Trying Data Science(7) : Cramming Data Visualizaion - Choropleth Map

Intro 

---

Hello, everyone!
Last time, I've posted about Seaborn, which can generates various plots. This time I'm going to learn how to visualize geographical data on maps. I will share some information about Choropleth maps.

What is a Choropleth map?

---

Choropleth map is a visualization method in which specific indicators or data values of geographic areas are represented in color. 

Action speaks louder than words! Just have a look at it below.

2014 World GDP

Importing

---

# Import plotly
import chart_studio.plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True) 

# Import pandas 
import pandas as pd

1. Import plotly. Plotly is a library that allows you to create interactive plots that you can use in websites.

2.  Import pandas to read the csv file.
   

How to use Choropleth map?

---

We need to begin to build data dictionary.
The way to do this is to use the dict() function.
There are some arguments in dict() function.

data = dict(type = 'choropleth',
            locations = ['AZ','CA','NY'],
            locationmode = 'USA-states',
            colorscale= 'Portland',
            text= ['text1','text2','text3'],
            z=[1.0,2.0,3.0],
            colorbar = {'title':'Colorbar Title'})

• type = 'choropleth',

• locations = list of states

• locationmode = 'USA-states'

• colorscale= 'pairs' | 'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' | 'Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' | 'YIGnBu' and so on

• text= list or array of text to display per point

• z= array of values on z axis (color of state)

• colorbar = {'title':'Colorbar Title'})

After making data, create the layout nested dictionary.

layout = dict(geo = {'scope':'usa'})

Then, set up the object that finally gets passed into iplot()

choromap = go.Figure(data = [data],layout = layout)
iplot(choromap)

Practice1 : US Agriculture Exports

---

1. Read data and check it.

df = pd.read_csv('2011_US_AGRI_Exports')
df.head()

2. Make data dictionary with some arguments.

data = dict(type='choropleth',
            colorscale = 'ylorbr',
            locations = df['code'],
            z = df['total exports'],
            locationmode = 'USA-states',
            text = df['text'],
            marker = dict(line = dict(color = 'rgb(255,255,255)',width = 2)),
            colorbar = {'title':"Millions USD"}
            ) 

3. Make layout dictionary with some arguments.

layout = dict(title = '2011 US Agriculture Exports by State',
              geo = dict(scope='usa',
                         showlakes = True,
                         lakecolor = 'rgb(85,173,240)')
             )

4. Set up an object(choromap) that gets passed into iplot() and call it.

choromap = go.Figure(data = [data],layout = layout)
iplot(choromap)

US Agriculture Exports by State

Practice2 : World Power Consumption

---

1. Read data and check it.

df = pd.read_csv('2014_World_Power_Consumption')
df.head()

2. Make data dictionary with some arguments.

data = dict(
        type = 'choropleth',
        colorscale = 'Viridis',
        reversescale = True,
        locations = df['Country'],
        locationmode = "country names",
        z = df['Power Consumption KWH'],
        text = df['Country'],
        colorbar = {'title' : 'Power Consumption KWH'},
      ) 

3. Make layout dictionary with some arguments.

layout = dict(title = '2014 Power Consumption KWH',
                geo = dict(showframe = False,projection = {'type':'mercator'})
             )

4. Set up an object(choromap) that gets passed into iplot() and call it.

choromap = go.Figure(data = [data],layout = layout)
plot(choromap,validate=False)

Outro

---

As what I discussed above, the Choropleth maps can display population density, convenience location, tourism information. As such, It is very useful and attractive. The following picture is a population distribution map of Seoul, Korea, at 1 p.m by using the Choropleth maps. Do you get the hang of it?

Distribution of population in Seoul (1:00 p.m.)

※ The picture source: https://story.pxd.co.kr/1408

エンジニアファーストの会社 株式会社CRE-CO
ソンさん