Technical Papers
Jul 28, 2023

Classifying Urban Functional Zones by Integrating POIs, Place2vec, and LDA

Publication: Journal of Urban Planning and Development
Volume 149, Issue 4

Abstract

The urban functional zone (UFZ) is a basic unit of urban management and eco-environmental research. The study of the UFZ classification has been given more and more attention. In these studies, points of interest (POIs) are important and outstanding data, and both their semantic information and local spatial relationship information are important for the classification of UFZs. Many studies use the semantic information or local spatial relationship information of POIs for the UFZ classification, but owing to the lack of available models, only a few studies consider both of them. Combining POIs, Place2vec, and Latent Dirichlet Allocation (LDA) models, this paper proposes a new UFZ classification model that combines the semantic information and the local spatial relationship information of POIs. First, the principle and implementation of the model was described. Then the proposed model was applied to classify the UFZs of an experimental area (Chaoyang, a district of Beijing). By analyzing with manual interpretation results, accuracy of the classification results manifested that the model is useful for UFZ classification. By comparing the classification result of the proposed model with those of the Place2vec model and the LDA model, the advantage of this model was shown. Compared with the Place2vec model, this model integrates the semantic information of POIs into the representation of UFZs through the integration of the LDA model, thus improving the classification accuracy of UFZs. Compared with the LDA model, this model integrates the local spatial relationships of POIs into the representation of UFZs through the integration of the Place2vec model, thus improving the classification accuracy of UFZs.

Get full access to this article

View all available purchase options and get full access to this article.

Acknowledgments

This research was funded by the National Natural Science Foundation of China (Grant No. 42201506) and the Natural Science Foundation of Shandong Province (Grant No. ZR2019BD019).

References

Andrade, R., A. Alves, and C. Bento. 2020. “POI mining for land use classification: A case study.” ISPRS Int. J. Geo-Inf. 9: 493. https://doi.org/10.3390/IJGI9090493.
Bao, H., D. Ming, Y. Guo, K. Zhang, and S. Du. 2020. “DFCNN-based semantic recognition of urban functional zones by integrating remote sensing data and POI data.” Remote Sens. 12: 1088. https://doi.org/10.3390/rs12071088.
Computer Network Information Center, Chinese Academy of Sciences. 2020. “Gaofen-1 WFV data product.” Geospatial Data Cloud. Accessed June 8, 2023. https://bjdl.gscloud.cn/sources/download/428/GF1_WFV2_4752002?sid=4Fopcv-_I8mn7bZLvJU5SQnLp4ZzJXhQYmlrLA0JbiPpkg&uid=22651.
Du, S. H., S. H. Du, B. Liu, and X. Y. Zhang. 2021. “Mapping large-scale and fine-grained urban functional zones from VHR images using a multi-scale semantic segmentation network and object based approach.” Remote Sens. Environ. 261: 112480. https://doi.org/ 10.1016/j.rse.2021.112480.
Feng, Y., Z. Huang, Y. L. Wang, L. Wan, Y. Liu, Y. Zhang, and X. Shan. 2021. “A SOE-based learning framework using multi-source big data for identifying urban functional zones.” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14: 7336–7348. https://doi.org/10.1109/JSTARS.2021.3091848.
Gao, S., K. Janowicz, and H. Couclelis. 2017. “Extracting urban functional regions from points of interest and human activities on location-based social networks.” Trans. GIS 21: 446–467. https://doi.org/ 10.1111/tgis.12289.
He, W. 2021. “Integrating aerial LiDAR and very-high-resolution images for urban functional zone mapping.” Remote Sens. 13: 2573. https://doi.org/10.3390/rs13132573.
Hu, S., Z. He, L. Wu, L. Yin, Y. Xu, and H. Cui. 2020. “A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data.” Comput. Environ. Urban Syst. 80: 101442. https://doi.org/ 10.1016/j.compenvurbsys.2019.101442.
Huang, X., and Y. Wang. 2019. “Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China.” ISPRS J. Photogramm. Remote Sens. 152: 119–131. https://doi.org/10.1016/j.isprsjprs.2019.04.010.
Huang, X., J. Yang, J. Li, and D. Wen. 2021. “Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery.” ISPRS J. Photogramm. Remote Sens. 175: 403–415. https://doi.org/ 10.1016/j.isprsjprs.2021.03.019.
Jing, Y., R. Sun, and L. Chen. 2022. “A method for identifying urban functional zones based on landscape types and human activities.” Sustainability 14: 4130. https://doi.org/ 10.3390/su14074130.
Li, T., J. Cao, M. Xu, Q. Wu, and L. Yao. 2020. “The influence of urban spatial pattern on land surface temperature for different functional zones.” Landscape Ecol. Eng. 16: 249–262. https://doi.org/10.1007/s11355-020-00417-8.
Liu, B. H., Y. B. Deng, M. Li, J. Yang, and T. Liu. 2021. “Classification schemes and identification methods for urban functional zone: A review of recent papers.” Appl. Sci. 11: 9968. https://doi.org/ 10.3390/app11219968.
Liu, X. P., J. L. He, Y. Yao, J. B. Zhang, H. L. Liang, H. Wang, and Y. Hong. 2017. “Classifying urban land use by integrating remote sensing and social media data.” Int. J. Geogr. Inf. Sci. 31: 1675–1696. https://doi.org/ 10.1080/13658816.2017.1324976.
Meng, X. S., Z. Y. Ouyang, G. F. Cui, W. F. Li, and H. Zheng. 2004. “Composition of plant species and their distribution patterns in Beijing urban ecosystem.” Acta Ecol. Sin. 24: 2200–2206. https://doi.org/10.1088/1009-0630/6/5/011.
Miao, R., Y. Wang, and S. Li. 2021. “Analyzing urban spatial patterns and functional zones using Sina Weibo POI data: A case study of Beijing.” Sustainability 13: 647. https://doi.org/10.3390/su13020647.
Niu, H., and E. A. Silva. 2021. “Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London.” Comput. Environ. Urban Syst. 88: 101651. https://doi.org/10.1016/j.compenvurbsys.2021.101651.
Pandharipande, A. 2021. “Social sensing in IoT applications: A review.” IEEE Sens. J. 21: 12523–12530. https://doi.org/ 10.1109/JSEN.2021.3049714.
Radim Řehůřek. 2022. “Gensim: Topic modelling for humans.” Accessed July 25, 2023. https://radimrehurek.com/gensim.
Scikit-Learn Developers. 2022. “Scikit-Learn: Machine learning in Python.” Accessed July 25, 2023. https://scikit-learn.org/stable/index.html.
Song, J., H. F. Xing, H. X. Zhang, Y. T. Xu, and Y. Meng. 2021. “An adaptive network-constrained clustering (ANCC) model for fine-scale urban functional zones.” IEEE Access 9: 53013–53029. https://doi.org/10.1109/ACCESS.2021.3070345.
Sun, Z., H. Jiao, H. Wu, Z. Peng, and L. Liu. 2021. “Block2vec: An approach for identifying urban functional regions by integrating sentence embedding model and points of interest.” ISPRS Int. J. Geo-Inf. 10: 339. https://doi.org/ 10.3390/ijgi10050339.
Tu, W., Z. W. Hu, L. F. Li, J. Z. Cao, J. C. Jiang, Q. P. Li, and Q. Q. Li. 2018. “Portraying urban functional zones by coupling remote sensing imagery and human sensing data.” Remote Sens. 10: 141. https://doi.org/10.3390/rs10010141.
Xu, S., L. Qing, L. Han, M. Liu, and L. Shen. 2020. “A new remote sensing images and point-of-interest fused (RPF) model for sensing urban functional regions.” Remote Sens. 12: 1032. https://doi.org/10.3390/rs12061032.
Yan, B., K. Janowicz, G. Mai, and S. Gao. 2017. “From ITDL to Place2Vec – reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts.” ACM SIGSPATIAL 2017: 35. https://doi.org/ 10.1145/3139958.3140054.
Yao, Y., X. Li, X. P. Liu, P. H. Liu, Z. T. Liang, J. B. Zhang, and K. Mai. 2017. “Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model.” Int. J. Geogr. Inf. Sci. 31: 825–848. https://doi.org/10.1080/13658816.2016.1244608.
Yu, B., Z. Wang, H. Mu, L. Sun, and F. Hu. 2019. “Identification of urban functional regions based on floating car track data and POI data.” Sustainability 11: 6541. https://doi.org/10.3390/su11236541.
Yu, Z., Y. Jing, G. Yang, and R. Sun. 2021. “A new urban functional zone-based climate zoning system for urban temperature study.” Remote Sens. 13: 251. https://doi.org/10.3390/rs13020251.
Yuan, J., Y. Zheng, and X. Xie. 2012. “Discovering regions of different functions in a city using human mobility and POIs.” In Proc., 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 186–194. New York: Association for Computing Machinery.
Yuan, N. J., Y. Zheng, X. Xie, Y. Z. Wang, K. Zheng, and H. Xiong. 2015. “Discovering urban functional zones using latent activity trajectories.” IEEE Trans. Knowl. Data Eng. 27: 712–725. https://doi.org/ 10.1109/TKDE.2014.2345405.
Zhai, W., X. Bai, Y. Shi, Y. Han, Z. R. Peng, and C. Gu. 2019. “Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs.” Comput. Environ. Urban 74: 1–12. https://doi.org/10.1016/j.compenvurbsys.2018.11.008.
Zhang, X., S. Du, and Q. Wang. 2017. “Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data.” ISPRS J. Photogramm. Remote Sens. 132: 170–184. https://doi.org/10.1016/j.isprsjprs.2017.09.007.
Zhang, X., W. Li, F. Zhang, R. Liu, and Z. Du. 2018a. “Identifying urban functional zones using public bicycle rental records and point-of-interest data.” ISPRS Int. J. Geo-Inf. 7: 459. https://doi.org/10.3390/ijgi7120459.
Zhang, X. Y., S. H. Du, and Q. Wang. 2018b. “Integrating bottom-up classification and top-down feedback for improving urban land-cover and functional-zone mapping.” Remote Sens. Environ. 212: 231–248. https://doi.org/10.1016/j.rse.2018.05.006.
Zhang, X. Y., S. H. Du, Q. Wang, and W. Q. Zhou. 2018c. “Multiscale geoscene segmentation for extracting urban functional zones from VHR satellite images.” Remote Sens. 10: 281. https://doi.org/10.3390/rs10020281.
Zhang, X. Y., S. H. Du, and Z. J. Zheng. 2020. “Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data.” ISPRS J. Photogramm. Remote Sens. 161: 1–12. https://doi.org/ 10.1016/j.isprsjprs.2020.01.005.
Zhong, Y. F., Q. Q. Zhu, and L. P. Zhang. 2015. “Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery.” IEEE Trans. Geosci. Remote Sens. 53: 6207–6222. https://doi.org/10.1109/TGRS.2015.2435801.
Zhou, W., D. Ming, X. Lv, K. Zhou, and H. Bao. 2019. “SO–CNN based urban functional zone fine division with VHR remote sensing image.” Remote Sens. Environ. 236: 111458. https://doi.org/10.1016/j.rse.2019.111458.

Information & Authors

Information

Published In

Go to Journal of Urban Planning and Development
Journal of Urban Planning and Development
Volume 149Issue 4December 2023

History

Received: Feb 6, 2023
Accepted: May 15, 2023
Published online: Jul 28, 2023
Published in print: Dec 1, 2023
Discussion open until: Dec 28, 2023

Permissions

Request permissions for this article.

ASCE Technical Topics:

Authors

Affiliations

Lecturer, School of Information and Control Engineering, Qingdao Univ. of Technology, Shandong 266520, China (corresponding author). Email: [email protected]
Yilai Yang
Master’s Student, School of Information and Control Engineering, Qingdao Univ. of Technology, Shandong 266520, China.
Xinqi Zheng
Professor, School of Information Engineering, China Univ. of Geosciences (Beijing), Beijing 10083, China.

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

  • Uncovering Critical Causes of Highway Work Zone Accidents Using Unsupervised Machine Learning and Social Network Analysis, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-13952, 150, 3, (2024).

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share