Disclaimer:
Please be aware that the content herein has not been peer reviewed. It consists of personal reflections, insights, and learnings of the contributor(s). It may not be exhaustive, nor does it aim to be authoritative knowledge.
Overview
Prepared by (Name of the experimenter)
Betty Chemier
On date (Day/Month/Year)
March 2024
Current status of experimental activity
Completed
What portfolio does this activity correspond to? If any
Collective Intelligence for Climate Action (Nesta Design Studio)
What is the frontier challenge does this activity responds to?
Climate Adaptation, Disaster and Risk Management
What is the learning question(from your action learning plan) is this activity related to?
How can collective intelligence be effectively leveraged for disaster response and management in urban areas prone to floods?
Please categorize the type that best identifies this experimental activity:
Experimental (Randomised assignment and/or control group)
Which sector are you partnering with for this activity? Please select all that apply
Public Sector, Civil Society/ NGOs
Please list the names of partners mentioned in the previous question:
Sistema Nacional de Proteccion Civil (SINAPROC), Municipio de Panama (MUPA), Junta Comunal de Juan Diaz y Don Bosco, Cognicity (Pentabencana), Civic Data Lab, Magenta
Design
What is the specific learning intent of the activity?
Understand the effectiveness of collective intelligence and behavior change strategies in improving flood preparedness, response and management in urban areas.
What is your hypothesis? IF... THEN....
IF people can report and access geolocated information on risks in real-time, THEN they can reduce their risks and significantly contribute to improving urban flood response through community participation.
Does the activity use a control group for comparison?
No, it does not use a control group
How is the intervention assigned to different groups in your experiment?
Non-random assignment
Describe which actions will you take to test your hypothesis:
The methodology testing involved adapting the Cognicity OSS platform for flood management in Panama, drawing on lessons from Pentabencana's experiences in Indonesia. The pilot entailed workshops and pilot tests in flood-prone urban areas, engaging the community in data generation and real-time platform evaluation. Simultaneously, a behavior change communication strategy was implemented to promote platform usage. This multifaceted approach aimed to assess platform functionality, community engagement, and the efficacy of behavior change techniques.
What is the unit of analysis of this experimental activity?
The unit of analysis for this experimental activity is level of engagement of the community or individuals within flood-prone urban areas in Panama.
Please describe the data collection technique proposed
The proposed data collection technique involves gathering quantitative and qualitative data through workshops, interviews, focus groups, social media reach, website visits, and reports generated on the platform.
What is the timeline of the experimental activity? (Months/Days)
5 months
What is the estimated sample size?
50-99
What is the total estimated monetary resources needed for this experiment?
More than 20,000 USD
Quality Check
This activity is relevant to a CPD outcome, The hypothesis is clearly stated, This activity offers strong collaboration oportunities, This activity offers a high potential for scaling, This activity has a low risk
Please upload any supporting images or visuals for this experiment.
Please upload any supporting links
What are the estimated non- monetary resources required for this experiment? (time allocation from team, external resources, etc) If any.
Non-monetary resources required for this experiment include time and effort from the team, expertise from partners such as UNDP and Nesta, and participation from the community in workshops and pilot tests.
Results
Was the original hypothesis (If.. then) proven or disproven?
Proven
Do you have observations about the methodology chosen for the experiment? What would you change?
The methodology chosen for the experiment seems robust, incorporating participatory approaches, technology adaptation, and behavior change strategies. One improvement would be to extend the length of the experiment to allow for more time for users' adoption of the tool.
From design to results, how long did this activity take? (Time in months)
5 months
What were the actual monetary resources invested in this activity? (Amount in USD)
Consultant for social marketing strategy based on behavior change: 50K, Consultants for the adaptation of the Cognicity Open-Source Software to Panama context (Pentabencana and Civic Data Lab) 10 K each
Does this activity have a follow up or a next stage? Please explain
The experiment has ongoing conversations with SINAPROC and the Municipality of Panama for potential adoption of the system and further scaling, nevertheless, the current political pre-electoral climate of Panama is not supporting this turnover process.
Is this experiment planned to scale? How? With whom?
The experiment has the potential to be scaled by expanding to other urban areas prone to floods in Panama and potentially other countries (we have presented the project to several COs in the region interested in this work), leveraging community feedback to improve the platform continuously, yet there are no concrete next steps for scaling.
Please add any supporting links that describe the planning, implementation, results of learning of this activity? For example a tweet, a blog, or a report.
Considering the outcomes of this experimental activity, which of the following best describe what happened after? (Please select all that apply)
This experiment led to partnerships
Learning
What do you know now about the action plan learning question that you did not know before? What were your main learnings during this experiment?
During the pilot, managing community expectations emerged as crucial, necessitating clear communication about the experimental nature of the project to align perceptions with achievable outcomes. Disassociating the platform from the government was also proven to be important in the communication phase to build trust and improve adoption, particularly amidst prevailing social discontent. Overcoming skepticism in emerging technology and addressing digital literacy gaps, especially among older demographics, underscored the need for ongoing engagement and inclusive design. The pilot highlighted the challenge of preparing communities for emergencies during periods of perceived safety, emphasizing the importance of sustained education efforts. Additionally, exploring the platform's potential for broader community issues beyond flood risks revealed its adaptability and the community's interest in addressing local challenges.
What were the main obstacles and challenges you encountered during this activity?
The project faced challenges in managing community expectations, navigating skepticism, and building trust. Overcoming disparities in digital literacy and ensuring inclusive access presented hurdles in platform adoption. Adapting to environmental conditions, like low rainfall periods, emphasized the need for continuous preparedness efforts. Scalability while maintaining relevance across diverse contexts required iterative approaches and adaptive strategies. These obstacles required careful navigation and continuous feedback loops to optimize the platform's utility and impact effectively.
Who at UNDP might benefit from the results of this experimental activity? Why?
Staff involved in disaster risk reduction and community resilience initiatives at UNDP would benefit from the results to inform future projects and strategies. Other COs working on flooding issues and citizens participation tools as well.
Who outside UNDP might benefit from the results of this experiment? and why?
Local governments, disaster management agencies, community organizations, and other development partners working on disaster risk reduction and climate adaptation could benefit from the results to improve flood response and management.
Did this experiment require iterations? If so, how many and what did you change/adjust along the way? and why?
Several rounds of iteration were part of the process to make adjustments based on community feedback and challenges encountered during the pilot.
What advice would you give someone wanting to replicate this experimental activity?
Prioritize community participation, adapt the platform to local contexts and needs, address trust and digital literacy issues, and maintain ongoing communication with stakeholders.
Can this experiment be replicated in another thematic area or other SDGs? If yes, what would need to be considered, if no, why not?
Yes, the experiment could be replicated in other thematic areas or SDGs by adapting the platform to address different disaster risks or community needs, such as earthquakes, tsunamis, or public health emergencies.
How much the "sense" and "explore" phases of the learning cycle influenced/shaped this experiment? In hindsight, what would you have done differently with your fellow Solution Mapper and Explorer?
The "sense" and "explore" phases likely influenced the experiment by emphasizing the importance of understanding community needs and exploring innovative solutions collaboratively. During this process we found out about the success of Coginicity in Indonesia and decided to test it in Panama.
What surprised you?
9Industry, innovation and infrastructure
11Sustainable cities and communities
13Climate action
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