Density vs Hot Spot Analysis

Density Density analysis takes known quantities of some phenomenon and spreads them across the landscape based on the quantity that is measured at each location and the spatial relationship of the locations of the measured quantities

Density surfaces show where point or line features are concentrated The cells nearer the measured points receive higher proportions of the measured quantity than those farther away Ex: create a surface showing the predicted distribution of the population throughout the landscape

You can calculate density using either simple or kernel calculations. In a simple density calculation, points or lines that fall within the search area are summed, then divided by the search area size to get each cell's density value. The difference between the Point Density and Line Density tools is that the first is applied to point features and the second to linear features. The two calculate the quantity specified by the Population field that falls within the identified neighborhood and divide that quantity by the area of the neighborhood.Point DensityLine Density The difference between the output of those two tools and that of Kernel Density is that in point and line density, a neighborhood is specified that calculates the density of the population around each output cell. Kernel density spreads the known quantity of the population for each point out from the point location. The resulting surfaces surrounding each point in kernel density are based on a quadratic formula with the highest value at the center of the surface (the point location) and tapering to zero at the search radius distance. For each output cell, the total number of the accumulated intersections of the individual spread surfaces is calculated.Kernel Density

How to…. – Spatial Analyst toolbox Density toolset

Density can tell you where clusters in your data exist, but not if your clusters are statistically significant Hotspot analysis uses vectors (not rasters) to identify the locations of statistically significant hot spots and cold spots in data

Hot Spot Analysis This tool identifies statistically significant spatial clusters of high values (hot spots) and low values (cold spots) – New Output Feature Class with a z-score, p-value, and confidence level bin (Gi_Bin) for each feature in the Input Feature Class – Uses Getis-Ord Gi* statistic

Gettis-Ord Gi* – Produces Z scores and P values – A high Z score and small P value for a feature indicates a significant hot spot. A low negative Z score and small P value indicates a significant cold spot. The higher (or lower) the Z score, the more intense the clustering. A Z score near zero means no spatial clustering. Gi/005p /

How to… – Data must be projected. – Spatial Statistics toolbox