Cyclone Shelter Capacity and Evacuation Route Planner for Coastal Bangladesh
When a cyclone warning is raised, coastal families must reach a shelter in time, but some shelters overflow while others sit half empty, and some villages are too far from any shelter. Build a planning tool that uses open maps and population data to assign villages to shelters within walking time and capacity, finds coverage gaps, and suggests where new shelters would help most. Test it on one coastal upazila.
Intermediate
A small team, or one strong student willing to learn something new.1 semester · 2 students
7 to ship
4 optional extrasSuggested stack
What you should ship
- A study area with shelter locations and capacities from official or open data
- Population estimates per village or grid cell from an open population dataset
- A walking network from OpenStreetMap with travel time estimates, including slower times for elderly people and children
- An assignment model that sends each village to a reachable shelter within capacity
- A coverage gap report: people beyond a walking time limit or with no shelter space
- A siting tool that suggests the best locations for one to three new shelters
- Maps and printable lists for local volunteers
If you have time left
- Scenarios for flooded road segments
- Separate capacity for women, people with disabilities and livestock
- A comparison with another coastal district
- A mobile-friendly view of the assigned shelter for each village
The problem: the right shelter, in time
Bangladesh has cut cyclone deaths dramatically over the decades through early warnings, volunteers and multi-purpose cyclone shelters. Problems remain: some shelters are too far for the elderly to reach in time, some are crowded beyond capacity while others are underused, and new settlements grow where no shelter was planned. Planners decide shelter assignments and new locations with paper maps and local knowledge.
What already exists and the gap
- The Cyclone Preparedness Programme (CPP), run by the Bangladesh government and the Bangladesh Red Crescent Society since the 1970s, mobilises volunteers who spread warnings and help evacuation.
- OpenStreetMap and the Humanitarian OpenStreetMap Team have mapped many roads and buildings in coastal Bangladesh.
- Disaster management agencies keep lists of shelters and their capacity.
The gap is a tool that combines shelters, people and walking routes to check, village by village, whether the plan works.
Inspiration
Facility location and capacity-constrained assignment are classic operations research problems, used for decades to place schools, clinics and fire stations. Bangladesh's own cyclone preparedness system, built around volunteers and shelters, shows exactly where such planning applies.
What you build
- Upazila disaster management officers see which villages are covered, which are not and where shelters overflow.
- CPP volunteers get printable lists: each village's assigned shelter and route.
- Planners test where new shelters would cover the most people.
The technically interesting part
- Data gaps. Shelter capacities may be outdated and OpenStreetMap incomplete. Document every assumption and show how results change if data is off.
- Travel time. Walking speed differs for children, older people and people carrying belongings, and roads flood. Build scenarios rather than one answer.
- Optimisation. Assigning villages to shelters within capacity is an integer programming problem. Solve it with OR-Tools and compare with a simple nearest-shelter rule.
- Siting. Choosing new shelter locations is a maximal coverage problem. Explain results in plain terms.
How to evaluate it
- Coverage: share of population within the time limit, before and after optimisation.
- Overflow: people assigned beyond capacity under the nearest-shelter rule versus your model.
- Sensitivity: how results change when walking speed or capacities change by 20 percent.
- Usefulness: feedback from a disaster management student, NGO worker or volunteer on the printed lists.
Suggested plan
- Weeks 1 to 3: choose the study area and gather shelter, population and road data.
- Weeks 4 to 6: network and travel times.
- Weeks 7 to 10: assignment and siting models.
- Weeks 11 to 13: maps, lists and sensitivity tests.
- Weeks 14 to 15: report and demo.
Path to a product
Disaster agencies and humanitarian NGOs across South Asia, East Africa and the Pacific face the same planning problem for cyclones, floods and earthquakes. A reusable planning toolkit with clear methods could be offered as consultancy software. The project shows geospatial and optimisation skills that are rare among graduates.
Scope warning
Your plan is an analysis, not an official evacuation order. Share results with experts before anyone uses them. Respect data licences and avoid publishing sensitive location data about vulnerable households.
Learn what you need on Sefism
- Data science roadmap: spatial analysis and sensitivity tests.
- DSA roadmap: shortest paths and graph algorithms.
- SQL and databases roadmap: storing spatial data in PostGIS.
- pandas User Guide: population and capacity tables.
- Streamlit documentation: the planning map.
Get help with this idea
Optimisation results are only as good as the data. Tauseef can help you build honest scenarios and explain results to non-technical readers. If you are new here, create a free account first.
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