Accessibility of Food compost sites in NYC


Lab Reports, Maps, Visualization

Background

For this lab report, I wanted to look at the accessibility of food composting in NYC. In the rise of our current environmental reckoning, more people are turning to food composting to help tackle global warming. As people feel the pressure to do their part, this has also ushered in a new phenomenon – ‘eco-shaming’. Eco-shaming is the occurrence in which people are ridiculed for acting unsustainably, either called out directly or indirectly (e.g., social media) (McMullin, 2019). While eco-shaming has certainly resulted in positive changes, namely with large corporations (Nestle stopped sourcing unethical palm oil as a direct result from being eco-shamed) – it’s not without its faults. At the individual level, there are barriers to some environmentally friendly practices; particularly cost and accessibility of eco-friendly products and practices, as well as education on how to be more eco-friendly (Nguyen et al., 2019). This report will examine the accessibility of current food composting practices in NYC, while looking at how household income and population size might have an effect.

Data Collection & Cleaning

The data on food scrap drop-off locations in NYC was provided by the Department of Sanitation and available on NYC Open Data. The base map for NYC was taken from the 2020 Census Tracts and a shapefile for NYC PUMAs (public use microdata areas) was pulled from NYC Open Data.

Both median income and population data were taken from the Census American Community Survey (ACS) for 2019 and filtered by PUMAs. While Tableau has an add-in data layer feature in their map layers that can populate income and population data for you, I didn’t feel that their income brackets were representative (particularly their lower and higher income brackets being such wide ranges – see Figure 1). Therefore, I decided to pull in my own data from the Census ACS to avoid Tableau’s pre-populated brackets.

Figure 1. Tableau’s pre-populated income brackets

I had to create a join key that linked the community districts shapefile with the median income, population, and food scrap location data. This was performed manually in excel using the community district ID from the community districts shapefile and entered as a field in the other datasets.

Figure 2. Join key field for community district IDs

Visual Inspiration

I was inspired by a graphic developed by the NY Department of Sanitation (DSNY) in its use of earth-colored tones (brown and green in this example) to keep in line with the environmental theme of their graphic.

Figure 3. DSNY Graphic on Waste Prevention, Reuse and Recycling

I was also inspired by another graphic I came across by NYC EDC because of its breakdown of NYC by PUMAs (or community districts). I had been struggling with figuring out the best way to breakdown the data by areas, as census tracts were too granular (195 in total) and the 5 boroughs were too aggregate. There are 59 community districts in NYC, making it a happy medium between census tracts and boroughs.

Figure 4. NYC EDC graphic showing PUMAs

Visualization Process

Using Tableau, I added the shapefile for NYC census tracts as my first layer and muted it considerably as these zones weren’t the focus of my analysis. I decided to add labels for only the 5 boroughs, as 59 community names would be too much detail.

Figure 5. Base map

I then layered the community districts map on top to show the division of these areas and added data based on color for both median income and total population (median income shown below).

Figure 6. Second map layer

I added a final layer to depict points for each of the food drop-off locations. I wanted to stick with earth-colored tones for my visuals, so I chose green to depict the median income scale (as its associated with money as well), blue for the population scale, and brown for food scrap drop-off points.

Figure 7. Third map layer

Results

The final visuals show that there are some more densely packed areas of food composting sites than others (my eye is first drawn to the Soho, Greenwich Village, Chelsea areas of district 1 & 2 and 4 & 5). For the boroughs of Brooklyn, Queens, the Bronx and Staten Island, food compost sites seem to be more prevalent the closer they are to Manhattan and more sparse the farther out they are. We see for districts with lower median incomes (e.g., Washington Heights and Inwood in Manhattan and Flushing and Murray Hill in Queens) that they have only 1 drop-off site – yet some districts with high income areas (e.g., Tottenville and Great Kills in Staten Island) have none. Population also doesn’t appear to have a large effect – for instance District 2 in Queens (which includes Sunnyside and Woodside) has a relatively low population in comparison (136,058), yet they have 6 drop-off sites.

Still, it’s interesting to see the clear divide in some areas with little to no drop-off sites as opposed to areas with many. This could have an evident impact on accessibility for many residents and prevent them from participating in this eco-practice.

Future Work and Limitations

While neither income or population size seemed to have a large effect (at least visually speaking) on the number of drop-off sites, it would be interesting to see if perhaps there are other factors at hand that might be driving the location of these drop-off sites – maybe nearby subway lines or the age demographic of an area.

My analysis is somewhat limited by time, as I was only able to pull demographic data from 2019 – though the food scrap drop-off location data is recent (2021).

References

The Ninh Nguyen, Thi Thu Hoai Phan, Tuan Khanh Cao, Hoang Viet Nguyen. (2017). Green purchase behavior: mitigating barriers in developing countries, Strategic Direction, Vol. 33 Issue: 8, pp.4-6, https://doi.org/10.1108/SD-04-2017-0064 Permanent link to this document: https://doi.org/10.1108/SD-04-2017-0064

McMullin, J. (2019). Sustainability Shaming: Helpful or Harmful? The Emerald Review. http://emeraldreview.com/sustainability-shaming-helpful-or-harmful/