Meaning
Theoretical baselines establish a spatial arrangement where every point in a study region is placed independently and with equal probability at any location. Achieving complete spatial randomness requires that the position of one point provides no information about the position of any other point in the set. This state corresponds to a homogeneous poisson process where the intensity remains constant across the entire window.
Null Hypothesis
The null hypothesis assumes no interaction between events. Using this model allows researchers to test if observed data contains identifiable structure or if it is merely the result of a random process.
Point Distribution
Probability distributions for point counts follow the poisson distribution. Under complete spatial randomness, the variance of the number of points in a sub-region equals the mean number of points. Distances to the nearest neighbor follow an exponential distribution which provides a mathematical benchmark for identifying clustering.
If the average distance between points is measurably lower than this benchmark, the pattern is considered clustered. Conversely, a higher average distance suggests a regular or repulsive arrangement where points are forced apart.
Deviation Measurement
Environmental factors often cause real world data to deviate from this model. A deviation measurement quantifies the strength of spatial interactions.
