Characterization of Jos City Road Network, Nigeria

ἀe performance of road network depends on its topological characteristics which help to deḀne its connectiv-ity. ἀis paper analyses the topological characteristic of Jos city road network and its bearing on traᴀc ᰀow situations. Simple graph theoretic measures oᬀered the framework on which the problem was approached. ἀe study requires the abstraction and analysis of the topological structure by selection of certain variables relating to the road connectivity. ἀese include the Beta, Gamma and Alpha index, the PI, Cyclomatic number, and the spread and density of the network. Information on these variables was obtained through the use of vector data model to abstract the road network graph from the Quick-bird satellite imagery used for the study. Results of the Ḁndings reveal that, the road network of Jos City Centre as a whole have achieved an average level of connectivity, showing Beta index values of 1.4049, Gamma index value of 47.06%, Alpha index Value of 20.63%; and a pi and cyclomatic number of 24.74 and 165 respectively, the spread of the network is moderate exhibiting a value of 23, even though some areas have more concentration of roads than the others; and has a road density of 52 links per km2. Based on these Ḁndings, the need for construction of new roads is imperative so as to improve the eᴀciency of connectivity and accessibility within the city

Visualising the structure of document search results: A comparison of graph theoretic approaches

This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion