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spatial_analysis_methods:ripley_s_k_and_pair_correlation_function

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 spatial_analysis_methods:ripley_s_k_and_pair_correlation_function [2012/06/06 10:31]jgillan spatial_analysis_methods:ripley_s_k_and_pair_correlation_function [2012/06/06 10:32] (current)jgillan Both sides previous revision Previous revision 2012/06/06 10:32 jgillan 2012/06/06 10:31 jgillan 2012/06/05 19:25 leandro 2012/06/05 19:23 leandro 2012/06/05 19:19 leandro 2012/06/05 19:17 leandro 2012/06/05 19:16 leandro 2012/06/05 19:15 leandro created 2012/06/06 10:32 jgillan 2012/06/06 10:31 jgillan 2012/06/05 19:25 leandro 2012/06/05 19:23 leandro 2012/06/05 19:19 leandro 2012/06/05 19:17 leandro 2012/06/05 19:16 leandro 2012/06/05 19:15 leandro created Line 11: Line 11: Using an illustrative example, the Ripley’s K function is used to describe the point pattern of pine trees (Fig. 1). In this example, pine trees are represented by green dots and other tree species are represented by red dots. The function counts the number of neighboring pine trees found within a given distance of each individual pine tree (Xm).   The number of observed neighboring pine trees is then traditionally compared to the number of pine trees one would expect to find based on a completely spatially random point pattern. If the number of pines found within a given distance of each individual pine is greater than that for a random distribution,​ the distribution is clustered. ​ If the number is smaller, the distribution is dispersed. Using an illustrative example, the Ripley’s K function is used to describe the point pattern of pine trees (Fig. 1). In this example, pine trees are represented by green dots and other tree species are represented by red dots. The function counts the number of neighboring pine trees found within a given distance of each individual pine tree (Xm).   The number of observed neighboring pine trees is then traditionally compared to the number of pine trees one would expect to find based on a completely spatially random point pattern. If the number of pines found within a given distance of each individual pine is greater than that for a random distribution,​ the distribution is clustered. ​ If the number is smaller, the distribution is dispersed. + Ripley’s K function is generally calculated at multiple distances allowing you to see how point pattern distributions can change with scale. For example, at near distances, the points could cluster, while at farther distances, points could be dispersed (Fig. 2). Ripley’s K function is generally calculated at multiple distances allowing you to see how point pattern distributions can change with scale. For example, at near distances, the points could cluster, while at farther distances, points could be dispersed (Fig. 2). + Ripley’s K function can be calculated in a univariate form where you are describing the spatial pattern of only pine trees (described above). Alternatively,​ K can be calculated in a bivariate form where you interested in the spatial pattern of pine trees compared with other species of trees. The computation is the same except the function counts the number of neighboring other trees found within a given distance of each individual pine tree. Ripley’s K function can be calculated in a univariate form where you are describing the spatial pattern of only pine trees (described above). Alternatively,​ K can be calculated in a bivariate form where you interested in the spatial pattern of pine trees compared with other species of trees. The computation is the same except the function counts the number of neighboring other trees found within a given distance of each individual pine tree. 