Using accessibility and connectivity metrics

New economic geography suggests that accessibility is a significant driver of the geographical distribution of economic activity and wealth. More specifically, it suggests that regions with better access to other markets can benefit from improved access to consumer, supplier or labour markets, thereby leveraging economies of agglomeration; diffusion and spillover of knowledge; innovation and technology; import and export expansion; and specialisation effects (Berg et al., 2017; Fujita et al., 1999; Knoben and Oerlemans, 2006; Lakshmanan, 2011). Transport can trigger those effects by reducing generalised transport costs (Lakshmanan, 2011). From a societal perspective, accessibility metrics subsequently offer insights into the potential of a specific location to interact with other regions.

Accessibility is a concept based on generalised transport costs. It provides insights into the interplay of both locational impacts and the transport network in determining generalised transport costs. Accessibility metrics are subsequently aggregate metrics which take into account geography and the role of transport links in driving the costs associated with overcoming geography. They are useful for understanding regions’ remoteness while taking into account the role of transport links in creating connections. However, decision makers must acknowledge that they do not have full control over accessibility, at least in the short and medium terms. This is because accessibility does not specifically describe the quality of transport links available to a region, since it is confounded by geographical factors.

Decision makers might be more interested in understanding the quality of transport links because their decisions can have significant impacts on these links. This reasoning can motivate the use of aspatial connectivity metrics in analyses focused on evaluating transport networks and the links they offer. A wide variety of connectivity-type metrics is available for analysing the quality of air transport networks, irrespective of spatial considerations. In line with our earlier discussion, these metrics are service based, thereby acknowledging how users rely on scheduled service offerings to use air transport. Furthermore, most metrics are defined at the airport level and provide insights into the aggregate service offerings. However, the metrics differ by scope. Centrality-type measures focus mainly on counting (direct and indirect) destinations or intermediate stops. In this way, they are more suitable for analysing air transport connectivity in less-complex networks, where service offerings are homogenous (e.g. direct flights only, focus on a specific airline or small networks). Feasible-link measures and link-quality measures analyse the quality characteristics for users in overcoming distances. They are subsequently more suitable for studying connectivity in more-complex networks, such as post-regulation hub-and-spoke networks. For this purpose, link-quality measures consider degreeness and closeness attributes associated with each connection, including connecting times, detours, airline agreements and flight frequency. The overall evaluation then requires analysts to consider the ease with which each connection bridges distances from the user perspective. Thus, the metrics need to rely on a sound calibration approach. As shown earlier, the modelling can have sizeable effects on the results of airport-level analyses. Although correlations generally point towards positive correlations between degreeness and more-complex link-quality metrics, these correlations do not hold among all subsets of airports. In particular, many small airports highly benefit from indirect connections through hub airports, which only link-quality metrics can capture properly.

Although connectivity metrics focus on analysing the quality of transport links, some applications require analysts to assign a value to each connection, which yields the market connectivity metrics. As shown earlier, such destination weights can have significant effects on airport-level results. However, this requires the use of justifiable destination weights, which is driven by the objective function of the analysis. For analyses that aim to understand schedule coordination (e.g. for airlines), analysts might disregard weighting or use weights based on traffic throughput. When analysing the impacts of air transport on an economy, analysts could consider the size of each destination market to capture the market potential at each destination. Other destination-based weights could include the number of hotel beds (for touristic connectivity) or population with ethnic ties (for connectivity associated with visiting-friends-and-relatives traffic).

Most connectivity indices are computed for airports, which serve as access points to air transport services. However, most users do not reside or work at the airport. In fact, large airports can serve wide-ranging catchment areas (Lieshout, 2012). Thus, if it is reasonable for potential users to use airports in surrounding regions, then an analysis of air transport connectivity for a specific region cannot be limited to the connectivity of airports in the region. Spatial access modelling is then needed to capture the impacts of airports in neighbouring regions.

Finally, analysts and decision makers can gain additional insights from analysing aspatial connectivity metrics alongside spatial accessibility indices. While the former can help understand the quality of transport networks, the latter can guide policymakers in understanding how transport links can offset locational disadvantages. Accessibility metrics subsequently guide decision makers in understanding the urgency for developing high-quality transport networks in an effort to mitigate geographical disadvantages. Alternatively, metrics of location - such as market potential, which is defined as the sum of destination

Measuring the quality of air transport networks 21 market size over distance - can be used alongside connectivity metrics. This approach would capture both spatial and aspatial impacts on generalised transport costs but would not shed light on the potential trade-offs between spatial and aspatial impacts inherent in fully defined accessibility metrics. However, understanding the trade-offs between spatial and aspatial impacts requires a careful calibration of the accessibility indices used.