Visualizing spatial data I

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# Geospatial visualizations

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## Geospatial visualizations

* Earliest form of information visualizations
* Geospatial data visualizations
* [Google Maps](https://www.google.com/maps)

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## Not that Jon Snow

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## Dr. John Snow

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## Designing modern maps

* Depict spatial features
* Incorporate additional attributes and information
* Major features
    * Scale
    * Projection
    * Symbols

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## Scale

* Proportion between distances and sizes on a map and their actual distances and sizes on Earth
* Small-scale map
* Large-scale map

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## Large-scale map

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## Small-scale map

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## Asgard

]

## Midgard

]

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## Not flat

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## Projection

* Process of taking a three-dimensional object and visualizing it on a two-dimensional surface
* No 100% perfect method for this
* Always introduces distortions

* Properties of projection methods
    1. Shape
    1. Area
    1. Angles
    1. Distance
    1. Direction

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## Symbols

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# `ggmap` for raster maps

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## `ggmap`

- Package for drawing maps using `ggplot2` and **raster** map tiles
- Static image files generated by mapping services
- Focus on incorporating data into existing maps
- Severely limits ability to change the appearance of the geographic map
- Don't have to worry about the maps, just the data to go on top

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## Bounding box

```r
nyc_bb <- c(
* left = -74.263045,
* bottom = 40.487652,
* right = -73.675963,
* top = 40.934743
)

nyc_stamen <- get_stamenmap(
  bbox = nyc_bb,
  zoom = 11
)
ggmap(nyc_stamen)
```

]

]
]

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## Level of detail

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## Identifying bounding box

> Use [bboxfinder.com](http://bboxfinder.com/#0.000000,0.000000,0.000000,0.000000) to determine the exact longitude/latitude coordinates for the bounding box you wish to obtain.

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## Types of map tiles

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## Import crime data

* New York City open data portal
* [Crime data from 2022](https://data.cityofnewyork.us/Public-Safety/NYPD-Complaint-Data-Current-Year-To-Date-/5uac-w243)

```r
crimes <- here("data", "nyc-crimes.csv") %>%
  read_csv()
```

```r
glimpse(crimes)
```

```
## Rows: 256,797
## Columns: 7
## $ cmplnt_num   <chr> "247350382", "243724728", "246348713", "240025455", "2461…
## $ boro_nm      <chr> "BROOKLYN", "QUEENS", "QUEENS", "BROOKLYN", "BRONX", "BRO…
## $ cmplnt_fr_dt <dttm> 1011-05-18 04:56:02, 1022-04-11 04:56:02, 1022-06-08 04:…
## $ law_cat_cd   <chr> "MISDEMEANOR", "MISDEMEANOR", "MISDEMEANOR", "FELONY", "F…
## $ ofns_desc    <chr> "CRIMINAL MISCHIEF & RELATED OF", "PETIT LARCENY", "PETIT…
## $ latitude     <dbl> 40.66904, 40.77080, 40.68766, 40.65421, 40.83448, 40.6973…
## $ longitude    <dbl> -73.90619, -73.81115, -73.83406, -73.95957, -73.85637, -7…
```

---

## Plot high-level map of crime

.panelset.sideways[

```r
nyc <- nyc_stamen
*ggmap(nyc)
```

<img src="index_files/figure-html/import-nyc-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Using `geom_point()`

.panelset.sideways[

```r
ggmap(nyc) +
* geom_point(
*   data = crimes,
*   mapping = aes(
*     x = longitude,
*     y = latitude
*   )
* )
```

<img src="index_files/figure-html/plot-crime-point-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Using `geom_point()`

.panelset.sideways[

```r
ggmap(nyc) +
  geom_point(
    data = crimes,
    mapping = aes(
      x = longitude,
      y = latitude
    ),
*   size = .25,
*   alpha = .01
  )
```

<img src="index_files/figure-html/plot-crime-point-alpha-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Using `geom_density_2d()`

.panelset.sideways[

```r
ggmap(nyc) +
* geom_density_2d(
    data = crimes,
    mapping = aes(
      x = longitude,
      y = latitude
    )
  )
```

<img src="index_files/figure-html/kde-contour-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Using `stat_density_2d()`

.panelset.sideways[

```r
ggmap(nyc) +
* stat_density_2d(
    data = crimes,
    mapping = aes(
      x = longitude,
      y = latitude,
*     fill = stat(level)
    ),
*   geom = "polygon"
  )
```

<img src="index_files/figure-html/kde-fill-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Using `stat_density_2d()`

.panelset.sideways[

```r
ggmap(nyc) +
  stat_density_2d(
    data = crimes,
    mapping = aes(
      x = longitude,
      y = latitude,
      fill = stat(level)
    ),
*   alpha = .2,
*   bins = 25,
    geom = "polygon"
  )
```

<img src="index_files/figure-html/plot-crime-density-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Looking for variation

.panelset.sideways[

```r
ggmap(nyc) +
  stat_density_2d(
    data = crimes %>%
      filter(ofns_desc %in% c(
        "DANGEROUS DRUGS",
        "GRAND LARCENY OF MOTOR VEHICLE",
        "ROBBERY",
        "VEHICLE AND TRAFFIC LAWS"
      )),
    aes(
      x = longitude,
      y = latitude,
      fill = stat(level)
    ),
    alpha = .4,
    bins = 10,
    geom = "polygon"
  ) +
* facet_wrap(facets = vars(ofns_desc))
```

<img src="index_files/figure-html/plot-crime-wday-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Exercise using `ggmap`

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# Geofaceting

---

---

## Daily US vaccine data by state

```r
us_state_vaccinations <- read_csv(here::here("data", "us_state_vaccinations.csv"))
```

```r
glimpse(us_state_vaccinations)
```

```
## Rows: 38,052
## Columns: 16
## $ date                                <date> 2021-01-12, 2021-01-13, 2021-01-1…
## $ location                            <chr> "Alabama", "Alabama", "Alabama", "…
## $ total_vaccinations                  <dbl> 78134, 84040, 92300, 100567, NA, N…
## $ total_distributed                   <dbl> 377025, 378975, 435350, 444650, NA…
## $ people_vaccinated                   <dbl> 70861, 74792, 80480, 86956, NA, NA…
## $ people_fully_vaccinated_per_hundred <dbl> 0.15, 0.19, NA, 0.28, NA, NA, NA, …
## $ total_vaccinations_per_hundred      <dbl> 1.59, 1.71, 1.88, 2.05, NA, NA, NA…
## $ people_fully_vaccinated             <dbl> 7270, 9245, NA, 13488, NA, NA, NA,…
## $ people_vaccinated_per_hundred       <dbl> 1.45, 1.53, 1.64, 1.77, NA, NA, NA…
## $ distributed_per_hundred             <dbl> 7.69, 7.73, 8.88, 9.07, NA, NA, NA…
## $ daily_vaccinations_raw              <dbl> NA, 5906, 8260, 8267, NA, NA, NA, …
## $ daily_vaccinations                  <dbl> NA, 5906, 7083, 7478, 7498, 7509, …
## $ daily_vaccinations_per_million      <dbl> NA, 1205, 1445, 1525, 1529, 1531, …
## $ share_doses_used                    <dbl> 0.207, 0.222, 0.212, 0.226, NA, NA…
## $ total_boosters                      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ total_boosters_per_hundred          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA…
```

]

---

## Facet by location

.panelset.sideways[
.panel[.panel-name[Code]

```r
ggplot(
  data = us_state_vaccinations,
  mapping = aes(x = date, y = people_fully_vaccinated_per_hundred)
) +
  geom_area() +
* facet_wrap(facets = vars(location))
```

]

]
]

---

## Data cleaning

```r
us_state_vaccinations <- us_state_vaccinations %>%
  mutate(location = if_else(location == "New York State", "New York", location)) %>%
  filter(location %in% c(state.name, "District of Columbia"))
```

---

## Geofacet by state

Using `geofacet::facet_geo()`:

.panelset.sideways[
.panel[.panel-name[Code]

```r
ggplot( 
  data = us_state_vaccinations,
  mapping = aes(x = date, y = people_fully_vaccinated_per_hundred)
) +
  geom_area() +
* facet_geo(facets = vars(location)) +
  labs(
    x = NULL, y = NULL,
    title = "Covid-19 vaccination rate in the US",
    subtitle = "Daily number of people fully vaccinated, per hundred",
    caption = "Source: Our World in Data"
  )
```

]

]
]

---

## Geofacet by state, with improvements

.panelset.sideways[
.panel[.panel-name[Plot]
<img src="index_files/figure-html/unnamed-chunk-38-1.png" width="95%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]
.small[

```r
ggplot(us_state_vaccinations, aes(x = date, y = people_fully_vaccinated_per_hundred, group = location)) +
  geom_area() +
  facet_geo(facets = vars(location)) +
* scale_y_continuous(
*   limits = c(0, 100),
*   breaks = c(0, 50, 100),
*   minor_breaks = c(25, 75)
*   ) +
* scale_x_date(breaks = c(ymd("2021-01-01", "2021-07-01", "2022-01-01")), date_labels = "%b-%y") +
  labs(
    x = NULL, y = NULL,
    title = "Covid-19 vaccination rate in the US",
    subtitle = "Daily number of people fully vaccinated, per hundred",
    caption = "Source: Our World in Data"
  ) +
  theme(
*   strip.text.x = element_text(size = 7),
*   axis.text = element_text(size = 8),
    plot.title.position = "plot"
  )
```
]
]
]

---

## Bring in 2020 Presidential election results

```r
election_2020 <- read_csv(here::here("data", "us-election-2020.csv"))
```

```r
election_2020
```

```
## # A tibble: 51 × 5
##    state                electoal_votes biden trump win       
##    <chr>                         <dbl> <dbl> <dbl> <chr>     
##  1 Alabama                           9     0     9 Republican
##  2 Alaska                            3     0     3 Republican
##  3 Arizona                          11    11     0 Democrat  
##  4 Arkansas                          6     0     6 Republican
##  5 California                       55    55     0 Democrat  
##  6 Colorado                          9     9     0 Democrat  
##  7 Connecticut                       7     7     0 Democrat  
##  8 Delaware                          3     3     0 Democrat  
##  9 District of Columbia              3     3     0 Democrat  
## 10 Florida                          29     0    29 Republican
## # … with 41 more rows
```

---

## Geofacet by state, color by presidential election result

```r
us_state_vaccinations %>%
  left_join(election_2020, by = c("location" = "state")) %>%
  ggplot(mapping = aes(x = date, y = people_fully_vaccinated_per_hundred)) +
* geom_area(aes(fill = win)) +
  facet_geo(facets = vars(location)) +
* scale_y_continuous(limits = c(0, 100), breaks = c(0, 50, 100), minor_breaks = c(25, 75)) +
  scale_x_date(breaks = c(ymd("2021-01-01", "2021-07-01", "2022-01-01")), date_labels = "%b") +
* scale_fill_manual(values = c("#2D69A1", "#BD3028")) +
  labs(
    x = NULL, y = NULL,
    title = "Covid-19 vaccination rate in the US",
    subtitle = "Daily number of people fully vaccinated, per hundred",
    caption = "Source: Our World in Data",
    fill = "2020 Presidential\nElection"
  ) +
  theme(
    strip.text.x = element_text(size = 7),
    axis.text = element_text(size = 8),
    plot.title.position = "plot",
*   legend.position = c(0.93, 0.15),
*   legend.text = element_text(size = 9),
*   legend.title = element_text(size = 11),
*   legend.background = element_rect(color = "gray", size = 0.5)
  )
```

]

]
]
]