---
title: Spatial data
author: "Colin Rundel"
date: "2018-04-03"
output:
  xaringan::moon_reader:
    css: "slides.css"
    lib_dir: libs
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
---
exclude: true

```{r setup, echo=FALSE, message=FALSE, warning=FALSE, include=FALSE}
options(
  htmltools.dir.version = FALSE, # for blogdown
  width = 80,
  tibble.width = 80
)

knitr::opts_chunk$set(
  fig.align = "center"
)

htmltools::tagList(rmarkdown::html_dependency_font_awesome())

library(dplyr)
library(sf)
library(purrr)
library(ggplot2)

ggplot2::theme_set(ggplot2::theme_bw())

options(width=100)
cols = c("#7fc97f","#386cb0","#beaed4","#fdc086")
```


---
class: middle
count: false

# Geospatial stuff is hard

---

## Projections

```{r echo=FALSE}
lat_lines  = map(seq(9.999, 89.999, length.out = 9), ~ cbind(seq(-179.999, -9.999, length.out=100), .))
long_lines = map(seq(-179.999, -9.999, length.out = 17), ~ cbind(., seq(9.999, 89.999, length.out=100))) 

lat_long = c(lat_lines, long_lines) %>% 
  st_multilinestring() %>%
  st_sfc() %>%
  st_set_crs("+proj=longlat +datum=WGS84 +no_defs")
```

```{r echo=FALSE, fig.align="center", message=FALSE, out.width="100%", fig.height=5}
data(wrld_simpl, package = "maptools")
world = st_as_sf(wrld_simpl)

NAm = world %>% filter(FIPS %in% c("CA","GL","MX","US"))
NAm_google = st_transform(NAm, "+init=epsg:3857")


par(mar=c(3,2,2,1),mfrow=c(2,3))
plot(lat_long, col=adjustcolor("grey",alpha.f = 0.5), axes=TRUE, main="Lat/Long (epsg:4326)")
plot(st_geometry(NAm), col="black", add=TRUE)

plot(st_transform(lat_long, "+init=epsg:3857"), col=adjustcolor("grey",alpha.f = 0.5), axes=TRUE, main="Google / Web Mercator (epsg:3857)", ylim=c(0, 2e7))
plot(st_transform(NAm, "+init=epsg:3857") %>% st_geometry(), col="black", add=TRUE)

lcc = "+proj=lcc +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs"
plot(st_transform(lat_long, lcc), col=adjustcolor("grey",alpha.f = 0.5), axes=TRUE, main="Lambert Conformal Conic:")
plot(st_transform(NAm, lcc) %>% st_geometry(), col="black", add=TRUE)

aea = "+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs"
plot(st_transform(lat_long, aea), col=adjustcolor("grey",alpha.f = 0.5), axes=TRUE, main="Alberts Equal Area")
plot(st_transform(NAm, aea) %>% st_geometry(), col="black", add=TRUE)

robinson = "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
plot(st_transform(lat_long, robinson), col=adjustcolor("grey",alpha.f = 0.5), axes=TRUE, main="Robinson")
plot(st_transform(NAm, robinson) %>% st_geometry(), col="black", add=TRUE)

mollweide = "+proj=moll +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
plot(st_transform(lat_long, mollweide), col=adjustcolor("grey",alpha.f = 0.5), axes=TRUE, main="Mollweide")
plot(st_transform(NAm, mollweide) %>% st_geometry(), col="black", add=TRUE)
```

---

## Dateline

How long is the flight between the Western most and the Eastern most points in the US?

--

```{r echo=FALSE, fig.align="center", fig.height=4, fig.width=8, warning=FALSE, message=FALSE}
par(mar=c(3,3,1,1))

ak = st_read("data/ak/states.shp", quiet = TRUE, stringsAsFactors = FALSE)
ak_geom = st_geometry(ak)

west_hem = st_polygon(list(matrix(c(-180,90, -180,-90, 0,-90, 0,90, -180,90), ncol=2,byrow=TRUE))) %>% 
  st_sfc() %>%
  st_set_crs("+proj=longlat +datum=WGS84")

east_hem = st_polygon(list(matrix(c(180,90, 180,-90, 0,-90, 0,90, 180,90), ncol=2,byrow=TRUE))) %>% 
  st_sfc() %>%
  st_set_crs("+proj=longlat +datum=WGS84")

ak_west = st_intersection(ak_geom, west_hem)
ak_east = st_intersection(ak_geom, east_hem)

ak_east_shift = (ak_east - c(360,0)) %>% st_set_crs("+proj=longlat +datum=WGS84")

ak_shift = st_union(ak_east_shift, ak_west)

plot(ak_shift, axes=TRUE, col="black", border=NA, xlim=c(-190, -130))
points(c(-360+179.776,-179.146), c(51.952,51.273),col='red')
abline(v=-180,col='blue',lty=2)
```
```{r echo=FALSE, fig.align="center", fig.width=8, fig.height=3}
plot(ak_shift, col="black", border=NA, xlim=c(-190, -170), ylim=c(50, 55))
points(c(-360+179.776,-179.146), c(51.952,51.273),col='red')
abline(v=-180,col='blue',lty=2)
box()
```

---

## Great circle distance

```{r echo=FALSE, fig.align="center", fig.width=8, fig.height=4}
par(mar=c(0,0,0,0))
ak1 = c(179.776,51.952)
ak2 = c(-179.146,51.273)
inter = geosphere::gcIntermediate(ak1, ak2, n=50, addStartEnd=TRUE)
plot(st_geometry(world), col="black", ylim=c(-90,90), axes=TRUE)
lines(inter,col='red',lwd=2,lty=3)
```

---

## Even plotting is hard

```{r echo=FALSE, fig.align="center", fig.width=8, fig.height=4, out.width="100%"}
plot(st_geometry(NAm), graticule=st_crs(NAm), col="black", border=NA, axes=TRUE)
```

---

## Relationships

<img src="imgs/taal_photo.jpg" width="250" height="250" style="display: block; margin: auto;"/>


<img src="imgs/taal_seq.png" width="1000" style="display: block; margin: auto;"/>


---
class: middle
count: false

# Geospatial Data and R

---

## Simple Features

```{r echo=FALSE}
pt = st_point(c(30, 10))
ls = st_linestring(matrix(c(30, 10, 10, 30, 40, 40), byrow=TRUE, ncol=2))
poly = st_polygon(list(matrix(c(30, 10, 40, 40, 20, 40, 10, 20, 30, 10), ncol=2, byrow=TRUE)))
poly_hole = st_polygon(
              list(
                matrix(c(35, 10, 45, 45, 15, 40, 10, 20, 35, 10), ncol=2, byrow=TRUE),
                matrix(c(20, 30, 35, 35, 30, 20, 20, 30), ncol=2, byrow=TRUE)
              )
            )

pts = st_multipoint(matrix(c(10, 40, 40, 30, 20, 20, 30, 10), ncol=2, byrow=TRUE))
lss = st_multilinestring(list( 
        matrix(c(10, 10, 20, 20, 10, 40), ncol=2, byrow=TRUE),
        matrix(c(40, 40, 30, 30, 40, 20, 30, 10), ncol=2, byrow=TRUE)
      ))
polys = st_multipolygon(list(
          list(matrix(c(30, 20, 45, 40, 10, 40, 30, 20), ncol=2, byrow=TRUE)),
          list(matrix(c(15, 5, 40, 10, 10, 20, 5, 10, 15, 5), ncol=2, byrow=TRUE))
        ))
polys_hole = st_multipolygon(list(
                list(matrix(c(40, 40, 20, 45, 45, 30, 40, 40), ncol=2, byrow=TRUE)),
                list(matrix(c(20, 35, 10, 30, 10, 10, 30, 5, 45, 20, 20, 35), ncol=2, byrow=TRUE),
                     matrix(c(30, 20, 20, 15, 20, 25, 30, 20), ncol=2, byrow=TRUE))
              ))
```

```{r echo=FALSE, fig.width=8, fig.height=8, fig.align="center", out.width="100%"}
par(mar=c(1,1,2,1), mfrow=c(4,4))

plot(pt, axes=FALSE, main="Point", pch=16); box()
plot(ls, axes=FALSE, main="Linestring");    box()
plot(poly, axes=FALSE, col="lightgrey", main="Polygon");  box()
plot(poly_hole, axes=FALSE, col="lightgrey", main="Polygon w/ Hole(s)");  box()

plot(pts, axes=FALSE, main="Multipoint", pch=16); box()
plot(lss, axes=FALSE, main="Multilinestring");    box()
plot(polys, axes=FALSE, col="lightgrey", main="Multipolygon");  box()
plot(polys_hole, axes=FALSE, col="lightgrey", main="Multipolygon w/ Hole(s)");  box()
```

---

## Analysis of geospatial data in R

R has a rich package ecosystem for read/writing, manipulating, and analyzing geospatial data.

<br/>

Some core packages:

* ~~`sp` - core classes for handling spatial data, additional utility functions.~~

* ~~`rgdal` - R interface to `gdal` (Geospatial Data Abstraction Library) for reading and writing spatial data.~~

* ~~`rgeos` - R interface to `geos` (Geometry Engine Open Source) library for querying and manipulating spatial data. Reading and writing WKT.~~

* `sf` - Combines the functionality of `sp`, `rgdal`, and `rgeos` into a single package based on tidy simple features.

* `raster` - classes and tools for handling spatial raster data.

<br/>

See more - [Spatial task view](http://cran.r-project.org/web/views/Spatial.html)

---

## Installing `sf`

This is the hardest part of using the `sf` package, difficulty comes from is dependence on several external libraries (`geos`, `gdal`, and `proj`).

* *Windows* - installing from source works when Rtools is installed (system requirements are downloaded from rwinlib)

* *MacOS* - install dependencies via homebrew: `gdal2`, `geos`, `proj`.

* *Linux* - Install development pacakages for GDAL (>= 2.0.0), GEOS (>= 3.3.0) and Proj.4 (>= 4.8.0) from your package manager of choice.


More specific details are included in the [repo readme on github](https://github.com/r-spatial/sf).


---

## Reading, writing, and converting simple features

- `sf`
    * `st_read` / `st_write` - Shapefile, GeoJSON, KML, ...
    * `st_as_sfc` / `st_as_wkt` - WKT
    * `st_as_sfc` / `st_as_binary` - WKB
    * `st_as_sfc` / `as(x, "Spatial")` - sp

See [sf vignette #2](https://cran.r-project.org/web/packages/sf/vignettes/sf2.html).

---

## Shapefiles

```{r}
fs::dir_info("data/gis/nc_counties/") %>% select(path:size)
```

---

## NC Counties

```{r}
nc  = st_read("data/gis/nc_counties/", quiet=TRUE, stringsAsFactors=FALSE) %>% tbl_df() %>% st_sf()

nc
```

---

## `sf` classes

.normal[
```{r}
str(nc, max.level=1)

class(nc)

class(nc$geometry)

class(nc$geometry[[1]])
```
]

---

```{r}
plot(nc)
```

---

## Other data

```{r}
air = st_read("data/gis/airports/", quiet=TRUE, stringsAsFactors=FALSE) %>% tbl_df() %>% st_sf()
hwy = st_read("data/gis/us_interstates/", quiet=TRUE, stringsAsFactors=FALSE) %>% tbl_df() %>% st_sf()
```

```{r echo=FALSE}
par(mar=c(3,3,3,0.1), mfrow=c(1,3))
plot(st_geometry(nc),  axes=TRUE, main="nc")
plot(st_geometry(air), axes=TRUE, pch=16, col="blue", main="air")
plot(st_geometry(hwy), axes=TRUE, col="red", main="hwy")
```

---

## Projections

```{r}
st_crs(nc)
st_crs(air)
st_crs(hwy)
```

---

```{r echo=FALSE, fig.align="center", fig.height=4, fig.width=8}
plot(st_geometry(nc),  axes=TRUE, main="Combined")
plot(st_geometry(air), add=TRUE, pch=16, col="blue")
plot(st_geometry(hwy), add=TRUE, col="red")
```

---

## UTM Zones

<img src="imgs/UTM_Zones.png" width="700" style="display: block; margin: auto;"/>

---

## Lat/Long

```{r}
nc_ll = nc
air_ll = air
hwy_ll = st_transform(hwy, st_crs(nc))
```

```{r echo=FALSE, fig.align="center", fig.height=4, fig.width=8}
plot(st_geometry(nc_ll),  axes=TRUE, main="Combined")
plot(st_geometry(air_ll), add=TRUE, pch=16, col="blue")
plot(st_geometry(hwy_ll), add=TRUE, col="red")
```

---

## UTM (Zone 15)

```{r}
utm17 = "+proj=utm +zone=17 +datum=NAD83 +units=m +no_defs"
nc_utm = st_transform(nc, st_crs(utm17))
air_utm = st_transform(air, st_crs(utm17))
hwy_utm = st_transform(hwy, st_crs(utm17))
```

```{r echo=FALSE, fig.align="center", fig.height=4, fig.width=8}
plot(st_geometry(nc_utm),  axes=TRUE, main="Combined")
plot(st_geometry(air_utm), add=TRUE, pch=16, col="blue")
plot(st_geometry(hwy_utm), add=TRUE, col="red")
```

---
class: middle
count: false

# Airport Example

---

## Sparse vs Full Results

```{r}
st_intersects(nc[20:30,], air) %>% str()
```

---

```{r}
st_intersects(nc, air, sparse=FALSE) %>% str()

st_intersects(nc, air, sparse=FALSE) %>% .[20:30, 260:270]
```

---

## Which counties have airports?

```{r}
nc_air = st_intersects(nc_utm, air_utm)
has_air = map_lgl(nc_air, ~ length(.) > 0)
nc %>% slice(which(has_air)) %>% pull(COUNTY)
```

```{r}
air_in_nc = nc_air %>% unlist() %>% unique()
air %>% slice(air_in_nc) %>% pull(AIRPT_NAME)
```
 
---

```{r fig.align="center"}
plot(st_geometry(nc))
plot(st_geometry(nc[has_air,]), add=TRUE, col="lightblue")
plot(st_geometry(air[air_in_nc,]), add=TRUE, pch=16, col="blue")
```

---
class: middle
count: false

# A Quick Gerrymandering Example

---

## (Fake) Data from Annearundel County, Maryland

```{r fig.align="center"}
(ac = st_read("data/gis/Annearundel/AnneArundelN.shp", quiet=TRUE) %>% tbl_df() %>% st_sf())
```

---

```{r fig.align="center"}
ac %>% select(DISTRICT) %>% plot()
```

---

## Adjacency matrix of precincts

```{r fig.align="center"}
adj = st_touches(ac, sparse=FALSE)

corrplot::corrplot(adj,method="color",type="full",tl.col="black",cl.pos = "n")
```

---

## Creating districts

```{r warning=FALSE, fig.align="center"}
ac_dists = ac %>% group_by(DISTRICT) %>% summarize(geometry = st_union(geometry))
plot(ac_dists)
```

---

## Polsby-Popper

This is a measure of district compactness

$$ PP(d) = 4\pi \frac{ A(d)}{P(d)^2} $$

where $A(d)$ and $P(d)$ are the area and perimeter of the district respectively.

--

```{r fig.align="center"}
circ = st_length(st_cast(ac_dists,"MULTILINESTRING"))
area = st_area(ac_dists)

4 * pi * area / circ^2
```

---
class: middle
count: false 

# Highway Example

---

## Highways

.top[
```{r echo=FALSE, out.width="90%", fig.height=4}
ggplot() +
  geom_sf(data=nc_utm) +
  geom_sf(data=hwy_utm, col='red')
```
]

---

## NC Interstate Highways

```{r}
hwy_nc_utm = st_intersection(hwy_utm, nc_utm)
```

```{r echo=FALSE, out.width="100%", fig.height=4}
ggplot() + 
  geom_sf(data=nc_utm) +
  geom_sf(data=hwy_nc_utm, col='red')
```

---

## Counties near the interstate (Buffering)

```{r}
buffer = hwy_nc_utm %>%
  st_buffer(10000)
```

```{r echo=FALSE, out.width="100%", fig.height=4}
ggplot() + 
  geom_sf(data=nc_utm) +
  geom_sf(data=hwy_nc_utm, color='red') +
  geom_sf(data=buffer, fill='red', alpha=0.3)
```

---

## Counties near the interstate (Buffering + Union)

```{r}
buffer = hwy_nc_utm %>%
  st_buffer(10000) %>%
  st_union() %>%
  st_sf()
```

```{r echo=FALSE, out.width="100%", fig.height=4}
ggplot() + 
  geom_sf(data=nc_utm) +
  geom_sf(data=hwy_nc_utm, color='red') +
  geom_sf(data=buffer, fill='red', alpha=0.3)
```

---

## Which counties?

```{r}
buffer = hwy_nc_utm %>%
  st_buffer(10000) %>%
  st_union() %>%
  st_sf()
near_counties = st_intersects(nc_utm, buffer) %>%
  map_lgl(~length(.x) != 0)
```

```{r echo=FALSE, out.width="100%", fig.height=4}
ggplot() + 
  geom_sf(data=nc_utm) +
  geom_sf(data=nc_utm[near_counties,], fill="lightblue", alpha=0.8) +
  geom_sf(data=hwy_nc_utm, color='red') +
  geom_sf(data=buffer, fill='red', alpha=0.3)
```