TOPICS COVERED SO FAR (as of 14 February 2011)
NOTE THAT THE EXAM WILL ALSO COVER GLOBAL CLUSTERING AND RIPLEY’S K-FUNCTIONS.
As per our discussions in class – this is a challenging exam and requires that you have completed
the readings online as well as the laboratory materials and kept up with my lectures and the inclass discussions.
GIS TOPICS
 Shapefiles and vectors vs gridded data and raster
 Discrete vs continuous
o Known locations with values or known cases occurrences
 Deterministic versus probabilistic techniques for deriving continuous surfaces
KDE VS IDW
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Kernel density estimates a measure of CONCENTRATION (SMOOTHING)
o Kernel – Spatial filter (exclude beyond the filter)
o Bandwidth is the size of the filter we use
 Size; WHAT IS OPTIMAL?
 Fotheringham et al. hopt
 Has a tendency to “oversmooth”
 BASED ON STANDARD DISTANCE
EXAMPLE:
Counts of phenomenon in certain geography
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Descriptive spatial statistics
o Mean Center – mean x, y of our point distribution
o Standard distance – Dispersion around the mean center
 THIS IS NOT A DIRECTIONAL MEASURE
o Standard deviation ellipse – this can be directional
o BOTH SD and SDEV are centered on the spatial mean
INTERPOLATION – estimating some value from an un-sampled location from a
known point distribution with measured values
o Inverse Distance Weighting (measures the phenomenon of distance
decay)
o 1/distance^power (default is sq but will see optimal routine in GEOSTAT
ANALYST)
o We calculate UNKNOWN LOCATIONS WITH THE GRIDDED SURFACE WE
ASSIGN
 Grid cell size, power value, number of neighbors (12…. User
assigned value)
o CROSS VALIDATION – hold out some known data from the model, train
the model with the rest, test the model with the hold out after the model
is built known as post hoc testing
 Training for model building
 Testing for model accuracy assessment or evaluation
o RMSE – measure of predicted value at a point versus the actual value at
that point; “lower is better measure”
o Kriging can leap from deterministic to probabilistic through the fitting of
a semi-variogram
 IDW IS NOT SENSITIVE TO DIRECTIONALITY
Example
Contour (these could be derived from the IDW gridded surface) of climate measure; disease
rate surface
Monte Carlo? WTH?
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POINT DISTRIBUTION IS RANDOM OR CLUSTERED?
CSR – Complete spatial randomness –
IN DMAP CSR IS DERIVED FROM 999 iterations of point distributions from a random
reassignment of case data to locations
Fixed lattice over the landscape (this is an x, y distribution)
FILTER SIZE TO DETERMINE
LATTICE SIZE (determine how far apart)
Adaptive filter in DMAP IS DEMONINATOR DEPENDENT – literally the filter gets as big as
required to meet the minimum denom setting
Selective filtering – URBAN SETTING; RURAL SETTING (Classes)
Fixed filter – IN DMAP THESE ARE ONLY CIRCULAR
CONFINDENTIALITY
NUMERATOR RULE: any count less than 5 associated with demographic data cannot be
published
DENOMINATOR RULE IN FL: 20k or less has to be aggregated up to a large areal unit until 20k is
exceeded
SPATIAL MASKS OR RANDOM SHAPES