Spatial statistics is mainly concerned with statistical analysis of point pattern data or geostatistical data.
Point pattern data arise at many spatial scales. Examples are observations of random locations of tumor and immune system cells in microscopy images of lymph nodes, locations of crime scenes or epicentres and times of earth quakes. Important hypotheses concern how occurrences of points depend on environmental variables and whether point patterns are clustered or regular. For crime scene data, for example, a key question is how crime events depend on socio-economic variables or policing strategies. It is also of interest to study how crimes cluster in space as well as the spatial dependence between different types of crimes.
Geostatistical data are georeferenced observations of some variable of interest, e.g. measurements of a soil nutrient across a field where the spatial locations of the soil samples are a crucial part of the data. For geostatistical data, the sampling locations are usually regarded as fixed and the objective is to analyse the variation of the measured variable over space.
The research of the spatial statistics group is mainly concerned with spatial and space-time point pattern data. Key research topics are construction of flexible parametric statistical models for spatial point patterns as well as computational and theoretical aspects of parameter estimation and inference procedures for such models. Another important topic is non-parametric summary statistics that allow to study scientific hypotheses without having to specify a model for the data. The spatial statistics group also conducts research in stochastic geometry which is concerned with probabilistic aspects of random geometric structures.