How to womble with location uncertainty

If you want to do clustering on classified data, first create fuzzy classes from the original data set.

  1. Go to the "Data" menu (at the top of the screen or by right-clicking in the BoundarySeer project window). Choose "Detect Boundary" and then "Location Uncertainty."

  2. "General" Tab:

    1. Choose the data set from the pull-down list of available data in the project.

    2. Select a name for the new boundary, or you can take the default name at "Name:"

    3. Choose the number of iterations for the randomization of the location of the data (default = 100) and the columns in the resulting raster (default = 50). Lowering the number of iterations will decrease the calculation time, though it will also decrease the number of randomization runs, and therefore the power of the analysis.

    4. You can choose to detect the boundary with all variables, weighting variables using a variable set, or with a single variable.

    5. To standardize the data set before analysis, check the box at the bottom of the tab.

  3. "Methods" Tab:

    1. Choose the location model, which sets how the data will be randomized.

      1. If you choose a completely randomized model, click on polygon model and then choose the data set that contains the polygons within which BoundarySeer will randomize the coordinates. If the data is a set of polygons, that data will already be chosen and that box grayed out.

      2. <not yet available> If you choose a population model, specify the file that contains the population information.

    2. Choose the boundary detection method from the pull-down list: either crisp or fuzzy wombling.

    3. Choose the thresholds for boundaries.

      1. For crisp or fuzzy wombling, the default is BLVs in the top 30%.

      2. For fuzzy wombling only, define the proportion of BLVs in the boundary core. The default value is 15%.

  4. Hit OK to start the analysis.

Next Step:

See also: