DVRPC Land Use - Sharswood

URBS 4000: Urban Studies Thesis

Author

Jed Chew

Published

December 8, 2025

Project Title: Mixed by Design

A Case Study of the Philadelphia Housing Authority’s (PHA) Choice Neighborhoods Redevelopment of Sharswood

For my Urban Studies thesis, I am researching the PHA’s Choice Neighborhoods redevelopment of the Sharswood neighborhood in North Philadelphia. I have two main research questions about the process and outcome of this redevelopment:

(1) the process by which the PHA aligned the politics, finance, and design for the redevelopment of Sharswood; and

(2) the early redevelopment outcomes relative to the Choice Neighborhoods Initiative (CNI) vision of mixed-partners, mixed-use, and mixed-income

Part 1: Land-Use Data from DVRPC

Land-Use Data from Delaware Valley Regional Planning Commission (DVRPC)

Data Source: https://catalog.dvrpc.org/dataset/?q=Land+Use
- 2023 Land-Use Data: https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/65bf5c69517342e088c61452f390d774_0/explore?
- 2020 Land-Use Data: https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/dd956104ece64920b242131bf4b6e4ce/explore
- 2015 Land-Use Data: https://dvrpc-dvrpcgis.opendata.arcgis.com/datasets/e207c88797a14c5d9350d083b9c823e4/explore
Date Downloaded: Nov 13, 2025
File Type: GeoJSON
Description: DVRPC’s land use files are based on digital orthophotography created from aerial surveillance conducted in the spring of the land use file year.
- These land-use files are consistently updated every five years which is more frequent than those from Philadelphia’s Department of Planning and Development.
- However, because these land-use files are based on aerial surveillance, they are less detailed and unable to distinguish between the types of multifamily buildings or identify vacant buildings.

Step 1: Load Philadelphia Census Tract and Block Group Data

Philadelphia census tracts, block groups, and neighborhood boundaries

# Load required packages
library(tidyverse)
library(tidycensus)
library(janitor)
library(sf)
library(tigris)
library(scales)
library(ggnewscale)
library(patchwork)
library(RColorBrewer)
library(viridisLite)
library(units)
library(knitr)
library(caret)

# Load spatial data
census_tracts <- tracts(state = "PA", county = "Philadelphia", year = 2020, class = "sf", cb = TRUE, progress = FALSE)
block_grps <- block_groups(state = "PA", county = "Philadelphia", year = 2020, class = "sf",cb = TRUE, progress = FALSE)

# Filter Block Groups for Sharswood
ids <- c("421010138001","421010138002","421010139001",
         "421010139002","421010139003")

sharswood_bg_sf <- block_grps |>
 filter(GEOID %in% ids) |> 
  st_transform(2272) # PA State Plane (in US Survey Feet)

Sharswood Land Parcels

Data Source: https://opendataphilly.org/datasets/department-of-records-property-parcels/
Date Downloaded: Oct 19, 2025
Department: Department of Records
File Type: GeoJSON
Description: Department of Records (DOR) boundaries of real estate property parcels derived from legal recorded deed documents

# Filter Property Parcels for Sharswood
prop_parcels <- st_read("data/DOR_Parcel.geojson") |>
  st_transform(st_crs(sharswood_bg_sf))

sharswood_parcels <- st_filter(prop_parcels, sharswood_bg_sf, .predicate = st_intersects)

st_write(
  sharswood_parcels, "data/sharswood_parcels.geojson",
  driver = "GeoJSON",
  delete_dsn = TRUE,
  layer_options = c("RFC7946=YES","COORDINATE_PRECISION=6","WRITE_BBOX=NO")
)

# Load Sharswood land parcels
sharswood_land_parcels <- st_read("data/sharswood_parcels.geojson")
sharswood_zip <- 19121

Step 2: Load DVRPC Land-Use Data

# Color Palette from PCPC
landuse_pal <- c(
  "Residential"                         = "#f5ff12", # 245,255,18
  "Commercial"                          = "#e60000", # 230,0,0
  "Mixed Commercial/Residential"        = "#e89579", # 232,149,121
  "Industrial"                          = "#8c00ff", # 140,0,255
  "Institutional"                       = "#0070ff", # 0,112,255
  "Transportation"                      = "#cccccc", # 204,204,204
  "Recreation"                          = "#9bccb3", # 155,204,179  
  "Park/Open Space"                     = "#63cc00", # 99,204,0
  "Water"                               = "#bfe8ff", # 191,232,255
  "Undeveloped"                         = "#a87000", # 168,112,0
  "Other/Unknown"                       = "#d2d2d2"  # 210,210,210 
)

2020 Land-Use Data

# Load and Spatially Filter Data
DVRPC_2020 <- st_read("data/Land_Use_2020.geojson") |> 
  st_make_valid()

sharswood_lu_2020 <- DVRPC_2020 |> 
  st_transform(st_crs(sharswood_bg_sf)) |> 
  st_crop(sharswood_bg_sf) |> 
  st_filter(sharswood_bg_sf, .predicate = st_within)

# Visualization
ggplot(sharswood_lu_2020) +
  geom_sf(aes(fill = lu20catn), color = NA) +
  scale_fill_manual(values = landuse_pal, name = "Land Use") +
  labs(title = "2020 DVRPC Land Use in Sharswood",
       subtitle = "Data Source: DVRPC") +
   theme(
    plot.title = element_text(size = 14, face = "bold"),
    plot.subtitle = element_text(size = 10)
  ) +
  theme_void()

ggplot(sharswood_lu_2020) +
  geom_sf(aes(fill = lu20subn), color = NA) +
  labs(title = "2020 DVRPC Detailed Land Use in Sharswood") +
  theme_void()

2015 Land-Use Data

# Load and Spatially Filter Data
DVRPC_2015 <- st_read("data/Land_Use_2015.geojson") |> 
  st_make_valid()

sharswood_lu_2015 <- DVRPC_2015 |> 
  st_transform(st_crs(sharswood_bg_sf)) |> 
  st_crop(sharswood_bg_sf) |> 
  st_filter(sharswood_bg_sf, .predicate = st_within)

# Visualization
ggplot(sharswood_lu_2015) +
  geom_sf(aes(fill = lu15catn), color = NA) +
  scale_fill_manual(values = landuse_pal, name = "Land Use") +
  labs(title = "2015 DVRPC Land Use in Sharswood",
       subtitle = "Data Source: DVRPC") +
   theme(
    plot.title = element_text(size = 14, face = "bold"),
    plot.subtitle = element_text(size = 10)
  ) +
  theme_void()

2023 Land-Use Data

# Load and Spatially Filter Data
DVRPC_2023 <- st_read("data/Land_Use_2023.geojson") |> 
  st_make_valid()

sharswood_lu_2023 <- DVRPC_2023 |> 
  st_transform(st_crs(sharswood_bg_sf)) |> 
  st_crop(sharswood_bg_sf) |> 
  st_filter(sharswood_bg_sf, .predicate = st_within)

# Visualization
ggplot(sharswood_lu_2023) +
  geom_sf(aes(fill = lu23catn), color = NA) +
  scale_fill_manual(values = landuse_pal, name = "Land Use") +
  labs(title = "2023 DVRPC Land Use in Sharswood",
       subtitle = "Data Source: DVRPC") +
   theme(
    plot.title = element_text(size = 14, face = "bold"),
    plot.subtitle = element_text(size = 10)
  ) +
  theme_void()

ggplot(sharswood_lu_2023) +
  geom_sf(aes(fill = lu23subn), color = NA) +
  labs(title = "2023 DVRPC Detailed Land Use in Sharswood") +
  theme_void()