Final Project Proposal Saving the world, one cigarette butt at a time.

Last May, the SF Mayor announced the Safe and Clean Neighborhoods Promise, an initiative to improve the quality of life in San Francisco. To deliver on this promise, the Mayor established the neighborhood-based Fix-It Teams. Collaborating with the Mayor’s Office of Civic Innovation (MOCI), we are one of the Fix-It teams focusing on litter in the Castro. Our team is trying to solve the problem of litter in an assigned region of the district by prototyping an intervention.

Since this is a complex problem, comprised of social, environmental, and logistical elements, we took a case-study approach to investigate it from different perspectives and analytical tools. We collected several types of data, quantitative and qualitative (observations, litter count, interviews, independent research). We analyzed it through an established framework (“empathy map”), to identify the most prominent problems that emerged from the data. Observing that cigarette butts were highest in quantity of litter in our area, as well as taking into account our constraints of scope, time and resources, the problem we chose to focus on is reducing cigarette butts.(1#estimation) Considered “the most common type of litter on earth” (Bonanomi, Incerti, Cesarano, Gaglione & Lanzotti, 2015.), and a problem cities worldwide are trying to approach, we continued breaking it down in order to come up with a hypothesis that will serve as the basis for our intervention in the specific area we are approaching. We are currently doing so by considering different ways we might tackle it through a quasi-experiment research design. The intervention we are proposing will be tested in its natural setting, which is why quasi-experiment is the most suitable method. (2#breakitdown)

We will present our research in an engaging way using Adobe Spark. Next to a textual explanation (3,000 words), we will include a photo and video documentation of the process as well as applicable visualizations. To explain the research design, we detail how a #casestudy approach helped designing a #quasiexperiment. We will expand on #nudge to explain the intention behind the intervention, and how we reached the solution by understanding #levelsofanalysis and keeping an #openmind. We will analyze our data using #descriptivestats and visualize the findings with #dataviz as appropriate for the intended #audience.

Studies have shown that a balanced team and one with clear roles tend to perform best (Partington, D. & Harris, H., 1999). Since our team was put together by Minerva, we worked within those constraints to optimize our team work and came up with a team charter. Consisting of diverse personalities and backgrounds, our team identified each person’s strengths and weaknesses and delegated general team roles like for instance Completer-Finisher for time management, team Worker for mitigating conflicts and Monitor-Evaluator as a contrarian. Since we all have strong, yet different, personalities - we agreed on alternating the leader-role each week. Because we have a non-Minervan team member we strongly emphasized team building in the beginning of the project, with organizing informal team dinners and social activities. (3#teamrole)

Word count: 499

Anna, Hana, Yasmin and Sherington

#organization - Adobe Spark allows to break down different contents into interactive sections, which we made use of to visualize different information we wanted to communicate in an engaging manner. After an introduction of our past research and future implementation, we reflect on the team roles and give an outlook on the complete final project.

#presentation - Since the proposal should convince the audience of its quality, we decided to present it with Adobe Spark - the application we intend to have for the final project itself. For that we balanced an engaging design with professional content and appropriate citations.

#infoneeded - The need to reduce litter in Castro was established by the government after thorough research and previously failed interventions. Research and our observations showed cigarette butts to be the most pressing issue, thus we decided on finding an intervention that reduces the amount of cigarette butts on the street.

1. #estimation - To understand whether our intervention could reach enough people, we used Fermi estimation to approximate the amount of people and smokers passing every day. We observed on average 3 people per minute, which equals to 200 per hour, and totaling to 2400 if we assume 12 busy hours. With a smoking rate of 11% in San Francisco, we can reach approximately 250 smokers per day through the intervention (Dowd, 2015). While not each of them will want to throw away the cigarette, they become aware of the intervention and might adjust their behavior.

2.#breakitdown - Our process of coming up with an effective intervention is done by breaking down this complex problems into more tractable components, in order to characterize exactly what our problem is and therefore what will a successful intervention look like.

3.#teamroles: This is a great use of the HC because we recognize the importance of team roles and having a contrarian. This is based on research that we refer to. We managed to work within our constraints - because we did not choose our own team and had a non-Minerva member - and built up a strong team spirit and trust.


Bonanomi, G., Incerti, G., Cesarano, G., Gaglione, S. A., & Lanzotti, V. (2015, January 21). Cigarette Butt Decomposition and Associated Chemical Changes Assessed by 13C CPMAS NMR. PLoS ONE, 10(1), e0117393. Retrieved March 11, 2017, from

Design Thinking. (2017, July 8). Empathy Mapping. Retrieved 13 March 2017, from

Dowd, K. (2015, December 1). Which counties smoke the most in the Bay Area?. Retrieved March 11, 2017, from

Partington, D., & Harris, H. (1999). Team role balance and team performance: an empirical study. Journal of Management Development, 18(8), 694-705. doi:10.1108/02621719910293783

Created By
Hana McMahon-Cole


Created with images by armsultan - "DSCF7742"

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