This document outlines a proposal to link disparate disaster response datasets and apply data mining/machine learning techniques to create useful information for disaster responders and affected communities. Specifically, it discusses linking datasets from needs assessments, situation reports, and other sources to identify patterns and predict needs. The goal is to increase coordination among responders and better understand community needs. The proposal involves interviewing responders and community members in Bangladesh to discover information needs, identify appropriate data sources, and design a prototype system that links and analyzes data to provide actionable insights.

1. Linking disparate
datasets and applying
‘data mining/machine
learning’ to create
actionable information
FOR THE PROFESSIONAL AND RESPONDING COMMUNITY IN
THE DISASTER RESPONSE AND PREPAREDNESS PHASE

2. Story about myself
WHO IS ROBERT MONNÉ?

3. My road till now…
• BSc Business Administration
• …Too broad
• MSc Business Informatics
• Focus on: DATA
• Aiming for a big consultancy or
technology firm
• But….
• Why don’t we apply technology
in a developing country?
• My vision:
Tech can be a catalyser for rapid
growth
• My advantage: I get to apply
innovative ideas in a
environment where it’s not
applied before…

4. Agenda
• Problem definition
• What is data linkage?
• What is data mining?
• Examples of envisioned prototype
• Goals of trip

5. Problem definition
• Information overload
• Iteration of JNA and D-Form is
quite long
• What if needs change?
• Information gap
JNA
D-Form
FFWC
OSM/Maps
Etc etc….
Professional Disaster
Responders
Affected/Responding
Community
???
???
???

6. 3 building blocks
MasterThesis
Information
Need
Data
linkage
methods
Data
mining
methods
Interviews
Literature Research
Expert Interviews
Literature Research
Prototype
Data
Sources

7. Data linkage
Issues: temporal/spatial component, difference in source, format, quality etc. etc.

8. Data Mining and Machine Learning
• Finding novel patterns in datasets to create value
• Creating actionable info from several large PDF’s
• Clustering customers in marketing
Data Information Knowledge

9. Envisioned prototype examples
• Visualising needs
• Predicting needs
• Advice for shelter or location of relief goods
• Delivered value:
• Knowing the real and urgent needs of affected
• Increased coordination among actors (from a fragmented approach)

10. Prototype (possible) data sources
• JNA
• D-Form
• TamTam project data
• Telephone data
• Social media data
• Open street map data
• Etc?

11. Trip goals:
• Discovering information need
• Technological context
• Identifying data sources
• Identifying data mining opportunities

12. Link to TamTam
• TamTam could use the data I collect in the field (e.g. the information
needs of disaster responders)
• TamTam could use the data fusion/linkage and data mining methods I
use in the prototype
• My prototype could use the dataTamTam aims to collect

13. Do you want to help?
I want to interview these people:
Community Organization Role
Professional Concern Universal  Disaster ResponseCoordinator
 Disaster Preparation Coordinator
 Ground level responder
MMS
Oxfam
Practical Action
Responding Local Disaster Responders  Imams
 Teachers
 Entrepreneurs
 UISC/Digital centre entrepreneurs
 Volunteer Disaster management
committees
Private Companies
Government Union Parishad  Disaster ResponseCoordinator
 Disaster Preparation Coordinator
 Ground level responder
Union Disaster Management
Committee
Upazila Disaster Management
Committee
Ansar andVillage Development
Police