Steven M. Manson

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 only what is taught in GIScience courses but also who is taught, why it is being taught, and how it 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.

Social issues, AI, and climate change are just a few of the disruptive focuses impacting science. The field of GIScience is well positioned to respond to accelerating disruptions due to the interdisciplinary nature of the field and the ability of GIScience approaches to be used in support of decision-making. This manuscript aims to start a conversation that will establish a research agenda for GIScience in an age of disruptions. We outline three guiding principles: (1) focusing on the relevance and real-world impact of research, (2) adopting systems-based thinking and contextual approaches and (3) emphasizing inclusive practices. We then outline prioritized research areas organized by what topics are important focal areas (Data and Infrastructure, Artificial Intelligence, and Causality and Generalizability), and what approaches to science we should be attentive to (Impactful Open Science, Collaborative and Convergent Science, and through Diverse Participation and Partnerships). We conclude with a call to increase impact by balancing slow science with practical and policy-oriented research. We also recognize that while broad adoption of spatial approaches is a signal of GIScience's success, we should continue to work together to advance core knowledge centered on spatial thinking and approaches.

The Health Insurance Portability and Accountability Act (HIPAA) was an important milestone in protecting the privacy of patient data; however, the HIPAA provisions specific to geographic data remain vague and hinder the ways in which epidemiologists and geographers use and share spatial health data. The literature on spatial health and select legal and official guidance documents present scholars with ambiguous guidelines that have led to the use and propagation of multiple interpretations of a single HIPAA safe harbor provision specific to geographic data. Misinterpretation of this standard has resulted in many entities sharing data at overly conservative levels, whereas others offer definitions of safe harbors that potentially put patient data at risk. To promote understanding of, and adherence to, the safe harbor rule, this paper reviews the HIPAA law from its creation to the present day, elucidating common misconceptions and presenting straightforward guidance to scholars. We focus on the 20,000-person population threshold and the 3-digit zip code stipulation of safe harbors, which are central to the confusion surrounding how patient location data can be shared. A comprehensive examination of these 2 stipulations, which integrates various expert perspectives and relevant studies, reveals how alternative methods for safe harbors can offer researchers better data and better data protection. Much has changed in the 20 years since the introduction of the safe harbor provision; however, it continues to be the primary source of guidance (and frustration) for researchers trying to share maps, leaving many waiting for these rules to be revised in accordance with the times.

This article addresses the challenge of sharing finer scale protected health information (PHI) while maintaining patient privacy by using regionalization to create higher resolution Health Insurance Portability and Accountability Act (HIPAA)-compliant geographical aggregations. We compare four regionalization approaches in terms of their fitness for analysis and display: max-p-regions, regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP), and self-organizing map (SOM) variants of each. Each method is used to create a configuration of regions that aligns with census boundaries, optimizes intraunit homogeneity, and maximizes the number of spatial units while meeting the minimum population threshold required for sharing PHI under HIPAA guidelines. The relative utility of each configuration was assessed with measures of model fit, compactness, homogeneity, and resolution. Adding the SOM procedure to max-p-regions resulted in statistically significant improvements for nearly all assessment measures, whereas the addition of SOM to REDCAP primarily degraded these measures. These differences can be attributed to the different impacts of SOM on top-down and bottom-up regionalization procedures. Overall, we recommend REDCAP, which outperformed on most measures. The SOM variant of max-p-regions (MSOM) could also be recommended, because it provided the highest resolution while maintaining suitable performance on all other measures.

Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. A er several decades of progress, ABMs remain di icult to develop and use for many students, scholars, and policy makers. This di iculty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling.

Group-randomized trials of communities often rely on the convenience of pre-existing administrative divisions, such as school district boundaries or census entities, to divide the study area into intervention and control sites. However, these boundaries may include substantial heterogeneity between regions, introducing unmeasured confounding variables. This challenge can be addressed by the creation of exchangeable intervention and control territories that are equally weighted by pertinent socio-demographic characteristics. The present study used territory design software as a novel approach to partitioning study areas for The Minnesota Heart Health Program’s “Ask about Aspirin” Initiative. Twenty-four territories were created to be similar in terms of age, sex, and educational attainment, as factors known to modify aspirin use. To promote ease of intervention administration, the shape and spread of the territories were controlled. Means of the variables used in balancing the territories were assessed as well as other factors that were not used in the balancing process. The analysis demonstrated that demographic characteristics did not differ significantly between the intervention and control territories created by the territory design software. The creation of exchangeable territories diminishes geographically based impact on outcomes following community interventions in group-randomized trials. The method used to identify comparable geographical units may be applied to a wide range of population-based health intervention trials. National Institutes of Health (Clinical Trials.gov), Identifier: NCT02607917 . Registered on 16 November 2015.

Big Geo Data promises tremendous benefits to the GIS Science community in particular and the broader scientific community in general, but has been primarily of use to the relatively small body of GIScientists who possess the specialized knowledge and methods necessary for working with this class of data. Much of the greater scientific community is not equipped with the expert knowledge and techniques necessary to fully take advantage of the promise of big spatial data. IPUMS-Terra provides integrated spatiotemporal data to these scholars by simplifying access to thousands of raster and vector datasets, integrating them and providing them in formats that are useable to a broad array of research disciplines. IPUMS-Terra exemplifies a new class of National Spatial Data Infrastructure because it connects a large spatial data repository to advanced computational resources, allowing users to access the needle of information they need from the haystack of big spatial data. The project is trailblazing in its commitment to the open sharing of spatial data and spatial tool development, including describing its architecture, process development workflows, and openly sharing its products for the general use of the scientific community.