Nam woo Kim
ABSTRACT
As urbanization continues to evolve and accelerate, understanding the interactions between urban geography and large-scale mobility data has generated a great interest in the urban studies in recent years. In this paper, we present a method to learn embeddings of urban zones by utilizing the spatiotemporal characteristics of urban mobility. We extract the mobility signature from taxi trajectory data in Seoul, South Korea. Then, we apply Skip-gram model on the mobility signature to obtain the embeddings of the urban zones. Finally, we apply the spherical k-means clustering on the learned embeddings of zones to identify the urban functional regions. Through proposed approach, region maps of Seoul that can readily identify regions of similar socio-economic activities such as mobility patterns are provided.
- Larissa Limongi Aguiar, Gustavo Garcia Manzato, and Antônio Nélson Rodrigues da Silva. 2020. Combining travel and population data through a bivariate spatial analysis to define Functional Urban Regions. Journal of Transport Geography 82 (2020), 102565.Google Scholar
Cross Ref
- Korea Transportation Safety Authority. 2018. Digital Tachograph Dataset. Retrieved Dec. 10, 2019 from http://www.kotsa.or.kr/Google Scholar
- Hugo Barbosa, Marc Barthelemy, Gourab Ghoshal, Charlotte R James, Maxime Lenormand, Thomas Louail, Ronaldo Menezes, José J Ramasco, Filippo Simini, and Marcello Tomasini. 2018. Human mobility: Models and applications. Physics Reports 734 (2018), 1--74.Google ScholarCross Ref
- Isaac Brodsky. 2018. H3: Hexagonal hierarchical geospatial indexing system. Uber Open Source. Retrieved (2018).Google Scholar
- Song Gao, Krzysztof Janowicz, and Helen Couclelis. 2017. Extracting urban functional regions from points of interest and human activities on location-based social networks. Transactions in GIS 21, 3 (2017), 446--467.Google ScholarCross Ref
- Yoav Goldberg and Omer Levy. 2014. word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv: 1402.3722 (2014).Google Scholar
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 855--864.Google ScholarDigital Library
- Kurt Hornik, Ingo Feinerer, Martin Kober, and Christian Buchta. 2012. Spherical k-means clustering. Journal of statistical software 50, 1 (2012), 1--22.Google ScholarCross Ref
- Hyunjoong Kim, Han Kyul Kim, and Sungzoon Cho. 2020. Improving spherical k-means for document clustering: Fast initialization, sparse centroid projection, and efficient cluster labeling. Expert Systems with Applications 150 (2020), 113288.Google ScholarCross Ref
- Xi Liu, Li Gong, Yongxi Gong, and Yu Liu. 2015. Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography 43 (2015), 78--90.Google ScholarCross Ref
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv.1301.3781 (2013).Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.Google Scholar
- Terry Ngo. 2011. Data mining: practical machine learning tools and technique, by ian h. witten, eibe frank, mark a. hell. ACM SIGSOFT Software Engineering Notes 36, 5 (2011), 51--52.Google ScholarDigital Library
- Seoul Urban Planning Portal. 2015. Biotope Dataset. Retrieved Mar. 12, 2021 from https://urban.seoul.go.kr/Google Scholar
- Xin Rong. 2014. word2vec parameter learning explained. arXiv preprint arXiv:1411.2738 (2014).Google Scholar
- Bo Yan, Krzysztof Janowicz, Gengchen Mai, and Song Gao. 2017. From itdl to place2vec: Reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts. In Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems. 1--10.Google ScholarDigital Library
- Dingqi Yang, Daqing Zhang, and Bingqing Qu. 2016. Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Transactions on Intelligent Systems and Technology (TIST) 7, 3 (2016), 1--23.Google ScholarDigital Library
- Zijun Yao, Yanjie Fu, Bin Liu, Wangsu Hu, and Hui Xiong. 2018. Representing urban functions through zone embedding with human mobility patterns. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18).Google ScholarCross Ref
- Jing Yuan, Yu Zheng, and Xing Xie. 2012. Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. 186--194.Google ScholarDigital Library
- Ye Zhi, Haifeng Li, Dashan Wang, Min Deng, Shaowen Wang, Jing Gao, Zhengyu Duan, and Yu Liu. 2016. Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data. Geo-spatial Information Science 19, 2 (2016), 94--105.Google ScholarCross Ref
- Shi Zhong. 2005. Efficient online spherical k-means clustering. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., Vol. 5. IEEE, 3180--3185.Google ScholarCross Ref
Index Terms
- Representation learning of urban regions via mobility-signature-based zone embedding: a case study of Seoul, South Korea
Recommendations
Discovery of urban functional regions based on Node2vec
AbstractIdentifying urban functional regions is a trending topic in urban computing. It helps understand the economic and cultural development of cities and assists decision-makers in land-use planning. However, the studies to date have not fully mined ...
Representing urban functions through zone embedding with human mobility patterns
IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial IntelligenceUrban functions refer to the purposes of land use in cities where each zone plays a distinct role and cooperates with each other to serve people's various life needs. Understanding zone functions helps to solve a variety of urban related problems, such ...
Detecting socioeconomic changes of Chicago regions between 2013 and 2020 using urban region representation learning
AbstractUrban region representation learning aims to extract valuable insights for understanding urban dynamics from diverse and intricate urban data. Since human mobility is greatly related to the socioeconomic status in a city, existing studies have ...
Comments