The goal of addNlcdData is to add varaibles from the National Landcover Database to your data based on nlcd_cell (an identifier for a 30 x 30 m NLCD grid cell) and year (2001, 2006, 2011, or 2016).
Install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("geomarker-io/addNlcdData")
-
Variables returned for point data include:
impervious: percent imperviouslandcover_class: landcover classfication category (broad)landcover: landcover classification (detailed)green: TRUE/FALSE if landcover classification in any category except water, ice/snow, developed medium intensity, developed high intensity, rock/sand/clay (based on published definitions)road_type: impervious descriptor category (or “non-impervious”)
-
Variables returned for polygon data include:
impervious: average percent impervious of all nlcd cells overlapping the polygongreen: percent ofgreen = TRUEnlcd cells overlapping polygonprimary_urban,primary_rural,secondary_urban,secondary_rural,tertiary_urban,tertiary_rural,thinned_urban,thinned_ruralnonroad_urban,nonroad_rural,energyprod_urban,energyprod_rural: percent of nlcd cells overlapping polygon classified as the corresponding impervious descriptor categorynonimpervious: percent of ncld cells overlapping polygon not classified as any of the impervious descriptior categories
-
Note that the NLCD categories correspond exactly to fraction imperviousness
nlcd category fraction impervious developed open < 20% developed low 20 - 49% developed medium 50 - 79% developed high 80 - 100% any other 0%
The NLCD product values are stored in fst files as “chunks” of the total
data and are names like nlcd_chunk_{chunk_number}.fst. Chunk files
will be automatically downloaded to the ./nlcd_fst/ folder in the
working directory; the number of chunk files needed depends on the
geographic extent of the input spatial data; their sizes vary, but each
file is 28.5 MB in size on average (all 1,685 files take about 48 GB on
disk). These files were created using code available at
https://github.com/geomarker-io/nlcd_raster_to_fst.
Point Data
library(addNlcdData)
point_data <- tibble::tribble(
~id, ~lon, ~lat,
51981, -84.69127387, 39.24710734,
77553, -84.47798287, 39.12005904,
52284, -84.47123583, 39.2631309,
96308, -84.41741798, 39.18541228,
78054, -84.41395064, 39.18322447
)
point_data <- get_nlcd_cell_numbers_points(point_data)
get_nlcd_data(point_data, product = c("nlcd", "impervious", "imperviousdescriptor"), year = 2016)
#> # A tibble: 5 × 10
#> id lon lat nlcd_cell year impervious landcove…¹ landc…² green road_…³
#> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> <lgl> <chr>
#> 1 51981 -84.7 39.2 7790589150 2016 0 forest decidu… TRUE non-im…
#> 2 77553 -84.5 39.1 7854743431 2016 65 developed develo… FALSE nonroa…
#> 3 52284 -84.5 39.3 7768829115 2016 36 developed develo… TRUE nonroa…
#> 4 96308 -84.4 39.2 7812350603 2016 8 developed develo… TRUE tertia…
#> 5 78054 -84.4 39.2 7813317754 2016 28 developed develo… TRUE nonroa…
#> # … with abbreviated variable names ¹landcover_class, ²landcover, ³road_typePolygon Data
library(sf)
library(tigris)
options(tigris_class = 'sf')polygon_data <- tigris::tracts(state = 'oh', county = 'hamilton') %>%
dplyr::slice(1:3) %>%
dplyr::select(GEOID)
#> | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 30% | |====================== | 31% | |====================== | 32% | |======================= | 32% | |======================= | 33% | |======================== | 34% | |======================== | 35% | |========================= | 35% | |========================= | 36% | |========================== | 36% | |========================== | 37% | |========================== | 38% | |=========================== | 38% | |=========================== | 39% | |============================ | 39% | |============================ | 40% | |============================= | 41% | |============================= | 42% | |============================== | 42% | |============================== | 43% | |=============================== | 44% | |================================ | 45% | |================================ | 46% | |================================= | 46% | |================================= | 47% | |================================= | 48% | |================================== | 48% | |================================== | 49% | |=================================== | 50% | |=================================== | 51% | |==================================== | 51% | |==================================== | 52% | |===================================== | 52% | |===================================== | 53% | |===================================== | 54% | |====================================== | 54% | |====================================== | 55% | |======================================= | 55% | |======================================= | 56% | |======================================== | 56% | |======================================== | 57% | |======================================== | 58% | |========================================= | 58% | |========================================= | 59% | |========================================== | 60% | |========================================== | 61% | |=========================================== | 61% | |=========================================== | 62% | |============================================ | 62% | |============================================ | 63% | |============================================= | 64% | |============================================== | 65% | |============================================== | 66% | |=============================================== | 66% | |=============================================== | 67% | |=============================================== | 68% | |================================================ | 68% | |================================================ | 69% | |================================================= | 70% | |================================================= | 71% | |================================================== | 71% | |================================================== | 72% | |=================================================== | 72% | |=================================================== | 73% | |==================================================== | 74% | |==================================================== | 75% | |===================================================== | 76% | |====================================================== | 77% | |====================================================== | 78% | |======================================================= | 78% | |======================================================= | 79% | |======================================================== | 79% | |======================================================== | 80% | |========================================================= | 81% | |========================================================= | 82% | |========================================================== | 82% | |========================================================== | 83% | |========================================================== | 84% | |=========================================================== | 84% | |=========================================================== | 85% | |============================================================ | 85% | |============================================================ | 86% | |============================================================= | 87% | |============================================================== | 88% | |============================================================== | 89% | |=============================================================== | 90% | |=============================================================== | 91% | |================================================================ | 91% | |================================================================ | 92% | |================================================================= | 92% | |================================================================= | 93% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%#> Simple feature collection with 12 features and 17 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -84.66381 ymin: 39.16639 xmax: -84.57982 ymax: 39.2207
#> Geodetic CRS: NAD83
#> First 10 features:
#> GEOID year impervious green primary_urban primary_rural
#> 1 39061020764 2001 35 75 0 0
#> 2 39061020764 2006 35 75 0 0
#> 3 39061020764 2011 35 75 0 0
#> 4 39061020764 2016 35 75 0 0
#> 5 39061020763 2001 32 82 0 0
#> 6 39061020763 2006 32 82 0 0
#> 7 39061020763 2011 32 82 0 0
#> 8 39061020763 2016 32 82 0 0
#> 9 39061020604 2001 20 84 3 0
#> 10 39061020604 2006 22 82 3 0
#> secondary_urban secondary_rural tertiary_urban tertiary_rural thinned_urban
#> 1 10 0 23 0 0
#> 2 10 0 23 0 0
#> 3 10 0 23 0 0
#> 4 10 0 23 0 0
#> 5 10 0 23 0 0
#> 6 10 0 23 0 0
#> 7 10 0 23 0 0
#> 8 10 0 23 0 0
#> 9 8 0 18 0 0
#> 10 8 0 19 0 0
#> thinned_rural nonroad_urban nonroad_rural energyprod_urban energyprod_rural
#> 1 0 64 0 0 0
#> 2 0 64 0 0 0
#> 3 0 64 0 0 0
#> 4 0 64 0 0 0
#> 5 0 64 0 0 0
#> 6 0 64 0 0 0
#> 7 0 64 0 0 0
#> 8 0 64 0 0 0
#> 9 0 40 0 0 0
#> 10 0 40 0 0 0
#> nonimpervious geometry
#> 1 3 MULTIPOLYGON (((-84.60153 3...
#> 2 3 MULTIPOLYGON (((-84.60153 3...
#> 3 3 MULTIPOLYGON (((-84.60153 3...
#> 4 3 MULTIPOLYGON (((-84.60153 3...
#> 5 4 MULTIPOLYGON (((-84.60085 3...
#> 6 4 MULTIPOLYGON (((-84.60085 3...
#> 7 4 MULTIPOLYGON (((-84.60085 3...
#> 8 4 MULTIPOLYGON (((-84.60085 3...
#> 9 31 MULTIPOLYGON (((-84.66314 3...
#> 10 30 MULTIPOLYGON (((-84.66314 3...
Points with buffers
get_nlcd_data_point_buffer(point_data, buffer_m = 400)#> # A tibble: 20 × 20
#> id lon lat nlcd_…¹ year imper…² green prima…³ prima…⁴ secon…⁵ secon…⁶
#> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 51981 -84.7 39.2 7.79e9 2001 0 100 0 0 0 0
#> 2 51981 -84.7 39.2 7.79e9 2006 0 100 0 0 0 0
#> 3 51981 -84.7 39.2 7.79e9 2011 0 100 0 0 0 0
#> 4 51981 -84.7 39.2 7.79e9 2016 0 100 0 0 0 0
#> 5 77553 -84.5 39.1 7.85e9 2001 12 50 0 0 12 0
#> 6 77553 -84.5 39.1 7.85e9 2006 12 50 0 0 12 0
#> 7 77553 -84.5 39.1 7.85e9 2011 12 50 0 0 12 0
#> 8 77553 -84.5 39.1 7.85e9 2016 12 50 0 0 12 0
#> 9 52284 -84.5 39.3 7.77e9 2001 38 66 0 0 6 0
#> 10 52284 -84.5 39.3 7.77e9 2006 38 66 0 0 6 0
#> 11 52284 -84.5 39.3 7.77e9 2011 38 66 0 0 6 0
#> 12 52284 -84.5 39.3 7.77e9 2016 38 66 0 0 6 0
#> 13 96308 -84.4 39.2 7.81e9 2001 23 90 0 0 9 0
#> 14 96308 -84.4 39.2 7.81e9 2006 23 89 0 0 9 0
#> 15 96308 -84.4 39.2 7.81e9 2011 23 89 0 0 9 0
#> 16 96308 -84.4 39.2 7.81e9 2016 24 89 0 0 9 0
#> 17 78054 -84.4 39.2 7.81e9 2001 21 91 0 0 6 0
#> 18 78054 -84.4 39.2 7.81e9 2006 21 91 0 0 6 0
#> 19 78054 -84.4 39.2 7.81e9 2011 21 91 0 0 6 0
#> 20 78054 -84.4 39.2 7.81e9 2016 21 90 0 0 6 0
#> # … with 9 more variables: tertiary_urban <dbl>, tertiary_rural <dbl>,
#> # thinned_urban <dbl>, thinned_rural <dbl>, nonroad_urban <dbl>,
#> # nonroad_rural <dbl>, energyprod_urban <dbl>, energyprod_rural <dbl>,
#> # nonimpervious <dbl>, and abbreviated variable names ¹nlcd_cell,
#> # ²impervious, ³primary_urban, ⁴primary_rural, ⁵secondary_urban,
#> # ⁶secondary_rural