2017
DOI: 10.3390/ijgi6120381
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An Improved Identification Code for City Components Based on Discrete Global Grid System

Abstract: City components are important elements of a city, and their identification plays a key role in digital city management. Various identification codes have been proposed by different departments and systems over the years, however, their application has been partly hindered by the lack of a unified coding framework. The use of a code identifying a city component for unified management and geospatial computation across systems is still problematic. In this paper, we put forward an improved identification code for… Show more

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Cited by 11 publications
(5 citation statements)
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References 28 publications
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“…The proposed air traffic highways in this research are constructed by 3D grids. Apart from UAV-related applications, GeoSOT-3D has been extensively investigated in remote sensing data management [ 38 , 39 ], city component identification [ 40 ], trajectory data storage [ 41 ], and urban expansion monitoring [ 42 ].…”
Section: Methodsmentioning
confidence: 99%
“…The proposed air traffic highways in this research are constructed by 3D grids. Apart from UAV-related applications, GeoSOT-3D has been extensively investigated in remote sensing data management [ 38 , 39 ], city component identification [ 40 ], trajectory data storage [ 41 ], and urban expansion monitoring [ 42 ].…”
Section: Methodsmentioning
confidence: 99%
“…Thus, they are not suited for large-scale querying in time and space. As a spatial-partitioning-based index model, the GeoSOT (geographical coordinate subdividing grid with one dimension integer coding on 2 n -tree) has been widely used for spatial data indexing and retrieval [24][25][26][27][28]. The above works proposed means of organizing and retrieving spatial data based on GeoSOT, but they only encode spatial information for the object without encoding its time information.…”
Section: Related Workmentioning
confidence: 99%
“…Activity hotspots are found to display scaling patterns in terms of the sublinear scaling relationships between the number of stopping locations and the number of points of interest (POIs), which indicates economies of scale of human interactions with urban land-use-inferred by stopping locations, such as the railway station. Qi et al [62] suggest identification codes for city components based on the discrete global grid system. The locations of city components are identified with one-dimensional integer codes, which outperform traditional codes in data query and geospatial computation.…”
Section: Insights From the Special Issue On Place-based Gismentioning
confidence: 99%