Data and Network Science in the Geography of Work

In recent years exciting advancements have been made at the intersection of data science and economic geography. Data-driven ideas and methods borrowed from computer science, mathematics, physics, and computational social science have been harnessed to investigate technological change in local labor markets and provide new fertilizing potential across disciplines. Examples are wide ranging. Among others, the application of text analysis to mine datasets such as job calls, curriculum vitaes, or social media, and the deployment of sophisticated data and network analysis tools to uncover patterns of individual careers, labor flows, co-worker interactions belong to this quickly evolving interdisciplinary area. Given the ever-increasing range of new types of data exploited to better understand economic processes, and the growing interest in the field, the time is ripe to foster further innovation and exploration at this interface.

This special session aims to highlight quantitative techniques, methodological developments and applications across a range of domains related to the geography of work, including economic complexity and evolutionary economic geography, urban mobility and planning, and processes on spatial and/or social networks. In particular, we wish to feature new methodological advancements specifically developed or tailored for applications in these areas, and novel uses of existing algorithms and tools derived from machine learning and network science.

We welcome submissions on a wide range of topics within these areas. Examples include: