Exploring a Big Data Approach to Building a List Frame for Urban Agriculture: A Pilot Study in the City of Baltimore

Young Linda J. (), Hyman Michael () and Rater Barbara R. ()
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Young Linda J.: USDA National Agricultural Statistics Service, Research and Development Division, 1400 Independence Avenue SW, Washington DC20250, U.S.A.
Hyman Michael: USDA National Agricultural Statistics Service, Research and Development Division, 1400 Independence Avenue SW, Washington DC20250, U.S.A.
Rater Barbara R.: USDA National Agricultural Statistics Service, Census and Survey Division, 1400 Independence Avenue SW, Washington DC20250, U.S.A.

Journal of Official Statistics, 2018, vol. 34, issue 2, 323-340

Abstract: The United States Department of Agriculture’s National Agricultural Statistics Service (NASS) has the responsibility of quantifying the nation’s agricultural production. Historically, it has focused on large production agriculture. With interest and activity increasing in urban areas, NASS has begun exploring how to better quantify urban agriculture. This segment of agriculture is particularly challenging to enumerate because the agricultural holdings tend to be small, diverse, widely dispersed, and more transient than the predominantly large farms in rural areas. In collaboration with the Multi-Agency Collaboration Environment (MACE), a new approach to list building was explored in a pilot study conducted in the City of Baltimore, Maryland. Using a big data approach, areas of potential agricultural activity were identified by gathering information (state and local permits, Facebook and twitter feeds, interest groups, etc.) via the web. A sample was drawn from the list, and an in-person survey was conducted to assess whether or not the identified areas had agricultural activity. The results of the pilot study are presented. Lessons learned from the study and next steps are discussed.

Keywords: Web scraping; urban agriculture; list building (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:34:y:2018:i:2:p:323-340:n:3

DOI: 10.2478/jos-2018-0015

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