Data mining for recognition of spatial distribution patterns of building heights using airborne lidar data. (January 2020)

• Record Type: Journal Article
• Authors: Shirowzhan, S.; Lim, S.; Trinder, J.; Li, H.; Sepasgozar, S.M.E.
• Is Part Of: Advanced engineering informatics. Volume 43(2020)
• Publication Date: 2020-01
• Subjects: GIS -- Digital Building Model -- Spatial statistics -- Spatial patterns -- Spatial data mining -- Kernel density -- Spatial big data; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2020.101033
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Extend theoritical analyze methods for spatio-temporal vertical urban development. Examine spatial data mining on spatial and non-spatial attributes of data in GIS. Employs theoretical models of Local Moran's I, Gi* and Kernel density on building heights. Explores building clusters based on the computations of height similarities. Abstract: There is an increasing demand for spatial big data visualisation in Geographic Information Systems (GIS) in building construction and urban development. Exploring building height patterns is required to obtain and visualize essential information about spatio-temporal vertical urban developments due to the trends towards increasing building heights in different urban fabrics. While metrics to characterize horizontal patterns of urban fabric using spectral information exist, theoritical-based metrics identifying the patterns of vertical urban developments using height values are still scarce. In addition, there is a lack of reliable methods to analyze height information for modeling the distribution of building heights and to automatically detect three-dimensional urban patterns. In this paper, we propose to apply the spatial statistics of Local Moran's I (LMI), G i ∗ and Kernel Density Estimation (KDE) on building heights to explore vertical urban patterns through detecting the concentration of relatively higher buildings. The proposed methods were applied on two different airborne lidar point cloud data sets. The results show overall good performance of LMI and Gi* methods compared to KDE. It is also found that there is a higher level of agreement between clusters of relatively higher buildings derived by the autocorrelation statistics of LMI and Gi*, compared with the patterns derived from the Kernel density. For the lower accuracies obtained from the KDE, the authors suggest to use either LMI or Gi* for this kind of study. The spatial closeness of clusters of higher buildings to major roads, defined by the mean distances of the clusters to major roads, were investigated and based on the Analysis of Variance (ANOVA) and Tukey's tests, the mean distances were found to be shorter than for all other buildings. Lastly, an analysis of clusters of the relatively higher buildings showed varying land uses for the two case studies.