Assoc. Prof. Riccardo Di Clemente, Ms Aydan Ege Guven (p), Dr. Nandini Iyer, Assoc. Prof. Marco Modica

Natural hazards are external shocks, but disasters emerge when pre-existing social and territorial vulnerabilities amplify their impacts. Critical infrastructures such as transport networks and service facilities such as hospitals and schools, are central to this process because they shape access to essential services and, in turn, people’s real opportunities to live, work, learn, and remain in place. When earthquakes disrupt roads and damage buildings, they do not only reduce network performance; they can reconfigure who is able to reach key destinations and effectively use services. This matters especially in peripheral regions where baseline service provision is limited and travel already relies on few routes. A capability-based view of territorial resilience therefore shifts attention from “how fast systems recover” to whether people retain substantive freedoms under disruption, and whether recovery restores those freedoms equitably. This study asks: to what extent do disaster-induced disruptions to critical infrastructure translate into accessibility-mediated capability loss, and how does this help explain uneven post-disaster depopulation? We examine the 2016-2017 Central Italy earthquake sequence, which affected four regions characterised by inner, mountainous municipalities with historically low accessibility. The event was followed by sustained population decline, particularly among working-age groups, deepening existing territorial inequalities.Using building-level post-earthquake surveys, damage-grade mapping, and mobility data, we model disruption to critical infrastructure and estimate changes in network-based accessibility to essential services before and after the earthquakes. Accessibility decline is operationalised as the increase in travel time caused by damaged assets and road disruptions. We then combine this accessibility shock with a social vulnerability index to measure capability loss as the interaction between pre-existing vulnerability and reduced access. Finally, we assess how estimated capability loss relates to subsequent depopulation patterns. We expect capability loss to concentrate in already peripheral locations where small disruptions generate large travel-time increases, and depopulation to be strongest where accessibility declines compound social vulnerability. Overall, the study offers a spatially explicit, people-centered measure of territorial resilience that links infrastructure disruption to the unequal distribution of post-disaster opportunities and demographic change.

Dr. András Igari (p)

The Coronavirus pandemic and the response (including both containment and proactive measures to mitigate the negative effects of the crisis) have had a wide range of economic impacts around the world, but the extent and modalities of these have varied. The crisis has both had short-term impacts and caused structural changes and has also affected many aspects of territorial structures: the economic structure, the enterprises and the labour market. The aim of this presentation is to highlight the territorial inequalities of the various economic impacts of the COVID-19 pandemic in multiple scales: at regional level in Europe, and at municipal and district level in Hungary.
To do this, I have collected and analysed NUTS3-level data (GDP, employment) for European regions, and settlement-level data (net sales revenue of economic organisations, SMEs, job seekers, employees by occupation) for Hungary. I have also collected literature to explore the processes that may have contributed to the economic and labour market downturn, restructure and bouncing-back. First, I have analysed the impact of the COVID-19 pandemic on economic and labour market trends based on the trends of the previous period (2015-2019), the pandemic period (2020-2021) and the following period. Second, I have examined the patterns of these indicators, categorising regions and settlements based on changes in economic and labor market curves. Third, I have run regression analyses to examine which social, economic, infrastructural, governance, and environmental factors may have influenced the extent of the shock effect. Finally, I compared the results of the two studies so that I could examine the economic impact of the pandemic from the European to the local level.
As a result, I have got that the different crisis effects showed various spatial patterns. In European context, the crisis hit mainly the economy and labour market of tourist regions (Southern Europe, Alpine region) and East-Central Europe, in relation to the economic structure of the regions, excess mortality caused by COVID-19, and the quality of governance. In Hungarian context, Budapest, tourist areas, and the relatively developed western border region with its manufacturing-based economy suffered the most in the short term. However, in the longer term, this more developed western border region and the least developed northeastern peripheries proved to be the most vulnerable in terms of the economy and the labour market.

Mr Mehmet Emin Sarıhan (p)

Within the metropolitan landscape of Istanbul, specialized clusters of small-scale production areas remain tightly integrated into residential neighborhoods. This complex urban spatial structure faces profound threats from seismic risk and compounding fragilities, compromising the resilience of both the city's inhabitants and its economic core. This study seek for traces of the "invisible" spatial intersection of small-scale production ecosystems and seismic vulnerability in Istanbul, a dimension often overlooked in traditional land-use planning. The aim is to show the relationship between small scale production density, socio-economic status, and predicted building damage to identify latent earthquake risk zones based on neighborhood level. Utilizing a "Geospatial Big Data" approach, the methodology employed to visualize in GIS around 48,000 Point of Interest (POI) data entries extracted by Python from Google Maps to serve as a proxy for small-scale production locations’ intensity. The study employs K-Means Cluster Analysis on Istanbul’s neighborhoods to identify non-linear spatial typologies. Most of the results reveal a significant "spatial mismatch": the highest structural vulnerability (Cluster 5) is found in residential zones with low production activity, driven by building fragility rather than function. However, a critical "mixed-use" typology (Cluster 3) was identified where high production intensity co-locates with high damage risk around urban residential areas, creating risks for secondary chain reaction hazards. The study concludes that urban risk mitigation must shift from broad policies to typology-specific interventions, specifically targeting these hidden, hybrid production behind residential clusters.