Mosaic Migrants
- Ishrat Jaben Bushra
- Kin Kwan Liu(Derek)
- Muhammad Zaka Shaheryar
This hands-on competition empowers undergraduate and graduate students to explore real-world migration issues using underutilized Statistics Canada datasets. Participants analyze disparities in access to essential services, like healthcare, education, and employment, across immigrants, temporary residents, and Canadian-born individuals. It is presented by Toronto Metropolitan University in collaboration with the Innovation Zones.
Curated datasets were prepared under the leadership of Dr. Feng Hou, Principal Researcher at Statistics Canada, provides a solid foundation for exploring spatial inequality and data-driven storytelling.
We focused on the following key questions:
- Which areas have a higher-than-average density of recent immigrants (2016–2021)?
- How do different immigrant classes (economic, family, refugee, and other) vary in areas with high vs. low workplace accessibility?
- How does generational composition differ in areas with high vs. low access to primary/secondary education?
- In areas with high populations of women refugees, do they face greater barriers to workplace accessibility?
Provided by Statistics Canada:
- ADA_profile_simplified.xlsx: 2021 Census Profile at the ADA level (immigration-related characteristics) (saved as csv for ease of use)
- ADA_acs_file.xlsx: Spatial Access Measures (aggregated at ADA level from Dissemination Block level) (saved as csv for ease of use)
- Shapefiles: Geographic boundary files in .shp format used for spatial visualizations.https://www12.statcan.gc.ca/census-recensement/2021/geo/sip-pis/boundary-limites/index2021-eng.cfm?year=21
- National Cencus 2021: https://www12.statcan.gc.ca/census-recensement/2021/dp-pd/prof/details/page.cfm?Lang=E&DGUIDList=2021A000011124&GENDERList=1,2,3&STATISTICList=1,4&HEADERList=0&SearchText=Canada
- Language: Python 3.11
- Platform: Google Colab for coding
- Visualization & Analysis Tools: Microsoft Excel for pivot tables, data exploration, intermediate calculation
- Libraries: pandas, numpy, matplotlib,plotly, geopandas, folium, ticker, patches
- Colab Notebooks & py files: Modular scripts for data processing and visualization.
- Charts & Tables: Stacked bar charts, Group bar charts, Choropleth maps, Table.
- Report: Final PDF with visual findings and analysis.
- Shapefiles: For geographic analysis.
- Presentation Slides: Summary of findings .
We gratefully acknowledge:
📍 Statistics Canada 📍 Immigration, Refugees and Citizenship Canada 📍 Government of Canada 📍 Experience Ventures 📍 Innovation Boost Zone (IBZ) 📍 Centre of Excellence on the Canadian Federation 📍 Environics Institute, Magnet, Pairity, and TMU's Master of Engineering Innovation & Entrepreneurship 👤BD-MDC organizers and mentors.
GNU General Public License v3.0