Although many statistical approaches have been developed to quantify and assess spatial point patterns, the challenge to analyze complicated patterns has yet to be met. Statistics that describe the level of clustering usually assume that point events are isotropic. Many point events are influenced by linear features and clusters of these point events often have an elongated shape. Existing statistical cluster detection approaches often ignore these types of processes. This study proposes a new method, termed an L-function analysis for clusters influenced by linear features (L-Function-l) to test anisotropic point patterns with respect to the orientation of nearby linear features. To explicitly account for the influence of the underlying linear features on the point events, a number of ellipses with varying lengths, orientations and eccentricities are used to replace the circles that are drawn in the original L-function analysis. A case study of testing anisotropically clustered patterns of mosquito larval sites is used to illustrate the application of this method. The results indicate that the proposed approach provides a more flexible and comprehensive description of point patterns than the original L-function analysis.