Data analytics of urban fabric metrics for smart cities

Abstract

Comprehensive understanding of the built environment, especially the urban form, is a prerequisite for building a smart city. Data analytics of urban fabric metrics using quantitative methods is critical to understanding a city’s complexity. This paper aims to study urban fabric using comprehensive computation methods. A series of morphological indexes of urban blocks are established to measure the blocks’ overall features and subtle differences. This study uses multiple statistical methods with computation techniques and machine learning to fulfill factor analysis and clustering to classify major block types and their spatial distribution, and this study aims to precisely position the important continuous zone and fracture locations within the study area based on a geo-information system (GIS), effectively revealing the potential morphological order of different block types in the urban fabric. The study provides accurate basis and technical support for the optimization of urban construction. It has important and practical significance for promoting the scientific and reasonable implementation of a new type of urbanization.