Due to limited sampling, the overall distribution and diversity pattern of a species in a specific geographic region usually have to be inferred from small-sized spatial biodiversity data collected via line transect surveys. Here, an index called one-dimensional nearest neighboring distance (1NND) is proposed to infer the overall distributional aggregation pattern of species from limited line-transect data. This index accounts for the sampling distances between the closest neighbors, avoiding the estimation bias from limited sampling. This index has also been extended to analyze the combined datasets from multiple line transects simultaneously (called LNND). Both extensive simulations and empirical applications showed that the proposed metrics 1NND and LNND always performed better in comparison to two-dimensional metrics like Clark and Evans’ metrics (CECI), which tended to overestimate aggregation. Moreover, empirically, 1NND and LNND performed as well as or better than the dispersion parameter of the negative binomial distribution and crossing-scale Ripley’s K function. These comparative results suggested that, when line-transect-derived spatially constrained biodiversity data were used, 1NND and LNND were the most applicable. In contrast, when the entire spatial point data were available (e.g., individual distributional coordinates in the entire forest plot for each tree were available), crossing-scale Ripley’s K function was recommended. CECI was not recommended due to its risk of overestimating aggregation. An R package, called (Lin)e transect-based nearest neighbor (d)istance (a)nalysis (Linda), is available for computing 1NND and LNND from line-transect data.