Integrating updates from timely sources such as volunteered geographic information (VGI) into the spatial data maintained at official agencies is becoming a more demanding requirement but presents many challenges. This paper proposes an approach to addressing the technical challenge of propagating updates from timely sources (e.g. OpenStreetMap) to spatial data maintained at separate map scales. The main idea is to establish a multiple representation database (MRDB) for datasets at different scales and time to facilitate incremental update, where linkages between corresponding objects at different datasets are made explicit. First, two ways in which the timely sources can be integrated into official data for incremental update are discussed. To derive the linkages between different datasets, a data matching procedure based on computer vision is presented and fine-tuned to match data in different scale ranges. Furthermore, the generalization history used to produce smaller scale data from the larger ones in official data is inferred based on the linkages, and is then used to guide the update propagation. Finally, a framework for incremental generalization in MRDBs is proposed, where crucial issues like strategies for update propagation, cartographic generalization, and the so-called ‘chain reaction’ are addressed. The framework is implemented as a fully automated process where operators like simplification, enlargement, compression, displacement and typification are incorporated into the incremental update process. By testing the framework against real world data sets (i.e. OpenStreetMap and official data at 1:10k, 1:50k and 1:100k), we show that the updates are integrated consistently into existing data in terms of spatial relations and cartographic quality. Our work suggests that making use of timely sources by official mapping agencies and companies in a continuous or event-driven data update is technically feasible, with further improvement and extensions discussed.

将来自诸如自愿地理信息（VGI）之类的及时资源的更新信息集成到官方机构维护的空间数据中，已成为一项越来越苛刻的要求，但也带来了许多挑战。本文提出了一种解决技术难题的方法，该技术难题是将更新从及时的源（例如OpenStreetMap）传播到以单独的地图比例尺维护的空间数据。主要思想是为不同比例和时间的数据集建立一个多重表示数据库（MRDB），以促进增量更新，其中使不同数据集上的相应对象之间的链接变得明确。首先，讨论了将及时资源整合到官方数据中以进行增量更新的两种方式。为了得出不同数据集之间的联系，提出了一种基于计算机视觉的数据匹配程序，并对其进行了微调以匹配不同比例范围内的数据。此外，基于链接推断用于从正式数据中的较大数据中生成较小规模数据的概括历史，然后将其用于指导更新传播。最后，提出了用于MRDB中增量泛化的框架，其中解决了诸如更新传播，制图泛化和所谓的“链式反应”之类的关键问题。该框架被实现为一个完全自动化的过程，在该过程中，诸如简化，放大，压缩，位移和类型化之类的运算符被合并到了增量更新过程中。通过针对现实世界的数据集（即OpenStreetMap和官方数据，以1：10k，1：50k和1：100k）测试框架，我们显示，在空间关系和制图质量方面，更新始终如一地集成到现有数据中。我们的工作表明，在持续的或事件驱动的数据更新中，利用官方制图机构和公司的及时资源在技术上是可行的，并进行了进一步的改进和扩展。