Automatically acquiring comprehensive, accurate, and real-time mapping information and translating this information into digital maps are challenging problems. Traditional methods are time consuming and costly because they require expensive field surveying and labor-intensive post-processing. Recently, the ubiquitous use of positioning technology in vehicles and other devices has produced massive amounts of trajectory data, which provide new opportunities for digital map production and updating. This paper presents an automatic method for producing road networks from raw vehicle global positioning system (GPS) trajectory data. First, raw GPS positioning data are processed to remove noise using a newly proposed algorithm employing flexible spatial, temporal, and logical constraint rules. Then, a new road network construction algorithm is used to incrementally merge trajectories into a directed graph representing a digital map. Furthermore, the average road traffic volume and speed are calculated and assigned to corresponding road segments. To evaluate the performance of the method, an experiment was conducted using 5.76 million trajectory data points from 200 taxis. The result was qualitatively compared with OpenStreetMap and quantitatively compared with two existing methods based on the F-score. The findings show that our method can automatically generate a road network representing a digital map.

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In the big data era, an enormous amount of spatial and spatiotemporal data are generated every day. However, spatial query result sets that satisfy a query condition are very large, sometimes over hundreds or thousands of terabytes. Interactive visualization of big geospatial data calls for continuous query requests, and large query results prevent visual efficiency. Furthermore, traditional methods based on random sampling or line simplification are not suitable for spatial data visualization with bounded errors and bound vertex numbers. In this paper, we propose a vertex sampling method—the Balanced Douglas Peucker (B-DP) algorithm—to build hierarchical structures, where the order and weights of vertices are preserved in binary trees. Then, we develop query processing algorithms with bounded errors and bounded numbers, where the vertices are retrieved by binary trees’ breadth-first-searching (BFS) with a maximum-error-first (MEF) queue. Finally, we conduct an experimental study with OpenStreetMap (OSM) data to determine the effectiveness of our query method in interactive visualization. The results show that the proposed approach can markedly reduce the query results’ size and maintain high accuracy, and its performance is robust against the data volume.

The landscape spatial pattern with large scale has been the research focus of the landscape ecology recently, but few researches pay attention to that how to choose a perspective to study the characteristics of the landscape pattern of the Qinghai-Tibet Plateau for its unique topography and the alpine ecosystem. This paper takes the 2010 Western Mapping Data as the main data source and uses GIS as research platform. The method of choosing river basin as statistical analysis unit to extract landscape indices was put forward and the theory of landscape ecology was applied to analyze the landscape spatial pattern of the Qinghai-Tibet Plateau and to explore the relationship of the indices of landscape patterns and the terrain factors. The results show that this method can reflect the unique characteristics of the landscape pattern of the Qinghai-Tibet Plateau, which can help landscape ecological planning and protecting. Besides, it can offer reference and help for optimizing the resources allocation adjusting the agriculture structure and developing ecological agriculture.