2. Read the NYC postal areas in Shapefiles into sf objects. As NYC
DOH publishes COVID-19 data by zip code, we will utilize the postal area
data later.
nyc_sf <- st_read("data/nyc/nyc_acs_tracts.shp")
## Reading layer `nyc_acs_tracts' from data source
## `H:\Hunter Class\GISr\Section_07\data\nyc\nyc_acs_tracts.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 2166 features and 113 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -74.25559 ymin: 40.49612 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS: NAD83
nyc_sf
## Simple feature collection with 2166 features and 113 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -74.25559 ymin: 40.49612 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS: NAD83
## First 10 features:
## UNEMP_RATE cartodb_id withssi withsocial withpubass struggling profession
## 1 0.0000000 1 0 0 0 0 0
## 2 0.0817052 2 228 353 47 694 0
## 3 0.1706029 3 658 1577 198 2589 36
## 4 0.0000000 4 0 0 0 0 0
## 5 0.0880372 5 736 1382 194 2953 19
## 6 0.0562960 6 0 99 0 337 745
## 7 0.0844740 7 261 1122 145 3085 18
## 8 0.0395738 8 0 10 0 17 49
## 9 0.1442177 9 39 264 5 131 35
## 10 0.2163820 10 638 895 334 1938 0
## popunemplo poptot popover18 popinlabou poororstru poor pacificune pacificinl
## 1 0 0 0 0 0 0 0 0
## 2 92 2773 2351 1126 2026 1332 0 0
## 3 549 8339 6878 3218 4833 2244 0 0
## 4 0 0 0 0 0 0 0 0
## 5 379 10760 8867 4305 7044 4091 13 13
## 6 321 7024 6637 5702 1041 704 0 0
## 7 432 10955 8932 5114 6376 3291 0 0
## 8 26 849 711 657 112 95 0 0
## 9 106 1701 1241 735 232 101 0 0
## 10 494 5923 4755 2283 4115 2177 0 0
## pacific otherunemp otherinlab otherethni onlyprofes onlymaster onlylessth
## 1 0 0 0 0 0 0 0
## 2 0 0 144 598 0 77 878
## 3 0 103 348 1175 102 412 2164
## 4 0 0 0 0 0 0 0
## 5 13 46 609 1799 20 292 3793
## 6 0 0 112 112 890 2162 121
## 7 0 0 184 377 116 408 4384
## 8 0 0 48 48 78 233 34
## 9 0 17 90 183 77 328 115
## 10 0 151 525 1664 0 16 1846
## onlyhighsc onlydoctor onlycolleg onlybachel okay mixedunemp mixedinlab mixed
## 1 0 0 0 0 0 0 0 0
## 2 1088 0 471 271 742 16 72 175
## 3 4068 66 2355 1269 3474 21 134 234
## 4 0 0 0 0 0 0 0 0
## 5 3868 1 2290 1322 3499 14 100 251
## 6 5508 145 5371 4685 5982 24 136 224
## 7 3882 98 2223 1262 4579 0 91 115
## 8 631 29 585 481 733 0 10 16
## 9 1093 42 884 687 1469 51 62 102
## 10 2343 0 1111 200 1798 31 140 334
## master maleunempl maleover18 malepro malemastr male_lesHS male_HS male_doctr
## 1 0 0 0 0 0 0 0 0
## 2 77 76 1101 0 10 302 607 0
## 3 310 349 3134 36 143 1063 1893 24
## 4 0 0 0 0 0 0 0 0
## 5 272 179 4068 19 126 1693 1651 0
## 6 1272 204 3557 473 1034 29 2985 77
## 7 292 197 3992 48 181 2013 1702 30
## 8 155 8 454 51 123 17 414 29
## 9 251 72 613 61 139 103 485 38
## 10 16 277 1935 0 0 714 985 0
## male_collg male_BA maleinlabo maledrop male16to19 male lessthanhi lessthan10
## 1 0 0 0 0 0 0 0 0
## 2 227 132 612 16 65 1318 878 212
## 3 1225 668 1640 8 253 3850 2164 760
## 4 0 0 0 0 0 0 0 0
## 5 997 562 2059 0 162 4796 3793 1100
## 6 2924 2411 3299 0 37 3840 121 289
## 7 871 499 2466 0 236 5244 4384 625
## 8 381 288 423 0 20 533 34 2
## 9 368 292 392 0 32 897 115 51
## 10 462 43 991 0 187 2514 1846 463
## households hispanicun hispanicin hispanic highschool geo_state geo_place
## 1 0 0 0 0 0 36 51000
## 2 986 30 357 1187 617 36 51000
## 3 3382 269 1171 3503 1713 36 51000
## 4 0 0 0 0 0 36 51000
## 5 3838 115 819 2608 1578 36 51000
## 6 4104 27 297 374 137 36 51000
## 7 3950 0 236 318 1659 36 51000
## 8 367 0 77 108 46 36 51000
## 9 739 68 201 360 209 36 51000
## 10 2224 335 1145 3478 1232 36 51000
## geo_county field_1 femaleunem femaleover fem_profes fem_master fem_lessHS
## 1 61 1 0 0 0 0 0
## 2 61 2 16 1250 0 67 576
## 3 61 3 200 3744 66 269 1101
## 4 61 4 0 0 0 0 0
## 5 61 5 200 4799 1 166 2100
## 6 61 6 117 3080 417 1128 92
## 7 61 7 235 4940 68 227 2371
## 8 61 8 18 257 27 110 17
## 9 61 9 34 628 16 189 12
## 10 61 10 217 2820 0 16 1132
## fem_HS fem_doctor fem_colleg fem_BA femaleinla femaledrop femal16_19 female
## 1 0 0 0 0 0 0 0 0
## 2 481 0 244 139 514 0 84 1455
## 3 2175 42 1130 601 1578 1 124 4489
## 4 0 0 0 0 0 0 0 0
## 5 2217 1 1293 760 2246 0 271 5964
## 6 2523 68 2447 2274 2403 0 2 3184
## 7 2180 68 1352 763 2648 0 126 5711
## 8 217 0 204 193 234 0 2 316
## 9 608 4 516 395 343 0 32 804
## 10 1358 0 649 157 1292 14 242 3409
## europeanun europeanin european doctorate com_90plus comm_5less comm_60_89
## 1 0 0 0 0 0 0 0
## 2 14 303 540 0 49 99 40
## 3 328 1641 4091 66 101 0 215
## 4 0 0 0 0 0 0 0
## 5 56 525 1181 1 36 1 370
## 6 188 4058 4975 145 61 220 136
## 7 71 492 929 98 82 46 687
## 8 26 540 717 29 17 26 32
## 9 22 543 1327 42 27 0 41
## 10 132 510 1645 0 178 0 331
## comm_5_14 comm_45_59 comm_30_44 comm_15_29 college bachelor asianunemp
## 1 0 0 0 0 0 0 0
## 2 121 142 217 352 200 194 62
## 3 226 171 923 970 1086 857 38
## 4 0 0 0 0 0 0 0
## 5 331 284 1442 1250 968 1030 207
## 6 913 287 1514 1840 686 2523 108
## 7 718 409 1118 1363 961 854 352
## 8 87 46 198 192 104 248 0
## 9 58 84 210 134 197 359 16
## 10 99 351 493 304 911 184 61
## asianinlab asian americanun americanin american africanune africaninl
## 1 0 0 0 0 0 0 0
## 2 559 1249 0 0 43 0 48
## 3 615 1724 0 0 0 59 434
## 4 0 0 0 0 0 0 0
## 5 2736 6549 0 57 57 43 265
## 6 1286 1598 1 1 1 0 109
## 7 4283 9448 0 0 0 9 64
## 8 42 51 0 0 0 0 17
## 9 35 75 0 0 0 0 5
## 10 491 922 24 24 51 95 593
## african puma ntaname ntacode ctlabel cdeligibil
## 1 0 3810 park-cemetery-etc-Manhattan MN99 1 I
## 2 168 3809 Lower East Side MN28 2.01 E
## 3 1115 3809 Lower East Side MN28 2.02 E
## 4 0 3810 park-cemetery-etc-Manhattan MN99 5 I
## 5 910 3809 Lower East Side MN28 6 E
## 6 114 3810 Battery Park City-Lower Manhattan MN25 7 I
## 7 86 3809 Chinatown MN27 8 E
## 8 17 3810 Battery Park City-Lower Manhattan MN25 9 I
## 9 14 3809 Lower East Side MN28 10.01 I
## 10 1307 3809 Lower East Side MN28 10.02 E
## boroname medianinco medianagem medianagef medianage househol_1 gini
## 1 Manhattan NA NA NA NA NA NA
## 2 Manhattan 17282 39.3 43.8 43.0 2.8 0.4714
## 3 Manhattan 24371 44.9 47.9 45.7 2.4 0.5679
## 4 Manhattan NA NA NA NA NA NA
## 5 Manhattan 18832 43.4 43.0 43.2 2.74 0.5565
## 6 Manhattan 123516 29.7 28.3 29.0 1.71 0.4935
## 7 Manhattan 31196 42.0 43.0 42.2 2.77 0.4585
## 8 Manhattan 144018 33.2 31.9 32.9 2.17 0.4241
## 9 Manhattan 80223 41.5 45.7 43.1 2.3 0.5151
## 10 Manhattan 15890 31.2 47.5 39.3 2.55 0.4723
## geo_stusab
## 1 ny
## 2 ny
## 3 ny
## 4 ny
## 5 ny
## 6 ny
## 7 ny
## 8 ny
## 9 ny
## 10 ny
## geo_name
## 1 Census Tract 1, New York city (part), Manhattan borough, New York County, New York
## 2 Census Tract 2.01, New York city (part), Manhattan borough, New York County, New York
## 3 Census Tract 2.02, New York city (part), Manhattan borough, New York County, New York
## 4 Census Tract 5, New York city (part), Manhattan borough, New York County, New York
## 5 Census Tract 6, New York city (part), Manhattan borough, New York County, New York
## 6 Census Tract 7, New York city (part), Manhattan borough, New York County, New York
## 7 Census Tract 8, New York city (part), Manhattan borough, New York County, New York
## 8 Census Tract 9, New York city (part), Manhattan borough, New York County, New York
## 9 Census Tract 10.01, New York city (part), Manhattan borough, New York County, New York
## 10 Census Tract 10.02, New York city (part), Manhattan borough, New York County, New York
## geo_geoid shape_leng shape_area ct2010 boroct2010
## 1 08000US360614491951000000100 11023.048 1844421.2 100 1000100
## 2 08000US360614491951000000201 4748.704 971628.2 201 1000201
## 3 08000US360614491951000000202 8568.208 3315121.2 202 1000202
## 4 08000US360614491951000000500 32407.899 9081808.1 500 1000500
## 5 08000US360614491951000000600 6973.204 2583270.4 600 1000600
## 6 08000US360614491951000000700 12076.323 2528569.6 700 1000700
## 7 08000US360614491951000000800 6353.031 2356896.9 800 1000800
## 8 08000US360614491951000000900 16484.099 3122058.8 900 1000900
## 9 08000US360614491951000001001 3924.837 867808.5 1001 1001001
## 10 08000US360614491951000001002 6315.120 2225016.1 1002 1001002
## borocode tractid popdty geo_fips geometry
## 1 1 1.00 0.000 3.60614e+20 MULTIPOLYGON (((-74.04388 4...
## 2 1 2.01 79619.050 3.60614e+20 MULTIPOLYGON (((-73.9845 40...
## 3 1 2.02 72326.460 3.60614e+20 MULTIPOLYGON (((-73.98237 4...
## 4 1 5.00 0.000 3.60614e+20 MULTIPOLYGON (((-74.01093 4...
## 5 1 6.00 109785.000 3.60614e+20 MULTIPOLYGON (((-73.99022 4...
## 6 1 7.00 71641.800 3.60614e+20 MULTIPOLYGON (((-73.998 40....
## 7 1 8.00 130376.500 3.60614e+20 MULTIPOLYGON (((-73.99191 4...
## 8 1 9.00 6530.409 3.60614e+20 MULTIPOLYGON (((-74.00078 4...
## 9 1 10.01 55484.370 3.60614e+20 MULTIPOLYGON (((-73.97519 4...
## 10 1 10.02 76331.140 3.60614e+20 MULTIPOLYGON (((-73.97351 4...
nycPostal_sf <- st_read("data/ZIP/ZIP_CODE_040114.shp")
## Reading layer `ZIP_CODE_040114' from data source
## `H:\Hunter Class\GISr\Section_07\data\ZIP\ZIP_CODE_040114.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 263 features and 12 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 913129 ymin: 120020.9 xmax: 1067494 ymax: 272710.9
## Projected CRS: NAD83 / New York Long Island (ftUS)
str(nycPostal_sf)
## Classes 'sf' and 'data.frame': 263 obs. of 13 variables:
## $ ZIPCODE : chr "11436" "11213" "11212" "11225" ...
## $ BLDGZIP : chr "0" "0" "0" "0" ...
## $ PO_NAME : chr "Jamaica" "Brooklyn" "Brooklyn" "Brooklyn" ...
## $ POPULATION: num 18681 62426 83866 56527 72280 ...
## $ AREA : num 22699295 29631004 41972104 23698630 36868799 ...
## $ STATE : chr "NY" "NY" "NY" "NY" ...
## $ COUNTY : chr "Queens" "Kings" "Kings" "Kings" ...
## $ ST_FIPS : chr "36" "36" "36" "36" ...
## $ CTY_FIPS : chr "081" "047" "047" "047" ...
## $ URL : chr "http://www.usps.com/" "http://www.usps.com/" "http://www.usps.com/" "http://www.usps.com/" ...
## $ SHAPE_AREA: num 0 0 0 0 0 0 0 0 0 0 ...
## $ SHAPE_LEN : num 0 0 0 0 0 0 0 0 0 0 ...
## $ geometry :sfc_POLYGON of length 263; first list element: List of 1
## ..$ : num [1:159, 1:2] 1038098 1038142 1038171 1038280 1038521 ...
## ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "names")= chr [1:12] "ZIPCODE" "BLDGZIP" "PO_NAME" "POPULATION" ...
plot(st_geometry(nycPostal_sf), main='NYC postal areas')
3. Read and process the NYS health facilities spreadsheet data.
Create sf objects from geographic coordinates.
NYS_H_Facilites_df <- read_csv("data/NYS_Health_Facility.csv",
show_col_types = FALSE,
lazy = FALSE)
#Data Cleaning
NYS_H_Facilites_clean <- NYS_H_Facilites_df %>%
mutate(
`Facility Latitude` = if_else(`Facility ID` == 5764, 43.211623, `Facility Latitude`),
`Facility Longitude` = if_else(`Facility ID` == 5764, -75.459349, `Facility Longitude`)
) %>%
filter(complete.cases(NYS_H_Facilites_df[, c("Facility Longitude", "Facility Latitude")]),
`Facility Longitude` != 0,
`Facility Latitude` != 0) %>%
mutate(`Facility Latitude` = abs(`Facility Latitude`)) %>%
mutate(`Facility Longitude` = abs(`Facility Longitude`) * -1)
NYC_H_Facilites_clean <- NYS_H_Facilites_clean %>%
filter(
`Facility County` == "Queens" |
`Facility County` == "Bronx" |
`Facility County` == "Kings" |
`Facility County` == "Richmond" |
`Facility County` == "New York"
)
NYC_H_Facilites_sf <- st_as_sf(NYC_H_Facilites_clean,
coords = c("Facility Longitude", "Facility Latitude"))
st_crs(NYC_H_Facilites_sf) <- 4326
4. Read and process the NYS retail food stores data. Create sf
objects from geographic coordinates for NYC.
df <- read_csv("data/nys_retail_food_store_xy.csv",
show_col_types = FALSE,
lazy = FALSE)
NYS_Food_df <- df %>%
rename(County = 1)
NYS_Food_clean <- NYS_Food_df %>%
filter(complete.cases(NYS_Food_df [, c("Y", "X")]),
X != 0,
Y != 0) %>%
mutate(Y = abs(Y)) %>%
mutate(X = abs(X) * -1)
NYC_Food_clean <- NYS_Food_clean %>%
filter(
County == "Queens" |
County == "Bronx" |
County == "Kings" |
County == "Richmond" |
County == "New York"
)
str(NYC_Food_clean)
## tibble [11,300 × 18] (S3: tbl_df/tbl/data.frame)
## $ County : chr [1:11300] "Bronx" "Bronx" "Bronx" "Bronx" ...
## $ License.Number : num [1:11300] 734149 606221 606228 723375 724807 ...
## $ Operation.Type : chr [1:11300] "Store" "Store" "Store" "Store" ...
## $ Establishment.Type: chr [1:11300] "JAC" "JAC" "JAC" "JAC" ...
## $ Entity.Name : chr [1:11300] "7 ELEVEN FOOD STORE #37933H" "1001 SAN MIGUEL FOOD CENTER INC" "1029 FOOD PLAZA INC" "1078 DELI GROCERY CORP" ...
## $ DBA.Name : chr [1:11300] NA "1001 SAN MIGUEL FD CNTR" "1029 FOOD PLAZA" "1078 DELI GROCERY" ...
## $ Street.Number : chr [1:11300] "500" "1001" "122" "1078" ...
## $ Street.Name : chr [1:11300] "BAYCHESTER AVE" "SHERIDAN AVE" "E 181ST ST" "EAST 165TH STREET" ...
## $ Address.Line.2 : logi [1:11300] NA NA NA NA NA NA ...
## $ Address.Line.3 : logi [1:11300] NA NA NA NA NA NA ...
## $ City : chr [1:11300] "BRONX" "BRONX" "BRONX" "BRONX" ...
## $ State : chr [1:11300] "NY" "NY" "NY" "NY" ...
## $ Zip.Code : num [1:11300] 10475 10456 10453 10459 10456 ...
## $ Square.Footage : num [1:11300] 0 1100 2000 1200 1500 2400 1000 1200 3400 500 ...
## $ Location : chr [1:11300] "500 BAYCHESTER AVE\r\nBRONX, NY 10475\r\n(40.869156, -73.831875)" "1001 SHERIDAN AVE\r\nBRONX, NY 10456\r\n(40.829061, -73.919613)" "122 E 181ST ST\r\nBRONX, NY 10453\r\n(40.854755, -73.902853)" "1078 EAST 165TH STREET\r\nBRONX, NY 10459\r\n(40.825105, -73.890589)" ...
## $ Coords : chr [1:11300] "40.869156, -73.831875" "40.829061, -73.919613" "40.854755, -73.902853" "40.825105, -73.890589" ...
## $ Y : num [1:11300] 40.9 40.8 40.9 40.8 40.8 ...
## $ X : num [1:11300] -73.8 -73.9 -73.9 -73.9 -73.9 ...
NYC_Food_sf <- st_as_sf(NYC_Food_clean,
coords = c("X", "Y"))
st_crs(NYC_Food_sf) <- 4326