Conference Agenda

Digital nomads and remote workers distinguish themselves from other labor groups through their ability to leverage geo-arbitrage—the strategic relocation across geographical spaces to optimize personal and professional benefits. This mobility allows them to decouple their place of residence from their place of work, enriching their exposure to diverse territories and cultures.

Often referred to as the "creative class" or "location-independent workers," these individuals have become central to talent attraction initiatives and, in some cases, tourism promotion strategies. Their growing presence is reshaping urban spatialities, compelling cities to navigate dual imperatives: fostering a thriving digital economy while preserving local socio-cultural dynamics.

These evolving work models highlight the transformative influence of artificial intelligence (AI) on spatial configurations, constantly redefining the relationship between people, technology, and space. To address the socio-economic imbalances associated with these changes, it is imperative to examine their implications for spatial development.

This study adopts a geohistorical lens and socio-spatial analysis to explore the impact of digital nomadism and remote working on the spatial dynamics of Barcelona. By employing the Quadruple Helix of Innovation model, the research investigates how collaboration among government, industry, academia, and civil society has informed strategies, practices, and policies that align global mobility with local needs.

The findings emphasize the importance of dynamic territoriality, where active civic participation reshapes socio-spatial relations to harmonize global forces with local contexts. Positioning digital nomads and remote workers as temporary inhabitants or long-term tourists underscores their significant role in redefining the socio-spatial configurations of the places they occupy. The insights presented at EUGEO will offer a framework for cities seeking to balance the complexities of global mobility and local sustainability in the AI era.

Distance is one of the basic geographical concepts used in various models. It can be defined in numerous ways, depending on context. The most frequently distinguished are physical distance, which is an empirically measured line between locations, and economic distance, understood as the cost of transport in space. Therefore, distance is a key element of various economic analyses, including those related to trade. One of the most often usage is the gravity model of trade. The popularity of its use is because of highly successful empirical analyses, despite its simplicity. However, there is a lack of uniformity in measurements of distance. The oldest works in this field use the physical distance between points in a straight line, along a road or railway track. Determining the distance between countries, which are spatial objects, becomes more complicated. There is assumption that the distances between them can be defined as the distance between their capitals or largest cities, less often between their centroids. However, these are not the only possibilities. An additional field for measuring distance is provided by remote sensing methods, which allow obtaining data for the entire world, varied in time and methodologically uniform. There is particularly great potential in data on light emissions at night because they are comparable on the actual place of residence of the population and economic activity. To correctly analyse this kind of data, the AI tools are paramount. Therefore, it is important to standardize this issue and determine the differences between various distance measures and their impact on the analyses.
To this aim, my work will develop a full gravity model of trade using many different measures of distance. Some of them come from a literature review, some are of the author's own origin based on cartographic and remote sensing methods enhanced by AI algorithms. Then, using the AIC and BIC information criteria, I will determine which of the distance measures give the best estimates. I expect that proprietary measures based on remote sensing data will provide the statistically better results.

The Covid-19 pandemic had a variety of impacts on health care in recent years which have varied over time and space, but one of the most marked consequences of all has been in digitalization processes. Digitalization in health care was already a prominent feature of the European Union's development policy before the pandemic, which led to the widespread availability of e-health (telemedicine) to health care providers and the general public.

The aim of the presentation is to provide an overview of the short, medium and long-term effects of the COVID-19 pandemic, its direct and indirect consequences on digitalization processes in health care, primarily through a Hungarian case. The research questions are the following:

How did health care respond to the challenges of the pandemic and what role did digital solutions play in this?

What were the differences in the application of digital solutions in public and private health care?

What opportunities does the application of artificial intelligence (AI) offer in e-health for the future?

Methodologically, the research is based on using literature review, statistical analysis, online questionnaire survey and semi-structured interviews. On the one hand, there is no enough data on using AI in health care, but on the other hand, there are many findings from interviews to evaluate the role of AI in health care.

Among the most important results, it can be mentioned that the pandemic initially caused the rapid and widespread spread of e-health (telemedicine) in private healthcare, and in comparison, the public health care sector was able to respond to the new challenges with a delay of one and a half years. The digitalization of health care caused by the Covid-19 pandemic manifested itself in two ways: it resulted in the acceleration of existing processes and it was accompanied by the spread of new digital solutions (e.g. such as using AI). AI offers many opportunities in health care, but there are currently no appropriate regulations for it.

The presentation is part of the project (K146833) which has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the NRDI Fund.

In the context of intensifying climate change and increasing occurrences of extreme heat events, heat exposure has emerged as a critical concern for public health and urban sustainability. Concurrently, the rise of remote work, facilitated by artificial intelligence and digital technologies, has begun to reshape traditional work–residence relationships, thereby altering patterns of human mobility. This paper examines how the proliferation of remote work affects population distribution and movement across different regions, and investigates the consequent implications for heat exposure and environmental justice.

Drawing on a combined geographic information system (GIS)-based spatial analysis and a multi-scenario remote work adoption model, we first characterize shifts in population density under different remote work scenarios. We then quantify resultant heat exposure levels across urban cores and peripheral areas under projected climate conditions. Preliminary findings suggest that enabling employees to decouple residence from the workplace may help reduce heat exposure in densely populated urban heat islands by redistributing populations to less congested or smaller urban clusters. However, such shifts can also generate new “secondary heat islands” in suburban or rural zones lacking adequate infrastructure or green space, thereby intensifying localized environmental disparities.

The study underscores the need for holistic urban and regional policy interventions that account for new mobility patterns prompted by remote work. These policies must include cross-regional coordination of heat mitigation strategies and targeted infrastructure investments aimed at supporting vulnerable populations and underserved communities. By aligning technological, policy, and social efforts, it is possible to forge a more equitable and resilient response to the dual challenges of climate change and evolving work paradigms.