基于时空语义挖掘的城市功能区识别研究

Discovering urban functional regions based on sematic mining from spatiotemporal data

摘要:

针对目前城市功能区划分大多依靠人工完成,且未充分使用城市中时空数据的问题,提出一种基于时空语义挖掘的城市功能区识别方案.首先,选取某城市矩形区域为研究样本,并以建筑物为划分依据将研究样本划分为有效的基础区域;然后,对各基础区域内的新浪微博位置签到数据及POI(Points of Interest)数据进行时空语义挖掘,采用狄利克雷多项式回归（DMR）主题模型生成区域的功能性向量;最后,通过向量聚类,依据POI类别比例完成区域的功能性识别.实验结果表明,本方案相比基于POI密度的kmeans聚类方案和基于潜在狄利克雷分布（LDA）主题模型的城市功能区识别方法具有更高的准确性,位置签到数据所表征出的人们活动模式可以揭示城市功能区之间的差异,在城市地理空间分析上具有良好的效果.

Abstract:

To tackle the problem that the current urban functional regions division are manual completed and do not fully use the spatiotemporal data in urban regions, an approach for detecting urban functional regions is proposed based on sematic mining from spatiotemporal data. In which, a rectangular area of the city is first selected as a research sample and divided it into some valid basis region units according to its buildings. Dirichlet multinomial regression (DMR) topic model is then implemented for the checkin and POI(points of interest) data from Sina weibo in these basis region units and the functional vectors of the basis region units are obtained. Finally，the functional regions are discovered with vector clustering algorithm and POI’s category proportion. The experimental results show that this approach has higher accuracy compared with the kmeans clustering method based on POI density and urban functional area detecting approach based on latent Dirichlet allocation (LDA) topic model. Therefore，The activity patterns of people identified by location checkin data can reveal the differences between urban functional areas and have a good effect on urban geospatial analysis.