Prof. Alan Murray (p)

Local and regional patterns of shape have received much attention and focus because of the implications for equity, fairness and sustainability. A major challenge in regional science has been the measurement and interpretation of shape. This paper examines the theoretical foundations of compactness metrics, and their implications for addressing questions of land use allocation, fair representation in political redistricting, trade area efficiency, environmental support and sustainability more broadly. The roles of geographic information systems and regional analytics are highlighted in the examination of recent legislative districting as well as land use mitigation efforts.

Prof. Ivo Mossig (p), Mr Hannes Lehmann (p)

Filling Data Gaps in the Measurement of Income Inequality.
A Complete Dataset of National Gini Coefficents 1995-2019

Income inequalities are a major societal challenge and research on their causes, consequences, and policy solutions has become central to social science (Grusky 2018; Polacko 2021). Despite criticism, the Gini coefficient – especially income-based Ginis – remain the most important indicators for measuring the extent and development of income inequality within a country. Common sources include the OECD, World Bank, Luxembourg Income Study (LIS), and World Income Inequality Database (WIID). Unfortunately, Gini coefficients based on comparable methodologies are only available to a very limited extent. To remedy this lack of data, we create a complete dataset of Gini coefficients from 1995 to 2019 for 160 countries through the combination of machine learning methods and a large dataset of social-policy indicators.

From a geographical perspective, data gaps are especially prevalent for countries in the Global South. Furthermore, pre-2005 data are often incomplete or missing even for the Global North. The most comprehensive data set available with consistent definitions for net income is the WIID Gini. With around 900 data points, this data set covers only 22% of the possible country-year combinations for the selected sample of 160 countries in the chosen observation period.

Therefore, we pursue two objectives: (1) to close existing data gaps through statistical imputation thereby creating a consistent and plausible dataset of Gini coefficients for 160 countries with over 1 Mio. inhabitants from 1995 to 2019 and (2) to identify the socioeconomic and political indicators that most strongly influence these imputations. To achieve this, missing data are estimated using a gradient boosting machine (GBM) drawing on over 1400 socioeconomic and political indicators from the WeSIS database (www.wesis.org; Mossig and Obinger 2023). The results are contextualized on the basis of an extensive literature review on the causes of income inequality.

According to our methodology, the following indicators are particularly suitable for predicting income inequality: (a) Gender Inequality Index, (b) Proportion of people living below 50% of median income, (c) Total population ages 65 and above, (d) Codetermination and information/consultation of workers, (e) De facto coverage of children by the child benefit for citizens/residents as well as (f) Capital city coordinates (Latitude).
With this novel dataset, we enable researchers to broaden their inquiry into causes and effects of socio-economic inequality on a formerly unachievable scale.

Dr. Hristo Dokov (p)

Inequality remains one of the most persistent barriers to achieving sustainable development, manifesting not only within countries but also across regions and at the global level. While the Sustainable Development Goals (SDGs) provide one of the most comprehensive frameworks for monitoring development progress, their potential to illuminate structural inequalities across countries and world regions remains underexplored. This paper maps global and macro-regional inequalities through the lens of the SDG indicator framework, examining how disparities in performance cluster across economic, social, and environmental dimensions of sustainability.
Drawing on harmonized SDG data, the study analyzes patterns of divergence and convergence across world regions, highlighting how inequality is embedded in multidimensional development trajectories. Beyond assessing average performance, the paper investigates distributional spreads, cross-regional gaps, and interlinkages among goals to identify systemic imbalances and structural constraints.
Methodologically, the analysis combines descriptive mapping with inequality metrics and comparative regional profiling. By situating SDG performance within a broader inequality perspective, the study moves beyond aggregate rankings to reveal uneven progress, regional clusters of deprivation and advancement, and persistent structural divides. The findings demonstrate that global development under the SDG framework is characterized not merely by varying speeds of progress, but by qualitatively distinct regional pathways shaped by historical, economic, and environmental asymmetries.
The paper argues that understanding sustainable development requires explicit attention to its spatial and distributional dimensions. Mapping inequalities through the SDGs thus provides critical insights for more targeted, region-sensitive policy strategies and for strengthening the equity-oriented ambitions of "leaving no one behind“ implied in the UN 2030 Agenda.

Mr Kyungjae Lee (p), Ass. Prof. Jonghoon Park , Prof. Seongwoo Lee

This study proposes a Spatially Weighted Relative Deprivation index (SWRD) that redefines the reference structure of relative deprivation in spatial terms. Conventional inequality measures summarize disparities into a single scalar and therefore do not assign region-specific deprivation values. Standard relative deprivation (RD) indices overcome this limitation by providing unit-level measures, but they retain an implicitly aspatial reference structure in which comparison sets are defined over the full distribution. As a result, neither approach captures the spatially structured nature of regional hierarchies. Building on Yitzhaki’s formulation, SWRD incorporates a spatial weight matrix so that upward comparisons are weighted by geographic proximity, allowing each region’s deprivation to be evaluated within a spatially constrained comparison environment. Using GRDP for 229 Korean districts from 2015 to 2021, the study first diagnoses the empirical spatial structure of regional economic performance through variogram analysis. A data-driven procedure based on spatial panel models and Akaike Information Criterion identifies a 65 km inverse-distance cutoff as the comparison range that best captures observed spatial dependence. The resulting SWRD reveals spatial patterns distinct from RD, highlighting locally intensified gaps around growth poles that are not detectable under global ranking. A cross-classification of RD and SWRD uncovers heterogeneous spatial typologies of regional disparity. Panel analyses of youth net migration further show that national and local deprivation interact: local comparison pressure amplifies the migration response to national disadvantage. The findings demonstrate that regional inequality is structured not only by aggregate rank but also by spatially bounded comparison processes.

Peter Nijkamp, Krzysztof Górnisiewicz (p), Waldemar Ratajczak, Michał Banaszak