Migrants in Maharashtra

A data visualisation case study of who moves to Maharashtra, where they settle, and why, built from Census of India migration tables.

Duration

2 weeks

Methods

Sheets to gather data and Canva to visualise it

Focus

Data Visualisation

OVERVIEW

OVERVIEW

OVERVIEW

Making a large dataset easier to read.

AREA

AREA

State to District
State to District

Two geographic scales to understand migration patterns.

Two geographic scales to understand migration patterns.

PEOPLE

PEOPLE

Gender and Residence
Gender and Residence

Comparing male and female migration across rural and urban contexts.

Comparing male and female migration across rural and urban contexts.

TIMELINE

TIMELINE

1991 to 2011
1991 to 2011

Historical context mapped across selected years in the source project.

Historical context mapped across selected years in the source project.

TIMELINE

TIMELINE

Research Question
Research Question

How is migration to Maharashtra distributed across source states and destination districts?

How is migration to Maharashtra distributed across source states and destination districts?

TIMELINE

TIMELINE

Supporting Questions
Supporting Questions

Which districts show the highest and lowest migrant counts? How do gender and residence patterns compare?

Which districts show the highest and lowest migrant counts? How do gender and residence patterns compare?

TIMELINE

TIMELINE

Storytelling Lens
Storytelling Lens

Connect numeric patterns with the social and economic reasons described in the project’s year-wise table.

Connect numeric patterns with the social and economic reasons described in the project’s year-wise table.

PROCESS

PROCESS

PROCESS

From raw data to visual narratives.

Step 01: Data Pickup
Step 01: Data Pickup

Collect migration data from the Census source.

Step 02: Data Cleaning
Step 02: Data Cleaning

Organise and prepare the dataset in Google Sheets.

Step 03: Visualise
Step 03: Visualise

Build maps, charts and comparisons in Flourish and Canva.

Step 04: Find Insights
Step 04: Find Insights

Compare district, demographic patterns & shape a narrative.

INDIA VIEW

INDIA VIEW

INDIA VIEW

Migration begins with a place of origin.

Most migrants came from Uttar Pradesh and Bihar, followed by Gujarat, Madhya Pradesh and Karnataka.

MAHARASHTRA VIEW

MAHARASHTRA VIEW

MAHARASHTRA VIEW

Migration is not evenly distributed.

Top 5: Higher Migration Counts
Thane, Pune, Mumbai Suburban, Nashik and Kolhapur.

Low 5: Lower Migration Counts
Hingoli, Gadchiroli, Washim, Gondiya and Bhandara.

DATA ANALYSIS

DATA ANALYSIS

DATA ANALYSIS

Higher Migration Count Districts

DATA ANALYSIS

DATA ANALYSIS

DATA ANALYSIS

Lower Migration Count Districts

REASONING

REASONING

REASONING

Numbers sit inside real-world conditions.

DATA PATTERNS

DATA PATTERNS

DATA PATTERNS

Migrants as per Gender

DATA PATTERNS

DATA PATTERNS

DATA PATTERNS

Migrants as per Residence

PEOPLE & PATTERN

PEOPLE & PATTERN

PEOPLE & PATTERN

Migrants in District as per Gender

CONCLUSION

CONCLUSION

CONCLUSION

Behind every number is a journey.

People from all over India have come to Maharashtra for jobs, studies, marriage, or a better life. While big cities like Mumbai, Pune, and Thane attracted men for work, many women moved to rural areas like Hingoli after marriage. Some were pushed by struggles like drought and unemployment, others were pulled by hopes of better opportunities. Behind every number is a real story — of dreams, courage, and the search for a new beginning.