>% scroll_box(height = "100%", width = "100%")

Summary Statistics for Distance from Closest Stop to Courthouse by County
County	Min	Median	Mean	Max
Barnstable	0.02	0.07	0.10	0.24
Berkshire	0.04	0.08	0.10	0.18
Bristol	0.03	0.04	0.08	0.26
Dukes	0.04	0.05	0.05	0.06
Essex	0.01	0.05	0.06	0.18
Franklin	0.10	0.14	0.14	0.17
Hampden	0.03	0.13	0.19	0.70
Hampshire	0.03	0.05	0.35	1.27
Middlesex	0.02	0.05	0.13	0.83
Nantucket	0.03	0.03	0.03	0.03
Norfolk	0.05	0.12	0.27	1.35
Plymouth	0.02	0.07	0.15	0.37
Suffolk	0.02	0.05	0.06	0.12
Worcester	0.01	0.79	0.91	2.69

It looks like Worcester County has the courthouse that is 2.69 miles from the closest bus or train stop.

# Restructure data to plot
Long_Stops_Summary <- Stops_Summary %>%
  pivot_longer(c(min, med, mean, max), names_to = "Summary_Type", values_to = "Value")

# Plot summary stats by county
Vec <- c("min", "med", "mean", "max")
DSgg <- ggplot(data = Long_Stops_Summary, 
               mapping = aes(x = factor(Summary_Type, 
                                        levels = unique(Vec)), 
                             y = Value)) + 
  geom_point(color = "darkblue", size = 2) +
  facet_wrap(vars(COUNTY), ncol = 5) +
  labs(title = "Distance from Closest Bus or Train Stop to Courthouse",
       subtitle = "Summary Statistics by County", 
       x = "Summary Statistics Type", 
       y = "Distance to Courthouse") +
  theme_bw(base_size = 12) +
  theme(text=element_text(family="serif"))+
  theme(axis.text.x = element_text(angle = 90))
DSgg

Most counties have a bus or train stop on average below 0.5 miles from their courthouses.

Area Outside of 5 Miles from a Stop for Each County

Let’s see which counties have more and less coverage based on county area and bus/train stop dispersion.

Steps to calculate in ArcGIS Pro:

Use ‘buffer’ analysis tool on the bus/train stops feature with a 5 mile radius set
Use ‘erase’ tool to remove the buffered points from the County data layer
Use ‘calculate geometry’ feature to calculate remaining area in each county
Export table to Excel and import in R

Outside5 <- read_excel("Counties_5miles.xls")
Outside5 <- select(Outside5, "COUNTIES_POLYM.COUNTY",
                   "COUNTIES_POLYM.area_mi", 
                   "5mille_stp_cnty.mil5_area") %>%
  rename("County" = "COUNTIES_POLYM.COUNTY", 
         "Area" = "COUNTIES_POLYM.area_mi", 
         "Outside_Area" = "5mille_stp_cnty.mil5_area")
Outside5$Outside_Area[is.na(Outside5$Outside_Area)] <- 0
Outside5 <- Outside5 %>%
  mutate(Percentage=paste0(round(Outside_Area/Area*100, 1),"%"))

kable(Outside5, col.names = c("County", "Area (sq mi)", 
                              "Area Outside 5 Miles of Stop", 
                              "% of County Outside 5 Miles of a Stop"), 
      align = c('c', 'c', 'c', 'c')) %>% 
  add_header_above(c("Percentage of Area per County Outside 5 Miles of a Bus or Train Stop" = 4)) %>% 
  kable_styling() %>% 
  scroll_box(height = "100%", width = "100%")

Percentage of Area per County Outside 5 Miles of a Bus or Train Stop
County	Area (sq mi)	Area Outside 5 Miles of Stop	% of County Outside 5 Miles of a Stop
BERKSHIRE	946.3980	442.98901	46.8%
BRISTOL	571.8640	72.11630	12.6%
DUKES	109.8960	11.99290	10.9%
ESSEX	514.9040	36.17110	7%
BARNSTABLE	412.4770	6.57529	1.6%
HAMPSHIRE	545.2690	243.94200	44.7%
MIDDLESEX	846.5920	91.96370	10.9%
FRANKLIN	724.5880	195.54900	27%
HAMPDEN	634.0580	324.04599	51.1%
NANTUCKET	49.0269	5.18355	10.6%
NORFOLK	408.5400	3.73558	0.9%
PLYMOUTH	689.7700	17.02720	2.5%
SUFFOLK	59.7386	0.00000	0%
WORCESTER	1579.2100	486.16901	30.8%

Concluding Discussion

Most courthouses lie within ½ mile of a bus/train stop – relatively accessible
- Large variation in proximity, especially in specific counties
Notable transit coverage gaps in Berkshire, Franklin, Hampden, Hampshire, and Worcester counties
Why relevant: 614,834 new case filings in 2021 fiscal year
- Criminal, Civil, Probate, Housing, Domestic Relations, Juvenile, Appeals, etc.
Limitations: missing bus routes, bus/train data from multiple sources
Future research ideas:
- Add layers of rural vs. urban & socioeconomic status
- Evaluate court usage/volume by county
- Consider other key structures (ex. medical facilities)

Sample: ArcGIS Pro with Additional Analysis