# Summary

A list of 196 countries ordered by “triangularity” can be found in the Results section of this post.

Details of the methods are described in the following two sections. I have included all my code at the very end of this post.

I was inspired to write this blog post after reading two excellent posts. One of these is about finding the most rectangular country and is by David Barry. The other is by Gonzalo Ciruelos and is about finding the roundest country. I’ll provide links to these posts below:

I thought it would be interesting to have a go at calculating the most triangular country. I followed a similar method to the previous posts. The first step is to define mathematically what “triangularity” means. The next step is to find a way to calculate triangularity for every country in the world.

In summary, the method involves picking a similarity measure between a triangle and a country, then, for each country, finding the triangle that maximises the similarity measure. This maximum similarity is the country’s triangularity. Finding a triangle that maximises similarity is an optimisation problem. A triangle can be uniquely defined by the coordinates of its three vertices. Hence, there are 6 free parameters in the optimisation problem (an $$x$$ and $$y$$ coordinate per vertex). In contrast, defining a rectangle requires five parameters, and a circle requires only three (e.g. the coordinates of the center of the circle and its radius). As a result of the greater number of free parameters, this optimisation is more prone to get stuck in local optima. I tackled the problem by running the optimisation multiple times per country with randomised initial conditions.

Overall, I am happy with the results.

# Theory

Following Gonzalo Ciruelos’ example, I will define the similarity of two sets $$A$$ and $$B$$ in $$\mathbb{R}^{2}$$ as the area of the intersection of the sets divided by the maximum of the areas of the sets.

$\textrm{similarity} (A,B) = \frac{\textrm{area}( A \cap B)}{\max \{ \textrm{area} {(A)},\textrm{area} (B) \} }$

This definition of similarity has the following properties:

• Because area is never negative, $$\textrm{similarity}(A,B) \geq 0$$.

• Because $$\textrm{area}(A \cap B) \leq \max \{\textrm{area}(A), \textrm{area}(B)\}$$, $$\textrm{similarity} (A,B) \leq 1$$.

• If $$A=B$$, then $$\textrm{similarity} (A,B) = 1$$.

• If $$A \cap B = \emptyset$$, then $$\textrm{similarity} (A,B) = 0$$.

Let the country $$C$$ be a set in the plane, $$C \subset \mathbb{R}^{2}$$. Let $$T(\textbf{x},\textbf{y})$$ be a set in the plane, $$T(\textbf{x},\textbf{y}) \subset \mathbb{R}^{2}$$, that is enclosed by the triangle with vertices: $$(x_{1},y_{1})$$, $$(x_{2},y_{2})$$, $$(x_{3},y_{3})$$.

Then, the triangularity of $$C$$ is the maximum similarity between $$C$$ and some $$T(\textbf{x},\textbf{y})$$:

$\textrm{triangularity} (C) = \max_{\textbf{x},\textbf{y}} \textrm{similarity} (C,T(\textbf{x},\textbf{y}))$

where

$\textbf{x} = [x_{1},x_{2},x_{3}]$ $x_{i} \in \mathbb{R}$ $\textbf{y} = [y_{1},y_{2},y_{3}]$ $y_{i} \in \mathbb{R}$

We have that

• For any triangle $$T$$, $$\textrm{triangularity} (T) = 1$$.

In practice, calculating the triangularity exactly for a country is not possible. However, using optimisation methods, it is possible to produce a reasonable approximation.

# Method

I used the freely available countries dataset from Natural Earth. The shapefile contains an ordered list of latitude and longitude points for the borders of 247 countries. Countries have one or more continuous parts (for example, each part might be an island belonging to the country). The part information is a series of indices that mark where the points for each part of the country begin and end.

The first step in processing the data from the shapefiles was to convert the latitude and longitude coordinates using an azimuthal projection centered on each country. To do the conversion, I adapted some code from this blog post. Then, using the python Shapely package, I converted the coordinates into a set of polygons for each country.

I wrote a function to calculate the similarity between the set of polygons that make up a country and a given triangle, making heavy use of the intersection and area methods from the Shapely package. I created an R-Tree for each country, using the STRtree class in Shapely. This makes it fast to query which parts of the country might intersect with the triangle. Then, for each of these parts, I calculated the area of intersection with the triangle. These are summed and divided by the maximum of the total area of the country and the area of the triangle, as per the definition of similarity. This worked for all countries, apart from Antarctica, due to a problem with the polygon intersecting with itself.

To find the triangle that maximised the triangularity of each country, I began with an initial guess: three random points on the country’s border. I then used the minimize function in scipy, using the Nelder-Mead simplex method, to find a triangle that maximised the triangularity of the country, i.e. minimising the cost function:

$f(\textbf{x},\textbf{y}) = 1 - \textrm{similarity} (C,T(\textbf{x},\textbf{y}))$

Interestingly, for a given country, the resulting triangles (and triangularities) after the optimisation varied quite significantly, depending on the initial guess. The problem is that the optimisation gets stuck in a local minimum of the cost function. In order to try and avoid local minima, I ran the optimisation routine 20 times for each country with a different initial guess each time, and used the estimate of triangularity from the best run. This is still no guarantee that local minima will be avoided, but does certainly help.

The final step was simply to save an image of each country and its most similar triangle, and create a CSV file with estimated triangularity for each country.

# Results

The results shown here are all the countries in the Natural Earth dataset that are also countries according to Encyclopedia Britannica. This means a slightly thinned down list of 196 countries from the original list of 247.

So these results would suggest that the most triangular country in the world is Nicaragua, followed closely by Bosnia and Herzegovina. (I will add that I found out an interesting fact about the flag of Bosnia and Herzegovina which is that it features a triangle that, according to Wikipedia, represents the approximate shape of the territory. Indeed, it does!)

I plotted the triangularity of each country by rank, labelling every 25th country. This shows that there is a quite gentle decrease in triangularity for the first 150 countries followed by a sharp drop off, seemingly due to countries that have multiple parts. The least triangular country is the Marshall Islands, hardly surprising considering the country is a collection of tiny islands spread over a large area of ocean. The least triangular country that isn’t a group of islands is Vietnam.

Looking at pictures of the best triangles, it does seem like the optimisation has worked as intended, at least to the human eye. I think I would have picked similar triangles if I were doing the process manually.

Below is the final list of triangular countries. If you want to view the images in more detail, you can either open the images in a new tab or download them.

Rank Country Triangularity Image
1 Nicaragua 0.918672
2 Bosnia and Herzegovina 0.913202
3 Namibia 0.907920
4 Mauritania 0.899915
5 Bolivia 0.898253
6 Bhutan 0.897231
7 Sri Lanka 0.896619
8 Barbados 0.892064
9 Botswana 0.889655
10 Dominican Republic 0.889356
11 Argentina 0.888404
12 Algeria 0.887851
13 Andorra 0.887847
14 Belarus 0.886588
15 Saint Lucia 0.885393
16 Mongolia 0.883835
17 Finland 0.883464
18 East Timor 0.883226
19 Lithuania 0.882420
20 Spain 0.880991
21 Iraq 0.880544
22 Nigeria 0.879989
23 Bahrain 0.879084
24 Central African Republic 0.878863
25 Niger 0.878834
26 Cambodia 0.878727
27 Libya 0.878326
28 Lebanon 0.877776
29 Chad 0.876590
30 Dominica 0.875869
31 Taiwan 0.875187
32 Brazil 0.874508
33 Saudi Arabia 0.874421
34 Zimbabwe 0.874192
35 Ecuador 0.872448
36 Venezuela 0.871525
37 Uruguay 0.869709
38 Benin 0.868978
39 Liberia 0.868948
40 Madagascar 0.867805
41 Democratic Republic of the Congo 0.866893
42 Ethiopia 0.866403
43 Syria 0.865422
44 Slovenia 0.864037
45 Liechtenstein 0.863911
46 Afghanistan 0.863858
47 Honduras 0.863776
48 Burkina Faso 0.863649
49 San Marino 0.863048
50 South Africa 0.860611
51 Vatican 0.860316
52 Belgium 0.859731
53 Poland 0.859164
54 Georgia 0.859137
55 Austria 0.858090
56 Hungary 0.857817
57 Ghana 0.857068
58 Nepal 0.857044
59 Slovakia 0.856166
60 El Salvador 0.856155
61 Republic of Serbia 0.856100
62 Armenia 0.856031
63 Morocco 0.855741
64 North Korea 0.855623
65 South Sudan 0.854143
66 Estonia 0.853185
67 Yemen 0.852291
68 Mauritius 0.852287
69 Burundi 0.852197
70 Cameroon 0.852041
71 Kuwait 0.852006
72 Luxembourg 0.851825
73 United Republic of Tanzania 0.850960
74 Monaco 0.850811
75 Kyrgyzstan 0.849767
76 Ivory Coast 0.849450
77 Guinea 0.849425
78 Romania 0.849085
79 South Korea 0.847823
80 Uganda 0.847599
81 Singapore 0.847415
82 Kenya 0.847016
83 Australia 0.844598
84 Cyprus 0.844447
85 United Arab Emirates 0.844364
86 Montenegro 0.842538
87 Macedonia 0.842095
88 Ireland 0.841491
89 Jamaica 0.840451
90 Senegal 0.840054
91 Suriname 0.839448
92 Turkey 0.838539
93 Pakistan 0.838210
94 Nauru 0.838165
95 Iceland 0.837977
96 Qatar 0.837840
97 Mali 0.837820
98 Sudan 0.837108
99 Albania 0.836750
100 Czechia 0.836644
101 Togo 0.836207
102 Iran 0.835827
103 Sierra Leone 0.835686
104 Kosovo 0.834849
105 eSwatini 0.834460
106 Gabon 0.834010
107 Bulgaria 0.833776
108 Angola 0.832625
109 Oman 0.832474
110 Lesotho 0.832316
111 Germany 0.832018
112 Rwanda 0.831329
113 Switzerland 0.830879
114 Belize 0.830612
115 Guatemala 0.830442
116 Turkmenistan 0.830046
117 Peru 0.829379
118 Moldova 0.829254
119 Myanmar 0.827051
120 China 0.825111
121 Kazakhstan 0.825079
122 Saint Vincent and the Grenadines 0.824970
123 Latvia 0.824709
124 Djibouti 0.824624
125 Equatorial Guinea 0.819325
126 Jordan 0.818978
127 Netherlands 0.818781
128 Paraguay 0.816157
129 Guinea-Bissau 0.815927
130 India 0.814979
131 Costa Rica 0.814253
132 Ukraine 0.813770
133 Colombia 0.813659
134 Grenada 0.812532
135 Egypt 0.812297
136 Portugal 0.811106
137 Republic of the Congo 0.811063
138 Tunisia 0.807452
139 Trinidad and Tobago 0.804956
140 Sweden 0.804381
141 Somalia 0.802796
142 Malawi 0.802539
143 Russia 0.800247
144 Zambia 0.799161
145 Guyana 0.798601
146 Azerbaijan 0.796271
147 Uzbekistan 0.795089
148 Thailand 0.794683
149 São Tomé and Principe 0.793244
150 Bangladesh 0.791780
151 United Kingdom 0.791691
152 Eritrea 0.789674
153 Malta 0.786289
154 Mozambique 0.786196
155 Tajikistan 0.785365
156 Laos 0.781655
157 United States of America 0.771845
158 Mexico 0.762001
159 Israel 0.758537
160 Brunei 0.752157
161 Papua New Guinea 0.748362
162 Canada 0.747720
163 Cuba 0.735471
164 France 0.734136
165 Italy 0.729978
166 New Zealand 0.723253
167 Samoa 0.719025
168 Greece 0.710513
169 Haiti 0.709348
170 Gambia 0.706181
171 Denmark 0.702239
172 Chile 0.694678
173 Saint Kitts and Nevis 0.694223
174 Palau 0.685319
175 Croatia 0.667609
176 Panama 0.650465
177 Japan 0.649205
178 Fiji 0.645017
179 Norway 0.635156
180 Vietnam 0.629038
181 Antigua and Barbuda 0.614450
182 Comoros 0.612388
183 Malaysia 0.561826
184 Federated States of Micronesia 0.536417
185 Philippines 0.536229
186 Indonesia 0.493143
187 Vanuatu 0.473048
188 Tonga 0.429500
189 The Bahamas 0.405978
190 Seychelles 0.388652
191 Solomon Islands 0.368949
192 Cabo Verde 0.309594
193 Maldives 0.203665
194 Tuvalu 0.144069
195 Kiribati 0.138684
196 Marshall Islands 0.084072

# Code

Python code to calculate the triangularity of every country in Natural Earth’s ne_10m_admin_0_countries.shp shapefile.