We report a robust nonparametric descriptor, J'(r), for quantifying the spatial organization of molecules in single-molecule localization microscopy. J'(r), based on nearest neighbor distribution functions, does not require any parameter as an input for analyzing point patterns. We show that J'(r) displays a valley shape in the presence of clusters of molecules, and the characteristics of the valley reliably report the clustering features in the data. More importantly, the position of the J'(r) valley (rJ'm) depends exclusively on the density of clustering molecules (ρc). Therefore, it is ideal for direct measurements of clustering density of molecules in single-molecule localization microscopy. We demonstrate that the rJ'm-ρc relation is robust against noises at high levels. We exploited this relation to characterize the clustering of H-NS proteins in E. coli bacteria, and measured that density of H-NS clusters increases by 27% upon cold shock.