A cluster analysis of Croatian counties as the base for an active demographic policy

Abstract

Abstract. This paper deals with Croatian counties cluster analysis as the base for developping a proactive demographic policy. Unfortunately, Croatia has no national demographic strategy and no national population policy is carried out. Some local governments are taking isolated policy measures but due to an unsystematic and distressed network at the national level it has to date given no significant effects. The Croatian nation is currently experiencing the initial process of demographic extinction. This process began even before the great emigration wave that started about a year and half ago. Since there are no financial resources for the simultaneous and complete implementation of an active demographic policy across the entire Croatian territory, this paper proposes a new approach. Namely, the main demographic indicators have been calculated and analyzed for each Croatian county. After that, using a multivariate methodology, fifteen demographic indicators that significantly differ from county to county were selected as criteria for clustering Croatian counties by k-means method. Clustering output defines several clusters consisting of a smaller number of counties with similar demographic characteristics. These clusters form a spatial county unit in which appropriate measures of an active demographic policy should be urgently implemented. In this way the process of active demographic policy can start with less financial resources and can be limited maybe only to spaces with poorest demographic characteristics. Moreover, the results of this study might very well stimulate "richer" government units to carry out the appropriate active demographic policy measures in their areas without waiting for the adoption of laws and regulations at the national state level.

A Review of the European Structural and Investment Funds in Health

“…Classification in Table 2 is presented based on dendrogram (a graphical representation of the sequence of clustering(Härdle & Simar, 2015)). As explained inHärdle & Simar (2015), the aim of the Ward procedure is to unify groups and the resulting groups are as homogeneous as possible.5 Application and more about the advantages of the K-means method can be found inJurun, Ratković and Ujević (2017). The number of clusters in our study was confirmed by analysis of variance.…”

Vulnerabilidad demográfica y distancia a grandes ciudades: Consecuencias sobre la población adulta-joven en Castilla y León

“…El objetivo de este apartado es la clasificación de los municipios de la región empleando técnicas de agregación para obtener los clústeres. Aunque este procedimiento es frecuente en ciencias como la genética, la microbiología, la epidemiología, la botánica o la ecología; también ha sido aplicado en diversos campos del estudio de la población (Billari, 2001;Hoang, Cong, Lanzi y Tho, 2012;Bivand, Wilk y Kossowski, 2017;Jurun, Ratkovic y Ujevic, 2017;Van Dijk, 2019). En la literatura española más reciente se ha aplicado esta técnica, con un grado variable de formalización matemática, principalmente en análisis demográficos en base a distritos censales en áreas urbanas y metropolitanas (Díaz Parra, 2017;Salom y Fajardo, 2017) o municipal para ámbitos territoriales más extensos (Marcu y Gómez, 2010;López y Aldrey, 2014).…”

Integrating County-Level Socioeconomic Data for COVID-19 Forecasting in the United States

“…Clustering approaches are commonly applied to demographic features in various applications for grouping users/subjects based on similarity. These include recommendation systems [29] , sociological analysis [30] , data mining for inductive analysis [31] , and for the development of policy [32] .…”