Guest Editorial: Spatial demography in regional science

Recent years have seen a resurgence of interest in the relation between networks and spatial context. This review examines critically a selection of the literature on how physical space affects the formation of social ties. Different aspects of this question have been a feature in network analysis, neighborhood research, geography, organizational science, architecture and design, and urban planning. Focusing primarily on work at the meso- and microlevels of analysis, we pay special attention to studies examining spatial processes in neighborhood and organizational contexts. We argue that spatial context plays a role in the formation of social ties through at least three mechanisms, spatial propinquity, spatial composition, and spatial configuration; that fully capturing the role of spatial context will require multiple disciplinary perspectives and both qualitative and quantitative research; and that both methodological and conceptual questions central to the role of space in networks remain to be answered. We conclude by identifying major challenges in this work and proposing areas for future research.

Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and sociology. We offer a brief introduction to this vast toolbox and illustrate its current uses in the social sciences, including distilling measures from new data sources, such as text and images; characterizing population heterogeneity; improving causal inference; and offering predictions to aid policy decisions and theory development. We argue that, in addition to serving similar purposes in sociology, machine learning tools can speak to long-standing questions on the limitations of the linear modeling framework, the criteria for evaluating empirical findings, transparency around the context of discovery, and the epistemological core of the discipline.