Spatial data & visualization

# Spatial data & visualization
### Becky Tang
### 07.14.2021

---

# Introduction

---

## Spatial data is important

- exploratory data analysis
- detecting spatial patterns and trends
- understanding spatial data relationships
- analysis of spatial data should reflect spatial structure

---

## 1854 London Cholera Outbreak

---

## Napoleon's 1812 Russia Campaign

---

## Bird abundances

**Many others!**

- [Mapping biodiversity habitats](https://pbgjam.env.duke.edu/web-mapper)
- [Migrations](http://maps.tnc.org/migrations-in-motion/#4/43.26/-112.02)
- [World Population Density](https://luminocity3d.org/WorldPopDen/#3/12.00/10.00)
- [Global Power](https://www.gocompare.com/gas-and-electricity/what-powers-the-world/)

---

## Spatial data is different

Our typical tidy data frame:

```
## # 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

Our (new) simple feature object:

```
## Simple feature collection with 100 features and 3 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## epsg (SRID):    4267
## proj4string:    +proj=longlat +datum=NAD27 +no_defs
## First 10 features:
##           name regstrd voted                       geometry
## 1         ASHE   19414  8428 MULTIPOLYGON (((-81.47276 3...
## 2    ALLEGHANY    7556  4101 MULTIPOLYGON (((-81.23989 3...
## 3        SURRY   46666 23660 MULTIPOLYGON (((-80.45634 3...
## 4    CURRITUCK   21803  7536 MULTIPOLYGON (((-76.00897 3...
## 5  NORTHAMPTON   13891  6196 MULTIPOLYGON (((-77.21767 3...
## 6     HERTFORD   14945  6955 MULTIPOLYGON (((-76.74506 3...
## 7       CAMDEN    8128  3472 MULTIPOLYGON (((-76.00897 3...
## 8        GATES    8294  3105 MULTIPOLYGON (((-76.56251 3...
## 9       WARREN   13441  6878 MULTIPOLYGON (((-78.30876 3...
## 10      STOKES   31649 14444 MULTIPOLYGON (((-80.02567 3...
```
]

<br/>

**What differences do you observe?**

---

## Raster versus vector spatial data

The choice to use vector or raster data depends on the problem context. We will focus on **vector** data.

---

## Simple features

A .vocab[simple feature] is a standard way to describe how real-world 
spatial objects (country, building, tree, road, etc) can be represented by a 
computer.

The package `sf` implements simple features and other spatial functionality 
using **tidy** principles.

---

## Simple features

Simple features have a geometry type. Common choices are below.

---

## A simple feature object

- Simple features are stored in a data frame, with the geographic information 
in a column called `geometry`.
- Simple features can contain both spatial and non-spatial data.
- Functions for spatial data in `sf` begin `st_`.

---

## A simple feature object

```
## Simple feature collection with 100 features and 6 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## epsg (SRID):    4267
## proj4string:    +proj=longlat +datum=NAD27 +no_defs
## First 10 features:
##           name regstrd voted mailed rejectd ml_rqst
## 1         ASHE   19414  8428     NA      NA    2666
## 2    ALLEGHANY    7556  4101     NA      NA     971
## 3        SURRY   46666 23660   4366       7    7088
## 4    CURRITUCK   21803  7536     NA      NA    2472
## 5  NORTHAMPTON   13891  6196    828       2    1441
## 6     HERTFORD   14945  6955     NA      NA    1524
## 7       CAMDEN    8128  3472    416       1     739
## 8        GATES    8294  3105     NA      NA     847
## 9       WARREN   13441  6878     NA      NA    1913
## 10      STOKES   31649 14444   2162       2    3648
##                          geometry
## 1  MULTIPOLYGON (((-81.47276 3...
## 2  MULTIPOLYGON (((-81.23989 3...
## 3  MULTIPOLYGON (((-80.45634 3...
## 4  MULTIPOLYGON (((-76.00897 3...
## 5  MULTIPOLYGON (((-77.21767 3...
## 6  MULTIPOLYGON (((-76.74506 3...
## 7  MULTIPOLYGON (((-76.00897 3...
## 8  MULTIPOLYGON (((-76.56251 3...
## 9  MULTIPOLYGON (((-78.30876 3...
## 10 MULTIPOLYGON (((-80.02567 3...
```
]

---

# Visualizing spatial data

---

## `nc_votes`

This data was pulled from the [North Carolina Early Voting Statistics](https://electproject.github.io/Early-Vote-2020G/NC.html) website and
is through 10-28-2020.

The dataset contains the following variables:

- `name`: county name
- `regstrd`: number of registered voters
- `voted`: number of individuals who have voted
- `mailed`: number of mail ballots returned
- `rejectd`: number of mail ballots rejected
- `ml_rqst`: number of mail ballots requested

---

## Getting `sf` objects

To read simple features from a file or database use the function `st_read()`.

```r
library(sf)
*nc <- st_read("data/nc_votes.shp", quiet = TRUE)
nc
```

---

## Plotting with `ggplot()`

```r
ggplot(nc) +
  geom_sf() +
  labs(title = "North Carolina counties")
```

---

## A look at some aesthetics

```r
ggplot(nc) +
* geom_sf(color = "lightblue") +
  labs(title = "North Carolina counties with theme and aesthetics") 
```

---

## A look at some aesthetics

```r
ggplot(nc) +
* geom_sf(color = "lightblue", size = 1.5) +
  labs(title = "North Carolina counties with theme and aesthetics")
```

---

## A look at some aesthetics

```r
ggplot(nc) +
* geom_sf(color = "lightblue", size = 1.5, fill = "purple") +
  labs(title = "North Carolina counties with theme and aesthetics")
```

---

## A look at some aesthetics

```r
ggplot(nc) +
* geom_sf(color = "lightblue", size = 1.5, fill = "purple", alpha = 0.50) +
  labs(title = "North Carolina counties with theme and aesthetics")
```

---

## A look back at some of our data

Let's incorporate these variables into our plot using `ggplot`.

---

## Choropleth map

```r
ggplot(nc) +
* geom_sf(aes(fill = voted)) +
  labs(title = "Higher population counties have more votes cast",
       fill = "Total votes cast") 
```

---

## Choropleth map

It is sometimes helpful to pick diverging colors;
[colorbrewer2](http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3)
can help.

---

## Choropleth map

One way to set fill colors is with `scale_fill_gradient()`.

```r
ggplot(nc) +
  geom_sf(aes(fill = voted)) +
* scale_fill_gradient(low = "#fee8c8", high = "#7f0000") +
  labs(title = "Higher population counties have more votes cast",
       fill = "Total votes cast") 
```

---

## Choropleth map

One way to set fill colors is with `scale_fill_gradient()`.

---

## "...it's just a population map!"

---

## Let's make it more informative

```r
ggplot(nc) +
* geom_sf(aes(fill = voted/regstrd)) +
  scale_fill_gradient(low = "#fff7f3", high = "#49006a") +
  labs(fill = "Votes cast per registered voter", 
       title = "Early vote turnout varies by county")
```

---

## Let's make it more informative

---

# 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](https://digital.ncdcr.gov/digital/collection/p15012coll6/id/425) available on all public Game Lands in NC.

```r
nc_game <- st_read("data/gamelands.shp", quiet = TRUE)
```

---

## A closer look

```r
nc_game
```

```
## Simple feature collection with 94 features and 6 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -84.29534 ymin: 33.98542 xmax: -75.54947 ymax: 36.58814
## epsg (SRID):    4267
## proj4string:    +proj=longlat +datum=NAD27 +no_defs
## First 10 features:
##    OBJECTID                   GML_HAB  SUM_ACRES GameLandID Shape__Are
## 1         1                     Alcoa 11395.9471          1   69931121
## 2         2           Alligator River 24439.0891          2  151120825
## 3         3                Angola Bay 34063.4468          3  204400526
## 4         4              Bachelor Bay  2786.2577          4   17219484
## 5         5             Bertie County  3883.7683          5   24044312
## 6         6 Bladen Lakes State Forest 33671.8426          6  202085696
## 7         7             Brinkleyville  1843.8439         92   11511489
## 8         8                  Buckhorn   491.3477         81    3046371
## 9         9                 Buckridge 17965.7187         10  110580903
## 10       10              Buffalo Cove  6630.9453         11   41161465
##    Shape__Len                       geometry
## 1   549030.42 MULTIPOLYGON (((-80.07347 3...
## 2   186792.83 MULTIPOLYGON (((-76.11832 3...
## 3   105421.80 MULTIPOLYGON (((-77.86947 3...
## 4    32891.84 MULTIPOLYGON (((-76.73896 3...
## 5    83468.94 MULTIPOLYGON (((-76.9209 35...
## 6   255198.44 MULTIPOLYGON (((-78.46171 3...
## 7    46838.19 MULTIPOLYGON (((-77.90555 3...
## 8    13445.00 MULTIPOLYGON (((-79.22056 3...
## 9   142923.83 MULTIPOLYGON (((-76.10961 3...
## 10   98754.34 MULTIPOLYGON (((-81.53307 3...
```
]

---

## Visualize `nc_game`

```r
ggplot(nc_game) +
  geom_sf() +
  labs(title = "North Carolina gamelands")
```

---

## Visualize `nc_game`

```r
ggplot(nc_game) +
  geom_sf(fill = "#ff6700") +
  labs(title = "North Carolina gamelands")
```

---

## Add layers

```r
ggplot(nc) +
  geom_sf() +
  geom_sf(data = nc_game, fill = "#ff6700", alpha = .5) +
  labs(title = "North Carolina gamelands and counties")
```

<img src="24-spatial_files/figure-html/nc_game_layer-1.png" style="display: block; margin: auto;" />
]

---

## Add layers and aesthetics

```r
ggplot(nc) +
  geom_sf() +
  geom_sf(data = nc_game, aes(alpha = SUM_ACRES), fill = "#ff6700") +
  labs(title = "North Carolina gamelands and counties")
```

<img src="24-spatial_files/figure-html/nc_game_aes-1.png" style="display: block; margin: auto;" />
]

---

# Spatial challenges

---

## Challenge #1

Different types of data exist (raster and vector).

---

## Challenge #2

The coordinate reference system (CRS) matters.

```r
    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
    *epsg (SRID):    4326
    *proj4string:    +proj=longlat +datum=WGS84 +no_defs
    # A tibble: 100 x 2
      NAME                                                geometry
      <chr>                                     <MULTIPOLYGON [°]>
    1 Ashe        (((-81.47276 36.23436, -81.54084 36.27251,  -...
    ```

---

##  Challenge #3

Manipulating spatial data objects is similar, but not identical to manipulating
data frames.

Note the core data-wrangling functions from `dplyr` do work.

---

# `dplyr` + `sf`

---

## `select`

```r
nc %>% 
  select(name, regstrd, voted)
```

---

## `filter`

```r
nc %>% 
  filter(regstrd > 100000)
```

```
## Simple feature collection with 20 features and 6 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## epsg (SRID):    4267
## proj4string:    +proj=longlat +datum=NAD27 +no_defs
## First 10 features:
##        name regstrd  voted mailed rejectd ml_rqst
## 1   FORSYTH  270818 134770  35664      75   67472
## 2  GUILFORD  381797 190530  42825     785   76615
## 3  ALAMANCE  110127  52434  12788      19   22091
## 4    ORANGE  111765  64016  22859      55   36560
## 5    DURHAM  243045 138264  41767     348   70046
## 6      WAKE  791821 421180 138483     781  244538
## 7   IREDELL  130013  67128  14775       2   22390
## 8  DAVIDSON  112872  54040  10012      87   16240
## 9      PITT  122925  57304   9268      37   17595
## 10  CATAWBA  108098  55018   8947      70   15004
##                          geometry
## 1  MULTIPOLYGON (((-80.0381 36...
## 2  MULTIPOLYGON (((-79.53782 3...
## 3  MULTIPOLYGON (((-79.24619 3...
## 4  MULTIPOLYGON (((-79.01814 3...
## 5  MULTIPOLYGON (((-79.01814 3...
## 6  MULTIPOLYGON (((-78.92107 3...
## 7  MULTIPOLYGON (((-80.72652 3...
## 8  MULTIPOLYGON (((-80.06441 3...
## 9  MULTIPOLYGON (((-77.47388 3...
## 10 MULTIPOLYGON (((-80.96143 3...
```

---

## `summarize`

```r
nc %>% 
  summarize(mean_registered  = mean(regstrd),
            median_voted = median(voted))
```

```
## Simple feature collection with 1 feature and 2 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## epsg (SRID):    4267
## proj4string:    +proj=longlat +datum=NAD27 +no_defs
##   mean_registered median_voted                       geometry
## 1        73183.36        16217 MULTIPOLYGON (((-76.54427 3...
```

---

## Geometries are "sticky"

The geometry is kept until it is deliberated dropped using `st_drop_geometry`.

```r
nc %>% 
  select(name, regstrd) %>% 
  filter(regstrd > 100000) %>% 
* st_drop_geometry()
```

```
##           name regstrd
## 1      FORSYTH  270818
## 2     GUILFORD  381797
## 3     ALAMANCE  110127
## 4       ORANGE  111765
## 5       DURHAM  243045
## 6         WAKE  791821
## 7      IREDELL  130013
## 8     DAVIDSON  112872
## 9         PITT  122925
## 10     CATAWBA  108098
## 11    BUNCOMBE  206178
## 12    JOHNSTON  142255
## 13 MECKLENBURG  789547
## 14    CABARRUS  149584
## 15      GASTON  150779
## 16  CUMBERLAND  225029
## 17       UNION  167068
## 18      ONSLOW  116300
## 19 NEW HANOVER  177056
## 20   BRUNSWICK  114115
```

---

## References

- [North Carolina Early Voting Statistics](https://electproject.github.io/Early-Vote-2020G/NC.html)
- [Simple Features for R vignette](https://r-spatial.github.io/sf/)
- [mapview vignette](https://r-spatial.github.io/mapview/index.html)
- [Coordinate Reference Systems in R](https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/OverviewCoordinateReferenceSystems.pdf)
- [Geocomputation with R](https://geocompr.robinlovelace.net/spatial-class.html)