Population and Demographics

Final project | Population | Demographics forecasting

Authors

Xinyi Liu

Benjamin Starostka

Show the code 
import pandas as pd
import cufflinks as cf
import plotly
import plotly.express as px
import plotly.graph_objects as go
import plotly.subplots as sp
%matplotlib inline
import math
from matplotlib import pyplot as plt
from sklearn import model_selection, tree, metrics, ensemble
from sklearn.preprocessing import MinMaxScaler
from bokeh import models, plotting
from bokeh.palettes import Spectral8, Spectral11
import seaborn as sns
import pycountry as pc
from bokeh.models import HoverTool
from bokeh.plotting import figure, show
from bokeh.models import  ColumnDataSource, Legend, LinearAxis, FactorRange
from bokeh.io import output_notebook, show, curdoc
from bokeh.palettes import Spectral6, Category20b
from bokeh.transform import factor_cmap
from bokeh.models import BoxAnnotation
from bokeh.models import Title
from bokeh.layouts import layout
from bokeh.models import Tabs, TabPanel, Range1d

from pandas_geojson import to_geojson

output_notebook()
import bokeh.models as bkm
from bokeh.resources import INLINE
import bokeh.io
from bokeh import *
bokeh.io.output_notebook(INLINE)
import regex as re

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot 
init_notebook_mode(connected=True)
cf.go_offline()
Loading BokehJS ...
Loading BokehJS ...

1 Global Population

Population growth is impacting our planet in many ways. According to Population Media Center, when looking back over the last several centuries, global population trends show continuous expansion, both in terms of volume and rapidity. Population is the key factor in the climate crisis, housing and infrastructure, economic growth of a country, and so on. Therefore, a proper forecast of population growth can guide the society to make better decisions in the future.

Looking into the data globally, we can get the insight from the expanding of population across the world and it’s correlations. To start with, we will bring the data from the World Bank and analyze the correlation from population to the other demographics features.

Show the code 
world_data = pd.read_csv('../data/countries-of-the-world.csv', decimal=',')

for col in world_data.columns.values:
    if world_data[col].isnull().sum() == 0:
        continue
    if col == 'Climate':
        guess_values = world_data.groupby('Region')['Climate'].apply(lambda x: x.mode().max())
    else:
        guess_values = world_data.groupby('Region')[col].median()
    for region in world_data['Region'].unique():
        world_data[col].loc[(world_data[col].isnull())&(world_data['Region']==region)] = guess_values[region]

plt.figure(figsize=(10,10))
sns.heatmap(data=world_data.iloc[:,2:].corr(),annot=True,fmt='.2f',cmap='coolwarm', 
            annot_kws={
                'fontsize': 7,
                'fontweight': 'bold'
            })
plt.title('Demographics correlation between different features')
plt.show()

2 Aging issue in Melbourne

In a global scale, the birthrate and deadrate have negative correlation to the population. Inspired by the fact, we want to look into the aging issue in Melbourne.

Show the code 
df_population = pd.read_csv('../data/city-of-melbourne-population-forecasts-by-small-area-2020-2040.csv')

df_age = df_population.drop(df_population[df_population['Age'].isin(['Total population', 'Average age'])].index)
df_age = df_age.drop(df_age[df_age['Geography'].isin(['City of Melbourne'])].index)

fig = px.bar(df_age, x='Year', y='Value', color='Age', barmode='group', title='Total population by Age from 2021 to 2041')
fig.show()

The age group from 25-40 is dominating the growth of total population in Melbourne, which shows that Melbourne is a young city. However, the aging issue is still a concern. Defined the elderly age group as 65+, from the data we can see that the population of elderly age group is increasing by the a fast pace.

Show the code 
df_elderly = df_population[df_population['Age'].isin(['Age 65-69', 'Age 70-74', 'Age 75-79', 'Age 80-84', 'Age 85+'])]

df_elderly = df_elderly.groupby(['Year', 'Age']).sum().reset_index()
fig = px.line(df_elderly, x='Year', y='Value', color='Age', title='Population of Elderly from 2021 to 2041')
fig.show()
/var/folders/b4/g666m_w53g9121tp3r4qmzth0000gn/T/ipykernel_30834/2492635057.py:3: FutureWarning:

The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.

As a young and developed city, Melbourne is also facing the aging issue. This is a challenge for the city to provide the service and proper resources such as healthcare, dwellings and social care for the elderly.

4 Takeaways

Based on the analysis, we can conclude that the population growth in the next 20 years: - The population of Melbourne is predominantly composed of young individuals, primarily between the ages of 25 and 40, driving the overall population growth. - Melbourne continues to face concerns regarding its aging population, as the number of elderly individuals is increasing at a rapid rate.

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