## Introduction

Several previous studies have found robust relationships between spatial properties of a country's **capital city** and that country's propensity for **conflict** and **misgovernance**.

Perceptions of this linkage also have an effect on "coup-proofing" decisions made by national governments. A recent BBC interview with Equatorial Guinea's President Teodoro Obiang, for example, highlighted this as a factor behind his decision to relocate the capital city:

> It's the remoteness of Oyala that makes it so appealing to President Obiang. In a rare interview he described how rebels had recently plotted a seaborne assault on his palace in the current capital, Malabo. 'We need a secure place for my government and for future governments. That's why we have created Oyala, to guarantee the government of Equatorial Guinea.' [@sackur_equatorial_2012]

This case is far from exceptional, as an even more recent *Washington Post* article points out with respect to Myanmar's decision to move its capital from Yangon to Naypyidaw:

> Analysts have described the decision as motivated by a desire to secure the military’s seat of power from any threat of protests or invasions. [@berger_myanmars_2021]

Most of these studies, however, are based on observations of **conflict events**. In this study, we study the more fundamental variable of a capital's distance from the **population centroid** of the country.

## Literature Review

@campante_capital_2019 analyzes the relationship between the location of a **capital city** and the degree of conflict and misgovernance in a given country. Their two key findings are that:

> Conflict is more likely to emerge (and dislodge incumbents) closer to the capital

and

> Isolated capitals are associated with misgovernance.

This first finding is illustrated in @fig-conflict-dist

![](images/conflict_dist.svg){#fig-conflict-dist}

## Methodology

The **population centroids** we use herein might require some explanation, since the term "centroid" can be ambiguous.

Here, the population centroids are drawn from @hall_population_2019

## Exploratory Data Analysis (EDA)

Here we plot the base GIS objects we're analyzing: the location of each **capital city** (in purple) and each **population centroid** (in yellow).

```{r}
#| label: imports
library(tidyverse) |> suppressPackageStartupMessages()
library(sf) |> suppressPackageStartupMessages()
library(mapview) |> suppressPackageStartupMessages()
library(units) |> suppressPackageStartupMessages()
cb_palette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
```

```{r}
#| label: load-eda
merged_long_sf <- readRDS("merged_long_sf.rds")
mapview(merged_long_sf, zcol="name", cex=4, label="geounit")
```

We then construct an **area-normalized** measure of capital-centroid distance $\text{dist}^{\textsf{AN}}$, using the formula

$$
\text{dist}^{\textsf{AN}}_i = \text{dist}_i / \sqrt{\text{area}_i}.
$$

A plot of this measure by country looks as follows:

```{r}
#| label: plot-area
merged_area_sf <- readRDS("merged_area_sf.rds")
mapview(merged_area_sf, zcol="scaled_dist")
```

## Hypothesis Testing (Regression)

```{r}
#| label: plot-regression
merged_sub_sf <- readRDS("merged_sub_sf.rds")
merged_sub_sf |> head()
```

```{r}
merged_sub_sf |> ggplot(aes(x=scaled_dist, y=total_score, label=NAME_ENGLI)) +
  geom_point() +
  geom_smooth(method='lm', formula= y~x) +
  geom_text(size=4, nudge_y = 0.075) +
  theme_classic()
```

## Discussion

## Conclusion

Our evidence indicates that the spatial dynamics of **conflict** differ from the spatial dynamics of **misgovernance**. Whereas 