Business Intelligence 2015 : Potenziale und Herausforderungen in der deutschsprachigen Schweiz

Studi

EU cohesion policy on the ground: Analyzing small-scale effects using satellite data

We present a novel approach to analyze the effects of EU cohesion policy on local economic activity. For all municipalities in the border area of the Czech Republic, Germany, and Poland, we collect project-level data on EU funding in the period between 2007 and 2013. Using night light emission data as a proxy for economic development, we show that receiving a higher amount of EU funding is associated with increased economic activity at the municipal level. Our paper demonstrates that remote sensing data can provide an effective way to model local economic development also in Europe, where comprehensive cross-border data are not available at such a spatially granular level

Learning income levels and inequality from spatial and sociodemographic data in Germany

This study explores the potential of predicting income inequality and income levels from attributes of the built, natural and social environment in Germany. Furthermore, it investigates differences in explanatory variables and estimation accuracy for municipalities with different social and spatial structure profiles. We use income tax data, the 2011 national census, and spatial data from various sources. The explanatory variables capture the spatial variation within the area of interest of characteristics of both the residents and the living environment. Our models explain 54% of the variability in inequality and 73% of the variability in median income levels for a sample of municipalities covering 97% of the country's population. Performance increases for the subsample of municipalities with at least 10,000 inhabitants, attaining 63% for inequality and 80% for income levels. Income inequality and top incomes are better identified in Western, urban, or central locations, while median income is best estimated in Eastern, rural and peripheral locations. The most important predictors are derived from attributes such as nationality, religious affiliation, household composition, residence construction year, as well as the size and density of residences and overall building stock. Our findings further the idea that the joint spatial analysis of population and the built environment can greatly improve our understanding of socioeconomic phenomena—at regional and local levels—beyond conventional data sources

Wohnverhältnisse und Wohnflächenverbrauch der Bevölkerung in der Stadt Zürich, 2003

Die Studie zeigt wichtige Zusammenhänge zwischen Wohnfläche und Wohnverhältnis einerseits und demographischen Merkmalen andererseits auf. Ende 2003 lebten in der Stadt Zürich insgesamt 364'528 Personen in 200'590 Wohnungen mit einer Fläche von total 15 Millionen Quadratmetern. Die mittlere Wohnfläche pro Person betrug damit 41.4 m2. Deutliche Unterschiede in der Wohnfläche ergeben sich nach Gebäude (Baujahr, Eigentumsverhältnisse, Gebäudeart) und nach Stadtquartier

Evaluating EU cohesion policy using satellite data

In December 2020, the Council of the European Union adopted the EU's long-term budget for the years 2021 to 2027. With a share of 31 percent of the total budget (around 330 billion Euro), cohesion policy remains an important priority area. Given the large amount of resources dedicated to reduce economic and social disparities between European regions, it is essential to learn about the impact of different funding instruments in previous budgetary periods. In this project, we illustrate a novel approach of evaluating the economic effects of the European Regional Development Fund and the Cohesion Fund since 2007. For a selected pilot region in the border area of the Czech Republic, Germany and Poland we collect data on EU funding at the municipality level. Using night light emission data as a proxy for economic development, we show that the receipt of a higher amount of EU funding is associated with higher growth in these areas. The results of this project suggest that remote sensing data can be used effectively to capture the small-scale impact of place-based policies on economic development, even in a pan-European context

The spatial and social structure of income inequality in Germany

Out of the multiple possible dimensions of inequality such as income, health or education, inequality of income is the highest. This study complements similar research on income inequality and socio-economic and spatial disparities with a comprehensive analysis of spatial descriptors of inequality in Germany. Using anonymized gross income tax declarations, we compute the Gini inequality index at the municipality level. We extract spatial variables from mostly open spatial datasets, and socioeconomic variables, from the openly available 2011 national census. We focus on measures of spatial variability, such as variation of spatial attributes or measures of population segregation and diversity between spatial units of one squared kilometer census cells within municipalities. Results show that a random forest model performs variable in predicting inequality across federal states, with a R2 statistic ranging between 0.3 and 0.58. Predictions are found significantly better for big municipalities with more than 10,000 inhabitants. For most states, inequality positively correlates with diversity of religion and nationality. Between the different states, inequality is associated with specific attributes of the built environment, from density of residential living space or size of residential annexes such as garages to coefficient of variation of building height or the amount of green spaces

Learning income levels and inequality from spatial and sociodemographic data in Germany - working paper

This study explores the potential of predicting income inequality and income levels from attributes of the built, natural and social environment in Germany. Furthermore, it investigates differences in explanatory variables and estimation accuracy for municipalities with different social and spatial structure profiles. We use income tax data, the 2011 national census, and spatial data from various sources. The explanatory variables capture the spatial variation within the area of interest of characteristics of both the residents and the living environment. Our models explain 54% of the variability in inequality and 73% of the variability in median income levels for a sample of municipalities covering 97% of the country's population. Performance increases for the subsample of municipalities with at least 10,000 inhabitants, attaining 63% for inequality and 80% for income levels. Income inequality and top incomes are better identified in Western, urban, or central locations, while median income is best estimated in Eastern, rural and peripheral locations. The most important predictors are derived from attributes such as nationality, religious affiliation, household composition, residence construction year, as well as the size and density of residences and overall building stock. Our findings further the idea that the joint spatial analysis of population and the built environment can greatly improve our understanding of socioeconomic phenomena—at regional and local levels—beyond conventional data sources

Learning income levels and inequality from spatial and sociodemographic data in Germany - working paper

This study explores the potential of predicting income inequality and income levels from attributes of the built, natural and social environment in Germany. Furthermore, it investigates differences in explanatory variables and estimation accuracy for municipalities with different social and spatial structure profiles. We use income tax data, the 2011 national census, and spatial data from various sources. The explanatory variables capture the spatial variation within the area of interest of characteristics of both the residents and the living environment. Our models explain 54% of the variability in inequality and 73% of the variability in median income levels for a sample of municipalities covering 97% of the country's population. Performance increases for the subsample of municipalities with at least 10,000 inhabitants, attaining 63% for inequality and 80% for income levels. Income inequality and top incomes are better identified in Western, urban, or central locations, while median income is best estimated in Eastern, rural and peripheral locations. The most important predictors are derived from attributes such as nationality, religious affiliation, household composition, residence construction year, as well as the size and density of residences and overall building stock. Our findings further the idea that the joint spatial analysis of population and the built environment can greatly improve our understanding of socioeconomic phenomena—at regional and local levels—beyond conventional data sources