Presenting the accessibility to general practitioners by meshblock using contours shows Accession has correctly identified that the meshblock centroids which are closer to general practitioners have a higher accessibility than those further away. Lower contour values (shown in red on figures A.10 to A.12) represent origins with a lower cost of access to general practitioners and therefore higher accessibility, while higher contour values (shown in blue) represent origins with a higher cost of access to general practitioners and therefore lower accessibility.
Testing the three interpolation options (aggressive, moderate and relaxed) indicates visual results can be heavily influenced by the interpolation type. Aggressive interpolation is more appropriate to use when results datasets have a high quantity of origin points. Relaxed interpolation is more appropriate when the dataset has sparse data points as origins.
The result sets shown in figures A.8 to A.10 do not include geodemographic data such as the population attributes of each meshblock. The results only use the OD matrix travel time values. A meshblock with high accessibility to a general practitioner is desirable, but if the population of the meshblock is very low, then the benefit of greater accessibility to the community is limited.
Further results can be calculated through the inclusion of geodemographic data. These results provide much richer information in terms of assessing and therefore potentially optimising a population’s access to an activity. Undertaking an analysis to determine the number or percentage of population within ‘accessible’ reach of particular activities or land uses provides a better assessment of accessibility.
The accessibility analysis using Accession does not consider the reach to multiple opportunities. This is a significant limitation of the simplified ‘closest facility approach’ used by Accession to assess accessibility.
It would be an interesting exercise to fully develop an Accession model for Christchurch including all modes, in particular, public transport and walking, and then compare the model results with the UK DfT indicators.