Talking Trash (and How Data Fits In)

This year’s Climate and Society class is out in the field (or lab or office) completing a summer internship or thesis. They’ll be documenting their experiences one blog post at a time. Read on to see what they’re up to.

Ximena Fonseca-Morales, C+S ’18

Boston kicked off “Neat Streets” in 2016, an interactive campaign to tackle litter behavior among cigarette smokers. (Source: City of Boston)

If you’ve visited New York, you’ve probably seen at least a little trash. Mountains of filled garbage bags, plastic bottles, unidentifiable objects (seriously), paper. You name it, it’s either in the streets or on the sidewalks. Trash is a real problem, and it has a negative impact on the health of a community. It’s no surprise that worldwide, cities have gotten creative to address this problem, organizing competitions to reduce street litter or designing interactive trash cans where people can answer a question by disposing of their litter. While these initiatives have been successful, how can data play a role to inform programs or policies to cut down on litter? This summer, I was part of research team that taught me just how to answer that question.

Data can help cities tailor litter reduction strategies by digging deeper to understand its root causes. By conducting research that is specific to an area, data-driven programs or policy recommendations—and lasting results—can become more feasible. As a research assistant with a group of researchers from Columbia’s School of International and Public Affairs, I helped gather and analyze data to provide data-driven policy recommendations to reduce litter in Harlem. The overarching goal of my research team was to foster a cleaner and healthier Harlem by providing policy recommendations to reduce sidewalk and street litter along three key business corridors.

Commerce has boomed in Harlem, especially along 125th St. In this bustling area, as well as other commercial neighborhoods, clean streets result in a healthier environment and improve the pedestrian experience, bringing more business and boosting the local economy. As with any other problem, the key to formulating a solution to 125th St. and other Harlem commercial hubs’ trash problem is understanding the causes of the problem itself.

To determine how to reduce the amount of litter found along 116th, 125th and 135th streets, my team conducted field research to gather data on the type of trash, where it came from, the overall amount and the location. Although the image of six graduate students walking around with heads bowed toward the ground counting litter may seem silly, it was a necessary step to obtain the data that ultimately guided our project and our policy recommendations. We counted over 15,500 pieces of litter over a span of three visits to six block stretches between Morningside and 5^th Ave on our target streets. I must confess that before this experience, there have been times when I thought data gathering would be a waste (no pun intended) of time because the problems and solutions seemed so obvious.

Why spend a month or longer gathering data when I already knew what the data would tell me? Why not spend that time actually trying to find solutions, draft policies or develop programs? The truth is that this internship has taught me that often, the data tells you a different story than you would expect and that proposing solutions based on assumptions is a true waste of time.

Filled garbage bags on street corners or along the block were a common sight throughout the field work. (Source: Ximena Fonseca-Morales)

Given that this project built upon a study conducted last year by some of the same researchers, we were able to compare findings. It was evident that some key findings based on last year’s data no longer held. For example, last year’s study found that a significant proportion of trash along 125th St. was bulk trash like fans, furniture, and cardboard boxes. This year, my team did not find this to be the case. Had we assumed that bulk items were still a significant part of the trash along this Harlem street, we would have recommended policies that added no value.

By relying on our specific findings, we developed policy recommendations to tackle the litter problems we observed. For instance, the New York City Department of Sanitation empties the trash baskets along 125th St. twice a day. Yet, paper scraps and napkins, which made up 30 percent of the trash we counted, do not always make it into a litter basket and often end up strewn throughout the street. To minimize the paper litter, the team recommended that sidewalk cleaning and sweeping machines should be deployed at least twice a week in addition to continued litter basket pick-up.

This all goes to say that this internship has emphasized that policy recommendations and program development must be data-driven. In any field or for any problem—especially one like climate change—solutions are not one-size fits all. While we can turn to other organizations, cities, or nations for best-practices, we must still focus on our specific research questions and gather the data that will be the key component to informing the solutions we propose.