Abstract: The Ripley’s K-function has widely been applied for many fields such as ecology, criminology, and geography. Because of the mathematical confusion of spatial point processes, the corresponding papers contain many errors in measuring the distributions of spatial objects. This paper contributes to an improved understanding for the application for Ripley’s K-function. The focus is first on the estimation methods of spatial point pattern analysis based on Ripley’s K-function. The formula of Ripley’s K-function is corrected, then various definitions of edge effect correction methods applied for the K-function analysis are detailed compared. The relative merits of various algorithm methods are identified by considering the dynamicity of random point pattern, the definition desirability of parameters, and estimation reliability of edge correction methods. The modified algorithms have been employed for the point pattern analysis of the fruit plants in Xinping County, Yunnan Province. The results show: the number of Monte Carlo simulations has a great influence over the analysis of the observed point patterns. Trapped in the dynamicity problems, significance tests of observed patterns are changed that is responsible for changes in Ripley’s K-function measures with uncertainty as ArcGIS software package running. There are advantages of modified algorithms of edge effect correction, the rectangle of study areas is generally extended to complex shape that is robust enough.