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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Spatial data with sf</title>
<meta charset="utf-8" />
<meta name="author" content="Shawn Santo" />
<link rel="stylesheet" href="slides.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Spatial data with sf
## Programming for Statistical Science
### Shawn Santo
---
## Supplementary materials
Full video lecture available in Zoom Cloud Recordings
Additional resources
- Simple Features for R [vignettes](https://r-spatial.github.io/sf/)
- [CRS in R](https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/OverviewCoordinateReferenceSystems.pdf)
by Melanie Frazier
- [Leaflet for R](https://rstudio.github.io/leaflet/)
---
class: inverse, center, middle
# Introduction
---
## Spatial data is different
Our **typical tidy data frame**:
.tiny[
```
#> # A tibble: 336,776 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
#> 1 2013 1 1 517 515 2 830 819
#> 2 2013 1 1 533 529 4 850 830
#> 3 2013 1 1 542 540 2 923 850
#> 4 2013 1 1 544 545 -1 1004 1022
#> 5 2013 1 1 554 600 -6 812 837
#> 6 2013 1 1 554 558 -4 740 728
#> 7 2013 1 1 555 600 -5 913 854
#> 8 2013 1 1 557 600 -3 709 723
#> 9 2013 1 1 557 600 -3 838 846
#> 10 2013 1 1 558 600 -2 753 745
#> # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
#> # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>
```
]
---
## Spatial data is different
A **simple features object**:
.tiny[
```
#> Simple feature collection with 100 features and 5 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> geographic CRS: NAD27
#> First 10 features:
#> AREA PERIMETER CNTY_ CNTY_ID NAME geometry
#> 1 0.114 1.442 1825 1825 Ashe MULTIPOLYGON (((-81.47276 3...
#> 2 0.061 1.231 1827 1827 Alleghany MULTIPOLYGON (((-81.23989 3...
#> 3 0.143 1.630 1828 1828 Surry MULTIPOLYGON (((-80.45634 3...
#> 4 0.070 2.968 1831 1831 Currituck MULTIPOLYGON (((-76.00897 3...
#> 5 0.153 2.206 1832 1832 Northampton MULTIPOLYGON (((-77.21767 3...
#> 6 0.097 1.670 1833 1833 Hertford MULTIPOLYGON (((-76.74506 3...
#> 7 0.062 1.547 1834 1834 Camden MULTIPOLYGON (((-76.00897 3...
#> 8 0.091 1.284 1835 1835 Gates MULTIPOLYGON (((-76.56251 3...
#> 9 0.118 1.421 1836 1836 Warren MULTIPOLYGON (((-78.30876 3...
#> 10 0.124 1.428 1837 1837 Stokes MULTIPOLYGON (((-80.02567 3...
```
]
---
Another simple features object:
.tiny[
```
#> Simple feature collection with 94 features and 1 field
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: 127456.7 ymin: 26544.91 xmax: 923528.7 ymax: 318097.4
#> projected CRS: NAD83 / North Carolina
#> # A tibble: 94 x 2
#> GML_HAB geometry
#> <chr> <MULTIPOLYGON [m]>
#> 1 Alcoa (((512096.2 183241.7, 512185.7 183203.4, 512226 183186.2…
#> 2 Alligator River (((869633.1 244541.9, 869739.4 243987.6, 869762.7 243999…
#> 3 Angola Bay (((713079.4 113954.7, 713110.9 113878.7, 713133.1 113925…
#> 4 Bachelor Bay (((813742.2 238618.7, 813730 238603.2, 813693.8 238525.7…
#> 5 Bertie County (((797133.8 247034.5, 797119.5 247030, 797112.2 247027.7…
#> 6 Bladen Lakes State… (((658970.6 95406.32, 660025.1 94245.76, 659839.4 94144.…
#> 7 Brinkleyville (((714741 276970.3, 714623.9 276970, 714622.1 277000, 71…
#> 8 Buckhorn (((589723.7 253224.6, 589568.5 252937.2, 589689.8 252937…
#> 9 Buckridge (((871137.4 219894.9, 871124.9 219827.8, 871124.2 219828…
#> 10 Buffalo Cove (((381445.9 260375.4, 381574.9 259668.3, 381915 259796.3…
#> # … with 84 more rows
```
]
---
## Spatial data plotting needs care
<img src="lec_11_files/figure-html/unnamed-chunk-5-1.png" width="100%" style="display: block; margin: auto;" />
---
<img src="lec_11_files/figure-html/unnamed-chunk-6-1.png" width="100%" style="display: block; margin: auto;" />
---
<img src="lec_11_files/figure-html/unnamed-chunk-7-1.png" width="100%" style="display: block; margin: auto;" />
---
class: center, middle
## Can we combine the two plots?
---
<img src="lec_11_files/figure-html/unnamed-chunk-8-1.png" width="100%" style="display: block; margin: auto;" />
Where are the game lands?
---
class: center, middle
## We can, but more care is needed.
---
<img src="lec_11_files/figure-html/unnamed-chunk-9-1.png" width="100%" style="display: block; margin: auto;" />
---
## Spatial data challenges
1. Different data types exist.
2. Special attention must be given to the coordinate reference system (CRS).
3. Manipulating spatial data objects is similar but not identical to
manipulating data frame objects.
---
class: inverse, center, middle
# Spatial data and R
---
## Analysis of spatial data in R
.pull-left[
<br/>
- Package `raster` contains classes and tools for handling spatial raster data.
<br/><br/>
- Package `sf` combines the functionality of `sp`, `rgdal`, and `rgeos`
into a single package based on tidy simple features.
]
.pull-right[
]
<br/>
Whether or not you use vector or raster data depends on the type of problem
and the data source. Our focus will be on vector data and package `sf`.
*Source:* https://commons.wikimedia.org/wiki/File:Raster_vector_tikz.png
---
## Installing package `sf`
From https://r-spatial.github.io/sf/index.html
**Windows**
Installing `sf` from source works under windows when Rtools is installed.
This downloads the system requirements from rwinlib.
**MacOS**
```bash
brew install pkg-config
brew install gdal
```
Once gdal is installed, you will be able to install sf package from source in R.
**Linux**
For Unix-alikes, GDAL (>= 2.0.1), GEOS (>= 3.4.0) and Proj.4 (>= 4.8.0) are
required.
---
## Features and simple features
- A **feature** is a thing or object in the real world: a house, a city, a park,
a forest, etc.
<br/><br/>
- A **simple feature** as defined by OpenGIS Abstract specification is to have
both spatial and non-spatial attributes. Spatial attributes are geometry
valued, and simple features are based on 2D geometry with linear
interpolation between vertices.
<br/><br/>
.tiny[
```r
Simple feature collection with 100 features and 1 field
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 123829.8 ymin: 14740.06 xmax: 930518.6 ymax: 318255.5
projected CRS: NAD83 / North Carolina
First 10 features:
NAME geometry
*1 Ashe MULTIPOLYGON (((387344.7 27...
2 Alleghany MULTIPOLYGON (((408601.4 29...
3 Surry MULTIPOLYGON (((478715.7 27...
4 Currituck MULTIPOLYGON (((878193.4 28...
5 Northampton MULTIPOLYGON (((769834.9 27...
6 Hertford MULTIPOLYGON (((812327.7 27...
7 Camden MULTIPOLYGON (((878193.4 28...
8 Gates MULTIPOLYGON (((828444.5 29...
9 Warren MULTIPOLYGON (((671746.3 27...
10 Stokes MULTIPOLYGON (((517435.1 27...
```
]
---
## Simple features examples
<img src="lec_11_files/figure-html/unnamed-chunk-12-1.png" width="100%" style="display: block; margin: auto;" />
---
## `sf` objects
.tiny[
```r
nc <- st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
nc
```
```
#> Simple feature collection with 100 features and 14 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> geographic CRS: NAD27
#> First 10 features:
#> AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID BIR74 SID74
#> 1 0.114 1.442 1825 1825 Ashe 37009 37009 5 1091 1
#> 2 0.061 1.231 1827 1827 Alleghany 37005 37005 3 487 0
#> 3 0.143 1.630 1828 1828 Surry 37171 37171 86 3188 5
#> 4 0.070 2.968 1831 1831 Currituck 37053 37053 27 508 1
#> 5 0.153 2.206 1832 1832 Northampton 37131 37131 66 1421 9
#> 6 0.097 1.670 1833 1833 Hertford 37091 37091 46 1452 7
#> 7 0.062 1.547 1834 1834 Camden 37029 37029 15 286 0
#> 8 0.091 1.284 1835 1835 Gates 37073 37073 37 420 0
#> 9 0.118 1.421 1836 1836 Warren 37185 37185 93 968 4
#> 10 0.124 1.428 1837 1837 Stokes 37169 37169 85 1612 1
#> NWBIR74 BIR79 SID79 NWBIR79 geometry
#> 1 10 1364 0 19 MULTIPOLYGON (((-81.47276 3...
#> 2 10 542 3 12 MULTIPOLYGON (((-81.23989 3...
#> 3 208 3616 6 260 MULTIPOLYGON (((-80.45634 3...
#> 4 123 830 2 145 MULTIPOLYGON (((-76.00897 3...
#> 5 1066 1606 3 1197 MULTIPOLYGON (((-77.21767 3...
#> 6 954 1838 5 1237 MULTIPOLYGON (((-76.74506 3...
#> 7 115 350 2 139 MULTIPOLYGON (((-76.00897 3...
#> 8 254 594 2 371 MULTIPOLYGON (((-76.56251 3...
#> 9 748 1190 2 844 MULTIPOLYGON (((-78.30876 3...
#> 10 160 2038 5 176 MULTIPOLYGON (((-80.02567 3...
```
]
---
## Class and other attributes: `sf`
```r
class(nc)
```
```
#> [1] "sf" "data.frame"
```
```r
names(attributes(nc))
```
```
#> [1] "names" "row.names" "class" "sf_column" "agr"
```
---
## `sfc` objects
```r
nc_polygons <- st_geometry(nc)
nc_polygons
```
```
#> Geometry set for 100 features
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> geographic CRS: NAD27
#> First 5 geometries:
```
---
## Class and other attributes: `sfc`
```r
class(nc_polygons)
```
```
#> [1] "sfc_MULTIPOLYGON" "sfc"
```
```r
names(attributes(nc_polygons))
```
```
#> [1] "n_empty" "crs" "class" "precision" "bbox"
```
<br/>
--
We see that `nc` has a class attribute `sf`, and object `nc_polygons` has a
class attribute `sfc`. What methods are available?
---
```r
methods(class = "sf")
```
```
#> [1] [ [[<- $<-
#> [4] aggregate anti_join arrange
#> [7] as.data.frame cbind coerce
#> [10] dbDataType dbWriteTable distinct
#> [13] dplyr_reconstruct filter full_join
#> [16] gather group_by group_split
#> [19] identify initialize inner_join
#> [22] left_join mapView merge
#> [25] mutate nest plot
#> [28] print rbind rename
#> [31] right_join sample_frac sample_n
#> [34] select semi_join separate_rows
#> [37] separate show slice
#> [40] slotsFromS3 spread st_agr
#> [43] st_agr<- st_area st_as_s2
#> [46] st_as_sf st_bbox st_boundary
#> [49] st_buffer st_cast st_centroid
#> [52] st_collection_extract st_convex_hull st_coordinates
#> [55] st_crop st_crs st_crs<-
#> [58] st_difference st_filter st_geometry
#> [61] st_geometry<- st_interpolate_aw st_intersection
#> [64] st_intersects st_is_valid st_is
#> [67] st_join st_line_merge st_m_range
#> [70] st_make_valid st_nearest_points st_node
#> [73] st_normalize st_point_on_surface st_polygonize
#> [76] st_precision st_reverse st_sample
#> [79] st_segmentize st_set_precision st_shift_longitude
#> [82] st_simplify st_snap st_sym_difference
#> [85] st_transform st_triangulate st_union
#> [88] st_voronoi st_wrap_dateline st_write
#> [91] st_z_range st_zm summarise
#> [94] transform transmute ungroup
#> [97] unite unnest
#> see '?methods' for accessing help and source code
```
---
```r
methods(class = "sfc")
```
```
#> [1] [ [<- as.data.frame
#> [4] c coerce format
#> [7] fortify identify initialize
#> [10] mapView obj_sum Ops
#> [13] print rep scale_type
#> [16] show slotsFromS3 st_area
#> [19] st_as_binary st_as_grob st_as_s2
#> [22] st_as_sf st_as_text st_bbox
#> [25] st_boundary st_buffer st_cast
#> [28] st_centroid st_collection_extract st_convex_hull
#> [31] st_coordinates st_crop st_crs
#> [34] st_crs<- st_difference st_geometry
#> [37] st_intersection st_intersects st_is_valid
#> [40] st_is st_line_merge st_m_range
#> [43] st_make_valid st_nearest_points st_node
#> [46] st_normalize st_point_on_surface st_polygonize
#> [49] st_precision st_reverse st_sample
#> [52] st_segmentize st_set_precision st_shift_longitude
#> [55] st_simplify st_snap st_sym_difference
#> [58] st_transform st_triangulate st_union
#> [61] st_voronoi st_wrap_dateline st_write
#> [64] st_z_range st_zm str
#> [67] summary type_sum vec_cast.sfc
#> [70] vec_ptype2.sfc
#> see '?methods' for accessing help and source code
```
---
## Reading and writing spatial data
- `st_read()` / `st_write()`, Shapefile, GeoJSON, KML, ...
- `st_as_sfc()`
- `st_as_text()`, well-known text format
- `st_as_binary()`, well-known binary format
<br/><br/><br>
See https://r-spatial.github.io/sf/articles/sf2.html for the full
set of driver availability.
---
## Plotting with `plot()`
```r
plot(nc)
```
<img src="lec_11_files/figure-html/unnamed-chunk-21-1.png" style="display: block; margin: auto;" />
---
```r
plot(nc["NAME"])
```
<img src="lec_11_files/figure-html/unnamed-chunk-22-1.png" style="display: block; margin: auto;" />
---
```r
par(oma=c(0,2,0,0))
plot(nc["AREA"], graticule = TRUE, axes = TRUE, las = 1)
```
<img src="lec_11_files/figure-html/unnamed-chunk-23-1.png" style="display: block; margin: auto;" />
---
## What is happening with `[` and the `sf` object?
```r
nc["AREA"]
```
```
#> Simple feature collection with 100 features and 1 field
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> geographic CRS: NAD27
#> First 10 features:
#> AREA geometry
#> 1 0.114 MULTIPOLYGON (((-81.47276 3...
#> 2 0.061 MULTIPOLYGON (((-81.23989 3...
#> 3 0.143 MULTIPOLYGON (((-80.45634 3...
#> 4 0.070 MULTIPOLYGON (((-76.00897 3...
#> 5 0.153 MULTIPOLYGON (((-77.21767 3...
#> 6 0.097 MULTIPOLYGON (((-76.74506 3...
#> 7 0.062 MULTIPOLYGON (((-76.00897 3...
#> 8 0.091 MULTIPOLYGON (((-76.56251 3...
#> 9 0.118 MULTIPOLYGON (((-78.30876 3...
#> 10 0.124 MULTIPOLYGON (((-80.02567 3...
```
---
```r
nc$AREA
```
```
#> [1] 0.114 0.061 0.143 0.070 0.153 0.097 0.062 0.091 0.118 0.124 0.114 0.153
#> [13] 0.143 0.109 0.072 0.190 0.053 0.199 0.081 0.063 0.044 0.064 0.086 0.128
#> [25] 0.108 0.170 0.111 0.180 0.104 0.077 0.142 0.059 0.131 0.122 0.080 0.118
#> [37] 0.219 0.118 0.155 0.069 0.066 0.145 0.134 0.100 0.099 0.116 0.201 0.180
#> [49] 0.094 0.134 0.168 0.106 0.168 0.207 0.144 0.094 0.203 0.141 0.070 0.065
#> [61] 0.146 0.142 0.154 0.118 0.078 0.125 0.181 0.143 0.091 0.130 0.103 0.095
#> [73] 0.078 0.104 0.098 0.091 0.060 0.131 0.241 0.082 0.120 0.172 0.121 0.163
#> [85] 0.138 0.098 0.167 0.204 0.121 0.051 0.177 0.080 0.195 0.240 0.125 0.225
#> [97] 0.214 0.240 0.042 0.212
```
---
```r
par(oma=c(0,2,0,0))
plot(nc["AREA"], col = "lightblue", graticule = TRUE,
axes = TRUE, las = 1)
```
<img src="lec_11_files/figure-html/unnamed-chunk-26-1.png" style="display: block; margin: auto;" />
---
```r
par(oma=c(0,2,0,0))
plot(st_geometry(nc), graticule = TRUE, axes = TRUE, las = 1)
```
<img src="lec_11_files/figure-html/unnamed-chunk-27-1.png" style="display: block; margin: auto;" />
---
## Plotting with `ggplot()`
```r
ggplot(nc) +
geom_sf() +
theme_bw(base_size = 16)
```
<img src="lec_11_files/figure-html/unnamed-chunk-28-1.png" style="display: block; margin: auto;" />
---
```r
ggplot(nc) +
geom_sf(aes(fill = AREA)) +
scale_fill_gradient(low = "#fee8c8", high = "#7f0000") +
theme_bw(base_size = 16)
```
<img src="lec_11_files/figure-html/unnamed-chunk-29-1.png" style="display: block; margin: auto;" />
---
class: center, middle
<img src="lec_11_files/figure-html/unnamed-chunk-30-1.png" style="display: block; margin: auto;" />
---
```r
p1 <- ggplot(nc) +
geom_sf(aes(fill = SID74)) +
scale_fill_gradient(low = "#fff7f3", high = "#49006a") +
theme_bw(base_size = 16)
p2 <- ggplot(nc) +
geom_sf(aes(fill = SID79)) +
scale_fill_gradient(low = "#fff7f3", high = "#49006a") +
theme_bw(base_size = 16)
p1 / p2
```
<br/><br/>
--
Visually, what is wrong with the last plot?
---
## Plotting with `mapview()`
```r
mapview(nc)
```
--
```r
mapviewOptions(legend.pos = "bottomright")
mapview(nc["SID74"], col.regions = sf.colors(10),
layer.name = "SID 1974")
```
<br/><br/>
*These should run in RStudio. There is an issue embedding this overlay in the slides*.
---
## Exercise
Use `ggplot` to create a choropleth map for the proportion of sudden infant
deaths, for the period of July 1, 1974 to June 30, 1979.
<img src="lec_11_files/figure-html/unnamed-chunk-34-1.png" width="100%" style="display: block; margin: auto;" />
???
```r
nc %>%
select(BIR74, SID74) %>%
mutate(SID74_prop = SID74 / (BIR74 + SID74)) %>%
st_as_sf() %>%
ggplot() +
geom_sf(aes(fill = SID74_prop)) +
scale_fill_gradient(low = "#fff7f3", high = "#49006a") +
labs(title = "July 1, 1974 to June 30, 1979",
fill = "",
subtitle = "Proportion of SID by county") +
theme_bw(base_size = 16)
```
---
class: inverse, center, middle
# Map layers
---
## Game Lands data
The North Carolina Department of Environment and Natural Resources,
Wildlife Resources Commission and the NC Center for Geographic Information and
Analysis has a shapefile data set available on all public Game Lands in NC.
https://www.nconemap.gov/datasets/e5ddff9b96204c6181be7c022e61d946_0
We can directly download and unzip the shapefile via
```r
download.file("https://opendata.arcgis.com/datasets/e5ddff9b96204c6181be7c022e61d946_0.zip?outSR=%7B%22latestWkid%22%3A32119%2C%22wkid%22%3A32119%7D",
destfile = "data/Gamelands.zip")
unzip("data/Gamelands.zip", exdir = "data/")
```
--
To see the available files
```r
list.files(path = "data/", pattern = "Game_Lands*")
```
```
#> [1] "Game_Lands_-_general.cpg" "Game_Lands_-_general.dbf"
#> [3] "Game_Lands_-_general.prj" "Game_Lands_-_general.shp"
#> [5] "Game_Lands_-_general.shx" "Game_Lands_-_general.xml"
```
---
## Read in the shapefile
```r
nc_gamelands <- st_read("data/Game_Lands_-_general.shp", quiet = TRUE)
```
.tiny[
```r
print(nc_gamelands, n = 5)
```
```
#> Simple feature collection with 94 features and 6 fields
#> geometry type: MULTIPOLYGON
#> dimension: XY
#> bbox: xmin: 127456.7 ymin: 26544.91 xmax: 923528.7 ymax: 318097.4
#> projected CRS: NAD83 / North Carolina
#> First 5 features:
#> OBJECTID GML_HAB SUM_ACRES GameLandID Shape__Are Shape__Len
#> 1 1 Alcoa 11109.559 1 44958790 438301.56
#> 2 2 Alligator River 24439.089 2 98901485 151120.16
#> 3 3 Angola Bay 34067.382 3 137865804 87094.49
#> 4 4 Bachelor Bay 2786.258 4 11275585 26613.27
#> 5 5 Bertie County 3881.466 5 15707735 67343.97
#> geometry
#> 1 MULTIPOLYGON (((512096.2 18...
#> 2 MULTIPOLYGON (((869633.1 24...
#> 3 MULTIPOLYGON (((713079.4 11...
#> 4 MULTIPOLYGON (((813742.2 23...
#> 5 MULTIPOLYGON (((797133.8 24...
```
]
---
## Metadata for each `sf` object
`nc`:
```r
Simple feature collection with 100 features and 14 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
*geographic CRS: NAD27
```
`nc_gamelands`:
```r
Simple feature collection with 94 features and 6 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 127456.7 ymin: 26544.91 xmax: 923528.7 ymax: 318097.4
*projected CRS: NAD83 / North Carolina
```
---
## Check the CRS
```r
st_crs(nc)
```
```r
Coordinate Reference System:
User input: NAD27
wkt:
GEOGCRS["NAD27",
DATUM["North American Datum 1927",
ELLIPSOID["Clarke 1866",6378206.4,294.978698213898,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["latitude",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["longitude",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4267]]
```
---
.tiny[
```r
st_crs(nc_gamelands)
```
```r
Coordinate Reference System:
User input: NAD83 / North Carolina
wkt:
PROJCRS["NAD83 / North Carolina",
BASEGEOGCRS["NAD83",
DATUM["North American Datum 1983",
ELLIPSOID["GRS 1980",6378137,298.257222101,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4269]],
CONVERSION["SPCS83 North Carolina zone (meters)",
METHOD["Lambert Conic Conformal (2SP)",
ID["EPSG",9802]],
PARAMETER["Latitude of false origin",33.75,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8821]],
⋮
PARAMETER["Northing at false origin",0,
LENGTHUNIT["metre",1],
ID["EPSG",8827]]],
CS[Cartesian,2],
AXIS["easting (X)",east,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["northing (Y)",north,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["unknown"],
AREA["USA - North Carolina"],
BBOX[33.83,-84.33,36.59,-75.38]],
ID["EPSG",32119]]
```
]
---
## Coordinate reference systems (CRS)
- CRS provide a standardized way of describing locations.
- Different CRS arise from various ways data were gathered, the locations,
and purposes of the data.
- A CRS is comprised of
- an ellipsoid, to define the earth's shape;
- a datum, to define the origin and orientation of coordinate axes;
- a projection, to go from 3D to 2D.
- It is important that you transform your spatial data to a common CRS before
plotting.
---
## Transform CRS
```r
nc_gamelands <- st_transform(nc_gamelands, crs = st_crs(nc))
```
--
Check they are equal:
```r
st_crs(nc) == st_crs(nc_gamelands)
```
```
#> [1] TRUE
```
---
## Map overlay with `plot()`
```r
plot(st_geometry(nc), axes = T, las = 1, main = "NC Public Game Lands",
cex.main = 3, cex.lab = 2, cex.axis = 1.5)
plot(st_geometry(nc_gamelands),
* add = T,
col = "#ff6700")
legend("bottomleft", legend = "WRC Game Lands", fill = "#ff6700")
```
---
## Map overlay with `plot()`
<img src="lec_11_files/figure-html/unnamed-chunk-49-1.png" style="display: block; margin: auto;" />
---
## Map overlay with `mapview()`
```r
nc_mapview <- mapview(nc, alpha.regions = .2, alpha = .9,
label = nc[, "NAME", drop = T],
layer.name = "NC Counties")
```
```r
nc_gamelands_mapview <- mapview(nc_gamelands, col.regions = "#ff6700",
label = round(nc_gamelands[, "SUM_ACRES", drop = T], 2),
layer.name = "NC Gamelands")
```
```r
nc_mapview + nc_gamelands_mapview
```
<br/><br/><br/>
*These should run in RStudio. There is an issue embedding this overlay in the slides*.
---
## Exercise
Create a map that includes NC county boundaries, Game Lands, and hazardous waste
sites. Data for the hazardous waste sites is available at
https://www.nconemap.gov/datasets/hazardous-waste-sites
This data set represents the location of sites within North Carolina that
are regulated by the hazardous waste portions of the
Resource Conservation and Recovery Act (RCRA).
---
class: inverse, center, middle
# Manipulating `sf` type objects
---
## Change the CRS
We'll make a quick change to the CRS to better manipulate the geometries.
```r
nc <- st_transform(nc, st_crs(32119))
nc_gamelands <- st_transform(nc_gamelands, st_crs(32119))
```
*Source*: https://spatialreference.org/ref/epsg/32119/
<br/>
--
To make it easier to view the tibbles, we'll drop some of the fields.
```r
nc <- nc %>%
select(NAME)