Research On Situation Visualization Of Road Transport Capacity Based On Spatial Clustering

Road transport plays an important role in national economic construction and military logistics support and transport capacity is a major evaluation of transportation level. With the rapid development of the national highway traffic, transport capacity data exploded in the last few years. Research on how to obtain valuable information from transport capacity data has become inevitable. Considered large road transport enterprises as the basic research unit, the spatial clustering analysis technique was used to analyze the thematic attribute which refers to capacity. Based on the clustering results, we designed an effective visualization method through some rational organization and standardization. Then, the knowledge of capacity situation obtained from these processes can provide the support for related analysis and decision-making.In this dissertation, we firstly proposed a system framework of capacity data visualization based on clustering, and researched the basic concept of transport capacity situation, as well as spatial clustering analysis and spatial data visualization technologies. After that, a clustering algorithm named ASCDT was used to analyze the road transport capacity data. Combined with the describing parameters of the clustering results, we designed a series of visual objects and constructed the mapping relationship. At last, the result was displayed and discussed through implementing. The main contributions of this dissertation are embodied in the following aspects:1. Applied spatial clustering to analyze the capacity data of road transport. Aiming at the large scale and complex structure of the data, spatial clustering algorithm represented by ASCDT was used to handling and analyzing the point entity oriented large highway transport enterprise, based on the spatial attributes and the thematic attribute of transport capacity. As a result, figures of transport capacity distribution were achieved.2. Established a dataset to describe the clustering results. By making full use of the intermediate results of the clustering process, we extracted and defined a relatively complete set of parameters to describe the main characteristics of spatial clusters. Then, the set was treated as a bridge between the clustering results and visual objects, which convert the boring data to vivid graphics and images.3. Designed and implemented a novel method to visualize transport capacity situation. Refer to the visualization approach of bubble chart and heat map, a kind of icons filled with gradient colors were designed to visually express the characteristics of clustering results including location, size, number and density end with being mapped onto the map. This method was implemented through a new development environment named Processing, and some discussion was made.4. Validated our approach by implementing the capacity situation visualization in a military transport information system with proposed method.