About the book
As a graphic representation of data, data visualization is a useful approach for communicating with viewers which offers them a representation of possible trends or patterns of relationships among the represented data. Data visualization can be considered in various dimensions including 1D, 2D, 3D, and 4D. Where the spatial and temporal information of data is involved, the dimension of data can be increased from 2D to 3D or 4D. Evolving technologies are available for data visualization, including web-based platforms, which increase the interaction and the efficiency of using data by viewers. Data science methods including data mining, machine learning, and deep learning can be applied to extract patterns, identify problems, or predict the future/expected outcomes. Using these methods helps to explore deeper insights, offer accurate predictions, or even generate suggestions or decisions to users and decision makers. The examples include smart cities or smart construction where information is modelled in 3D, 4D, 5D, 6D and 7D and can be analysed in either Building Information Modelling (BIM) or Geographic Information System (GIS) environments. The integration of data mining techniques with BIM and GIS has resulted in significant improvements in data-driven decision-making and has offered efficient communication tools, reduction of reworks, higher productivity, and better time management.
This book tends to discuss different relevant areas of data visualizations and point to new avenues for developing innovative approaches, novel methods, learn from the smart data visualization literature across data science, engineering and the built environment fields that have been gateways for extracting hidden patterns and relationships among variables and agents with a broad impact to be used on other similar fields. This book calls for chapters proposing innovative and impressive data visualizations by increasing understanding of data and improving the insight from data for better decision making related to the analysis methods.