• Journal of Geo-information Science
  • Vol. 22, Issue 6, 1370 (2020)
Jingjing LIU1、1、2、2, Yusi LIU1、1、2、2, Disheng YI1、1、2、2, Jing YANG1、1、2、2, and Jing ZHANG1、1、2、2、*
Author Affiliations
  • 1. 首都师范大学 三维信息获取与应用教育部重点实验室,北京 100048
  • 1MOE Key Lab of 3D Information Acquisition and Application, Capital Normal University, Beijing 100048, China
  • 2. 首都师范大学资源环境与旅游学院,北京 100048
  • 2College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
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    DOI: 10.12082/dqxxkx.2020.190594 Cite this Article
    Jingjing LIU, Yusi LIU, Disheng YI, Jing YANG, Jing ZHANG. Extracting Mixed Topic Patterns within Downtown Beijing at the Block Level[J]. Journal of Geo-information Science, 2020, 22(6): 1370 Copy Citation Text show less

    Abstract

    Cities with different land use types influenced by rapid urbanization and urban expansion support various human activities, such as shopping, eating, living, working, and recreation. The mixed use of land can stimulate the vitality of the city, enable the city togather enough people at different points in time, thus producing more interaction, promoting diversified consumption, and improving the economic and social benefits of the city.Mixed characteristics of land use types in cities gain more popularity in many researches due to the huge practical meanings. However, previous researches on mixed characteristics calculation mainly focused on POI data,and there is a lack of consideration for detecting urban topics. Human activities usually take place in different types of points of interest, the potential relationships and spatial interactions between the different types of adjacent POIs can work together to express the potential semantics of locations. In this paper, from an urban topic perspective, a method for the consideration of the relationship between POIs was proposed, and the Hill Numbers Diversity Index was applied to calculate the mixed degree of topics at the block level. Specifically,LDA (Latent Dirichlet Allocation) topic model was firstly used to generate topic vectors of the block and the co-occurrence patterns of POIs. Secondly, the diversity index was introduced to measure the mixed degree of blocks. Then, according to the Goodness of Variance Fit (GVF) and the nature break method, the blocks wereclassified into three groups: (1) high mixed blocks, (2) medium mixed blocks, and (3) low mixed blocks. Finally, multiple linear regression was applied based on mixed degree and topics in the blocksto uncover the significant topics and mixed pattern.Results show that different mixed blocks haddifferent mixed patterns.For high mixed blocks, the topic of teahouse restaurant was significant; the topics of company, enterprise, and residence weresignificant in medium mixed blocks; and the most typical two patterns in low mixed blocks werethe existence of landscape and famous scenery topic and teahouse restaurant topic. To sum up,starting from the urban topic, this paper reveals the mixed pattern of block, and the results show thatdifferent mixed patterns reflect the characteristics of different mixed areas and present certain rules in spatial distribution, which is conducive to the deep understanding of the cityareas, so as to provide a reference for the construction of Beijing mixed city, and also provide suggestions for other mixed cities.
    Jingjing LIU, Yusi LIU, Disheng YI, Jing YANG, Jing ZHANG. Extracting Mixed Topic Patterns within Downtown Beijing at the Block Level[J]. Journal of Geo-information Science, 2020, 22(6): 1370
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