Many objects in space can best be modeled statistically by using point processes. Examples are fires in an urban environment, herds of animals in large areas, earthquakes and forest fires and large speckles on a radar image. Modern developments in point process theory now much better than before allow us to make statistical models to explain the observed patterns. In this paper, we will address the way that point processes can be modeled in space and time. The first application draws from domestic fires at the city level, where we apply a statistical point pattern analysis to derive major causes from related layers of information. The second application considers earthquakes as a marked point process. For earthquakes, large and complex data sets exist including many possibly relevant covariates that may influence their occurrence. The Strauss point process model is explored to analyze earthquake data in Pakistan recorded since 1973, in particular the major earthquake event occurring in 2005. The model, despite some limitations, is rigorous for applying it to such a marked point pattern, representing well the clustering behaviour as determined by a number of environmental factors. Finally, the Strauss point process model is suggested for the use in identifying and explaining the occurrences of speckles in a radar image.
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