Density vs Hot Spot Analysis

Density vs Hot Spot Analysis
• 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.
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.
• 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.
• How to…
– Data must be projected.
– Spatial Statistics toolbox

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