Spatial
Analysis - Boolean Intersection
Spatial
Analysis - Boolean Intersection
With the data and methodology plan in hand, I could proceed to conduct my analysis at last. Since I chose to apply Boolean intersection to retrieve the spatial analysis result, my first step would be to standardize all my criteria factors and constraint into Boolean measure.
The constraint to be standardized is the percentage of Chinese population
in the Greater Vancouver
Region. My Uncle was hoping that his business would locate at regions within
the Greater Vancouver District that
contain at least 25% of Chinese population.
Using the above raster data image, I applied the reclass method from
IDRISI to obtain the Boolean image of at least 25% of Chinese population
in GVRD regions. From this image, we could clearly see that there were
only a handful regions with the GVRD with more than 25% of Chinese population.
This would be the greatest constraint to be considered for choosing a location
for my Uncle's Business.
The next factor to be examined was the concentration of youth between the age 15-24 in the GVRD region
.JPG)
To obtain the percentage of youth age 15-24 in GVRD, I created an overlay
image that compared the total population of youth between the age 15-24
with the total population of GVRD. From this image I could observe
that the highest percentage of youth age 15-24 in the GVRD
was roughly about 19%, while most regions appeared to have at least 12%
of youth age 15-24 in their region. As a result, I set aside 15% as the
indicator for high percentage of youth in the GVRD region.
Compared to the Boolean image of Chinese population, this time there
appeared to be more regions with a higher concentration of youth age 15-24.
My Uncle acknowledged that another important factor which required
my evaluation was the percentage of never married children between the
age of 18-24. He reasoned that children between the age of 15-17 were very
much likely
to stay
home, but a region with a high percentage of never married children among
the age group 18-24 who stay home could become a good business region for
him, since my Uncle believed that the disposal income among this group
was high and therefore would serve to be a significant factor in the spatial
analysis of finding him the ideal region for his business.
To create this image "never married children between the age of 18-24",
I had to first export the appropriate value from the census data table
to the matching raster frame, followed by the overlay operation with
the total population of youth age 15-24 in GVRD.
The result revealed the percentage of never married children age
18-24 ranged approximately between 10% to 60% among the age group of 15-24
years old in GVRD. My Uncle thought that a region with 50% or more
of never married children between the age of 18-24 would be good enough
for his business.
The two factors that I would also look at were the percentage of part-time worker and the average income in GVRD regions. I believed that a region, with an average income of more than $30,000 annually, a respectful among of part time workers and a high concentration of youth between the age 15-24 would match the ideal economic condition that my Uncle was seeking for his teahouse business.
I reasoned that a region with a higher average annual income while having
a high concentration of youth and part time workers could mean that it
is an affluent neigbourhood, with stay at home youth who have part time
jobs and therefore holding strong spending powers.
The overlay of part time worker in GVRD regions, as compared to the total
population, showed that most regions have about 20% to 30% of their population
working part-time. Hence I believed a Boolean image that indicated
a region with at least 20% part time worker will be reasonable.
Lastly, below is the Boolean image of GVRD regions with an annual average
income over $30,000
(Back to Top)
(To Result of Analysis)