🐾 Visualization: Animal Rights by Country

Introduction

In this personal mini-project, I created a visualization to show how animal rights are recognized around the world.

I utilized the "Principal laws on animal rights" table published on a Wikipedia page, which provides comprehensive coverage on the subject.

For each of the 93 countries in the table, the following five questions are addressed:

• Recognition of animal sentience
• Recognition of animal suffering
• Anti-cruelty laws meet OIE* standards
• Any laws against animal cruelty
• Support at the United Nations

*OIE: the World Organisation for Animal Health (WOAH), formerly the Office International des Epizooties (OIE), is an intergovernmental organisation founded in 1924, coordinating, supporting and promoting animal disease control.

Tools

• Python: A versatile programming language for data manipulation and analysis.
• Pandas: A library for data analysis and manipulation.
• Re: A library for regular expression operations.
• Jupyter Notebook: An interactive environment for running and documenting code.
• Tableau: A tool for creating interactive data visualizations.

Data Source

• en.wikipedia.org/wiki/Animal_rights_by_country_or_territory
• Date: the 12th of July, 2024
• Permission is granted to copy, distribute and/or modify Wikipedia's text under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License and, unless otherwise noted, the GNU Free Documentation License, unversioned, with no invariant sections, front-cover texts, or back-cover texts.

Outcome

Figure 1: Screenshot of the visualization.

Click here to view the interactive Tableau visualization

Hovering over a country reveals a tooltip displaying the following details:

• Recognition of animal sentience
• Recognition of animal suffering
• Anti-cruelty laws meet OIE standards
• Any laws against animal cruelty
• Support at the United Nations

Figure 2: Example of tooltips for different countries.

Table of Contents

• Data Preprocessing
  • Importing the Necessary Libraries
  • Scraping the Data from Wikipedia
  • Cleaning the Data
  • Converting String Values to Numerical Scores
  • Saving the Result to a CSV File
• Creating an Interactive Visualization

Project Workflow

Data Preprocessing

Importing the Necessary Libraries

# pandas library for data manipulation and analysis.
import pandas as pd

# re (regular expression) library for string matching.
import re

Scraping the Data from Wikipedia

The data is structured as a table on a Wikipedia page. I use the pandas library to scrape it.

# URL of the page.
url = 'https://en.wikipedia.org/wiki/Animal_rights_by_country_or_territory'

# Using pandas to read the tables directly from the page.
tables = pd.read_html(url)

# The table of interest is the forty-first on the page.
df = tables[40]

# Displaying the first several rows to see the result of scraping.
display(df.head())

Output:

	Country	Recognition of animal sentience	Recognition of animal suffering	Anti-cruelty laws meet OIE standards[10]	Any laws against animal cruelty	Support at the United Nations [a]
0	Algeria[11][12]	No	No	No	Yes -unenforced	No
1	Angola[13]	No	No	No	No	No
2	Argentina[14]	Yes	Yes	Yes	Yes	No
3	Australia[15]	Yes	Yes	Partial - varies internally	Yes	No
4	Austria[16]	Yes	Yes	Yes	Yes	Partial - support from various internal departments

Cleaning the Data

The original table contains footnotes and references, such as [11] and [a]. These are preserved in the scraped data. I will remove them for clarity.

# Function to clean the country names.
def clean_country_name(name):
    # Removing non-letter characters using regex.
    cleaned_name = re.sub(r'[^a-zA-Z\s]', '', name)
    return cleaned_name.strip()

# Applying the function to the "Country" column.
df['Country'] = df['Country'].apply(clean_country_name)

# Cleaning column names.
df.rename(columns={
    'Anti-cruelty laws meet OIE standards[10]': 'Anti-cruelty laws meet OIE standards',
    'Support at the United Nations [a]': 'Support at the United Nations'
}, inplace=True)

# Displaying the first several rows to see the result.
display(df.head())

Output:

	Country	Recognition of animal sentience	Recognition of animal suffering	Anti-cruelty laws meet OIE standards	Any laws against animal cruelty	Support at the United Nations
0	Algeria	No	No	No	Yes -unenforced	No
1	Angola	No	No	No	No	No
2	Argentina	Yes	Yes	Yes	Yes	No
3	Australia	Yes	Yes	Partial - varies internally	Yes	No
4	Austria	Yes	Yes	Yes	Yes	Partial - support from various internal departments

Converting String Values to Numerical Scores

To assign colors to the countries in Tableau, I need to convert the string values to numerical scores and calculate an "average score". I decide to use a weighted average for the score.

# Function to convert values to numerical scores.
def convert_to_score(value):
    if value == "No":
        return 0
    elif value == "Yes":
        return 1
    elif "Partial" in value:
        return 0.5
    elif "unenforced" in value:
        return 0.5
    elif value == "Unknown":
        return None
    return None

# Creating new columns for numerical scores by applying the conversion function.
for col in df.columns[1:]:
    df[col + ' (Score)'] = df[col].apply(convert_to_score)

# Defining weights for each column to use in the weighted average calculation.
weights = {
    'Recognition of animal sentience (Score)': 0.2,
    'Recognition of animal suffering (Score)': 0.2,
    'Anti-cruelty laws meet OIE standards (Score)': 0.2,
    'Any laws against animal cruelty (Score)': 0.3,
    'Support at the United Nations (Score)': 0.1
}

# Function to calculate the weighted average score for each row.
def calculate_weighted_average(row):
    total_weight = 0
    weighted_sum = 0
    for col, weight in weights.items():
        if pd.notna(row[col]):
            weighted_sum += row[col] * weight
            total_weight += weight
    if total_weight == 0:
        return None
    return weighted_sum / total_weight

# Applying the function to each row to calculate the overall score.
df['Weighted Overall Score'] = df.apply(calculate_weighted_average, axis=1)

# Displaying the first several rows to see the result of the calculations.
display(df.head())

Output:

	Country	Recognition of animal sentience	Recognition of animal suffering	Anti-cruelty laws meet OIE standards	Any laws against animal cruelty	Support at the United Nations	Recognition of animal sentience (Score)	Recognition of animal suffering (Score)	Anti-cruelty laws meet OIE standards (Score)	Any laws against animal cruelty (Score)	Support at the United Nations (Score)	Weighted Overall Score
0	Algeria	No	No	No	Yes -unenforced	No	0.0	0.0	0.0	0.5	0.0	0.15
1	Angola	No	No	No	No	No	0.0	0.0	0.0	0.0	0.0	0.00
2	Argentina	Yes	Yes	Yes	Yes	No	1.0	1.0	1.0	1.0	0.0	0.90
3	Australia	Yes	Yes	Partial - varies internally	Yes	No	1.0	1.0	0.5	1.0	0.0	0.80
4	Austria	Yes	Yes	Yes	Yes	Partial - support from various internal departments	1.0	1.0	1.0	1.0	0.5	0.95

Saving the Result to a CSV File

The result looks satisfactory, so I save it to a CSV file.

df.to_csv('animal_rights_by_country.csv', index=False)

print("Table saved as animal_rights_by_country.csv")

Creating an Interactive Visualization

The resulting Tableau dashboard consists of a single world map visualization, with country colors determined by their weighted average score.

Hovering over a country reveals a tooltip displaying the following details:

• Recognition of animal sentience
• Recognition of animal suffering
• Anti-cruelty laws meet OIE standards
• Any laws against animal cruelty
• Support at the United Nations

To enhance the user experience, the values for each feature are conditionally colored: "Yes" is green, "No" is red, most other values are yellow, and "Unknown" is gray. This effect is achieved by creating calculated fields for each feature that output the value based on its content.

Example of a calculated field formula:

IF CONTAINS(ATTR([Recognition of animal sentience]), "Partial")
THEN ATTR([Recognition of animal sentience])
END

The tooltip example:

The finished visualization:

Click here to view the interactive Tableau visualization