基于时空语义挖掘的城市功能区识别研究
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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.
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.
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.
引文
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[18] 徐健锐, 詹永照. 基于Spark的改进k-means快速聚类算法 [J]. 江苏大学学报: 自然科学版, 2018, 39: 316.
[19] Zhan X, Ukkusuri S V, Zhu F. Inferring urban land use using large-scale social media check-in data [J]. Networks & Spatial Economics, 2014, 14: 647.
[2] Tian G, Wu J, Yang Z. Spatial pattern of urban functions in the Beijing metropolitan region [J]. Habitat International, 2010, 34: 249.
[3] 史玉峰, 王艳. 基于自组织神经网络的城市功能分区研究 [J]. 计算机工程, 2006, 32: 206.
[4] Pu Y, Song X, Ge Y. An improved framework to discover functional urban regions using pedestrian trajectories [C]// Proceedings of the International Conference on Geoinformatics. [s.l.]:IEEE, 2016.
[5] 王永坤, 王海洋, 潘平峻, 等. 面向公共安全的时空数据挖掘综述 [J]. 重庆邮电大学学报: 自然科学版, 2018, 30: 40.
[6] 向峰. 基于移动网络数据的用户行为与城市感知研究 [D]. 武汉: 华中科技大学, 2014.
[7] Brilhante I, Macedo J A, Nardini F M, et al. Where shall we go today?: planning touristic tours with tripbuilder [C]// Proceedings of the Acm International Conference on Information & Knowledge Management. [s.l.]: ACM, 2013.
[8] Cranshaw J, Schwartz R, Hong J I, et al. The livehoods project: utilizing social media to understand the dynamics of a city [J]. Soc Sci Electron Publish, 2012: 1.
[9] Pan G, Qi G, Zhang D, et al. Land-use classification using taxi GPS traces [J]. IEEE Trans Intell Transp Syst, 2013, 14: 113.
[10] 陈世莉, 陶海燕, 李旭亮, 等. 基于潜在语义信息的城市功能区识别——广州市浮动车GPS时空数据挖掘 [J]. 地理学报, 2016, 71: 471.
[11] Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation [J]. J Mach Learn Res, 2003, 3: 993.
[12] Mimno D, Mccallum A. Topic models conditioned on arbitrary features with dirichlet multinomial regression [J]. U Mass Amherst, 2012, 2008: 411.
[13] Xie Z, Ye Z, Wu L. Research on building polygon map generalization algorithm [C]// Proceedings of the Eighth Acis International Conference on Software Engineering. [s.l.]: IEEE, 2007.
[14] Shapiro L G, Stockman G. Computer vision [M]. 2ed edit. Upper Saddle River, NJ, USA: Prentice Hall, 2001.
[15] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space [J]. Comput Sci, 2013, 2013: 1.
[16] 阳馨, 蒋伟, 刘晓玲. 基于多种特征池化的中文文本分类算法 [J]. 四川大学学报: 自然科学版, 2017, 54: 287.
[17] 杨杰明, 吴启龙, 曲朝阳, 等. MapReduce框架下基于抽样的分布式k-means聚类算法 [J]. 吉林大学学报: 理学版, 2017, 55: 109.
[18] 徐健锐, 詹永照. 基于Spark的改进k-means快速聚类算法 [J]. 江苏大学学报: 自然科学版, 2018, 39: 316.
[19] Zhan X, Ukkusuri S V, Zhu F. Inferring urban land use using large-scale social media check-in data [J]. Networks & Spatial Economics, 2014, 14: 647.
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