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Spatial Analysis. Longley et al., Ch 14,15. Transformations . Buffering (Point, Line, Area) Point-in-polygon Polygon Overlay Spatial Interpolation Theissen polygons Inverse-distance weighting Kriging Density estimation. Basic Approach. Map. New map. Transformation. Point-in-polygon.

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spatial analysis

Spatial Analysis

Longley et al., Ch 14,15

  • Buffering (Point, Line, Area)
  • Point-in-polygon
  • Polygon Overlay
  • Spatial Interpolation
    • Theissen polygons
    • Inverse-distance weighting
    • Kriging
    • Density estimation
basic approach
Basic Approach


New map


point in polygon

Select point known to be outside

Select point to be tested

Create line segment

Intersect with all boundary


Count intersections



combining maps
Combining maps
  • As long as maps have same extent, resolution, etc, overlay is direct (pixel-to-pixel)
  • Otherwise, needs interpolation
  • Use map algebra (Tomlin)
  • Tomlin’s operators
    • Focal, Local, Zonal
combining maps8
Combining Maps
  • A problem
creating new zones
Creating new zones

Town buffer

River buffer

other spatial analysis methods
Other spatial analysis methods
  • Centrographic analysis (mean center)
  • Dispersion measures (stand. Dist)
  • Point clustering measures (NNS)
  • Moran’s I: Spatial autocorrelation (Clustering of neighboring values)
  • Fragmentation and fractional dimension
  • Spatial optimization
    • Point
    • Route
  • Spatial interpolation
moran s i
Moran’s I

spatial autocorrelation
Spatial autocorrelation
  • Correlation of a field with itself




spatial optimization
Spatial optimization

linear interpolation
Linear interpolation



Half way from A to B,

Value is (A + B) / 2


nonlinear interpolation
Nonlinear Interpolation
  • When things aren\'t or shouldn’t be so simple
  • Values computed by piecewise “moving window”
  • Basic types:1. Trend surface analysis / Polynomial

2. Minimum Curvature Spline

3. Inverse Distance Weighted 4. Kriging

1 trend surface polynomial
1. Trend Surface/Polynomial
  • point-based
  • Fits a polynomial to input points
  • When calculating function that will describe surface, uses least-square regression fit
  • approximate interpolator
    • Resulting surface doesn’t pass through all data points
    • global trend in data, varying slowly overlain by local but rapid fluctuations
1 trend surface cont
1. Trend Surface cont.
  • flat but TILTED plane to fit data
    • surface is approximated by linear equation (polynomial degree 1)
    • z = a + bx + cy
  • tilted but WARPED plane to fit data
    • surface is approximated by quadratic equation (polynomial degree 2)
    • z = a + bx + cy + dx2 + exy + fy2
2 minimum curvature splines
2. Minimum Curvature Splines
  • Fits a minimum-curvature surface through input points
  • Like bending a sheet of rubber to pass through points
    • While minimizing curvature of that sheet
  • repeatedly applies a smoothing equation (piecewise polynomial) to the surface
    • Resulting surface passes through all points
  • best for gently varying surfaces, not for rugged ones (can overshoot data values)
3 inverse distance weighted
3. Inverse Distance Weighted
  • Each input point has local influence that diminishes with distance
  • estimates are averages of values at n known points within window R

where w is some function of distance (e.g., w = 1/dk)

  • IDW is popular, easy, but problematic
  • Interpolated values limited by the range of the data
  • No interpolated value will be outside the observed range of z values
  • How many points should be included in the averaging?
  • What about irregularly distributed points?
  • What about the map edges?
idw example
IDW Example
  • ozone concentrations at CA measurement stations

1. estimate a complete field, make a map

2. estimate ozone concentrations at specific locations (e.g., Los Angeles)

ozone in s cal text example
Ozone in S. Cal: Text Example

measuring stations and concentrations

(point shapefile)

CA cities (point shapefile)

CA outline (polygon shapefile)

DEM (raster)

cross validation
Cross validation
  • removing one of the n observation points and using the remaining n-1 points to predict its value.
  • Error = observed - predicted
4 kriging
4. Kriging
  • Assumes distance or direction betw. sample points shows a spatial correlation that help describe the surface
  • Fits function to
    • Specified number of points OR
    • All points within a window of specified radius
  • Based on an analysis of the data, then an application of the results of this analysis to interpolation
  • Most appropriate when you already know about spatially correlated distance or directional bias in data
  • Involves several steps
    • Exploratory statistical analysis of data
    • Variogram modeling
    • Creating the surface based on variogram
  • Breaks up topography into 3 elements:Drift (general trend), small deviations from the drift and random noise.

To be stepped over

explore with trend analysis
Explore with Trend analysis
  • You may wish to remove a trend from the dataset before using kriging. The Trend Analysis tool can help identify global trends in the input dataset.
kriging results
Kriging Results
  • Once the variogram has been developed, it is used to estimate distance weights for interpolation
  • Computationally very intensive w/ lots of data points
  • Estimation of the variogram complex
    • No one method is absolute best
    • Results never absolute, assumptions about distance, directional bias
kriging example
Kriging Example

Surface has no constant mean

Maybe no underlying trend

surface has a constant mean,

no underlying trend

allows for a trend

binary data

kriging result
Kriging Result
  • similar pattern to IDW
  • less detail in remote areas
  • smooth
idw vs kriging
IDW vs. Kriging
  • Kriging appears to give a more “smooth” look to the data
  • Kriging avoids the “bulls eye” effect
  • Kriging gives us a standard error