Modelling Spatial Density: Data, Methods, and R Applications in Statistics, Econometrics, and Machine Learning

In an era where geo-located point data has become the backbone of socio-economic, environmental, and urban research, understanding spatial density is crucial. Yet the tools for analysing this data have remained scattered and incomplete. Modelling Spatial Density fills a significant gap by providing a comprehensive, practical, and user-friendly guide to modelling spatial density using cutting-edge quantitative methods.

Bridging the worlds of spatial statistics, spatial econometrics, and spatial machine learning, Kopczewska introduces a range of established and novel techniques, made accessible through intuitive explanations, open data, and reproducible R code. Lesser and well-known methods are elegantly combined and discussed in non-mathematical language that is accessible to social scientists. The book makes a significant contribution to the synthesis, development, and application of spatial quantitative methods for spatial density in the social and environmental sciences.

Writing for researchers, policymakers, and analysts, the author demystifies complex methods, making them accessible to non-mathematicians while maintaining the rigour expected by specialists. With a focus on practical applications, empirical examples, and actionable insights, this resource empowers readers to turn data into evidence for decision-making. Whether you are exploring urban dynamics, environmental challenges, or socio-economic phenomena, this book provides the essential tools for spatial analysis, bringing clarity and precision to your research.

Katarzyna Kopczewska, Professor of Economics, Faculty of Economic Sciences, University of Warsaw

Katarzyna Kopczewska is Associate Professor at the University of Warsaw, Poland, Faculty of Economic Sciences. She is a quantitative economist focusing on regional socio-economic development in a spatial context. Her research includes spatial modelling of geo-localised economic processes. She conducts methodological research on the implementation of machine learning methods to spatial analysis and combining them with classical spatial statistics and econometrics. She is a member of the European board (EOC) of the European Regional Science Association, Councillor-At-Large of the Regional Science Association International, and Editor of Spatial Economic Analysis and Networks and Spatial Economics.