Caglar Koylu

PURPOSE: Cancer registries are often asked to present cancer data for small geographic areas to inform and facilitate targeted interventions and prevention programs. However, it is challenging to compute and visualize reliable cancer estimates for areas with small case counts and populations to support cancer control planning. METHODS: Leveraging a user-centered design process, we developed a visual analytics platform and interactive graphics to display modeled cancer risk estimates for small areas. Development of our visual analytics platform was informed by cancer registry and public health professionals through focus groups and surveys. The reliable cancer risk estimates for small areas that we displayed on this platform were created using a Bayesian hierarchical model that borrows strength from neighboring areas and over time to produce cancer estimates for small areas. RESULTS: The Cancer Analytics and Maps for Small Areas tool (CAMSA) provided age-adjusted cancer incidence and mortality rates and risk probabilities for eight cancers at the county and ZIP-code tabulation area levels. It allowed the user to identify areas of high cancer incidence, including among subgroups defined by sex and race/ethnicity. Potential end users were enthusiastic about the opportunity to implement CAMSA within their practice, emphasizing the tool's potential for increased collaborative opportunities at local and state levels. Suggestions for improvement included adding map overlays such as additional cancer risk variables and incorporating functionalities such as exportable data tables. CONCLUSION: CAMSA presented cancer rate and risk estimates for small geographic areas where they may have previously been suppressed. Through our user-centered design process, we developed statistical models and data visualizations to support the needs of an array of potential end users.

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.

Despite the progress made toward generating and utilizing population-scale family trees to study historical population dynamics, little is known about their representativeness for the entire population. In this article, we confront the inherent complexities and biases in historical data collection and shed light on the extensive areas of history that remain unknown, unrecorded, or inaccurately portrayed. Although we do not provide definitive solutions for these data gaps, we aim to initiate a dialogue on these critical issues, contributing to the discourse on ethical data collection and representation in historical research. We first report on the preliminary results of a record linkage experiment between family tree records and a historical census, emphasizing the need for methods that integrate historical data from multiple sources to systematically evaluate representativeness. The experiment reveals significant underrepresentation of certain groups in the United States, notably Native American, Black, and Mexican persons, as well as those from eastern Europe, southern Europe, and Ireland. These findings underscore the ethical responsibilities that should guide historical research, including the need to address underrepresentation and improve methodologies to better reflect the diversity of population dynamics and migration patterns. To complement these efforts, we advocate for the use of interactive story maps to amplify the qualitative narratives of underrepresented populations and integrate them into the broader historical narrative. Our endeavor to map migration and demographic changes is not just about tracing the past; it’s about shaping a more equitable and comprehensive understanding of history that honors the diversity of all its participants.

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.

Studying migration over a long period is challenging due to lack of data, uneven data quality, and the methodological challenges that arise when analyzing migration over large geographic areas and long time spans with constantly changing political boundaries. Crowd-sourced family tree data are an untapped source of volunteered geographic information generated by millions of users. These trees contain information on individuals such as birth and death places and years, and kinship ties, and have the potential to support analysis of population dynamics and migration over many generations and far into the past. In this article, we introduce a methodology to measure and map long-term changes in migration flows using a population-scale family-tree data set. Our methodology includes many steps such as extracting migration events, temporal periodization, gravity normalization, and producing time-series flow maps. We study internal migration in the continental United States between 1789 and 1924 using birthplaces and birthyears of children from a cleaned, geocoded, and connected set of family trees from Rootsweb.com. To the best of our knowledge, the results are the first migration flow maps that show how the internal migration flows within the U.S. changed over such a long period of time (i.e. 135 years).