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

Open access


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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Baltimore Office of Sustainability. 2016. Urban Agriculture. Available at: http://www.baltimoresustainability.org/projects/baltimore-food-policy-initiative/homegrown-baltimore/urban-agriculture-2/ (accessed April 25 2018).

  • Baret F. and S. Buis. 2008. “Estimating Canopy Characteristics from Remote Sensing Observations: Review of Methods and Associated Problems.” In Advances in Land Remote Sensing edited by S. Liang 173–201. Netherlands: Springer.

  • Chen M. S. Mao and Y. Liu. 2014. “Big Data: A Survey.” Mobile Networks and Applications 19: 171–209.

  • Colasanti K. C. Litjens and M. Hamm. 2010. Growing Food in the City: The Production Potential of Detroit’s Vacant Land. East Lansing MI: The C.S. Mott Group for Sustainable Food Systems at Michigan State University.

  • Congedo L. 2014. Semi-Automatic Classification Plugin User Documentation. Release Technical Report. Available at: https://www.researchgate.net/profile/Luca_Congedo/publication/265031337_Semi-Automatic_Classification_Plugin_User_Manual/links/57cafe2d08ae59825183576d.pdf (accessed May 28 2017).

  • Conner D. A.D. Montri D.N. Montri and M.W. Hamm. 2009. “Consumer Demand for Local Produce at Extended Season Farmers’ Markets: Guiding Farmer Marketing Strategies.” Renewable Agriculture and Food Systems 24: 251–259.

  • Despommier D. 2010. The Vertical Farm: Feeding the World in the 21st Century. New York: Dunne Books/St. Martin’s Press.

  • Dumbacher B. and C. Capps. 2016. “Big Data Methods for Scraping Government Tax Revenue from the Web.” In 2016 Proceedings of the Joint Statistical Meetings Section on Statistical Learning and Data Science: 2940–2954.

  • European Association of Remote Sensing Laboratories. 2017. “Introduction to Remote Sensing.” Available at: http://www.seos-project.eu/modules/remotesensing/remotesensing-c01-p05.html (accessed September 30 2017).

  • Forster D. Y. Buehler and T.W. Kellenberger. 2009. “Mapping Urban and Peri-Urban Agriculture Using High Spatial Resolution Satellite Data.” Journal of Applied Remote Sensing 3(1): 033523. Doi: https://dx.doi.org/10.1117/1.3122364

  • Goldsmith S. 2014. “Milwaukee’s Push to Turn Vacant Land into Urban Farms.” Governing the States and Localities. April 16 2014. Available at: http://www.governing.com/blogs/bfc/gov-milwaukee-mayor-tom-barrett-home-grown-vacant-lots-urban-agriculture.html (accessed April 25 2018).

  • Hartley H.O. 1962. “Multiple Frame Surveys.” Proceedings of the Social Statistics Section of the American Statistical Association: 203–206.

  • Hirschberg J. and C.D. Manning. 2015. “Advances in Natural Language Processing.” Science 349: 261–266.

  • Kausar M.A. V.S. Dhaka and S.K. Singh. 2013. “Web Crawler: A Review.” International Journal of Computer Applications (0975-8887) 63: 31–36. Available at: https://pdfs.semanticscholar.org/7086/cfbc441e1ae956e4600a115b45c8cc84e4a7.pdf (accessed May 29 2017).

  • Krijnen D. R. Bot and G. Lampropoulos. 2014. “Automated Web Scraping APIs.” Online: http://mediatechnology.leiden.edu/images/uploads/docs/wt2014_web_scraping.pdf (accessed April 25 2018).

  • Law M. and A. Collins. 2015. Getting to Know ArcGIS 4th Ed. pp. 768: ESRI Press.

  • Lohr S. 2011. “Alternative Survey Sample Designs: Sampling with Multiple Overlapping Frames.” Survey Methodology 37: 197–213.

  • Lohr S.L. 2010. Sampling: Design and Analysis 2nd Ed. Brooks/Cole: Cengage Learning.

  • Lohr S. and J.N.K. Rao. 2006. “Estimation in Multiple-Frame Surveys.” Journal of the American Statistical Association 101: 1019–1030.

  • Lovell S.T. 2010. “Multifunctional Urban Agriculture for Sustainable Land Use Planning in the United States.” Sustainability 2: 2499–2522. Doi: http://dx.doi.org/10.3390/su2082499.

  • Manjunath B.S. and W.Y. Ma. 1996. “Texture Features for Browing and Retrieval of Image Data.” IEEE Transactions on Pattern Analysis and Muchine Intelligence 18: 837–842.

  • Marin Master Gardeners. 2017. Community Gardens. University of California Division of Agriculture and Natural Resources. Available at: http://ucanr.edu/sites/MarinMG/Great_Gardening_Information/Marin_Community_Gardens/ (accessed May 26 2017).

  • Mayer-Schönberger V. and K. Cukier. 2014. Big Data: A Revolution That Will Transform How We Live Work and Think. Eamon Dolan/Mariner Books.

  • Mohr G. M. Stack I. Ranitovic D. Avery and M. Kimpton. 2004. “An Introduction to Heritrix: An Open Source Archival Quality Web Crawler.” 4th International Web Archiving Workshop. Available at: http://iwaw.europarchive.org/04/Mohr.pdf (accessed May 29 2017).

  • Neteler M. M.H. Bowman M. Landa and M. Metz. 2012. “GRASS GIS: A Multi-Purpose Open Source GIS.” Environmental Modelling & Software 31: 124–130.

  • Policy Link. 2012. Growing Urban Agriculture: Equitable Strategies and Policies for Improving Access to Healthy Food and Revitalizing Communities. Available at: http://www.policylink.org/sites/default/files/URBAN_AG_FULLREPORT.PDF (accessed April 24 2018).

  • Polidoro F. R. Grannini R.L. Conte S. Mosca and F. Rossetti. 2015. “Web Scraping Techniques to Collect Data on Consumer Electronics and Airfares for Italian HICP Compilation.” Statistical Journal of the IAOS 31: 165–176.

  • Popovitch T. 2014. “10 American Cities Lead the Way with Urban Agriculture Ordinances.” SeedStock Newsletter. May 27 2014. Available at: http://seedstock.com/2014/05/27/10-american-cities-lead-the-way-with-urban-agriculture-ordinances/ (accessed April 24 2018).

  • Rhodes B.B. A.F. Kim and B.R. Loomis. 2015. “Vaping the Web: Crowdsourcing and Web Scraping for Establishment Survey Farm Generation.” Proceedings of the 2015 Federal Committee on Statistical Methodology Research Conference. Available at: https://fcsm.sites.usa.gov/files/2016/03/H3_Rhodes_2015FCSM.pdf (accessed October 30 2016).

  • Santo R. A. Palmer and B. Kim. 2016. Vacant Lots to Vibrant Plots: A Review of the Benefits and Limitations of Urban Agriculture. Johns Hopkins Center for a Livable Future. Available at: http://www.jhsph.edu/research/centers-and-institutes/johnshopkins-center-for-a-livable-future/_pdf/research/clf_reports/urgan-ag-literature-review.pdf (August 19 2016).

  • Schowengerdt R.A. 2007. Remote Sensing: Models and Methods for Image Processing 3rd Ed. Elsevier: San Diego CA USA.

  • Singh S.M. and K. Hemachandran. 2012. “Content-Based Image Retrieval Using Color Moment and Gabor Based Image Retrieval Using Color Moment and Gabor Texture Feature.” UCSI International Journal of Computer Science Issues 9: 1694-0814. Available at: http://s3.amazonaws.com/academia.edu.documents/33984493/IJCSI-9-5-1-299-309.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1495994274&Signature=XS%2F%2FueQk9Kg1xteMNIf%2BfswT4HI%3D&response-content-disposition=inline%3B%20filename%3DIJCSI-9-5-1-299-309.pdf (accessed May 30 2017).

  • Specht K. R. Siebert I. Hartmann U.B. Feisinger M. SawickaA. Werner S. Thomaier D. Henckel H. Walk and A. Dierich. 2014. “Urban Agriculture of the Future: An Overview of Sustainability Aspects of Food Production in and on Buildings.” Agriculture and Human Values 31: 33–51. Doi: http://dx.doi.org/10.1007/s10460-013-9448-4.

  • Stoney W.E. 2008. ASPRS Guide to Land Imaging Satellites. Noblis Inc. Available at: http://www.asprs.org/a/news/satellites/ASPRS_DATABASE_021208.pdf (accessed September 30 2017).

  • Taylor J.R. and S.T. Lovell. 2014. “Mapping Public and Private Spaces of Urban Agriculture in Chicago Through the Analysis of High-Resolution Aerial Images in Google Earth.” Landscape and Urban Planning 108(1) : 57–70.

  • United States Census Bureau. 2011. Urban Area Criteria for the 2010 Census: Notice. Federal Register 76. No 164.

  • United States Department of Agriculture (USDA). 2016. Urban Agriculture. https://newfarmers.usda.gov/ (accessed April 25 2018).

  • Vidhya K.A. and G. Aghila. 2010. “A Survey of Naīve Bayes Machine Learning Approach in Text Document Classification.” International Journal of Computer Science and Information Security 7: 200–211. Available at: https://pdfs.semanticscholar.org/6861/d02328e18e84fe98b30658100b1c8e7d9891.pdf (accessed May 29 2017).

  • Wikipedia. 2017. Google Earth. Online: https://en.wikipedia.org/wiki/Google_Earth (accessed September 30 2017).

  • Young L.J. A.C. Lamas and D.A. Abreu. 2012. “The 2012 Census of Agriculture: A Capture-Recapture Analysis.” Journal of Agricultural Biological and Environmental Statistics 22: 523–539. Doi: https://dx.doi.org/10.1007/s13253-017-0303-8 (accessed September 30 2017).

  • Zujovic J. T.N. Pappas and D.L. Neuhoff. 2009. “Structural Similarity Metrics for Texture Analysis and Retrieval.” Proceedings of the International Conference on Image Processing. Cairo Egypt. 2225–2228.

Journal information
Impact Factor

IMPACT FACTOR 2018: 0.837
5-year IMPACT FACTOR: 0.934

CiteScore 2018: 1.04

SCImago Journal Rank (SJR) 2018: 0.963
Source Normalized Impact per Paper (SNIP) 2018: 1.020

Cited By
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 999 518 22
PDF Downloads 665 345 21