Introducing a Financial Twin App for Exploration and Comparison of Municipal Financial Patterns

Kumar, Pankaj and Hestermann, Sibylle and Mayer, Julia (2025) Introducing a Financial Twin App for Exploration and Comparison of Municipal Financial Patterns. URBAN INNOVATION: TO BOLDLY GO WHERE NO CITIES HAVE GONE BEFORE. Medium sized cities and towns as a major arena of global urbanisation. Proceedings of REAL CORP 2025, 30th Intl. Conference on Urban Development, Regional Planning and Information Society. pp. 633-643. ISSN 2521-3938

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Abstract

The availability of financial resources determines the extent to which municipalities can proactively manage critical aspects such as sustainable urban planning, infrastructure development, public service improvement, and regional development (Zimmermann, 2018). The foresighted management of these financial resources is, therefore, crucial for municipal decision-making. This project introduces a decision-support tool designed to analyze and visualize financial patterns among municipalities in North Rhine-Westphalia, Germany. Drawing on municipal financial data and population records for the 22 urban municipalities in North Rhine-Westphalia from 2009 to 2022, the application is built on a Flask-based web framework integrated with Python libraries, including pandas, scikit-learn, and GeoPandas. These libraries enable robust data processing, analysis, and visualization. GeoPandas facilitates geospatial analyses and visualizations, which are integral to understanding spatial trends in municipal finance (Jordahl et al., 2014). The system identifies “financial twins” – municipalities exhibiting comparable financial behavior in terms of income and expenditures across domains such as education, public safety, and infrastructure. The inclusion of population records allows for per-capita and absolute value analyses, ensuring contextual relevance. The tool provides a list of financial twins for all 22 urban municipalities based on cosine similarity and Euclidean distance. It offers corresponding data for each year from 2009 to 2022, with the flexibility to analyze twins either with or without factoring in the population of each municipality for a given year. The application also employs the k-means clustering technique to group municipalities into intuitive categories, highlighting shared financial characteristics. The tool can serve as a resource for: • Benchmarking and Collaboration: Identifying peer municipalities to share successful financial strategies. • Exploration and Comparison: Highlighting similarities and differences, and their impacts on financial decision-making. • Policy Development: Offering actionable insights into resource allocation and prioritization. This initiative underscores the critical role of data-driven approaches in addressing the complexities of urban financial planning in public administration. By making sophisticated clustering and visualization accessible to planners, the tool bridges the gap between raw data and actionable policy insights. Future work will explore predictive modelling and integration with socio-economic data to further enhance its relevance for strategic urban development.

Item Type:	Article
Uncontrolled Keywords:	big data, financial twin app, decision support, financial data, clustering
Subjects:	H Social Sciences > HG Finance J Political Science > JS Local government Municipal government T Technology > T Technology (General)
Depositing User:	The CORP Team
Date Deposited:	25 May 2025 14:47
Last Modified:	07 Jul 2025 10:07
URI:	http://repository.corp.at/id/eprint/1226

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