Nearest neighbor analysis examines the distances between each point and the closest point to it, and then compares these to expected values for a random sample of points from a CSR (complete spatial randomness) pattern.

There are a ton of ‘smart’ algorithms that assist data scientists do the wizardry. k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification.

Nearest Neighbour Analysis measures the spread or distribution of something over a geographical space. It provides a numerical value that describes the extent to which a set of points are clustered or uniformly spaced.

This 1.27 Rn value (which becomes 1.32 when reworked with an alternative nearest neighbour formula provided by David Waugh) shows there is a tendency towards a regular pattern of tree spacing.

A is calculated by (Xmax – Xmin) * (Ymax – Ymin). Refined nearest neighbor analysis involves comparing the complete distribution function of the observed nearest neighbor distances, , with the distribution function of the expected nearest neighbor distances for CSR, .

The nearest neighbor heuristic is another greedy algorithm, or what some may call naive. It starts at one city and connects with the closest unvisited city. It repeats until every city has been visited. The time complexity of the nearest neighbor algorithm is O(n^2) .

The Rn value, or normalized reporter value, is the fluorescent signal from SYBR Green normalized to (divided by) the signal of the passive reference dye for a given reaction. The delta Rn value is the Rn value of an experimental reaction minus the Rn value of the baseline signal generated by the instrument.

Rn = 0 Random distributions occur where there is no pattern at all. Rn equals The usual pattern for settlement is random with a tendency for clustering or regularity Regular patterns are perfectly uniform. They have a Rn value of 2.15 which means that each place is equidistant.