Search for: All records

The accessibility of official statistics to non-expert users could be aided by employing natural language processing and deep learning models to dataset lexicons. Specifically, the semantic structure of FIPS codes would offer a relatively standardized data dictionary of column names and string variable structure to identify: two-digits for states, followed by three-digits for counties. The technical, methodological contribution of this paper is a bibliometric analysis of scientific publications based on FIPS code analysis indicated that between 27,954 and 1,970,000 publications attend to this geo-identifier. Within a single dataset reporting national representative and longitudinal survey data, 141 publications utilize FIPS data. The high incidence shows the research impact. Yet, the low proportion of only 2.0 percent of all publications utilizing this dataset also shows a gap even among expert users. A data use case drawn from public health data implies that cracking the code of geo-identifiers could advance access by helping everyday users formulate data inquiries within intuitive language. 

Many publicly available datasets exist that can provide factual answers to a wide range of questions that benefit the public. Indeed, datasets created by governmental and nongovernmental organizations often have a mandate to share data with the public. However, these datasets are often underutilized by knowledge workers due to the cumbersome amount of expertise and embedded implicit information needed for everyday users to access, analyze, and utilize their information. To seek solutions to this problem, this paper discusses the design of an automated process for generating questions that provide insight into a dataset. Given a relational dataset, our prototype system architecture follows a five-step process from data extraction, cleaning, pre-processing, entity recognition using deep learning, and questions formulation. Through examples of our results, we show that the questions generated by our approach are similar and, in some cases, more accurate than the ones generated by an AI engine like ChatGPT, whose question outputs while more fluent, are often not true to the facts represented in the original data. We discuss key limitations of our approach and the work to be done to bring to life a fully generalized pipeline that can take any data set and automatically provide the user with factual questions that the data can answer. 

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.