Methodology
Data collection lays the basis for spatial analysis of Vancouver International Airport, and the next step to location analysis is methodology. The first procedure of methodology is data format conversion in which all ESRI shape files are imported to IDRISI and georeferenced in UTM-10N. The second procedure is rasterization of these converted vector files. The third procedure is creation of cost distance images from the rasterized files of all transport means using the 'distance' module,. The cost distance image of transport modes are essential for the 'multi-criteria evaluation' module that take s place during spatial analysis. Furthermore, the airport and highways factors only requires a simple, 'distance' operation to demonstrate that close-by areas gain more accessibility to the airport and highways.
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Transport mode factor images
- Transport mode distance images
For this analysis, land proximity is used to make an assumption that certain types of landuses appreciate the airport vicinity whereas others do not. The 'assign' module realizes this concept by assigning a 'fondness' score to all types of landuse. For example, the score 0 represents places that completely do not like the nearness of it and the score 255 represents those that completely enjoy it. In addition, the commercial and residential / mixed commercial types of landuse are emphasized because their proximity may have important economic implications (i.e., some tourists like CBD to be close to airport). The 'assign' module is used to separate these landuses from others, which then allows the 'distance' module to create their cost distance image.
- Land proximity factor images
- Commercial landuses factor and distance images
In order to evaluate the point data of air quality and noise, it is essential to create “surfaces” to take the distribution and magnitude of these abstract data into consideration. This step is completed with point data interpolation, and the end results are continuous surfaces of data across GVRD. For air quality, all interpolated images accumulate to one summary image that includes all air components, and this is achieved with the 'overlay' module and 'multiply' operation. Moreover, due to the fact that noise data are aggregated for about six to thirteen years, such data can be processed as time series data (.ts). To reflect changes in values of data over time, the module of 'time series analysis' (TSA) can be used to produce images of these changes (these images include NMTs that add spatial reference of terminus and noise radii). Eventually, together with the distance images mentioned above, it is ready to proceed to MCE, or spatial analysis.
- Air quality interpolation and overlay images
- Noise interpolation and TSA images
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