Abstract

The spatial distribution of socio-economic activity across territory is, by its very nature uneven and locally diverse. This is rooted in natural and historical conditions, and is strenghthened by infrastructure, market forces interactions and modern challenges such as climate change. At a local level, this matters for the efficiency of public policy, as many public services require a critical mass and minimum capacity thresholds, or dedicated solutions for overcrowded places. Different policy designs, targets, key performance indicators and cost-efficiency thresholds are needed for low, middle and high-density places. Efficient policies happen in sweet spots – optimal density places. However, what is still poorly understood are the density thresholds that indicate which public goods and services should be provided and how, depending on the characteristics of the place. Official statistics and academic research need low-granulation up-to-date data both on public sector activity, including inputs and outputs, as well as on local socio-economic density. This will enable advancing the research that make us understanding those density-related mechanisms. Except data, also suitable methods of measuring spatial density and agglomeration are needed.
The presentation will cover three major aspects: data, methods and policy. Firstly, it will highlight the theoretical approach to U-shaped denisty-related policy efficiency on the ground of economics and regional science, to justify this approach. Secondly, it will provide an overview of the data needs and data availability at a low level of granularity that will enable obtaining necessary inputs for the discussed models. Thirdly, it will focus on old and novel quantitative methods dealing with spatial density and agglomeration, encompassing spatial statistics, spatial econometrics and spatial machine learning. The available methodological toolbox will be discussed together with R implementations. The presentation will target a wide audience, ranging from „data people”, through „methods people” to „policy people”.