Abstract:

To address the challenges of difficult visual management and inefficient localization in large-scale， multi-category urban spatiotemporal data， an interactive visualization method for multi-category urban spatiotemporal big data based on point aggregation was proposed. Firstly， two efficient aggregation methods were introduced： geographic location-based aggregation and geographic hierarchy-based aggregation， to meet the government personnel’s needs for efficient visual management across diverse scenarios. Secondly， on the basis of efficient point aggregation， a conditional parsing algorithm was proposed o enable real-time parsing and conversion of spatiotemporal conditions and category visibility， improving data localization efficiency. Finally， 270 000 pieces of urban entity data from Beijing were used to conduct geographically hierarchical parsing algorithms and aggregation interaction experiments. Experimental results demonstrate that the average processing time of the two proposed aggregation methods were reduced by approximately 69.66% and 63.15%， respectively， compared to K-means in different scenarios， confirming the efficiency and stability of data processing and aggregation services in the system during data storage. Besides， this aggregation application service has been successfully deployed in a city governance project， supporting million-scale urban data aggregation with proven applicability.

Key words: point aggregation, geographically hierarchical parsing, human-computer interaction, visual management system, spatiotemporal data

摘要：

针对大规模多类别城市时空数据可视化管理难、定位效率低的问题，提出基于点聚合的多类别城市时空大数据交互式可视化方法。首先，分别提出基于地理位置与基于地理层级的高效聚合方法，满足政务人员在不同场景下的高效可视化管理需求；其次，在高效点聚合功能基础上，提出条件解析算法实现对时空条件和类目显隐的实时解析转换，提高数据定位效率；最后，采用北京市27万条城市实体数据进行地理层级解析算法与聚合交互实验。实验结果表明，在不同场景下，所提出的2个聚合方法的平均耗时比K-means方法分别缩短了约69.66%和63.15%，充分说明了系统存储数据时数据处理与聚合服务的高效性与稳定性；而且该聚合应用服务目前已成功在某城市治理项目示范应用，支撑了百万级城市数据聚合服务，具有较高的可用性。

关键词: 点聚合, 地理层级解析, 人机交互, 可视化管理系统, 时空数据