Developing Interactive Maps for Spatial Data Visualization

Developing Interactive Maps for Spatial Data Visualization

Creating an Interactive Choropleth Map in Python to Show Life Insurance Discontinuance Rates by Geographic Regions

In a recent development, life insurance companies have expressed concern over the impact of lockdown measures on the discontinuance rate of life insurance policies. To address this issue, an interactive choropleth map has been created using the Python Folium package, providing a visual representation of discontinuance rates by geographic regions in Australia.

Steps to Create the Map

1. Prepare your data: Gather a dataset containing geographic regions (such as states or counties) and their corresponding life insurance discontinuance rates. This data is typically in a CSV or DataFrame format with a region identifier (like a state name or FIPS code) and discontinuance rate values.
2. Obtain a GeoJSON or shapefile for the geographic regions: This file contains the geometries (polygons) of each region to draw on the map. For example, U.S. states' GeoJSON files are common and freely available.
3. Install and import needed libraries: Install the Folium and pandas packages using pip, and import them into your code.
4. Load your data and GeoJSON: Load your data and GeoJSON files into your Python script.
5. Create the Folium Map: Initialize the base map centered over your geographic region.
6. Add Choropleth Layer: Use to bind your data to the map polygons.
7. Add tooltips or popups for interaction: Create or to show discontinuance rates on hover or click.
8. Save the map to an HTML file and display: Save the map to an HTML file, which can be opened in a web browser.

The Impact of Lockdown Measures

Lockdown measures in various regions limited the number of people allowed to leave their homes for non-essential activities and temporarily closed local businesses providing non-essential services such as cafes and restaurants. These measures had an impact on spending and people's incomes, potentially affecting the life insurance industry by increasing the discontinuance rate of policies, particularly those sold shortly after purchase.

Customizing the Map

The map created in this example uses the base layer tile styling 'OpenStreetMap'. The added layer of the map shows discontinuance rates by Local Government Areas (LGAs) in New South Wales, with each LGA represented by a block of land highlighted in green. The tooltip layer can be customized to show additional information, such as income data by LGA.

Data Sources

Income data by LGA can be sourced from the Australian Bureau of Statistics website. Geometry data for geographic regions, such as Local Government Areas (LGAs), are most commonly available in json or shapefile format and can be read using the Python geopandas package.

Example Code Snippet

Here's an example code snippet tailored to a dataset format and geojson details. Replace the and variables with your own data and geojson files.

```python import folium import pandas as pd

data_path = 'your_life_insurance_data.csv' geojson_path = 'your_geographic_regions.geojson'

data = pd.read_csv(data_path)

m = folium.Map(location=[latitude_center, longitude_center], zoom_start=6)

folium.Choropleth( geo_data=geojson_path, data=data, columns=['region_id', 'discontinuance_rate'], key_on='feature.properties.region_id_property', # adjust to your geojson properties fill_color='YlOrRd', fill_opacity=0.7, line_opacity=0.2, legend_name='Life Insurance Discontinuance Rate (%)' ).add_to(m)

folium.GeoJson( geojson_path, name='Regions', tooltip=folium.features.GeoJsonTooltip( fields=['region_name_property', 'discontinuance_rate_property'], aliases=['Region:', 'Discontinuance Rate:'], localize=True ) ).add_to(m)

m.save('life_insurance_discontinuance_map.html') ```

This approach is elaborated in various tutorials on Folium, including data loading, choropleth creation, and interactivity with tooltips. A typical workflow also involves ensuring geographic identifiers in your data align perfectly with the GeoJSON properties your polygons carry.

Summary

By following these steps, you can create an interactive choropleth map displaying life insurance discontinuance rates by region using Folium in Python. This map can help life insurance companies and policymakers gain insights into the impact of lockdown measures on the discontinuance rate of life insurance policies.

1. To effectively use technology for uncovering patterns in the life insurance industry, data-and-cloud-computing techniques can be employed for developing and executing complex statistical models and visualizations, such as in constructing choropleth maps.
2. In order to create impactful visual representations of insurance data using technology-driven solutions like Python and its libraries (such as Folium for map creation), it's essential to ensure high-quality, clean, and well-formatted data is available to enable data-driven decision-making and insights.

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