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In this paper, we build on feminist geographical methodological innovations that link theories about data transparency and multiplicity with praxis, suggesting how feminist methods can better reflect the messiness of data. We argue that two interrelated strategies, feminist periscoping and feminist visualization, can highlight the strengths across messy data sets while also being transparent about the gaps within the data. We illustrate this argument using two examples from research into public information campaigns developed to dissuade unauthorized migration to the US and Australia. Taken together, we argue, feminist periscoping and feminist visualization approaches are an effective way of analyzing and disseminating messy data. 

Geographers have been central to identifying and exploring the shifting spatialities of border enforcement and how different enforcement strategies alter the geography of state sovereignty. Migration-related public information campaigns (PICs) are one strategy that has received increasing attention from geographers and social scientists more broadly in recent years. While existing research examines the sites and spaces where PICs are distributed, as well as the affective content of their messaging, little research has examined the development of campaigns and the transnational connections that enable their deployment. This article draws on work in the fields of carceral circuitry and transnational enforcement networks in order to expand our understanding of affective governmentality as a transnational strategy of border governance. Based on data collected as part of a large-scale comparative study of the use of PICs by the US and Australian governments, we argue that this form of affective governmentality relies upon transnational circuits through which people, money, and knowledge move to enable the development and circulation of affective messaging. In doing so, we develop the concept of transnational affective circuitry to refer to the often contingent, temporary relations and connections that enable PICs to operate as a form of transnational affective governmentality aimed at hindering unauthorized migration. Our analysis illustrates the transnational connections that enable increasingly expansive and creative forms of border enforcement to emerge while also expanding the scope of examinations of affective governmentality to attend to the relations that undergird and enable this form of transnational governance. 

Hydrologists and water managers increasingly face challenges associated with extreme climatic events. At the same time, historic datasets for modeling contemporary and future hydrologic conditions are increasingly inadequate. Machine learning is one promising technological tool for navigating the challenges of understanding and managing contemporary hydrological systems. However, in addition to the technical challenges associated with effectively leveraging ML for understanding subsurface hydrological processes, practitioner skepticism and hesitancy surrounding ML presents a significant barrier to adoption of ML technologies among practitioners. In this paper, we discuss an educational application we have developed—Sandtank-ML—to be used as a training and educational tool aimed at building user confidence and supporting adoption of ML technologies among water managers. We argue that supporting the adoption of ML methods and technologies for subsurface hydrological investigations and management requires not only the development of robust technologic tools and approaches, but educational strategies and tools capable of building confidence among diverse users.