Search for: All records

Abstract The Spatial Data Lab (SDL) project is a collaborative initiative by the Center for Geographic Analysis at Harvard University, KNIME, Future Data Lab, China Data Institute, and George Mason University. Co-sponsored by the NSF IUCRC Spatiotemporal Innovation Center, SDL aims to advance applied research in spatiotemporal studies across various domains such as business, environment, health, mobility, and more. The project focuses on developing an open-source infrastructure for data linkage, analysis, and collaboration. Key objectives include building spatiotemporal data services, a reproducible, replicable, and expandable (RRE) platform, and workflow-driven data analysis tools to support research case studies. Additionally, SDL promotes spatiotemporal data science training, cross-party collaboration, and the creation of geospatial tools that foster inclusivity, transparency, and ethical practices. Guided by an academic advisory committee of world-renowned scholars, the project is laying the foundation for a more open, effective, and robust scientific enterprise. 

Abstract Urban informatics appears to be a suitable area for the application of digital twins. Definitions of the term share some characteristics, but these definitions do not agree on what exactly constitutes a digital twin. The term has the potential to be misleading unless adequate attention is paid to the inherent uncertainty in any replica of a real system. The question of uncertainty is addressed, together with some of the issues that make its quantification problematic. Digital twins for urban informatics pose questions of purpose, governance, and ethics. In the final section the paper suggests some research issues that will need to be addressed if digital twins are to be successful. 

ABSTRACT GIS and GIScience education have continually evolved over the past three decades, responding to technological advances and societal issues. Today, the content and context in which GIScience is taught continue to be impacted by these disruptions, notably from technology through artificial intelligence (AI) and society through the myriad environmental and social challenges facing the planet. These disruptions create a new landscape for training within the discipline that is affecting not onlywhatis taught in GIScience courses but alsowhois taught,whyit is being taught, andhowit is taught. The aim of this paper is to structure a direction for developing and delivering GIScience education that, amid these disruptions, can generate a capable workforce and the next generation of leaders for the discipline. We present a framework for understanding the various emphases of GIScience education and use it to discuss how the content, audience, and purpose are changing. We then discuss how pedagogical strategies and practices can change how GIScience concepts and skills are taught to train more creative, inclusive, and empathetic learners. Specifically, we focus on how GIScience pedagogy should (1) center on problem‐based learning, (2) be open and accelerate open science, and (3) cultivate ethical reasoning and practices. We conclude with remarks on how the principles of GIScience education can extend beyond disciplinary boundaries for holistic spatial training across academia. 

The number of reproduction and replication studies undertaken across the sciences continues to rise, but such studies have not yet become commonplace in geography. Existing attempts to reproduce geographic research suggest that many studies cannot be fully reproduced, or are simply missing components needed to attempt a reproduction. Despite this suggestive evidence, a systematic assessment of geographers’ perceptions of reproducibility and use of reproducible research practices remains absent from the literature, as does an identification of the factors that keep geographers from conducting reproduction studies. We address each of these needs by surveying active geographic researchers selected using probability sampling techniques from a rigorously constructed sampling frame. We identify a clear division in perceptions of reproducibility among geographic subfields. We also find varying levels of familiarity with reproducible research practices and a perceived lack of incentives to attempt and publish reproduction studies. Despite many barriers to reproducibility and divisions between subfields, we also find common foundations for examining and expanding reproducibility in the field. These include interest in publishing transparent and reproducible methods, and in reproducing other researchers’ studies for a variety of motivations including learning, assessing the internal validity of a study, or extending prior work.