Abstract.This article presents a summary of our conversation on the past, present and future of the new economic geography, which took place with the help of an interlocutor in San Juan, Puerto Rico in November 2002. Following the introduction, we explain what the new economic geography is, and we describe some basic models. The discussion of its various critical aspects is presented subsequently, and the article concludes with the discussion of future issues and challenges facing the field.
Market area models, such as the Huff model and its extensions, are widely used to estimate regional market shares and customer flows of retail and service locations. Another, now very common, area of application is the analysis of catchment areas, supply structures and the accessibility of healthcare locations. The huff Python package provides a complete workflow for market area analysis, including data import, construction of origin-destination interaction matrices, basic model analysis, parameter estimation from empirical data, calculation of distance or travel time indicators, and map visualization. Additionally, the package provides several methods of spatial accessibility analysis. The package is modular and object-oriented. It is intended for researchers in economic geography, regional economics, spatial planning, marketing, geoinformation science, and health geography. The software is openly available via the Python Package Index (PyPI) (https://pypi.org/project/huff/); its development and version history are managed in a public GitHub Repository (https://github.com/geowieland/huff_official) and archived at Zenodo (https://doi.org/10.5281/zenodo.18639559).
Objectives. To synthesize contemporary scientific approaches to interpreting physical education of schoolchildren within the logic of a managed learning process and to clarify the role of pedagogical control, modelling, and age-related developmental regularities in shaping learning outcomes.
Materials and Methods. The study was conducted as a narrative review of publications addressing physical education theory, pedagogical control, modelling of the learning process, age-related developmental regularities, and the teaching of physical exercises in general secondary education. The analysis was carried out from systems-based and learning-oriented perspectives on the organisation of physical education.
Results. The review supports interpreting physical education of schoolchildren as a managed learning process in which learning outcomes emerge through the interaction of pedagogical control, modelling, and learners’ age-related developmental characteristics. Age-related regularities are best treated as parameters of learning models that define the boundaries for valid interpretation of pedagogical-control results. Pedagogical control acquires a regulatory function only when embedded within a model of the learning process. The synthesis also allows the learning of physical exercises to be interpreted as the formation and dynamics of learning states that can serve as objects of pedagogical control and regulation.
Conclusions. The proposed synthesis enables interpreting outcomes of physical education as consequences of the organisation of the learning process rather than as autonomous normative indicators. This narrative review delineates theoretical and methodological frames for further research aimed at empirically testing models of managed physical education and refining tools of pedagogical control in general secondary education practice.
We develop a Schumpeterian quality-ladder spatial model in which innovation arrivals depend on regional knowledge spillovers. A parsimonious reduced-form diffusion mechanism induces the convergence of regions' average distance to the global frontier quality. As a result, regional differences in knowledge levels stem residually from asymmetries in the spatial distribution of researchers and firms. We analytically characterize the processes of innovation and knowledge diffusion. We then explore how the weight of intra-relative to inter-regional knowledge spillovers interacts with freer trade to shape the spatial distribution of economic activities. If intra-regional spillovers are relatively stronger, a higher economic integration leads to progressive agglomeration. If inter-regional spillovers dominate, researchers and firms may re-disperse after an initial phase of agglomeration as integration increases. This happens because firms and researchers have incentives to relocate to the smaller region, where they can leverage the concentrated knowledge base of the larger region while avoiding congestion in innovation. The smoothness of the dispersion process depends on the particular weight of intra-regional spillovers. If inter-regional spillovers become stronger as trade becomes freer, then the latter induces a monotone dispersion process. When integration is high enough, stable long-run equilibria always maximize the growth rate of the global frontier quality and the average distance to the frontier, irrespective of whether spillovers are mainly local or global.
The rapid advancement of multimodal large language models (LLMs) has opened new frontiers in artificial intelligence, enabling the integration of diverse large-scale data types such as text, images, and spatial information. In this paper, we explore the potential of multimodal LLMs (MLLM) for geospatial artificial intelligence (GeoAI), a field that leverages spatial data to address challenges in domains including Geospatial Semantics, Health Geography, Urban Geography, Urban Perception, and Remote Sensing. We propose a MLLM (OmniGeo) tailored to geospatial applications, capable of processing and analyzing heterogeneous data sources, including satellite imagery, geospatial metadata, and textual descriptions. By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems. Results demonstrate that our model outperforms task-specific models and existing LLMs on diverse geospatial tasks, effectively addressing the multimodality nature while achieving competitive results on the zero-shot geospatial tasks. Our code will be released after publication.
Georeferenced Access Points as a Strategic Node in the Evolution of Territorial Information Systems - This paper explores the strategic role of georeferenced access points and civic numbering as foundational components of advanced Territorial Information Systems (SIT) within Italian public administrations. The quality and consistency of georeferenced street and building numbers represent a fundamental component of territorial data infrastructures, enabling reliable integration between cadastral datasets, administrative services and emergency response systems.
Drawing from the experience of LdP Progetti GIS — involving more than 130 municipalities across five regions — the article demonstrates how the integration of Accesses, Buildings and Street Toponyms enables an interoperable Web-GIS ecosystem supporting digital services, data governance and operational decision-making. Real case studies from the municipalities of Siena, Arezzo, Empoli and Pistoia illustrate concrete applications such as emergency management, fiscal intelligence (TARI compliance), housing planning and economic activity monitoring. The results highlight significant improvements in administrative efficiency, transparency and open-data availability, positioning geospatial infrastructures as a key enabler of digital transformation in the Public Sector.
Kyle Buettner, Sina Malakouti, Xiang Lorraine Li
et al.
Existing object recognition models have been shown to lack robustness in diverse geographical scenarios due to domain shifts in design and context. Class representations need to be adapted to more accurately reflect an object concept under these shifts. In the absence of training data from target geographies, we hypothesize that geographically diverse descriptive knowledge of categories can enhance robustness. For this purpose, we explore the feasibility of probing a large language model for geography-based object knowledge, and we examine the effects of integrating knowledge into zero-shot and learnable soft prompting with CLIP. Within this exploration, we propose geography knowledge regularization to ensure that soft prompts trained on a source set of geographies generalize to an unseen target set. Accuracy gains over prompting baselines on DollarStreet while training only on Europe data are up to +2.8/1.2/1.6 on target data from Africa/Asia/Americas, and +4.6 overall on the hardest classes. Competitive performance is shown vs. few-shot target training, and analysis is provided to direct future study of geographical robustness.