Rachel S. Franklin

1. Foundational economy (FE) has emerged over the last decade as a highly influential agenda in urban and regional studies amidst the proliferation of ‘beyond GDP’ (gross domestic product) and ‘pos...

Generative artificial intelligence (GenAI) is rapidly reshaping GIScience, yet the field has grown faster than its foundations have been systematically assessed. This review synthesizes how large language models (LLMs), vision‐language models (VLMs), diffusion models, and other generative approaches are deployed across geographic research. Screening an initial pool of 1146 records from the Web of Science, we identified 295 peer‐reviewed journal and conference papers published between November 2022 and February 2026 that demonstrate substantive engagement with geospatial analysis. Utilizing a 31‐variable extraction framework, this review combines corpus‐level descriptive statistics, thematic synthesis, and cross‐cutting evaluations of methodological trends, openness, trustworthiness, and geographic grounding. The literature reveals explosive growth, with LLMs remaining the dominant model family while multimodal pipelines and hybrid systems become increasingly prominent. However, our cross‐cutting synthesis exposes persistent structural gaps: formal uncertainty quantification remains rare; hallucination and geographic bias are unevenly addressed; full code‐and‐data openness is heavily limited; and explicit engagement with geographic theory remains incomplete, particularly within technically intensive areas like remote sensing and spatiotemporal modeling. Looking forward, we argue that disciplinary maturity depends on establishing five strategic pillars: geographically grounded uncertainty standards, spatial equity and justice frameworks, geospatially specific reproducibility norms, theory‐informed model design, and agentic GIS architectures that systematically transfer mature computer‐science mechanisms—including tool use, multi‐agent planning, reflection, persistent memory, and trajectory‐based evaluation—into spatially constrained, auditable geographic systems.

Mental-health geomatics require reliable ways to convert high-frequency GPS trajectories into meaningful place types that support indicators such as homestay, location entropy, and spatial extent of daily activities. Raw coordinates are typically noisy and carry little semantic information. We introduce H3-MOSAIC(H3-based Multimodal OSM-and-Satellite AI for Classification), a multimodal generative framework that fuses OpenStreetMap (OSM) building text and satellite imagery on H3 grids to infer place semantics from high-frequency GPS. Raw GPS was smoothed by minute-level speed filtering, then assigned to Level 10 H3 hexagons. Cells were retained if the mean speed was ≤ 1.2 m/s and the cumulative duration was ≥ 15 min, contiguous cells were merged, and home was defined as the cell with the longest dwell from 23:45 to 06:00. We compared text-only OSM classification with image-based and fused approaches across open-source models (DeepSeek, CLIP, LLaVA, Qwen-VL) and proprietary models (GPT-4o-mini, Gemini-2.5-flash-lite). Performance was assessed by accuracy, Cohen’s kappa, precision, recall, F-measure, and confusion matrices. Day level associations between H3 semantic exposures and stress were examined by a random forest model and explainable methods. Multimodal methods outperformed single-modality baselines. In the 11-class task, accuracies were: CLIP 0.179, LLaVA 0.269, Qwen-VL 0.565, GPT-4o-mini 0.779, and Gemini-2.5-flash-lite 0.790. In the 5-class consolidation, accuracies rose to 0.702 (Qwen-VL), 0.849 (GPT-4o-mini), and 0.858 (Gemini-2.5-flash-lite). Text-only OSM baselines were lower (≈ 0.60–0.68). Across 3,845 hexagons with OSM text, closed-source models agreed on 79% of labels; disagreements concentrated in mixed-use, office, and green classes. Error modes reflected area-dominant versus keyword-triggered reasoning, hybrid-parcel ambiguity, tag sparsity, and symbolic artifacts. Stabilized semantics support more robust computation of homestay, entropy, and activity space and are suitable for privacy-aware, cross-city reuse. In a day-level case study, minutes at Home related to lower stress; Green showed a U-shaped pattern. H3-MOSAIC provides a scalable, auditable pipeline for semantic place detection from high-frequency GPS. Multimodal fusion markedly improves accuracy and consistency. Proprietary models are most robust on hard classes and open-source models are practical for coarse taxonomies. H3 day level exposures show stress patterns consistent with established mental health pathways, supporting face validity. The framework enables downstream exposure analyses with reduced misclassification and improved interpretability.

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.

‘Left behind places’ have received increasing attention in the Global North, acknowledging growing regional inequalities between and within countries. Yet the trajectories followed by these places have mostly been investigated by assessing changes between two distant time-points. Aiming to provide more detailed insights into regional development pathways, we combine k-means clustering and sequence analysis to study detailed regional trajectories between 1982 and 2017 for EU15 NUTS3 regions. The resulting typology of trajectories evidences how some regions have increasingly or more recently ‘fallen behind’, some have remained ‘left behind’, and still others have experienced overall positive change over decades, at least temporarily catching-up on wealthier regions. As such, our findings suggest different transitions in and out of demographic and economic ‘left-behindness’.

For over a decade, concern has mounted about places in Europe and North America that have been ‘left behind’ by the growth and prosperity experienced in more economically dynamic regions. This briefing paper summarises the findings from the ‘Beyond Left Behind Places’ project. Filling a gap in the policy debate, this study included qualitative research with residents of economically ‘left behind’ regions in France, Germany and the UK to gather their experiences and perceptions. The qualitative research was focused on six case studies areas, two in each country. It aimed to give agency and voice to people living in ‘left behind’ areas and draw on their experiences and priorities to inform the development of locally tailored policy responses. The case studies were designed to explore residents’ employment activities and access to services, alongside their perceptions of their areas and of recent place-based policies. Based on our findings, we outline a set of directions and recommendations on policies ‘for’ and ‘with’ ‘left behind places.