The number of employment places in a location can be obtained from census information. It is then possible to randomly distribute points within mesh blocks to represent each of the employment opportunities. The travel time across the network to each of these points can then be calculated. Repeating this process with multiple iterations of the random distribution (Monte Carlo simulation) would yield a statistically robust result.
However, the computational power and time required to calculate such a result for a city the size of Christchurch rapidly expands to prohibitive levels. For example, there are approximately 145,000 jobs in Christchurch, which could mean a potential network calculation of up to 3000 * 145,000 = 435 million calculations. Conversely, by modelling places of employment at the centroids of mesh blocks they reside within, the maximum potential calculations can be reduced to 3000 * 3000 = 9 million calculations. This is achieved by solving the network path cost to the meshblock centroid, applying the negative exponential equation to the path cost and then multiplying this result by the number of jobs in the meshblock. Taking the sum of all these values yields the number of accessible co-located job equivalents.