Cartographic Models:

- Click here for the model using the Boolean Overlay Approach

- Click here for the model using the Weighted Linear Combination

    The project began with a critical look at what data is available to find areas of suitable areas for Urban development in the North Shore Mountains area.  Of the layers that were of use, the ones regarding human/environmental were selected for analysis.  The layers chosen were: a landuse and a digital elevation model for the area of study.

    From these 2 layers, I was able ot generate a total of 2 constraints and 5 factors that were applied to the spatial analysis or this project.

Constraints
- water areas versus non water areas
- underdeveloped land versus developed land

Factors
- Distance from protected watersheds
- Distance from protected parks/forest
- Distance from open water bodies
- Slope of the land
- Elevation of the land

Note: Further and detailed descriptions of each constraint and each factor are discussed in section 5.1 (Spatial Analysis using the Boolean Overlay Approach).

    When these factors and constraints are defined, the production of Boolean/distance/slope images for them began. After the Boolean/distance/slope images are produced, the next phase of the project is to determine what type of evaluation/analysis would be used.  There are a variety of multi-criteria evaluation (MCE) including Boolean Overlay and Weighted Linear Combination (WLC). Performing an analysis using the Boolean overlay approach would somewhat have satisfied the project objective: Suitable Areas for Development in the North Shore Mountains

    However, the Boolean overlay methodology has little flexibility, The hard and arbitrary nature of Boolean standardization limits the flexibility and utility of any approach using constraints. The North Shore Mountains area can be categorized either as suitable (1), or as unsuitable (0) for urban development. Boolean operations tend to produce extreme solutions, depending on the operator used.  For example, if a location does not meet a criteria (i.e., elevation) but meets all of the remaining criteria, it is likely in reality, that the site may still be selected in the final review regarding the location of suitable urban development.  With this in mind, in addition to Boolean Overlay, using the Weighted Linear Combination method of analysis was necessary for comparison purposes.

    Another limitation of the simple Boolean approach used in this project is that all factors had equal importance in the final suitability map. However, in reality, this is not likely to be the case. Some criteria may not be very important in determining the overall suitability for a given area while others may be of only marginal importance. By performing another spatial analysis using WLC, it allow me to overcome this limitation by weighting the factors and aggregating them with a calculated weighted linear average. The weights that were  assigned, govern the degree to which a factor can be compensated for another factor. Although this can be done with the Boolean images that were produced for the MCE - Boolean overlay approach,   I decided to do them using the MCE - WLC to compare the 2 final suitability maps with each other.

The following 7 constraint criteria images were used to generate the final image using the MCE - Boolean overlay approach:

- WATERCON.rst
- LANDCON.rst
- WASTERBOOL2.rst
- WATERSHED2.rst
- FOREST_PARKBOOL2.rst
- SLOPEBOOL.rst
- ELEVATION.rst

    The Weighted Linear Combination, uses the factors in this project in such away that they are not reduced to simple Boolean Constraints. Instead, they are standardized to a continuous scale of suitability from 0 (the least suitable for urban development) to 255 (the most suitable). By rescaling the factors to a standard, continuous scale, this enables the comparison and the combination of the  factors, as in the Boolean case. In this method of spatial analysis, I have used a soft or "fuzzy" concept to give all locations in the North Shore Mountains a value (ranging from 0 to 255) which represented its degree of suitability. The constraints, however, will retain their "inflexible" Boolean attributes.

    This procedure not only allowed the ability to retain the variability from the continuous factors, it also given the ability to have the factors to trade off with each other. For example, a low suitability score in the slope factor for any given location can be compensated for by a high suitability score in another factor (such as distance from protected watersheds). How the factors of this project trade off with each other have been determined by a set of Factor Weights that have indicated the relative importance of each factor.

Standardization of Factors to a Continuous Scale (fuzziness):
- Distance from protected watersheds  (100 - 100 meters)
- Distance from protected parks/forest (250 - 500 meters)
- Distance from open water bodies (100 - 800 meters)
- Slope of the land (0 - 20 degrees)
- Elevation of the land (0 - 1000 meters)

Weights:
    Slope is the single most important factor because it determines not only the price of development but also influences the chance of  property/people being harmed by landslides (by soil erosion). All other factors become secondary to slope.  Note that all values chosen below are arbitrary and should not be used for any professional purposes because this requires highly complex modeling which is beyond the scope of my knowledge to perform this particular task.

Weighted Values:
DEM_VANFUZZ  -         0.2058
SLOPEFUZZ -                0.3316
FOREST_PARKFUZZ -  0.00769
WATERFUZZ -              0.1652
WATERSHEDFUZZ-     0.2205

    Once the weights of each factor have been calculated The final image of the MCE - Linear Weight Combination will be based upon the output of the above  weighted factors. As the possible locations are found using each image/analysis, a vector layer (COASTLINE.rst) showing the extent of the North Shore Mountains area is placed over each map encountered in this project to provide land/water contrast..

Note: All Procedures are summarized in the cartographic model of the Boolean Overlay Approach or the Weighted Linear Combination.