Add to ORKGSpatial point processes play a fundamental role in spatial statistics. They provide stochastic models describing occurences of some events in the space (e.g. tree locations in the forest or epicentres of earthquakes). The variety of applications is quite broad. The analysis of spatial point pattern data is needed in forestry, seismology, spatial epidemiology, materials science, astronomy, geography, ecolog
Spatial processes are mathematical models for spatial data; that is, spatially ar-ranged measurement...
We summarize and discuss the current state of spatial point processtheory and directions for future ...
Ripley’s Kt function is a tool for analyzing com-pletely mapped spatial point process data (see Poi...
This chapter gives a brief introduction to spatial point processes, with a view to applications. The...
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
We summarize and discuss the current state of spatial point process theory and directions for future...
Written by a prominent statistician and author, the first edition of this bestseller broke new groun...
The paper presents introduction to spatial point processes and their characteristics. The reader is ...
The general theory of point processes has in the past been focused on temporal developments. In this...
The general theory of point processes has in the past been focused on temporal developments. In this...
Abstract: Spatial statistics is concerned with statistical methods that explicitly analyses spatial ...
Copyright ©CSIRO 2010 This is a detailed set of notes for a workshop on Analysing spatial point patt...
A spatial marked point process describes the locations of randomly distributed events in a region, w...
Many objects in space can best be modeled statistically by using point processes. Examples are fires...
Spatial processes are mathematical models for spatial data; that is, spatially ar-ranged measurement...
We summarize and discuss the current state of spatial point processtheory and directions for future ...
Ripley’s Kt function is a tool for analyzing com-pletely mapped spatial point process data (see Poi...
This chapter gives a brief introduction to spatial point processes, with a view to applications. The...
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
Spatial-temporal point processes have been useful for applications in many fields, including the stu...
We summarize and discuss the current state of spatial point process theory and directions for future...
Written by a prominent statistician and author, the first edition of this bestseller broke new groun...
The paper presents introduction to spatial point processes and their characteristics. The reader is ...
The general theory of point processes has in the past been focused on temporal developments. In this...
The general theory of point processes has in the past been focused on temporal developments. In this...
Abstract: Spatial statistics is concerned with statistical methods that explicitly analyses spatial ...
Copyright ©CSIRO 2010 This is a detailed set of notes for a workshop on Analysing spatial point patt...
A spatial marked point process describes the locations of randomly distributed events in a region, w...
Many objects in space can best be modeled statistically by using point processes. Examples are fires...
Spatial processes are mathematical models for spatial data; that is, spatially ar-ranged measurement...
We summarize and discuss the current state of spatial point processtheory and directions for future ...
Ripley’s Kt function is a tool for analyzing com-pletely mapped spatial point process data (see Poi...