The Moving Mapper: Participatory Action Research With Big Data. Issue 2 (3rd April 2022)

• Record Type: Journal Article
• Authors: Daepp, Madeleine I. G.; Binet, Andrew; Gavin, Vedette; Arcaya, Mariana C.
• Is Part Of: Journal of the American Planning Association. Volume 88:Issue 2(2022)
• Page Start: 179
• Page End: 191
• Publication Date: 2022-04-03
• Subjects: big data -- community engagement -- participatory action research -- residential mobility; Planning -- Periodicals; City planning -- Periodicals; Regional planning -- Periodicals; 711.4097305; 361.60973
• DOI: 10.1080/01944363.2021.1957704
• Languages: English
• ISSNs: 0194-4363

Abstract

Abstract: Problem, research strategy, and findings: Big data promises new insights for planning but threatens to exclude community expertise from knowledge creation and decision-making processes. Participatory methods are needed to ensure that big data is marshaled to address problems of importance to communities, that hypotheses and interpretations are shaped by evidence from lived experience, and that results are ultimately useful to residents. In this study we used a participatory action research (PAR) framework to engage Boston (MA)–area residents in leveraging a longitudinal consumer credit database to understand shared planning challenges. We describe how residents, community organizations, and academic researchers collaborated to co-design an interactive map of residential moves across Massachusetts. The resulting estimates were largely consistent with residents' understandings of local moving patterns, providing a case of big data analysis confirming, and further specifying, phenomena identified through centering lived experience. Collaborative data analysis also generated new insights; for example, showing misalignment between regional planning boundaries and low-credit movers' moving patterns. This work shows how sustained PAR partnerships can combine the strengths of community expertise and big data analyses to inform planning. Takeaway for practice: PAR with big data is feasible, combines the power of lived experience and large-scale quantitative analysis, and can mitigate the risks of exclusion that threaten emerging uses of big data.