The aggregation of point-based observations into quadrats results in a substantial loss of information regarding the underlying distribution of events. The procedure is also highly susceptible to issues relating to the shape, size, orientation and contiguity of the quadrats. By retaining the specific locations of the original points, assuming that this information is available, more detailed analysis can be undertaken. Typically this form of spatial analysis examines the distances between point pairs — either between events, or between events and random or control points. Distances are typically computed as Euclidean, but spherical distances may also be provided by some packages. Network-based analyses are rarely if ever provided (the SANET software being an important exception), other than indirectly through the use of the L₁ metric or other L_p‑type measure (e.g. such metrics are supported in Crimestat and LOLA).

For analysis of this type of data, events should be distinct objects (possibly weighted) and not the result of computations (e.g. zone or point set centroids). All events in a meaningful delimited study region are normally included in an analysis — analysis of subsets of the data (samples) should be undertaken with caution. For more complete discussions of basic point pattern analysis see O’Sullivan and Unwin (2003, Ch.4), Mitchell (2005, Ch.3) and the free and extensive Crimestat Manual (most chapters). More specialised books covering this subject area include Cressie (1991), Diggle (1983) and Bailey and Gatrell (1995).