In spatial science and urban applications, “space" is presented by multiple disciplines as a notion referencing our living environment. Space is used as a general term to help understand particular characteristics of the environment. However, the definition and perception of space varies and these variations have to be harmonised. For example, space may have diverse definitions and classification, the same environment may be abstracted/modelled by contradicting notions of space, which can lead to inconsistencies and misunderstandings. In this paper, we seek to investigate and document the state-of-the-art in the research of “space” regarding its definition, classification, modelling and utilization (2D/3D) in spatial sciences and urban applications. We focus on positioning, navigation, building micro-climate and thermal comfort, landscape, urban planning and design, urban heat island, interior design and planning, transportation and intelligent space. We review 147 research papers, technical reports and on-line resources. We compare the presented space concepts with respect to five criteria—classification, boundary, modelling components, use of standards and granularity. The review inventory is intended for both scientists and professionals in the spatial industry, such as companies, national mapping agencies and governments, and aim to provide a reference to better understand and employ the “space” while working across disciplines.

To explore the relationship between the objective morphological features and subjective scenic beauty preference of landscape open space units, this study improves the research method for morphology quantification, scenic beauty preference survey and relationship analysis. Fourteen morphology factors representing the features of boundary, domain and enclosure are quantified based on the point cloud data of 35 open space units. Scenic beauty evaluation is conducted online with dynamic panoramic photos. Principal component analysis is implemented to convert 14 correlated form factors into five principal components representing morphological principle. The multiple linear regression model explains the contribution of each principal component to scenic beauty preference values, showing a significance sequence of penetration, scale, naturalness, complexity and rhythm. The first three principal components have positive impacts on scenic beauty preference, while the last two principal components are negative. This work aims to reveal the regularity of public’s scenic beauty preference for open space morphology.

No abstract

No abstract