Holding a bank account represents access to an important bank service for achieving the formal financial inclusion of an individual. Unfortunately, at present, people living in different regions of India seem to experience a different level of access to this basic bank service and are therefore experiencing different levels of financial market exclusion. This paper studies to what extent social and cultural barriers hinder this effective banking access across the regions of India. Normative caste-based exclusion can be modified by the rigidity of the local context in following inherited norms. This overall cultural rigidity of the context can be termed cultural openness. The Culture Based Development (CBD) approach to quantify cultural openness through the Cultural Entropy measure is employed here. Using data from the National Family Health Survey (NFHS) and National Sample Survey Organization (NSSO), I conduct a probit model estimation to test how Cultural Entropy (CE) modifies the caste discrimination effect in access to bank services. I find that the there is strong evidence that regional Cultural Entropy modifies significantly the individual caste discrimination experience across the regions of the country.

Quality of Life in urban settings is intricately tied to active transportation, public health, and physical activity, capturing the growing societal emphasis on sustainable mobility and well-being. This study develops a composite indicator to evaluate regional Quality of Life across Italy, utilizing data from the 2021 ISTAT (Italian National Institute of Statistics) survey “Aspects of Daily Life.” Employing Principal Component Analysis for weighting, the indicator identifies key dimensions of urban well-being, including active transportation, physical activity, and general health. The results reveal pronounced disparities between central-northern and southern Italy, driven by differences in infrastructure, access to health resources, and socio-economic conditions. Northern regions consistently outperform southern counterparts, where structural challenges hinder improvements in Quality of Life. These findings underscore the urgent need for targeted, equity-focused policies to address regional disparities and enhance urban environments, with a particular focus on improving accessibility and public health in underperforming regions. The study provides valuable insights for policymakers aiming to promote sustainable and inclusive urban development.

Contemporary cities are increasingly exposed to cascading shocks that propagate through complex and interconnected urban networks, challenging the explanatory power of traditional risk models. This study introduces the General Urban Distortion Propagation Law (GUDPL), an innovative dynamic mathematical framework that integrates functional connectivity, effective distance, time delays, structural vulnerability, recovery dynamics, and irreducible residual effects. The model provides a unified representation of how urban shocks emerge, spread, and attenuate across interconnected systems, and can, in principle, support the identification of vulnerable urban nodes and resilience-oriented planning. To illustrate the operational behavior of the proposed framework, a proof-of-concept simulation based on a stylized cascading power outage scenario is conducted, highlighting delayed propagation, heterogeneous impacts, and recovery processes. GUDPL is adaptable to different crisis types and compatible with time-dependent and real-time modeling contexts, offering a flexible analytical foundation for urban resilience, sustainability, and crisis management research.

This study aims to determine and identify the effects of property characteristics including exposure time, elevation adjustment, and topographic adjustment and disaster characteristics, such as seismicity, on the value of auctioned assets (collateral). The research employs multiple regression analysis using the ordinary least squares method. The findings indicate that property characteristics, including elevation, topography, exposure time, and seismicity-related disaster characteristics, significantly affect the value of auctionedassets. Specifically, the Topography and Seismic Adjustment variables have a positive and significant influence on the Auction Market Value of collateral property assets. This research introduces a novel approach to understanding asset auction dynamics in earthquake-affected areas by integrating economic valuation with disaster analysis. The study's originality lies in combining an economicanalysis framework with disaster risk factors, an area seldom emphasized in previous asset auction research, which has typically addressed economic valuation and disaster impacts separately.

This study contributes to a deeper understanding of the complex relationship between migration, integration, and self-employment across European Union countries. Migration presents multifaceted challenges for host societies, particularly in terms of social cohesion and inclusive development. The Action Plan on Integration and Inclusion (2021-2027) emphasises the importance of migrant entrepreneurship in enhancing livelihoods and promoting sustained economic growth. However, empirical evidence on how integration shapes migrant entrepreneurship remains scarce, especially concerning the role of ethnic diversity. This research aims to fill this gap by examining the link between self-employment, integration, and ethnic concentration, distinguishing between migrants originating from European Union and non-European Union countries and further disaggregating the analysis by gender. It also investigates whether ethnic concentration mediates the effects of integration on migrant self-employment. Findings suggest that the relationship between integration and self-employment is multifaceted, with ethnic concentration having a significant influence and sometimes reversing this correlation.

Street view imagery, capturing detailed streetscapes at human eye-level, has received significant attention in the past decade. Street view images can be leveraged to observe the built environment from both element- and scene-levels. This chapter provides an introduction to methodologies for street view image-based analytics, including downloading street view images using Google Places API, and employing advanced computer vision techniques such as Deep Convolutional Neural Networks to detect and quantify urban elements and scenes. Particularly, this chapter introduces the use of image semantic segmentation to identify distinct urban elements, and image classification techniques to categorize and predict urban scene types. Further, an example of the calculation of the Green View Index (GVI) is provided to demonstrate how street view imagery analysis could contribute to urban data analytics. Through these methods, street view imagery not only helps model the digital environment, enhances our understanding of urban environments, but also offers a variety of insights into geography and urban studies.

An increasing number of studies focus on the impact of firm location on firm productivity. Here, location refers to localised resources or externalities available to firms. These studies have become possible due to advancements in firm-level data availability and productivity estimation methods. There is a growing need to better identify the effects of localised externalities -- "the firm-region nexus" -- on firms, which are the primary targets of many regional development policies. However, relying solely on administrative regions risks committing the ecological fallacy: attributing to all firms in a region the effects observed for a region as a whole. This article presents the state of the art as of 2025, highlighting key issues in this area of research. A total of 165 articles were selected, mainly published in the past decade. From a methodological perspective, there is no real consensus on the models utilised, whether to estimate productivity or to investigate the relationships between the "firm-region nexus" and firm productivity. Spatial integration is still not adequately taken into account: (a) Total Factor Productivity estimations do not account for spatial dependence among methodological concerns; (b) 70% of the articles do not discuss methodological issues linked to the use of firm locations. A deeper grasp of firm location, particularly the "head office bias", emerges as critical to improving the robustness of analyses. Quantifying the "firm-region nexus" remains heterogeneous across national and local contexts (diverging points of interest, data availability, etc.). Comparing the effects of various types of externalities across countries therefore appears ambitious. Some articles focus on the effect of one category of localised externalities, aiming not only to identify a relationship but also its type: spatial or temporal effect, linear or non-linear relationships, and threshold effects.