Uncovering JAPA

The Need for Human Experience in Big Data Research

Planners are increasingly equipped with data analysis tools to inform decisions. Yet, planners can make misinformed decisions when an over-reliance on existing datasets distracts from community goals. Mistaking data for a hard truth leads to ethical implications in planning where algorithms become prioritized over the long-standing knowledge of lived experiences.

Community Engagement Enhances Big Data Analysis

How can planners leverage big data to make decisions without overlooking the importance of community expertise? In "The Moving Mapper" (Journal of the American Planning Association, Vol. 88, No. 2), Madeleine I. G. Daepp, Andrew Binet, Vedette Gavin, Mariana C. Arcaya, and The Healthy Neighborhoods Research Consortium advocate for collaborative data analysis. The article presents new insights big data can provide when community members ("resident researchers") identify research questions, choose data sets, decide on analytic methods, and interpret results.

Daepp et al. explain how they engaged with 45 resident researchers in the Boston area as part of the Healthy Neighborhoods Research (HNR) Consortium, a network of academic researchers, community partners, government officials, advocacy groups, and recruited residents in the Boston area. They use participatory action research (PAR) to engage with "those most affected by the phenomena" of a chosen research topic.

In the article, Daepp et al. present a specific PAR project, "The Moving Mapper," which generated a web-based tool for residents to "compare their experiential knowledge with spatial patterns in residential moving."

Figure 2. The research approach of the Healthy Neighborhoods Study (top row), as applied to the development of the Moving Mapper tool (bottom row).

Figure 2. The research approach of the Healthy Neighborhoods Study (top row), as applied to the development of the Moving Mapper tool (bottom row).

Community Knowledge Drives Data Analysis Innovation

By describing the participatory structure of the research study, the authors identify opportunities for community knowledge to lead and guide data analysis. For example, one resident researcher combined her knowledge of local immigration patterns with visualizations provided by the Moving Mapper tool to suggest needed school district improvements from Haitian residents migrating to a specific neighborhood.

Yet, as the authors present, planners do not need big data to justify lived experiences as an actionable form of data. Instead, the study presents big data as a tool to augment lived experience data, which remains planners' most essential source of knowledge.

This research builds upon a broader practice of participatory and grassroots approaches to urban planning. However, the specific methodology of Daepp et al. is novel for planners because it embeds participatory collaboration in all stages of the research process. Daepp et al.'s method empowers community members to decide on the very research questions and hypotheses guiding the study.

As a current planning student, I find this article especially exciting in the context of lessons discussed in quantitative analysis courses. Although planners have growing access to more data, we need to critically examine when big data is the appropriate tool to inform decisions. As Daepp et al. remind planners, community members still hold the best source of knowledge when understanding community life and community needs.

Top Image: Orbon Alija, E+

About the AUthor
Mike Lidwin is a master in urban planning candidate at Harvard University's Graduate School of Design.

September 1, 2022

By Mike Lidwin