The District of North Vancouver


Methodology

Overview

The analysis that I decided to do included both Boolean multi-criteria evaluation and weighted linear combination to investigate the best possible locations for housing identification developments.  I wished to do these two forms of spatial analysis in order to compare the end results, and determine which analysis accomplishes the best results.  Both methods involved rasterizing vector data, exporting attribute values files to form new images, distance operations and reclassing.  I completed the Boolean multi-criteria evaluation first and then compared it to the weighted linear combination.

Layers Used
zoning.vct
parks.vct
creeks.vct

 from DNV
STREETS_SHP

Boolean Multi-criteria Evaluation

In preparation for the Boolean multi-criteria evaluation, I had to decide what was going to be considered as the factors and constraints of my analysis.  Based upon the data that I had, as well as the common criteria for sustainable development in cities, the area for development would have to be within a development zone, close or adjacent to multiresidential zones, within 1km of an elementary school, within 500m of a park, more than 2km away from an industrial zone, and more than 100m from creeks.  I was unable to find the specific bylaws concerning the required distance a building must be from creeks.  As a result I  guessed what an appropriate distance would be.  In this case I chose 100m as the buffer zone required for development.  I chose to look at development zones near to multiresidential zones in an attempt to maintain the character of the community as much as possible.  Having an elementary school within walking distance from a development will encourage families to move into the area.  It has also been stated that green space is extremely important to denser developments. Each unit does not necessarily receive any private green space and therefore it is very important that the residents have access to public green space where they can go to relax and enjoy the outdoors without having to travel a great distance.  It should be similar to having a backyard.  And lastly, I chose to distance any development more than 2km form industrial zones to maximize aesthetic and heath considerations.

I decided to use the zoning data from the SIS drive as the base map for my analysis. In order to find out what the various zoning categories are, I examined the database within the database workshop to identify what information was given.  The zoning categories were separated into eight major groups with several subgroups within them.  The Eight main categories were: MULTIRESIDENTIAL, SINGLE-FAMILY RESIDENTIAL, INDUSTRIAL, NULL, PARKS AND PROTECTED AREAS, PUBLIC ASSEMBLY, COMMERCIAL, AND COMPREHENSIVE DEVELOPMENT.

Steps
From the ZONING image, I created vector images of multiresidential zones, development zones, null zones, and industrial zones

PARKS and CREEKS ELEMENTARY I created a distance surface for all the images except DEVELOPRAS and MULTIRESRAS.  In an attempt to created a notion of adjacency, I performed a cost distance (costpush) upon MULTIRESRAS by  creating a frictional surface of DEVELOPRAS.  My intent was to illustrate that there was a low friction upon development zones and high friction for all other zones.  I then proceeded with the cost distance from multiresidential zones to extract which development zones were adjacent to development zones.  However, this process was not successful as it simply showed all development zones within DNV.  I will discuss this problem later in the problems section.  Problems

From the distance and cost surfaces, I was able to create Boolean images of each of the criteria.
The parameters for RECLASS  were:
 
Parks within 500m
Industrial Zones >2km away
Elementary Schools within 1km
Creeks >100m away
Multiresidential Zones within 1m

The resulting Boolean images were then used in a Boolean multi-criteria evaluation to produce the final image showing the best locations for dense housing development.


Weighted Linear Combination

For this process I needed to create distance surfaces of all images and then perform the FUZZY decision support operation upon them to create a more transitional zone around each of the criteria to take into account those areas very near the parameters.

In order to proceed with this analysis, I was required to create a raster image of the complete zones of DNV.  TO do this I reexamined the database and created the field CLASS_ID.  I then exported this field as an attribute values file and assigned this to the previously created ZONINGRAS to create the image ZONERASDB
I divided the the criteria into factors and constraints.
Constraints
Factors
INDUSTDISTBOOL MULTIFUZZ
CREEKDISTBOOL PARKFUZZ
ZONEFUZZ ELEMFUZZ

In the creation of ZONEFUZZ, I assigned Development Zones a value of 255 and Null Zones a value of 200 to indicate that development zones are perfect sites for building upon, but there is also potential to gain permission to develop on areas that currently are not zoned for anything specific. All other zones were given a value of zero indicating that they are not to be considered.

Weight Calculation
ELEMFUZZ 1
MULTIFUZZ 7 1
PARKFUZZ 3 1/5 1

Resulting Weights
ELEMFUZZ 0.0810
MULTIFUZZ 0.7306
PARKFUZZ 0.1884

In order to analyze the factors, taking into consideration the constraints, I performed a weighted linear combination of the criteria.  The resulting image designated sites that were best suited to the purpose of denser housing developments.


Spatial Analysis

Back to Table of Contents