POL 304: Using Data to Understand Politics and Society

class: center, middle, inverse, title-slide

.title[
# POL 304: Using Data to Understand Politics and Society
]
.subtitle[
## Drawing Maps
]
.author[
### Olga Chyzh [www.olgachyzh.com]
]

---

## Dependence in Observational Data

- Individuals are nested in social networks

+ Individual decisions are influenced by their friends.

- Provinces are surrounded by other provinces

+ Provinces mimic one another's policies

- Country-level outcomes are often a result of negotiations with other countries:
    
    + Economic or environmental policies
    
---
## Three Mechanisms for Spatial Dependence

- Homophily---similarity in outcomes is endogenous, units are similar because they self-select into the same outcome (e.g., partisan geo-sorting)

- Common exposure---similarity in outcomes is driven by an exogenous factor that affects nearby units (the effect of earthquakes on housing prices)

- Diffusion---nearby units affect each other through learning, imitation, etc (e.g., policy diffusion)

---

Source: van Weezel S. "On climate and conflict: Precipitation decline and communal conflict in Ethiopia and Kenya." *Journal of Peace Research*. 2019;56(4):514--528.

---

Source: Chyzh, Olga V. and R. Urbatsch. 2021. "Bean Counters: The Effect of Soy Tariffs on Change in Republican Vote Share Between the 2016 and 2018 Elections."*Journal of Politics* 83 (1): 415--419.
  
---
## What You Need

- Latitude/longitude points for all map boundaries

- Need to know to which boundary/state lat/long points belong

- Need to know the order to connect points within each group

---
## Making Maps

```r
library(tidyverse)
library(mapproj)
library(maps)
library(mapdata)
states <- map_data("state")

head(states)
```

```
##        long      lat group order  region subregion
## 1 -87.46201 30.38968     1     1 alabama      <NA>
## 2 -87.48493 30.37249     1     2 alabama      <NA>
## 3 -87.52503 30.37249     1     3 alabama      <NA>
## 4 -87.53076 30.33239     1     4 alabama      <NA>
## 5 -87.57087 30.32665     1     5 alabama      <NA>
## 6 -87.58806 30.32665     1     6 alabama      <NA>
```

---
## A Basin (Rather Hideous) Map

```r
library(ggplot2)
ggplot() +  geom_path(data=states, aes(x=long, y=lat, group=group),color="black", size=.5)
```

---
## A Bit Nicer of a Map

```r
#Set theme options:
theme_set(theme_grey() + theme(axis.text=element_blank(),
                               axis.ticks=element_blank(),
                               axis.title.x=element_blank(),
                               axis.title.y=element_blank(),
                               panel.grid.major = element_blank(),
                               panel.grid.minor = element_blank(),
                               panel.border = element_blank(),
                               panel.background = element_blank(),
                               legend.position="none"))
ggplot() +  geom_path(data=states, aes(x=long, y=lat, group=group),color="black", size=.5)+ coord_map()
```

---
## Polygon instead of Path

```r
ggplot() +  geom_polygon(data=states, aes(x=long, y=lat, group=group),color="black", size=.5)+ coord_map()
```

---
## Incorporate Information About States

- Add other geographic information (e.g., counties) by adding geometric layers to the plot

- Add non-geographic information by altering the fill color for each state

- Use geom = "polygon" to treat states as solid shapes to add color
    
    - Incorporate numeric information using color shade or intensity
    
    - Incorporate categorical informaion using color hue
    
---
## Categorical Information Using Hue

If a categorical variable is assigned as the fill color then ggplot will assign different hues for each category.

Let’s load in a state regions dataset:

```r
statereg<- read.csv("./data/statereg.csv")

head(statereg)
```

```
##        State StateGroups
## 1 california        West
## 2     nevada        West
## 3     oregon        West
## 4 washington        West
## 5      idaho        West
## 6    montana        West
```

---
## Join the Data

```r
states.class.map <- left_join(states, statereg, by = c("region" = "State"))
head(states.class.map)
```

```
##        long      lat group order  region subregion StateGroups
## 1 -87.46201 30.38968     1     1 alabama      <NA>       South
## 2 -87.48493 30.37249     1     2 alabama      <NA>       South
## 3 -87.52503 30.37249     1     3 alabama      <NA>       South
## 4 -87.53076 30.33239     1     4 alabama      <NA>       South
## 5 -87.57087 30.32665     1     5 alabama      <NA>       South
## 6 -87.58806 30.32665     1     6 alabama      <NA>       South
```

---
## Plot the Regions

```r
ggplot() +  geom_polygon(data=states.class.map, aes(x=long, y=lat, group=group, fill = StateGroups), colour = I("black"))+ coord_map()+theme(legend.position="bottom")
```

---
## Map of Canada

```r
library(devtools)
install_github("mccormackandrew/mapcan", build_vignettes = TRUE)
library(mapcan)
canada_map<-mapcan(boundaries = "province", type="standard",territories=TRUE)
head(canada_map)
```

```
##      long     lat order  hole piece pr_sgc_code group                pr_english
## 1 2278204 1261783     1 FALSE     1          10  10.1 Newfoundland and Labrador
## 2 2294272 1251745     2 FALSE     1          10  10.1 Newfoundland and Labrador
## 3 2300435 1250534     3 FALSE     1          10  10.1 Newfoundland and Labrador
## 4 2306656 1243299     4 FALSE     1          10  10.1 Newfoundland and Labrador
## 5 2315548 1241630     5 FALSE     1          10  10.1 Newfoundland and Labrador
## 6 2313319 1237013     6 FALSE     1          10  10.1 Newfoundland and Labrador
##                 pr_french pr_alpha
## 1 Terre-Neuve-et-Labrador       NL
## 2 Terre-Neuve-et-Labrador       NL
## 3 Terre-Neuve-et-Labrador       NL
## 4 Terre-Neuve-et-Labrador       NL
## 5 Terre-Neuve-et-Labrador       NL
## 6 Terre-Neuve-et-Labrador       NL
```

---

## Map of Canada

ggplot(canada_map, aes(long, lat, group = group)) +
  geom_polygon(color="black", fill="white")
```

---

## Map of Canada
<img src="11_maps_files/figure-html/unnamed-chunk-12-1.png" style="display: block; margin: auto;" />

---

## Canada Election Results

```r
library(tidyverse)
library(magrittr)
data("federal_election_results") 
federal_election_results %>% as.data.frame() %>% 
  dplyr::filter(election_year=="2015")->electdata

canada_ridings<-mapcan(boundaries = "ridings", type="standard",territories=TRUE)
head(canada_ridings)

canada_ridings %>% left_join(electdata, by="riding_code") %>%
           ggplot(aes(long, lat, group = group, fill=factor(party)))+
  geom_polygon(color="black") +scale_fill_discrete("Party", type="qual") + theme(legend.position="bottom")
```

---
## Canada Election Results

---
## Your Turn

1. Check out the help file for the `map_data` function to find out how to make a map of the world.

2. Can you figure out how to remove Antarctica from the map?

3. Use the GDP data you scraped as part of our web-scraping class to shade countries based on their GDP.

---
## Your Turn (Advanced)

1. Read in the animal.csv data:

2. Plot the location of animal sightings on a map of the region
3. On this plot, try to color points by class of animal and/or status of animal
4. Use color to indicate time.