For administration efficiency most countries subdivide their national territory into administrative regions. These regions are used to delineate areas which are internally well connected and relatively cohesive, especially compared with the links between regions. Hence, many countries seek to delineate local labour markets (LLMs): geographical regions where the majority of the local population seeks employment and from which the majority of local employers recruit labour. LLM boundaries are often based on functional regions, which represent the aggregate commuting patterns of the local population. A number of regionalisation procedures for objectivity delineating functional regions have been suggested, though many of these procedures require the use of ad hoc parameters to control the size and number of regions. Recently, a range of network-based alternatives have been developed in the literature. One of the most successful such methods is based on the concept of modularity: the extent to which there are dense connections within functional regions, but only sparse connections between functional regions. In this paper we maximise the modularity of a network of commuting flows to produce a regionalisation that exhibits less interaction than expected between regions. We demonstrate the effectiveness of this type of regionalisation procedure on a simulated geographical network, as well as using commuting data for the Republic of Ireland. We suggest that this new method has specific advantages over existing regionalisation procedures, particularly in the context of disaggregate commuting patterns of socioeconomic subgroups.

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Land cover to land use Modularity a b s t r a c tThis paper applies three algorithms for detecting communities within networks. It applies them to a network of land cover objects, identified in an OBIA, in order to identify areas of homogenous land use. Previous research on land cover to land use transformations has identified the need for rules and knowledge to merge land cover objects. This research shows that Walktrap, Spinglass and Fastgreedy algorithms are able to identify land use communities but with different spatial properties. Community detection algorithms, arising from graph theory and networks science, offer methods for merging sub-objects based on the properties of the network. The use of an explicitly geographical network also identifies some limitations to network partitioning methods such as Spinglass that introduce a degree of randomness in their search for community structure. The results show such algorithms may not be suitable for analysing geographic networks whose structure reflects topological relationships between objects. The discussion identifies a number of areas for further work, including the evaluation of different null statistical models for determining the modularity of geographic networks. The findings of this research also have implications for the many activities that are considering social networks, which increasingly have a geographical component.