See yourself in data : building a framework for databased community engagement events

Abstract

Over the past decade or so, government data has been released through open data portals to improve efficiency, enable data-driven policy research and decision-making, increase transparency, and open a new avenue by which citizens may engage with the public sector. While open data has been a boon for researchers, journalists, technologists, and entrepreneurs, benefits from their publication have not necessarily flowed down to community organizations and residents. As unequal access to open data threatens to widen information gaps, models of citizen participation in the data-driven city have not fully developed. This thesis reviews possibilities and barriers of several forms of data-based participation, focusing particularly on participatory data interpretation as a liberating process and its pre-requisites of data awareness and literacy. It synthesizes a general framework for community-based data events, based on insights from Public Participatory GIS, Data Feminism, Data Activism, and Data and Digital Justice, and compares that framework to open data awareness and literacy-raising events in Pittsburgh and Los Angeles. Compared to the choices and achievements of these two cases, the framework holds as a guide for meaningful considerations that future community-based data events may take into account.