Question-Generating Datasets: Facilitating Data Transformation of Official Statistics for Broad Citizenry Decision-Making

Abstract

Citizenry decision-making relies on data for informed actions, and official statistics provide many of the relevant data needed for these decisions. However, the wide, distributed, and diverse datasets available from official statistics remain hard to access, scrutinise and manipulate, especially for non-experts. As a result, the complexities involved in official statistical databases create barriers to broader access to these data, often rendering the data non-actionable or irrelevant for the speed at which decisions are made in social and public life. To address this problem, this paper proposes an approach to automatically generating basic, factual questions from an existing dataset of official statistics. The question generating process, now specifically instantiated for geospatial data, starts from a raw dataset and gradually builds toward formulating and presenting users with examples of questions that the dataset can answer, and for which geographic units. This approach exemplifies a novel paradigm of question-first data rendering, where questions, rather than data tables, are used as a human-centred and relevant access points to explore, manipulate, navigate and cross-link data to support decision making. This approach can automate time-consuming aspects of data transformation and facilitate broader access to data.