Objective 4: To Evaluate the Strengths and Limitations of the Broadband MCE Model.
The strengths of the MCE model consists of practical uses of numeric data, such as the considerations which population density has on the ability to locate areas of potential improvement without reducing the population variable to a hard constraint. An MCE model worked well for the purposes of this study as files throughout the preprocessing stage can be reused, making it easy to automate the entire process in python as well as retaining the consistency of the data after numerous operations. For the model, wireless data was the easiest data to collect and implement as wireless is the only data that is inherently open source[Data Table]. This allowed for the accurate implementation of the model to find and list the top five most applicable areas for potential broadband improvements.
The weaknesses ingrained in the MCE model often stemmed from the scale at which data was acquired. The population layer takes into account the total population of a census district at the census district level, however, there is no way to know the actual dispersion of the population within the census division polygon without further investigation. This creates an issue where large population centers are weighed lower if within a large subdivision area, as well as small population centers being weighed higher when within a small area. As such, the municipalities identified by the MCE should be investigated further for ways to improve their connectivity that are appropriate for their own population distribution. Secondly, the literature standards regarding the effective range of different types of broadband vary widely, suggesting that broadband connectivity varies. The weakness then lies with the average distance used to conduct the MCE’s. If the distance is set to 25km and the effective range varies from this depending on location within Canada, the numbers may be skewed or incorrect in areas with, for example, extreme elevation differences or impenetrable infrastructure such as tall buildings near cellular towers (Dalke et al., 2000). Finally, the data available for both copper and fibre networks are incomplete. Data for the locations of existing copper and fibre lines are not open to the public, which forced assumptions to be made in its stead. These data sets are kept private as access to them can pose a real threat to Canada's broadband network if used maliciously. To get around this, it is assumed both fibre and copper broadband lines exist somewhere near the known communication lines data set, which is where all calculations were based from. One final limitation with this study is the data describing broadband age and exact location does not currently exist, optimally, if this data was available regions which have dated broadband lines could be prioritized for improvements over regions with newer lines.
When it came to assigning weights to each factor, some level of subjectivity should be noted. For example, the decision was made for the population to matter little when deciding to place a new cellular tower to ensure remote communities are not excluded. In the Future, further research could be done specifically targeted at analyzing remote. This value can be increased and other weights lowered to produce a completely different result, like only considering urbanized areas for example. This is both beneficial and detrimental; detrimental as it shows the results of the analysis are biased towards the party who created the weights, and beneficial in that the model is flexible and easily modified.