GTECH 361 Final Project Proposal

Determine the Best Site for a New Urban Agriculture Project

NYC Planning Suitability Data

Source: https://data.cityofnewyork.us/City-Government/Suitability-of-City-Owned-and-Leased-Property-for-/4e2n-s75z

• The city has assessed potential suitability of over 400 sites for urban agriculture. 

• Of those sites only ~120 had Longitude/Latitude Points. 

• This could be remedied in Arcgis via geocoding. 

• Concern: The currently visble locations are highly clustered
  

  • This may be an issue if the other ~ 75% of the locations are similarly distributed. 
  • Spatial analysis tools may not be as useful as if the locations were more evenly distributed. 
  • Hopefully, these specific locations have coordinates because they were surveyed by more experienced city employees,
    and the other locations are in different areas.

Benefits of Urban Agriculture:

*Lead Remediation

Source: Brooklyn College Urban Soils Lab

• Below, a map of lead level tests from private and community gardens in NYC. 

• Plant uptake of lead is surprisingly very limited, leading to <1ppm in dry matter. 

• The main pathway for human exposure to lead is dust.

  • This can occur via inhalation or more commonly, ingestion.
  • Dust on clothing and skin, and that which settles on edible plant surfaces are the biggest concern.
    
    

• Cultivating green spaces is the best method for remediation.

  • Soil Organic Matter Stabilizes Lead, and other toxic elements (compost, cover crops).
  • Gardeners deliberately cover native soils with mulch and straw to prevent weed growth and lead contamination. 

*Serve Resource-Constrained Populations

• Step 1: Identify resource constrained populations in NYC via enrichment of census tracts with ACS income data.
  
  • Map percentage below the poverty line. 
    *Step 2: Use aggregation and intersection tools to compute the number of food retailers per x number of residents.
  • In effect, an index of accessibility, which measures the need for low-cost food options based on the number of low-income residents per retail operation.
  • If that number is high, the number of residents that each store serves can be said to be higher and access by income restricted. 

*Drawbacks: population vs. number of stores within a tract does not prove lack of access. Stores could be located outside, but near the boundaries of an area that falls within the higher range of this index, lowering the relevance of this method of analysis.

• Alternatively, each store can be said to be a centroid with a particular buffer range, serving a particular area, and therefore a particular number of residents within reason. Any areas not covered and also having low income could be especially targeted. 

• Potential Bottlenecks: It is difficult to distinguish via the data what constitutes a food store that provides “fresh” or “healthy” food options. NYers are acquainted with the concept of a bodega/deli that has nothing with any nutritional value on the shelves, yet these are classified the same way as larger grocery stores. Key words like “deli” or “bodega” could be used to filter the data, or more reliably, only stores above a certain square footage could be included. 

• Another viable alternative is to recreate the below map as a layer for analysis, using the same data.

  • Improve the nuance by creating bins for income and sales data within census polygons to enable comparison.
  • This might be the best metric of the three options.