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Visualizing Geographic Data with Choropleth Maps in Python

Introduction to Data Science

Data science is a multidisciplinary field that involves using algorithms, statistical models, and machine learning to extract insights and knowledge from structured, unstructured and noisy data. Data science has two main components: data analytics and predictive analytics.

Data analytics focuses on analyzing historical data to identify patterns and trends, while predictive analytics involves using machine learning algorithms to make predictions based on historical data. Python and R are two of the most popular programming languages used in data science, with Python being very popular in recent years due to its simplicity and wide range of libraries.

Data visualization is another integral part of data science, and Plotly is one of the most popular libraries used for creating interactive visualizations in Python.

Choropleth Maps

Choropleth maps are maps that use colors to represent different values of a data variable. The choropleth() method in Plotly is a powerful tool for creating these maps in Python.

Here are some tips and tricks for working with choropleth maps:to

Choropleth Maps and the choropleth() method

Choropleth maps are a great way to visualize data that is organized geographically. Whether you’re analyzing global trends or drilling down to the state or county level, choropleth maps can help you quickly identify patterns and trends.

The choropleth() method in Plotly allows you to create these maps with just a few lines of code. To create a choropleth map using the choropleth() method, you need to provide the following parameters:

– the geographical features you want to map, such as countries or states;

– the data you want to visualize;

– the colors you want to use for each data value.

Here is an example of how to create a choropleth map of the United States using the choropleth() method:

“`

import plotly.express as px

data = px.data.election()

fig = px.choropleth(data, locations=”abbr”, color=”winner”,

scope=”usa”, locationmode=”USA-states”,

color_discrete_map={“Obama”: “blue”, “Romney”: “red”})

fig.show()

“`

This example uses a dataset from Plotly containing election results for each US state. The choropleth() method takes this data and maps it to the state abbreviations using the `locations` parameter.

The `color` parameter specifies the variable we want to visualize, which in this case is the winner of the election. Finally, the `color_discrete_map` parameter specifies the colors we want to use for each value of the `winner` variable.

Highlighting Areas on Choropleth Map

Sometimes, you may want to highlight specific areas on your choropleth map, such as states that meet a certain criteria. You can do this by setting the `locationmode` parameter to the appropriate value and providing a list of location names or IDs that you want to highlight.

Here is an example of how to highlight a few states in the US:

“`

import plotly.express as px

data = px.data.election()

fig = px.choropleth(data, locations=”abbr”, color=”winner”,

scope=”usa”, locationmode=”USA-states”,

color_discrete_map={“Obama”: “blue”, “Romney”: “red”},

location=[‘NY’, ‘CA’, ‘TX’])

fig.show()

“`

In this example, we set the `locationmode` parameter to “USA-states” to tell Plotly that we’re mapping states. We then provide a list of state abbreviations using the `location` parameter to highlight three states – New York, California, and Texas.

Choropleth Maps for Countries Other Than USA

Creating choropleth maps for countries other than the US requires a bit more work, as you need to provide the GeoJSON data for the country or region you want to map. GeoJSON is a format for encoding geographical features, such as countries or states, in JavaScript Object Notation (JSON) format.

Here is an example of how to create a choropleth map of Brazil using GeoJSON data:

“`

import json

import pandas as pd

import plotly.express as px

with open(‘brazil.geojson’) as f:

geojson = json.load(f)

data = pd.read_csv(‘soybean-production.csv’)

fig = px.choropleth(data, geojson=geojson,

featureidkey=”properties.NOME_1″, locations=”STATE”,

color=”SOYBEAN_PRODUCTION”, projection=”mercator”)

fig.show()

“`

In this example, we first load the GeoJSON data for Brazil using the `json.load()` method. We then read in a CSV file containing soybean production data at the state level for Brazil.

We use the `choropleth()` method to map the soybean production data to the GeoJSON data using the `featureidkey` and `locations` parameters. The `projection` parameter tells Plotly to use a mercator projection when mapping the data.

Soybean Production in Brazil

Soybeans are Brazil’s top agricultural commodity, and the country is one of the world’s largest producers of soybeans. Let’s explore soybean production in Brazil using a choropleth map.

We start by obtaining a dataset containing soybean production data for each state in Brazil. This dataset can be obtained from a government database or from private sources.

Next, we load in the GeoJSON data for Brazil and use the `choropleth()` method to create a choropleth map of soybean production. We map the soybean production data to the GeoJSON data using the `featureidkey` and `locations` parameters, and specify a color scale that makes it easy to identify regions with high or low production levels.

The resulting choropleth map can provide valuable insights into soybean production trends and patterns in Brazil. For example, you may notice that the southern states of Rio Grande do Sul, Parana, and Mato Grosso do Sul tend to have higher production levels than other states.

Active COVID19 Cases in India

India has been hit hard by the COVID19 pandemic, with millions of confirmed cases and thousands of deaths. Let’s create a choropleth map of active COVID19 cases in India to visualize the distribution of cases across the country.

We start by obtaining a dataset containing the number of active COVID19 cases for each state in India. This dataset can be obtained from a government database or from private sources.

Next, we obtain the shape or coordinates of India and its states, which can be found online or using mapping libraries like GeoPandas. We use these shapes to create a GeoJSON file for India.

Finally, we use the `choropleth()` method to create a choropleth map of active COVID19 cases in India. We use the `featureidkey` and `properties.ST_NM` parameters to map the data to the GeoJSON data, and specify a color scale that makes it easy to identify regions with high or low numbers of active cases.

The resulting choropleth map can provide valuable insights into the distribution of active COVID19 cases in India. For example, you may notice that the western states of Maharashtra and Gujarat tend to have higher numbers of active cases than other states, while northeastern states like Sikkim and Arunachal Pradesh have relatively low numbers of active cases.

Conclusion

Data science provides powerful tools for analyzing and visualizing data of all types, including geographic data. Choropleth maps are a great way to visualize data that is organized geographically, and the choropleth() method in Plotly provides a powerful and flexible way to create these maps in Python.

Whether you’re analyzing soybean production in Brazil or the distribution of COVID19 cases in India, choropleth maps can help you quickly identify patterns and trends in your data. Data science is a multidisciplinary field that helps to extract insights and knowledge from structured, unstructured, and noisy data.

Choropleth maps are powerful tools that leverage geographical data to visualize patterns in data. Using Python and Plotly, choropleth maps can be created to map different regions and visualize the distribution of data across these regions.

The article highlights the importance of data visualization, introduces the choropleth() method in Plotly, and provides tips on highlighting areas, mapping other countries, and analyzing data sets such as soybean production in Brazil and COVID19 cases in India. Overall, the article emphasizes the importance of utilizing data science tools in analyzing geographic data to understand patterns and trends.

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