Geographies of Trash in New York City

New York City’s relationship with sanitation infrastructure and services is complex and fraught. I first began to examine this relationship when living in East New York, Brooklyn. I noticed many blocks had black trash bags attached to trees that neighbors or building superintendents regularly emptied and replaced. When I asked my roommate about it, she said that she called 311 to request a basket at the end of our block, but the New York Department of Sanitation (DSNY) rejected her claim because the area was residential.

When I noticed the same trash bag placements where I live now in East Flatbush, I began to wonder why I didn't directly observe the same thing in other residential neighbhorhoods like Kensington or Bed Stuy. I also wondered if the amount of trash on the streets I saw in East New York and Flatbush was related to the availability of litter baskets, which are often overflowing or non-existent in my neighborhood.

Many communities through New York are facing similar sanitation issues, no doubt influenced by DeBlasio's COVID-related budget cuts that reduced DSNY's budget by about $100 million in 2020. According to  Politico , "that [meant] nearly $38 million less for residential trash collection and street cleaning and a $5.6 million cut for crews emptying litter baskets that dot street corners" (Goldenberg, Dunn). These cuts reportedly had a direct effect on the quality of litter basket throughout the city and led to "led to a massive increase in citywide 311 complaints for overflowing litter baskets — from 58 by Feb. 2020 to 790 by July," according to Hogan and Morphet from the NY Post.

New York mayor Eric Adams has noted these issues, no doubt pressured by the increase in 311 trash-related complaints since he has taken office. He vowed to increase DSNY budget to $101 billon, with $22 million of that dedicated to litter basket services. Even more, Adams "[promised] the city’s approximately 23,000 trash bins will be emptied “50,000 times more” than ex-Mayor Bill de Blasio’s administration," according to Shen-Berro of  Politico .

Research on these issues reveals often complex and layered causes of the issues above -- attributing its roots to DSNY operational constraints, waste hauling privatization, equipment limitations, neighborhood proximity to transfer waste zones, and more. But how can we understand New York City's relationship to trash spatially?

This project explores this question through an examination of the City's litter baskets and socioeconomic factors like income and population density. What patterns emerge when we examine the spatial relationship between population density, income, litter basket inventory, and litter basket-related complaints?

To examine the spatial and social characteristics of 311 litter basket-related complaints, I have created a series of maps and charts that offer a visual story of where such complaints are taking place by location and complaint type with increasing levels of granularity. The visualizations are can be examined alongside population density and median household income for the geographic area specified. It is my hope that the combination of visualizations throughout the story allows a deep exploration of 311 complaints as they relate to space, each other, and the socioeconomic variables listed above.

Datasets used in this project came from the American Community Survey, the United States Census Bureau, NYC OpenData, the New York Department of Planning, and various ArcGIS users who published their findings for public use. Software used includes Tableau Public for chart visualizations and data exploration, ArcGIS for map creation and story map development, and R and Google Sheets for data exploration and cleaning.

To create the series of maps and charts below, I first began by exploring the “311 Service Requests from 2010 to Present” dataset from NYC OpenData. Because the dataset is large, I filtered for complaints starting January 1st, 2017. I thought this date would provide enough pre-pandemic data so that my analysis would not contain a large number of outliers. I then explored the many different types of complaints related to the New York Department of Sanitation (DSNY). I decided to focus only on complaints related explicitly to litter baskets because the data was more manageable and provided a more focused analysis directly related to my research questions. I also downloaded a dataset named “DSNY Litter Basket Inventory” that provided a map and figures of all DNSY litter baskets in New York City.

All the sections below begin with visualizing the number of 311 complaints by the specified geographic region. To construct these maps, I performed spatial joins of the 311 data to the geographic data, enabling me to visualize the total count of 311 complaints per community district, neighborhood tabulation area, and census tract. The same process was repeated to visualize the spatial distribution of each type of litter basket complaint per geographic area. These maps required the extra step of filtering the 311 data for specific complaint types and joining these results to the geographic area that served as my point of exploration. I chose choropleth maps to visualize the amount and frequency of the variables, whether number of complaints, number of complaints by type, population density, or median household income. I chose the specific geographic areas of community district, neighborhood tabulation area, and census tract to allow users to explore as granularly as they would like. Additionally, I thought broader areas like districts and neighborhoods might be more understandable and useful for many users. I still include complaint visualization by census tract, however, because the socioeconomic data I used was from the ACS and so collected via census tract.

The charts and graphs created with Tableau offer a visual breakdown of complaint type per community district as well as a breakdown of the most frequent complaint type across all of New York City. With these visualizations, users can easily compare complaint type by community district and by neighborhood. The neighborhood data were calculated via the “calculated” field function in Tableau using the “community board” values in the 311 datasets. As such, the neighborhoods are broken down per community board and function as neighborhood “groups.” I chose to visualize the data this way after class feedback that community board information was not understandable to users without previous knowledge.

Lastly, the visualizations are presented via an ArcGIS Storymap to create a narrative of the visualizations. The Storymap allows additional context and a way to navigate through the visualizations in an interactive way.

To being our exploration of the spatial dynamics of litter baskets, litter basket complaints, and the socioeconomic factors listed above, I begin with a series of charts that visualize the amount and frequency of 311 complaints by type, borough, and community board -- a New York City geographic unit explained below. The dataset used for these visualization is 311 Service Requests from 2010 to Present from NYC OpenData.

Fig. 1.0 demonstrates the breakdown of each 311 litter basket-related complaint: 1) litter basket request 2) overflowing litter basket and 3) general basket complaints. This chart shows the most common type of type of complaint since 2017 is the request for a new litter basket. An interactive chart is available  here .

Fig 1.2 examines the location of the most frequent request and reveals that most litter basket requests are in Brooklyn. While not yet mapped, these two charts offer a start to understanding where requests are taking place and what types of requests are most frequent.

Fig 1.3  highlight table  offers a different and perhaps quicker way to understand the information above. We see a chart of all Brooklyn community boards/districts, their assigned neighborhoods, and the total count of requests for a new litter basket. This view is more understandable for users unfamiliar with community boards/districts and easily communicates the varying number of complaints with a sequential color palette.

Figure 1.4, interactive here, zooms into Brooklyn to examine more granularly where not only litter basket requests are taking place, but all 311 basket-related complaints. Thee neighborhoods are grouped by community district due to the structure of the 311 data -- each complaint was attached to a community/board district, which are high level geographic composed of various neighborhoods, which are themselves composed of various census tracts. The interactive treemap allows users to compare complaint count and see community board/community district information across Brooklyn.

While the above visualizations offer a great start in understanding where 311 complaints are happening and what reporters are complaining about, I have chosen to visualize these variables alongside income and population density to examine any patterns that may emerge. The following maps examine litter basket placement, 311 complaints, and socioeconomic factors by NYC geographic areas from largest to smallest. I start with community districts, followed with neighborhoods, and end with census tracts.

Community districts, also called community district tabulation areas are NYC established geographic regions. The City is divided into 59 CDs, which are themselves composed of neighborhood tabulation areas (explained in the next section), which are themselves composed of census tracts. The visualizations in this area offer a high level view of the spatial distribution of variables named within each map. I have chosen choropleth maps because they are an accessible way to visualize amounts. Some maps contain diverging scales while others contain sequential scales, depending on which aspect the data I highlight. I have chosen color scales that should be colorblind friendly and that do not contain more than 5 classes for the sake of accessbility and legibility. All maps are interactive and searchable.

Neighborhood tabulation areas are the next geographic level down from CDTAs. They are built by aggregating census tracts that approximate, but do not exactly match, established New York City neighborhood boundaries. In this geographic level, because it is recognizable by most people, I introduce population density as a point of comparison for the different complaint types and totals.

Census tracts are a small unit of geographic measurement and are aggregations of census blocks. I chose to visualize data by census tract because of the usefulness of census data available spatially, such as income and population density.

Geographies of Trash in New York City uncovered various spatial patterns between 311 complaints, litter basket inventory, population density, and median household income. The opportunity to explore the above findings further through quantitative analysis would provide an additional layer of information and exploration of the complex world of waste management and sanitation services. An additional direction I I would like to take this project is the development of community district or neighborhood sanitation profiles, similar to the Community Profile dashboards here from the Department of City Planning. Such a resource would be helpful in understanding many different aspects of not only the waste management puzzle and allow many different stakeholders to see the ways that community members advocate for their communities and public space.

1. "Trash Complaints Pile Up Under Adams,"  Politico 
2. "NYC Budget Cuts Include Funding for Rat Proof Trash Cans,"  New York Post 
3. "Budget Cuts Threaten Sanitation Commissioner's Mayorial Ambitions,"  Politico 
4. "12 Things New Yorkers Should Know About Their Garbage,"  Citizen's Budget Commission 
5. "Annual Report: New York City Curbside and Containerized Municipal Refuse and Recycling Statistics," New York Department of Sanitation
6. "NYC 2020 Census Geographic Reconfiguration Atlas," NYC Department of City Planning, Population Division
7. "311 Service Requests from 2010 to Present," NYC OpenData
8. "2020 Census Tracts to 2020 NTAs and CDTAs Equivalency," NYC OpenData
9. "DSNY Litter Basket Inventory," NYC OpenData
10. "Census Demographics at the Neighborhood Tabulation Area (NTA) level," NYC OpenData
11. "2020 Census Blocks - Tabular," NYC OpenData