geography

The map below displays the final results of my project. The proportional dot symbols represent the data values for the Average Household Income Calculated for each secondary school catchment area. The blue to red spanning colours represent either losses or gains in the numbers of students at each school between 2001 and 2006. The proportional dots symbols have been adjusted using Flannery Compensation given that they are relatively close and bunched together.
As can clearly be seen on the map, there is a great discrepancy between the west and east end schools in Vancouver.

While I only explored a very limited number of options in regards as to possible explanations as to why these discrepancies occur, My research into the topic did lead me to believe that these were the most probable causes. Reports of parents going to the extremes, like changing or falsifying their addresses, simply to get their child into a west-end school are not unheard of.

While most native Vancouverites are familiar with the “wealthy” vs. “non-wealthy” neighborhoods that define the west and east sides, respectively, I was personally shocked to see that the autospacial correlations for the gains and losses of students in secondary schools was greater than the average household income as far as west vs. east are concerned. While this is a far from thorough examination of the phenomena, it definitely would seem to put into question the Vancouver School Board’s claim that the lack of students in east end schools is simply due to a population decline, specifically in that area, and that the smaller catchment areas of east Vancouver schools are also to blame. While an investigation most definitely needs to be completed into the size of the catchment area and how this effects the trend (as it is true, the catchment areas in the east end are slightly smaller than those in the west), the startling contrast is likely due to more than just simply the size of the catchment areas. The school board’s claim that it is an effect of population is also not true, as decline in population of high school students occurs just as readily in the west end as in the east. The only areas where the population of high school students is on the rise can be eyeballed and explained by the large amounts of urban growth and housing occurring in the downtown core and of course that the age group I have selected for encompasses those students who live at and go to UBC.

Nevertheless, the results of this project are intriguing, and a deeper look into the geographical trends of early education in Vancouver would likely bring to light many issues that the school board will inevitably have to face in the future.

Global Moran's I Summary
Moran's Index:	0.430109
Expected Index:	-0.055556
Variance:	0.059002
z-score:	1.999409
p-value:	0.045564

Global Moran's I Summary
Moran's Index:	-0.022240
Expected Index:	-0.055556
Variance:	0.048773
z-score:	0.150854
p-value:	0.880091

Global Moran's I Summary
Moran's Index:	0.681071
Expected Index:	-0.055556
Variance:	0.065722
z-score:	2.873364
p-value:	0.004061

Global Moran's I Summary
Moran's Index:	0.375055
Expected Index:	-0.055556
Variance:	0.059217
z-score:	1.769547
p-value:	0.076803

Global Moran's I Summary
Moran's Index:	0.364850
Expected Index:	-0.055556
Variance:	0.059634
z-score:	1.721562
p-value:	0.085149

Moran’s Index Scores:

A Moran’s Index is a method of calculating the overall spatial autocorrelation of these various maps. The maps themselves are simply a visual representation of the data, whereas the Moran’s Index value is a mathematical method of calculating how clustered or random the results are. A Moran’s value ranges from -1 (completely heterogeneous) to 1 (homogeneous), with a score of above about 0.3 representing spatial autocorrelation. The P-Values indicate the likelihood that this pattern occurred randomly. In the autocorrelation for the “Difference in Population of 10-19year Olds in Each Catchment Area (01-06)”, the Moran’s Index value and P-Value indicated that this pattern was most likely random. However, for all the other autocorrelations, the Index score and P-Value would indicate a significant autocorrelation. For the two Fraser Institute Scores from 2001 and 2006, the P-Value indicates a less than 10% likelihood that they are not spatially autocorrelated. For the Average Income 2006 map, there is a less than 5% likelihood that these values are not spatially autocorrelated. The map showing the highest level of spatial autocorrelation, however, is that displaying the change in school population (01-06), with a less than 1% likelihood that this is not spatially autocorrelated. As mentioned above, this is what indicated to me that my hypothesis was most likely correct.