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<https://discovery.ucl.ac.uk/id/eprint/16409> <http://purl.org/dc/terms/title> "Spectral comparison of large urban graphs"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/16409> <http://purl.org/ontology/bibo/abstract> "The spectrum of an axial graph is proposed as a means for comparison between spaces,\r\nparticularly for measuring between very large and complex graphs. A number of methods have\r\nbeen used in recent years for comparative analysis within large sets of urban areas, both to\r\ninvestigate properties of specific known types of street network or to propose a taxonomy of urban\r\nmorphology based on an analytical technique. In many cases, a single or small range of predefined,\r\nscalar measures such as metric distance, integration, control or clustering coefficient have\r\nbeen used to compare the graphs. While these measures are well understood theoretically, their\r\nlow dimensionality determines the range of observations that can ultimately be drawn from the data.\r\nSpectral analysis consists of a high dimensional vector representing each space, between which\r\nmetric distance may be measured to indicate the overall difference between two spaces, or\r\nsubspaces may be extracted to correspond to certain features. It is used for comparison of entire\r\nurban graphs, to determine similarities (and differences) in their overall structure.\r\nResults are shown of a comparison of 152 cities distributed around the world. The clustering of\r\ncities of similar properties in a high dimensional space is discussed. Principal and nonlinear\r\ncomponents of the data set indicate significant correlations in the graph similarities between cities\r\nand their proximity to one another, suggesting that cultural features based on location are evident in\r\nthe city form and that these can be quantified by the proposed method. Results of classification\r\ntests show that a city’s location can be estimated based purely on its form.\r\nThe high dimensionality of the spectra is beneficial for its utility in data-mining applications that can\r\ndraw correlations with other data sets such as land use information. It is shown how further\r\nprocessing by supervised learning allows the extraction of relevant features. A methodological\r\ncomparison is also drawn with statistical studies that use a strong correlation between human\r\ngenetic markers and geographical location of populations to derive detailed reconstructions of\r\nprehistoric migration. Thus, it is suggested that the method may be utilised for mapping the transfer\r\nof cultural memes by measuring comparison between cities."^^<http://www.w3.org/2001/XMLSchema#string> .
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