Demography in the Big Data Revolution: Changing the Culture to Forge New Frontiers

A nineteenth-century urban Ottoman population micro dataset: Data extraction and relational database curation from the 1840s pre-census Bursa population registers

In recent decades, the "big microdata revolution" has transformed access to transcribed historical census data for social science research. However, the population records of the Ottoman Empire, spanning Southeastern Europe, Western Asia, and Northern Africa, remained inaccessible to the big microdata ecosystem due to their prolonged unavailability. This publication marks the inaugural release of complete population data for an Ottoman urban center, Bursa, derived from the 1839 population registers. The dataset presents originally non-tabulated register data in a tabular format integrated into a relational Microsoft Access database. Thus, we showcase the extensive and diverse data found in the Ottoman population registers, demonstrating a level of quality and sophistication akin to the censuses conducted worldwide in the nineteenth century. This valuable resource, whose potential has been massively underexploited, is now presented in an accessible format compatible with global microdata repositories. Our aim with this dataset is to enable historical demographic studies for the Ottoman realm and beyond, while also broadening access to the datasets constructed by our large research team.

Has demography witnessed a data revolution? Promises and pitfalls of a changing data ecosystem

Over the past 25 years, technological improvements that have made the collection, transmission, storage, and analysis of data significantly easier and more cost efficient have ushered in what has been described as the 'big data' era or the 'data revolution'. In the social sciences context, the data revolution has often been characterized in terms of increased volume and variety of data, and much excitement has focused on the growing opportunity to repurpose data that are the by-products of the digitalization of social life for research. However, many features of the data revolution are not new for demographers, who have long used large-scale population data and been accustomed to repurposing imperfect data not originally collected for research. Nevertheless, I argue that demography, too, has been affected by the data revolution, and the data ecosystem for demographic research has been significantly enriched. These developments have occurred across two dimensions. The first involves the augmented granularity, variety, and opportunities for linkage that have bolstered the capabilities of 'old' big population data sources, such as censuses, administrative data, and surveys. The second involves the growing interest in and use of 'new' big data sources, such as 'digital traces' generated through internet and mobile phone use, and related to this, the emergence of 'digital demography'. These developments have enabled new opportunities and offer much promise moving forward, but they also raise important ethical, technical, and conceptual challenges for the field.

Estimating Nantucket’s Effective Population

This paper extends demographers’ traditional approaches to estimating local populations using symptomatic data. We augmented those approaches in order to track one community’s de facto population—both its permanent residents (“Census population”) and other sojourners—and assorted others in residence for shorter spells of time (“impermanent residents”). We illustrate how a new type of mobility data—the anonymous “pings” emitted by people’s personal mobile devices—can unveil the presence and mobility patterns of de facto populations within a community by month, week, and day. We use these data to gauge the seasonal ebb and flow of population on Nantucket Island, MA, a seasonal resort community whose effective population far outnumbers its “Census population.” We distinguish the following factors: (1) Permanent Residents, for whom Nantucket is their “usual place of residence” and where one votes and files one’s tax return; (2) Commuting Workers, who reside off-island and regularly commute to jobs on-island via high-speed ferry or air taxi; and (3) Sojourners of three types: (a) Seasonal residents, most occupying a second home they either own or rent; (b) Seasonal workers, present for several months to fill many hospitality, landscaping, and other temporary jobs from April through September; and (c) Visitors, present for shorter stays, as vacationers or on business. For each segment, we highlight the estimation methodologies we devised and evaluate their strengths and limitations. Our research exemplifies the evolution of traditional demographic methodologies to address practical concerns at local community scales using “Big Data.” Resort communities and winter “snowbird” destinations in Sunbelt locales experience regular annual influxes of visitors and/or seasonal residents in particular months. Just as daytime urban populations strain downtown infrastructure and transportation, such impermanent residents—however, brief or lengthy their stay—impose seasonal strains on local infrastructure and public services.