In Germany identification of logistics land is done rarely, among other things due to anonymization of employment and building data. The paper at hands presents a method for identifying logistics land based on publicly available data, in order to present an image of the existing spatial structure of logistics land. Identified spatial hotspots are mostly located in Metropolises/Regiopolises and their suburbs, along highways in areas with flat relief and in vicinity to large inland teminals/inland harbours. Correlation analyses show, that the area of identified logistics land shows only weak but significant correlations with other variables. In order to identify possible existing types of logistics hotspots, a cluster analysis is carried out for the identified logistics hotspots. The formation of the resulting three clusters is mainly driven by the existing logistics employment and company structure, the accessibility (e. g. of inland terminals) and population density. A comparison of the identified clusters with the remaining potential logistics land shows that extensive land reserves are only available in the low-density sururbs and rural areas cluster. Thus, the appearance of logistics sprawl is therefore to be expected solely due this reason.:1 Introduction 2 Background 2.1 Location factors of logistics facilities 2.2 The phenomenon of logistics sprawl 2.3 Further trends in the spatial distribution of logistics facilities 2.4 Difficulties in the study of spatial patterns of logistics in Germany 3 Study area 4 Identifying logistics land and data preparation 4.1 Used data sets and their preparation 4.2 Identification of logistics land 4.3 Accessibility analyses 4.4 Determination of land values 4.5 Spatial aggregation and further variables 4.6 Excursus: Identifying potential logistics/industrial land 5 Analysis of spatial autocorrelation 5.1 Global spatial autocorrelation 5.2 Local spatial autocorrelation 6 Correlation analysis 7 Cluster analysis 7.1 Prinicipal components analysis (PCA) 7.2 K-means Clustering 7.2.1 Identified clusters 7.3 Comparison of the results of the cluster analysis with other types of land 8 Conclusions Publication bibliography