The Social Progress Index (SPI) is a measure that gives an understanding of social progress globally. It helps in understanding how much countries care about the overall development of the citizens. If you want to know how to analyze the social progress index, this article is for you. This article will take you through Social Progress Index analysis using Python.
Social Progress Index Analysis
The Social Progress Index score is calculated by analyzing the overall development of the citizens of a country. Below are all the factors considered for calculating the Social Progress Score:
- Basic human needs
- Wellbeing
- Opportunities
- Nutritional and basic medical care
- Water and sanitation
- Shelter
- Personal Safety
- Access to basic knowledge
- Access to information and communication
- Health and wellness
- Environment quality
- Personal rights
- Personal freedom and choice
- Inclusiveness
- Access to advanced education
So these are the primary factors considered while calculating the SPI score of a country. I found a dataset on Kaggle that contains all these factors. It will be helpful to analyze the Social Progress Index. You can download the dataset from here.
Before moving forward, I want to let you know that if you want to create an advanced Data Science project on Social Progress Index Analysis, I recommend you use Tableau as such datasets are better visualized and analyzed on dashboards. For example, have a look at the dashboard made by socialprogress.org.
The section below will take you through Social Progress Index Analysis using Python.
Social Progress Index Analysis using Python
Let’s start this task by importing the necessary Python libraries and the dataset:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objs as go
from plotly.offline import iplot
data = pd.read_csv("spi.csv")
print(data.head())spi_rank country spi_score basic_human_needs wellbeing \ 0 1.0 Norway 92.63 95.29 93.30 1 2.0 Finland 92.26 95.62 93.09 2 3.0 Denmark 92.15 95.30 92.74 3 4.0 Iceland 91.78 96.66 93.65 4 5.0 Switzerland 91.78 95.25 93.80 opportunity basic_nutri_med_care water_sanitation shelter \ 0 89.30 98.81 98.33 93.75 1 88.07 98.99 99.26 96.48 2 88.41 98.62 98.21 94.92 3 85.04 98.99 98.82 93.16 4 86.28 98.72 98.96 92.97 personal_safety access_basic_knowledge access_info_comm health_wellness \ 0 90.29 98.66 95.80 89.32 1 87.75 96.32 95.14 85.73 2 89.46 97.44 98.18 85.15 3 95.66 99.51 93.12 91.02 4 90.35 98.60 95.07 91.50 env_quality personal_rights personal_freedom_choice inclusiveness \ 0 89.44 96.34 91.16 83.77 1 95.15 96.13 88.10 82.81 2 90.20 97.08 90.03 81.64 3 90.93 95.14 88.01 77.63 4 90.05 96.69 90.65 74.81 access_adv_edu 0 85.92 1 85.23 2 84.89 3 79.39 4 82.99
According to the SPI rank, Norway tops the Social Process Index globally. Let’s have a look at the column insights before moving forward:
print(data.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 169 entries, 0 to 168 Data columns (total 18 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 spi_rank 168 non-null float64 1 country 169 non-null object 2 spi_score 169 non-null float64 3 basic_human_needs 169 non-null float64 4 wellbeing 169 non-null float64 5 opportunity 169 non-null float64 6 basic_nutri_med_care 169 non-null float64 7 water_sanitation 169 non-null float64 8 shelter 169 non-null float64 9 personal_safety 169 non-null float64 10 access_basic_knowledge 169 non-null float64 11 access_info_comm 169 non-null float64 12 health_wellness 169 non-null float64 13 env_quality 169 non-null float64 14 personal_rights 169 non-null float64 15 personal_freedom_choice 169 non-null float64 16 inclusiveness 169 non-null float64 17 access_adv_edu 169 non-null float64 dtypes: float64(17), object(1) memory usage: 23.9+ KB None
Now let’s have a look at the average, highest, and minimum of the SPI score so that we can categorize scores as High and low:
print("Highet SPI Score : ", data["spi_score"].max())
print("Lowest SPI Score : ", data["spi_score"].min())
print("Average SPI Score: ", data["spi_score"].mean())Highet SPI Score : 92.63 Lowest SPI Score : 32.5 Average SPI Score: 67.43313609467457
As 67 is the average SPI score and 92 is the highest, we can consider 85 as the qualifying score for a high SPI score. So let’s analyze some data about the countries with high SPI scores.
Let’s start with a look at the countries with better basic human needs facilities:
fig = px.scatter(data.query("spi_score >= 85"),
x="basic_human_needs",
y="spi_score",
size="spi_score",
color="country",
hover_name="country",
title= "Countries with Better Basic Human Needs",
log_x=True, size_max=60)
fig.show()
Iceland and Japan are the top 2 countries with better basic human needs facilities. Now let’s have a look at the countries with better opportunities:
fig = px.scatter(data.query("spi_score >= 85"),
x="opportunity",
y="spi_score",
size="spi_score",
color="country",
hover_name="country",
title= "Countries with Better Opportunities",
log_x=True, size_max=60)
fig.show()
Norway, Denmark, and Finland are the top 3 countries with better opportunities. Now let’s have a look at the countries with better nutrition and medical care facilities:
fig = px.scatter(data.query("spi_score >= 85"),
x="basic_nutri_med_care",
y="spi_score",
size="spi_score",
color="country",
hover_name="country",
title = "Countries with Better Basic Nutrition & Medical Care",
log_x=True, size_max=60)
fig.show()
Finland, Iceland, and Norway are the top 3 countries with better nutrition and medical care facilities. Now let’s have a look at the countries with better water sanitation:
fig = px.scatter(data.query("spi_score >= 85"),
x="water_sanitation",
y="spi_score",
size="spi_score",
color="country",
hover_name="country",
title = "Countries with Better Water Sanitation",
log_x=True, size_max=60)
fig.show()
Finland and Switzerland are the top 2 countries with better water sanitation.
So, this is how we can analyze all the factors considered to calculate the SPI score. Now let’s create a visualization to analyze the overall Social Progress Index scores globally using a choropleth map:
values = dict(type = 'choropleth',
locations = data['country'],
locationmode = 'country names',
colorscale='Blues',
z = data['spi_score'],
text = data['country'],
colorbar = {'title':'Social Progress Index'})
layout = dict(title = 'Social Progress Index',
geo = dict(showframe = True,
projection = {'type': 'azimuthal equal area'}))
figure = go.Figure(data = [values], layout=layout)
iplot(figure)
So this is how you can analyze the Social Progress Index using the Python programming language.
Summary
The Social Progress Index (SPI) is a measure that gives an understanding of social progress globally. It helps in understanding how much countries care about the overall development of the citizens. I hope you liked this article on Social Progress Index Analysis using Python. Feel free to ask valuable questions in the comments section below.
