Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities

• Published in: ISPRS international journal of geo-information 2021-07, Vol.10 (7), p.432
• Main Authors: Moos, Nicolai, Juergens, Carsten, Redecker, Andreas P.
• Format: Article
• Language: English
• Subjects: annual income; Bivariate analysis; Capital assets; Cities; Clustering; Cultural heritage; dasymetric mapping; Data; Datasets; Decision making; Demographics; disaggregation; Economic analysis; economic disparities; Economics; economy; Income; Marketing; Methods; Polygons; Population density; Purchasing power; Sociodemographics; Socioeconomic factors; Socioeconomics; Sociology; Spatial analysis; Spatial data; Urban areas; Urban planning
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi10070432

This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons.