Geomatics Department, Humboldt-Universität zu Berlin

Research Projects of the Geomatics Lab

Spatial cluster analysis and geographically weighted regression -
exploring patterns and underlying processes of social and health characteristics in urban areas

(German: räumliche Clusteranalyse und geographisch gewichtete Regression; Analyse von Mustern und Prozessen sozialer und gesundheitlicher Charakteristika in urbanen Räumen)

Processes and characteristics of urban areas at the human-environment interface, such as social segregation or health disparities, depend on a diverse set of socio-demographic, economic and environmental factors. Due to the heterogeneity of urban areas, one can assume that strength and direction of influence of these factors vary over space. Geostastical techniques allow detecting spatial clustering on a global city-wide and local level to explore the spatial distribution of social and health phenomena.
To study the influence of the explanatory variables on a target variable, traditionally, global statistical regression approaches e.g. Ordinary Least Squares (OLS) are applied. Since these approaches emphasize similarities across space by building means, information about the spatial variance is lost. To take this spatial variation into account, a Geographically Weighted Regression (GWR) is utilized, extending the global regression by adding spatial information. This leads to a more detailed picture of the studied target variable.

We apply spatial cluster analysis and GWR:

Principal Investigators:
Tobia Lakes (Geomatics Lab, Geography Department, Humboldt-Universität zu Berlin)
Julius Fobil (Department of Biological, Environmental & Occupational Health Sciences, School of Public Health, University of Ghana, Legon, Ghana)

Project Colaborators:
Alexander Krämer (University of Bielefeld)
Jürgen May (Infectious Epidemiology Unit, Bernhard-Nocht Institute for Tropical Medicine, Hamburg, Germany)
Christian Levers (Geomatics Lab, Geography Department, Humboldt-Universität zu Berlin)


			Projects
	overview Research Projects of the Geomatics Lab: Data integration and data mining DHAKA-INNOVATE DeSurvey Environmental justice Urban Environmental Monitoring Graduate School on Urban Ecology Land changes in Albania and Kosovo Modeling cropland dynamics in Romania Modeling with domain-specific languages Risk model of Dengue Disease in Malaysia Social and health characteristics in urban areas Urban growth in Greater Tirana Research Collaborations: ESF Exploratory Workshop: EuCaRe EARSeL workshop Post-USSR land cover Rapid urbanization Other Projects of the Geomatics Lab: Geodateninfrastruktur imageSVM		Research Projects of the Geomatics Lab Spatial cluster analysis and geographically weighted regression - exploring patterns and underlying processes of social and health characteristics in urban areas (German: räumliche Clusteranalyse und geographisch gewichtete Regression; Analyse von Mustern und Prozessen sozialer und gesundheitlicher Charakteristika in urbanen Räumen) Processes and characteristics of urban areas at the human-environment interface, such as social segregation or health disparities, depend on a diverse set of socio-demographic, economic and environmental factors. Due to the heterogeneity of urban areas, one can assume that strength and direction of influence of these factors vary over space. Geostastical techniques allow detecting spatial clustering on a global city-wide and local level to explore the spatial distribution of social and health phenomena. To study the influence of the explanatory variables on a target variable, traditionally, global statistical regression approaches e.g. Ordinary Least Squares (OLS) are applied. Since these approaches emphasize similarities across space by building means, information about the spatial variance is lost. To take this spatial variation into account, a Geographically Weighted Regression (GWR) is utilized, extending the global regression by adding spatial information. This leads to a more detailed picture of the studied target variable. We apply spatial cluster analysis and GWR: to explore the spatial distribution and influencing factors of the social status in Berlin, Germany, and to explore the spatial distribution and influencing factors of malaria mortalities in the city of Accra, Ghana In both studies a comprehensive set of socio-demographic, economic and environmental data is used. Preliminary results show a better model fit for the GWR than for a global regression approach underlining the necessity to consider spatial variations of explanatory variables when explaining geographical phenomena by regression techniques. Principal Investigators: Tobia Lakes (Geomatics Lab, Geography Department, Humboldt-Universität zu Berlin) Julius Fobil (Department of Biological, Environmental & Occupational Health Sciences, School of Public Health, University of Ghana, Legon, Ghana) Project Colaborators: Alexander Krämer (University of Bielefeld) Jürgen May (Infectious Epidemiology Unit, Bernhard-Nocht Institute for Tropical Medicine, Hamburg, Germany) Christian Levers (Geomatics Lab, Geography Department, Humboldt-Universität zu Berlin)