GIScience research at the 2021 Esri User Conference

The first six articles in this issue of Transactions in GIS were gathered from a call for abstracts and will be presented in two research sessions scheduled during the 2021 Esri User Conference. From the 19 abstracts submitted, the journal editors selected six for preparation as full journal articles. Each of these manuscripts passed through the usual journal peer review process and the final versions included here have been revised in light of both the reviewers' and the editors' feedback. The six articles included in this issue cover a wide range of topics and address some of the fundamental concepts and applications of geographic information science from a variety of perspectives. The first offers a review that the authors hope will expand and solidify the scope of place-based GIS. The second proposes a privacy-preserving framework for location recommendation using decentralized collaborative machine learning. The third proposes a method to extract rules for agent-based models from movement data using classification trees and build data-driven simulations of the movements of an olive baboon troop. The fourth uses GIS to reconstruct spatiotemporal events from narratives and decide whether scenarios are conceivable (or not) in the narrative world. The fifth uses GIS and convolutional neural networks to extract buildings in support of humanitarian work. The sixth proposes a new soft computing logic method and tool for site suitability analysis. In the first article, by Vicente Tang and Marco Painho, the authors present a literature review to describe the distinct but overlapping frameworks that scholars have proposed to bridge the gap between space, place and GIS. The review shows that most studies focus on knowledge-based models in urban settings drawing on concepts from human geography. This article synthesizes the current state-of-the-art to encourage new conceptual and methodological approaches to expand and solidify the scope of place-based GIS. The second article, by Jinmeng Rao, Song Gao, Mingxiao Li, and Qunying Huang, presents a privacy-preserving framework for location recommendation using decentralized collaborative machine learning. This framework uses information about transportation infrastructure, public safety, and flow-based spatial interaction (similar to traditional centralized learning approaches) but keeps users' data on their own devices and trains the models locally. The framework then aggregates and updates local model parameters via secure multi-party computation to garner the benefits of collaborative learning among users while preserving privacy. The third article, by Jugal Patel, Jeffrey Katan, Liliana Perez, and Raja Sengupta, leverages machine learning to determine the environmental features associated with moving behaviors of a troop of olive baboons in Kenya. Their workflow performs path segmentation using thresholding to label training data, an agent-based rule extraction using classification trees to associate the relative Euclidian distance between a point and environmental features with behavior, and implements this information in an agent-based model that provides data-driven simulations of troop movements toward set destinations. The authors conclude by noting that their framework is scalable and that it will be able to support larger and more varied data inputs in future applications. The fourth article, by Vincent van Altena, Jan Krans, Henk Bakker, and Jantien Stoter, examines the use of GIS to reconstruct spatiotemporal events from narratives to examine whether a scenario is conceivable within the narrative world. The authors use least-cost path analysis, network analysis, and space-time cubes to examine the narrative about Paul’s escape from Berea (Acts 17:14–15) and they conclude that the aforementioned methods can help a modern reader to better understand and appreciate the conceivability of stories from the narrative world. The fifth article, by Dirk Tiede, Gina Schwendemann, Ahmad Alobaidi, Lorenz Wendt, and Stefan Lang, uses a Mask R-CNN deep learning approach and Pléiades very high-resolution satellite imagery to extract 1.2 million dwellings and buildings for Khartoum, Sudan. Their method strikes a balance between the need for timely information during the COVID-19 pandemic and the accuracy of the result by combining the outputs of three different models tailored to specific types of buildings. This work, which aimed to support humanitarian organizations in response to the COVID-19 pandemic, illustrates the potential for using convolutional neural networks with GIS for dwelling extraction from satellite imagery. The sixth and final article, by Shuoge Shen, Suzana Dragićević, and Jozo Dujmović, extends the logic scoring method of preference (LSP) as a general multicriteria evaluation method implemented within a GIS environment. The authors used an urban densification suitability analysis in the Metro Vancouver Region, Canada, to illustrate and validate the new LSP.GIS method. The results, in turn, show how this method provides a flexible and sensitive workflow for generating outcomes bounded by stakeholders’ goals and requirements. These six articles, taken as a whole, illustrate the breadth and depth of geographic information science scholarship and best practice across a variety of settings, including a call to expand place-based GIS, new workflows to reconstruct spatiotemporal events from narratives and extract buildings from satellite data, and new methods to make privacy-preserving location recommendations, delineate the environmental features associated with wildlife mobility, and assess site suitability. Special thanks are owed to the authors, and especially to those who provided the peer reviews, for helping to move the initial abstracts to full, peer-reviewed articles in just a few months. I trust that all involved will see how these contributions bore fruit when you read the final versions of the articles in this thirtieth issue of Transactions in GIS organized around several research sessions hosted by Esri and given a prominent place in its User Conference program.

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