000			01962nam a22002417a 4500
003			ZW-GwMSU
005			20221129160249.0
008			221129b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100			_ade Almeida, J.-P. _eauthor
245			_aA graph-based algorithm to define urban topology from unstructured geospatial data _ccreated by J.-P. de Almeida ,J.G. Morley &I.J. Dowman
264			_aLondon: _bTaylor & Francis, _c2013.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_vVolume , number ,
520			_aInterpretation and analysis of urban topology are particularly challenging tasks given the complex spatial pattern of the urban elements, and hence their automation is especially needed. In terms of the urban scene meaning, the starting point in this study is unstructured geospatial data, i.e. no prior knowledge of the geospatial entities is assumed. Translating these data into more meaningful homogeneous regions can be achieved by detecting geographic features within the initial random collection of geospatial objects, and then by grouping them according to their spatial arrangement. The techniques applied to achieve this are those of graph theory applied to urban topology analysis within GIS environment. This article focuses primarily on the implementation and algorithmic design of a methodology to define and make urban topology explicit. Conceptually, such procedure analyses and interprets geospatial object arrangements in terms of the extension of the standard notion of the topological relation of adjacency to that of containment: the so-called ‘containment-first search’. LiDAR data were used as an example scenario for development and test purposes.
650			_aurban topology
650			_agraph theory
650			_ascene analysis
856			_uhttps://doi.org/10.1080/13658816.2012.756881
942			_2lcc _cJA
999			_c160672 _d160672