mdmaps

mdmaps is an R data package providing pre-projected sf objects and related tibbles for making maps of Maryland. It is modeled on nycmaps.

All geometries are projected to EPSG:26985 (NAD83 / Maryland, meters).

Installation

# install.packages("remotes")
remotes::install_github("NewsAppsUMD/mdmaps")

What’s included (v1)

Statewide layers only in v1.

Administrative boundaries

• md_state_sf — state outline
• md_counties_sf — 24 county-equivalents (23 counties + Baltimore City)
• md_counties — county/FIPS lookup table
• md_sha_districts_sf — 7 MDOT State Highway Administration engineering districts

Political districts

• md_congressional_districts_sf — 8 U.S. House districts (119th Congress)
• md_legislative_districts_sf — 47 state legislative districts (Senate 1:1)
• md_delegate_subdistricts_sf — 71 House of Delegates electing units (whole-LD multi-member and A/B/C sub-districts)

Census geographies

• md_census_tracts_{2000,2010,2020}_sf — decennial census tracts
• md_census_blocks_2020_sf — 2020 census blocks (~84k features)
• md_pumas_{2010,2020}_sf — Public Use Microdata Areas
• md_zcta_sf — ZIP Code Tabulation Areas with 2019 ACS population
• md_zips — ZCTA-to-county lookup table

Later releases will add municipalities, school districts, shoreline, court districts, EMS regions, a dated precinct snapshot, and per-jurisdiction layers for Baltimore City.

Notes on specific layers

• md_sha_districts_sf and Baltimore City. SHA is responsible for state-maintained roads in the 23 counties but not inside Baltimore City, which runs its own DOT. The counties attribute in each district row therefore does not list Baltimore City, even though the polygons themselves cover its land area (District 4 wraps around the city). Treat the counties field as the SHA maintenance footprint, not as a strict geographic county list.

Examples

library(mdmaps)
library(ggplot2)

Counties

ggplot(md_counties_sf) +
  geom_sf() +
  theme_void()

Congressional Districts

md_congressional_districts_sf
#> Simple feature collection with 8 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 185230.5 ymin: 24695.61 xmax: 575762 ymax: 230941.9
#> Projected CRS: NAD83 / Maryland
#>   geoid district_num            district_name congress   land_area water_area
#> 1  2401            1 Congressional District 1      119 10013949639 4256275598
#> 2  2402            2 Congressional District 2      119  2100961804  106733050
#> 3  2403            3 Congressional District 3      119  1296561183  288741049
#> 4  2404            4 Congressional District 4      119   562456426   18570004
#> 5  2405            5 Congressional District 5      119  3928528635 2063211529
#> 6  2406            6 Congressional District 6      119  6203292326   96421134
#> 7  2407            7 Congressional District 7      119   337275331  132139459
#> 8  2408            8 Congressional District 8      119   708198478   17751413
#>                         geometry
#> 1 MULTIPOLYGON (((425830.6 22...
#> 2 MULTIPOLYGON (((373253.9 21...
#> 3 MULTIPOLYGON (((383869.4 18...
#> 4 MULTIPOLYGON (((396537.3 11...
#> 5 MULTIPOLYGON (((371797.8 85...
#> 6 MULTIPOLYGON (((185387.6 18...
#> 7 MULTIPOLYGON (((417678.1 18...
#> 8 MULTIPOLYGON (((370264 1594...

ggplot(md_congressional_districts_sf) +
  geom_sf(aes(fill = factor(district_num))) +
  scale_fill_brewer(palette = "Set2", name = "District") +
  theme_void()

ZIP Code Tabulation Areas

md_zcta_sf
#> Simple feature collection with 468 features and 3 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 185230.9 ymin: 29251.62 xmax: 570294.2 ymax: 230941.9
#> Projected CRS: NAD83 / Maryland
#> First 10 features:
#>     zcta   zcta_name population                       geometry
#> 1  20601 ZCTA5 20601      25517 MULTIPOLYGON (((400081.9 10...
#> 2  20602 ZCTA5 20602      26550 MULTIPOLYGON (((405443.7 10...
#> 3  20603 ZCTA5 20603      31975 MULTIPOLYGON (((395836.3 10...
#> 4  20606 ZCTA5 20606        374 MULTIPOLYGON (((420655.6 66...
#> 5  20607 ZCTA5 20607      10843 MULTIPOLYGON (((392994.5 11...
#> 6  20608 ZCTA5 20608        860 MULTIPOLYGON (((421644.1 10...
#> 7  20609 ZCTA5 20609       1151 MULTIPOLYGON (((418360.2 64...
#> 8  20611 ZCTA5 20611       1634 MULTIPOLYGON (((398160 8730...
#> 9  20612 ZCTA5 20612        203 MULTIPOLYGON (((427469.9 93...
#> 10 20613 ZCTA5 20613      14020 MULTIPOLYGON (((402888.8 11...

ggplot(md_zcta_sf) +
  geom_sf(aes(fill = population), color = NA) +
  scale_fill_viridis_c(name = "Population\n(2019 ACS)", labels = scales::comma) +
  theme_void()

Building the data

Data objects are rebuilt from source via data-raw/build_data.R:

source("data-raw/build_data.R")

Most Tier-1 datasets are pulled from Census TIGER/Line via the tigris package, so no shapefiles are checked into this repo.