Multi-scale features of urban planning spatial data

Abstract

Geospatial data is the digital carrier of geographic information. Spatial data and its manipulation or processing is related to cognitive ability, and the ability to understand the scale effect and behavior of spatial data would affect the accuracy of the acquired spatial knowledge. Scale is an important feature of spatial data, representing the depth and scope of people's cognition about spatial objects and spatial phenomena. The multi-manipulation and representation of spatial data are common techniques for spatial cognition and spatial analysis, and they much conform to our habits of inference. Urban planning data is a special kind of spatial data and it has a typical application feature of multi scale of spatial data. This paper analyses the major characteristics of urban planning spatial data, and it points out that the key elements to perform multi-scale visualization are to build a multi-resolution space and to determine its appropriate spatial data model. When researching on scale effect and scale transformation rules, we should take the attributes of urban planning spatial data into account at semantic level, as well as the changes of its spatial and temporal scale, when researching on scale effect and scale transformation rule. The generation of new dataset should be bidirectional; it not only could derive sketchy dataset at any scale from a more detailed spatial dataset, but also could carry out re-construction perfectively. Spatial accuracy and spatial features should be corresponded to its scale in the deriving process.