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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.
Nicolai Moos; Carsten Juergens; Andreas Redecker. Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities. ISPRS International Journal of Geo-Information 2021, 10, 432 .
AMA StyleNicolai Moos, Carsten Juergens, Andreas Redecker. Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities. ISPRS International Journal of Geo-Information. 2021; 10 (7):432.
Chicago/Turabian StyleNicolai Moos; Carsten Juergens; Andreas Redecker. 2021. "Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities." ISPRS International Journal of Geo-Information 10, no. 7: 432.
This chapter is devoted to the overview of the data fundamentals as regards data models and sources accompanied by geomatics, remote sensing, and economy. Description of such data sources is complemented with the basics from respective disciplines to provide a thematic context to the reader. The chapter starts with a summary of the most commonly used data models, starting with tabular and attribute formats. It is then followed by the spatial data models, including vector and raster data core principles. Since the geospatial domain is heterogeneous in terms of different data formats, the list of interoperability data sources and services is provided. Emphasis is also given to the international and selected national data sources, both non-spatial and spatial. This part is mainly covering the economic (socio-demographic) topics. At last, a remote sensing perspective on data sources is introduced, pointing out the most important Earth observation data. The whole chapter focuses on the major data models and sources, so it serves as a gateway to further exploration of existing data storages.
Vít Pászto; Andreas Redecker; Karel Macků; Carsten Jürgens; Nicolai Moos. Data Sources. Spationomy 2019, 3 -38.
AMA StyleVít Pászto, Andreas Redecker, Karel Macků, Carsten Jürgens, Nicolai Moos. Data Sources. Spationomy. 2019; ():3-38.
Chicago/Turabian StyleVít Pászto; Andreas Redecker; Karel Macků; Carsten Jürgens; Nicolai Moos. 2019. "Data Sources." Spationomy , no. : 3-38.
For processing geodata there are many different approaches of which all of them require their own specific input data and parameters to generate an outcome that suits the respective case of application. This chapter introduces the most common analyses that are conducted using a GIS. From basic tools like buffering certain vector geometries or merging operations of two different datasets to interpolating area wide raster datasets out of point data there is a huge variety of different toolsets that can be applied when using geodata. To understand why and how these toolsets are utilised, how they are parametrized and which other things are important to make proper use of all the different possibilities these toolsets are providing, this chapter sums up the analyses in reasoned groups and illustrates the many different approaches of spatial analyses through proper examples and depictions.
Andreas Redecker; Jaroslav Burian; Nicolai Moos; Karel Macků. Spatial Analysis in Geomatics. Spationomy 2019, 65 -92.
AMA StyleAndreas Redecker, Jaroslav Burian, Nicolai Moos, Karel Macků. Spatial Analysis in Geomatics. Spationomy. 2019; ():65-92.
Chicago/Turabian StyleAndreas Redecker; Jaroslav Burian; Nicolai Moos; Karel Macků. 2019. "Spatial Analysis in Geomatics." Spationomy , no. : 65-92.
Economic data analyses play a very important role in decision-making processes. Nowadays, the importance of the geospatial component inherent with most economic data is rapidly increasing. Therefore, added value from bringing geospatial aspects in economic data analyses is highly appreciated. The aim of the Spationomy project is to improve students’ interdisciplinary skills by interconnecting the different fields of business management, business informatics, geomatics and geoinformatics. At this students are supposed to learn and adopt combined methodologies, techniques and tools. They will be actors in “economic/business analytics games”, deployed to structure group-based and student-led investigations of advanced economic and spatial data analyses. They will simulate “economic/business analytics issues from real world” via games during short-term intensive courses striving to bring economics/business and (geo)information together. This article focuses on the presentation of an interdisciplinary project called SPATIONOMY in the frame of ERASMUS+ after the first year of implementation.
Carsten Jürgens; Nicolai Moos; Andreas Redecker. Spationomy — Spatial Exploration of Economic Data — an Interdisciplinary Geomatics Project. KN - Journal of Cartography and Geographic Information 2018, 68, 66 -71.
AMA StyleCarsten Jürgens, Nicolai Moos, Andreas Redecker. Spationomy — Spatial Exploration of Economic Data — an Interdisciplinary Geomatics Project. KN - Journal of Cartography and Geographic Information. 2018; 68 (2):66-71.
Chicago/Turabian StyleCarsten Jürgens; Nicolai Moos; Andreas Redecker. 2018. "Spationomy — Spatial Exploration of Economic Data — an Interdisciplinary Geomatics Project." KN - Journal of Cartography and Geographic Information 68, no. 2: 66-71.
Jürgen Dodt; Carsten Jürgens; Andreas Redecker. Sachverständige für die Verdachtsflächenerfassung: Neue Anforderungen im Sachbereich Luftbildauswertung? altlasten spektrum 2011, 1 .
AMA StyleJürgen Dodt, Carsten Jürgens, Andreas Redecker. Sachverständige für die Verdachtsflächenerfassung: Neue Anforderungen im Sachbereich Luftbildauswertung? altlasten spektrum. 2011; (1):1.
Chicago/Turabian StyleJürgen Dodt; Carsten Jürgens; Andreas Redecker. 2011. "Sachverständige für die Verdachtsflächenerfassung: Neue Anforderungen im Sachbereich Luftbildauswertung?" altlasten spektrum , no. 1: 1.