GTECH38520 Progress Check
Cassie Borra
2026-05-01
Ingestion and Transformation of Data
NYCTracts <- st_read("Data/NYCTractsWData.gpkg")## Reading layer `nyc_tracts_complete' from data source
## `C:\Users\student\OneDrive\.HUNTER\[4] SPRING 26\GTECH38520\work\project\Data\NYCTractsWData.gpkg'
## using driver `GPKG'
## Simple feature collection with 2324 features and 78 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 913141.2 ymin: 120096.3 xmax: 1067338 ymax: 272752.9
## Projected CRS: NAD83 / New York Long Island (ftUS)st_transform(NYCTracts, crs = 4326)## Simple feature collection with 2324 features and 78 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -74.25571 ymin: 40.49604 xmax: -73.70017 ymax: 40.91528
## Geodetic CRS: WGS 84
## First 10 features:
## GISJOIN STATEFP COUNTYFP TRACTCE GEOID NAME NAMELSAD
## 1 G3600050000100 36 005 000100 36005000100 1 Census Tract 1
## 2 G3600050000200 36 005 000200 36005000200 2 Census Tract 2
## 3 G3600050000400 36 005 000400 36005000400 4 Census Tract 4
## 4 G3600050001600 36 005 001600 36005001600 16 Census Tract 16
## 5 G3600050001901 36 005 001901 36005001901 19.01 Census Tract 19.01
## 6 G3600050001902 36 005 001902 36005001902 19.02 Census Tract 19.02
## 7 G3600050001903 36 005 001903 36005001903 19.03 Census Tract 19.03
## 8 G3600050001904 36 005 001904 36005001904 19.04 Census Tract 19.04
## 9 G3600050002001 36 005 002001 36005002001 20.01 Census Tract 20.01
## 10 G3600050002002 36 005 002002 36005002002 20.02 Census Tract 20.02
## MTFCC FUNCSTAT ALAND AWATER INTPTLAT INTPTLON Shape_Leng
## 1 G5020 S 1677210 1035011 +40.7929362 -073.8812065 5337.031
## 2 G5020 S 452832 852406 +40.8081797 -073.8566781 4211.545
## 3 G5020 S 770689 690794 +40.8087867 -073.8514252 7138.512
## 4 G5020 S 485079 0 +40.8188478 -073.8580764 2950.265
## 5 G5020 S 205007 0 +40.8067879 -073.9280462 1840.168
## 6 G5020 S 469741 0 +40.8036830 -073.9159297 4283.803
## 7 G5020 S 908739 0 +40.7999843 -073.9086940 7059.697
## 8 G5020 S 82079 1138170 +40.7963439 -073.8983770 1566.781
## 9 G5020 S 199398 0 +40.8173941 -073.8678806 2083.542
## 10 G5020 S 204892 0 +40.8176370 -073.8644248 2183.417
## Shape_Area Sector_unique Total_Pop_2000 Total_Pop_2010 Total_Pop_2020
## 1 1677210.38 NA 12780 11091 3772
## 2 452835.50 1 3545 4334 4779
## 3 770687.98 1 3314 5503 6272
## 4 485076.24 1 5237 5643 5795
## 5 205006.81 1 NA NA 2292
## 6 469739.28 1 NA NA 1613
## 7 908737.55 NA NA NA 2
## 8 82079.25 NA NA NA 0
## 9 199397.67 1 NA NA 5007
## 10 204893.57 NA NA NA 4584
## Total_Pop_2024 Pop_Change_2000_2020 Pct_White_2000 Pct_White_2010
## 1 2950 -9008 12.4 15.5
## 2 5061 1234 32.0 30.7
## 3 6649 2958 36.1 27.2
## 4 5611 558 34.5 27.6
## 5 2260 NA NA NA
## 6 2079 NA NA NA
## 7 0 NA NA NA
## 8 0 NA NA NA
## 9 4199 NA NA NA
## 10 3609 NA NA NA
## Pct_White_2020 Pct_White_2024 Pct_White_Change_2000_2020
## 1 16.9 33.5 4.5
## 2 9.2 12.1 -22.8
## 3 10.8 2.2 -25.3
## 4 9.0 2.8 -25.5
## 5 13.6 11.0 NA
## 6 18.8 4.3 NA
## 7 0.0 NA NA
## 8 NA NA NA
## 9 12.9 10.7 NA
## 10 8.6 13.6 NA
## Pct_White_Change_2020_2024 Pct_Black_2000 Pct_Black_2010 Pct_Black_2020
## 1 16.6 61.8 58.7 62.3
## 2 2.9 25.6 29.2 25.1
## 3 -8.6 28.0 31.5 30.1
## 4 -6.2 34.3 40.6 38.4
## 5 -2.6 NA NA 39.9
## 6 -14.5 NA NA 39.9
## 7 NA NA NA 0.0
## 8 NA NA NA NA
## 9 -2.2 NA NA 43.0
## 10 5.0 NA NA 31.3
## Pct_Black_2024 Pct_Black_Change_2000_2020 Pct_Black_Change_2020_2024
## 1 44.7 0.5 -17.6
## 2 33.3 -0.5 8.2
## 3 35.8 2.1 5.7
## 4 40.9 4.1 2.5
## 5 53.1 NA 13.2
## 6 19.5 NA -20.4
## 7 NA NA NA
## 8 NA NA NA
## 9 35.5 NA -7.5
## 10 25.1 NA -6.2
## Pct_Hispanic_2000 Pct_Hispanic_2010 Pct_Hispanic_2020 Pct_Hispanic_2024
## 1 26.9 34.1 33.4 21.4
## 2 69.5 69.1 66.4 60.6
## 3 68.0 68.2 63.3 64.1
## 4 62.1 61.7 60.0 57.3
## 5 NA NA 47.5 43.1
## 6 NA NA 52.9 50.9
## 7 NA NA 100.0 NA
## 8 NA NA NA NA
## 9 NA NA 60.4 61.3
## 10 NA NA 66.7 77.7
## Pct_Hispanic_Change_2000_2020 Pct_Hispanic_Change_2020_2024
## 1 6.5 -12.0
## 2 -3.1 -5.8
## 3 -4.7 0.8
## 4 -2.1 -2.7
## 5 NA -4.4
## 6 NA -2.0
## 7 NA NA
## 8 NA NA
## 9 NA 0.9
## 10 NA 11.0
## Pct_Foreign_Born_2000 Pct_Foreign_Born_2012 Pct_Foreign_Born_2022
## 1 0.0 15.9 13.7
## 2 15.0 25.6 38.0
## 3 25.4 23.4 17.0
## 4 20.1 28.0 29.7
## 5 NA NA 25.3
## 6 NA NA 19.4
## 7 NA NA NA
## 8 NA NA NA
## 9 NA NA 17.4
## 10 NA NA 29.0
## Pct_Foreign_Born_2024 Median_Income_2000 Median_Income_2012
## 1 9.1 0 NA
## 2 38.7 42539 71250
## 3 14.5 39013 75833
## 4 33.6 24552 32328
## 5 23.8 NA NA
## 6 24.5 NA NA
## 7 NA NA NA
## 8 NA NA NA
## 9 14.1 NA NA
## 10 31.5 NA NA
## Median_Income_2022 Median_Income_2024 Median_Income_Change_2000_2022
## 1 NA NA NA
## 2 115064 123729 72525
## 3 100553 105924 61540
## 4 41362 52147 16810
## 5 49500 58083 NA
## 6 67375 48953 NA
## 7 NA NA NA
## 8 NA NA NA
## 9 24684 22311 NA
## 10 NA 18649 NA
## Median_Income_Change_2022_2024 Median_Rent_2000 Median_Rent_2012
## 1 NA 0 NA
## 2 8665 730 1339
## 3 5371 640 1569
## 4 10785 558 856
## 5 8583 NA NA
## 6 -18422 NA NA
## 7 NA NA NA
## 8 NA NA NA
## 9 -2373 NA NA
## 10 NA NA NA
## Median_Rent_2022 Median_Rent_Change_2000_2022 Median_Home_Value_2000
## 1 NA NA 0
## 2 1972 1242 147500
## 3 1854 1214 157900
## 4 1111 553 177000
## 5 1642 NA NA
## 6 1984 NA NA
## 7 NA NA NA
## 8 NA NA NA
## 9 483 NA NA
## 10 NA NA NA
## Median_Home_Value_2012 Median_Home_Value_2022
## 1 NA NA
## 2 422200 562500
## 3 355300 484300
## 4 413800 721000
## 5 NA NA
## 6 NA 451700
## 7 NA NA
## 8 NA NA
## 9 NA NA
## 10 NA 479400
## Median_Home_Value_Change_2000_2022 Pct_Renter_2000 Pct_Renter_2010
## 1 NA NA NA
## 2 415000 43.5 47.0
## 3 326400 39.8 34.4
## 4 544000 83.9 84.0
## 5 NA NA NA
## 6 NA NA NA
## 7 NA NA NA
## 8 NA NA NA
## 9 NA NA NA
## 10 NA NA NA
## Pct_Renter_2020 Pct_Renter_Change_2000_2020 Poverty_Count_2000
## 1 NA NA 0
## 2 51.0 7.5 723
## 3 40.3 0.5 417
## 4 84.6 0.7 1112
## 5 99.5 NA NA
## 6 87.2 NA NA
## 7 66.7 NA NA
## 8 NA NA NA
## 9 98.7 NA NA
## 10 74.8 NA NA
## Poverty_Count_2012 Poverty_Count_2022 Poverty_Count_2024 Owner_Occupied_2000
## 1 0 0 0 0
## 2 846 688 652 637
## 3 420 378 297 633
## 4 861 893 808 291
## 5 NA 623 507 NA
## 6 NA 586 807 NA
## 7 NA 0 0 NA
## 8 NA 0 0 NA
## 9 NA 1847 2155 NA
## 10 NA 1307 1526 NA
## Renter_Occupied_2000 Total_Occupied_2010 Owner_Mortgage_2010 Owner_Free_2010
## 1 0 0 0 0
## 2 491 1351 560 156
## 3 418 1786 1034 137
## 4 1519 1925 233 75
## 5 NA NA NA NA
## 6 NA NA NA NA
## 7 NA NA NA NA
## 8 NA NA NA NA
## 9 NA NA NA NA
## 10 NA NA NA NA
## Renter_Occupied_2010 Total_Occupied_2020 Owner_Mortgage_2020 Owner_Free_2020
## 1 0 0 0 0
## 2 635 1517 594 149
## 3 615 2110 1053 207
## 4 1617 2042 250 65
## 5 NA 988 5 0
## 6 NA 484 39 23
## 7 NA 3 1 0
## 8 NA 0 0 0
## 9 NA 1864 9 15
## 10 NA 1561 295 98
## Renter_Occupied_2020 initiative_count heatmap_density density_class
## 1 0 0 6.492718e-05 None
## 2 774 1 3.086711e-03 Low-Medium
## 3 850 1 2.734316e-03 Low-Medium
## 4 1727 1 3.380687e-03 Low-Medium
## 5 983 1 3.585246e-02 High
## 6 422 1 1.732319e-02 Low-Medium
## 7 2 0 1.390509e-02 None
## 8 0 0 3.460678e-03 None
## 9 1840 1 5.684785e-03 Low-Medium
## 10 1168 0 4.794947e-03 None
## geom
## 1 MULTIPOLYGON (((-73.88683 4...
## 2 MULTIPOLYGON (((-73.85653 4...
## 3 MULTIPOLYGON (((-73.85552 4...
## 4 MULTIPOLYGON (((-73.85514 4...
## 5 MULTIPOLYGON (((-73.92427 4...
## 6 MULTIPOLYGON (((-73.92576 4...
## 7 MULTIPOLYGON (((-73.90218 4...
## 8 MULTIPOLYGON (((-73.89733 4...
## 9 MULTIPOLYGON (((-73.86522 4...
## 10 MULTIPOLYGON (((-73.86246 4...NYCSolidarityData <- read.csv("Data/SolidarityMap.csv")
NYCGardens <- NYCSolidarityData %>%
filter(NYCSolidarityData[[1]] == "Gardens")
NYCGardenssf <- st_as_sf(NYCGardens, coords = c("lon", "lat"), crs = 4326)Determination of Gentrification
As defined by the NYU Furman Center; Was a tract below median income in 2000 and did it experience rent growths higher than the city as a whole?
NYCTractsGentrifying <- NYCTracts %>%
mutate(Gentrifying = ifelse(
Median_Income_2000 > 38293,
"High-Income",
ifelse(Median_Rent_Change_2000_2022 > 1074,
"Gentrifying",
"Not Gentrifying")
))
ggplot(NYCTractsGentrifying) +
geom_sf(aes(fill = Gentrifying)) +
scale_fill_manual(
values = c("Gentrifying" = "#990000",
"Not Gentrifying" = "#009900",
"High-Income" = "#00a"),
na.value = "grey50"
) +
coord_sf(xlim = c(-74.26, -73.70), ylim = c(40.50, 40.92), default_crs = sf::st_crs(4326) ) +
theme_minimal() +
labs(title = "Gentrification Status"
)
Overlay of Gardens on Gentrification Data
ggplot(NYCTractsGentrifying) +
geom_sf(aes(fill = Gentrifying)) +
scale_fill_manual(
values = c("Gentrifying" = "#990000",
"Not Gentrifying" = "#009900",
"High-Income" = "#00a"),
na.value = "grey50"
) +
geom_sf(data=NYCGardenssf, colour="white", size=0.01) +
coord_sf(xlim = c(-74.26, -73.70), ylim = c(40.50, 40.92), default_crs = sf::st_crs(4326) ) +
theme_minimal() +
labs(title = "Gentrification Status"
)