CISUC

Automatic Classification of Points-of-Interest for Land-use Analysis

Authors

Filipe Rodrigues
Francisco Câmara Pereira
Ana Cristina da Costa Oliveira Alves
Shan Jiang
Joseph Ferreira

Abstract

This paper describes a methodology for automatic classification of places according to the North American Indus- try Classification System. This taxonomy is applied in many areas, particularly in Urban Planning. The typical approach is to manually classify places/Points-of-Interest that are collected with field surveys. Given the financial costs of the task some semi-automatic approaches have been taken before, but they are still based on field surveys and official census. In this paper, we apply machine learning to fully automatize the classification of Points-of-Interest collected from online sources. We compare the adequacy of several algorithms to the task, using both flat and hierarchical approaches, and validate the results in the Urban Planning context.

Keywords

machine learning; space analysis; points-of-interest; urban planning; GIS

Related Project

Crowds - Understanding urban land use from digital footprints of crowds

Conference

GEOProcessing 2012 : The Fourth International Conference on Advanced Geographic Information Systems, Applications, and Services, January 2012

DOI


Cited by

Year 2019 : 1 citations

 Yao, Y., Liu, P., Hong, Y., Liang, Z., Wang, R., Guan, Q., & Chen, J. (2019). Fine-scale intra- and inter-city commercial store site recommendations using knowledge transfer. Transactions in GIS, 23(5), 1029-1047.

Year 2018 : 4 citations

 Wong, J., Husain, A. M., & Panjwani, S. D. (2018). U.S. Patent No. 9,945,676. Washington, DC: U.S. Patent and Trademark Office.

 Yu, Y., Li, J., Zhu, C., & Plaza, A. (2018). Urban Impervious Surface Estimation from Remote Sensing and Social Data. Photogrammetric Engineering & Remote Sensing, 84(12), 771-780.

 Fang, F., Yuan, X., Wang, L., Liu, Y., & Luo, Z. (2018). Urban Land-Use Classification From Photographs. IEEE Geoscience and Remote Sensing Letters, (99), 1-5.

 Zhang, X., Li, W., Zhang, F., Liu, R., & Du, Z. (2018). Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data. ISPRS International Journal of Geo-Information, 7(12), 459.

Year 2017 : 3 citations

 Liu, Xiaoping, et al. "Classifying urban land use by integrating remote sensing and social media data." International Journal of Geographical Information Science (2017): 1-22.

 Yao, Yao, et al. "Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model." International Journal of Geographical Information Science 31.4 (2017): 825-848.

 Wood, S., Muthyala, R., Jin, Y., Qin, Y., Rukadikar, N., Rai, A., & Gao, H. (2017, December). Automated industry classification with deep learning. In Big Data (Big Data), 2017 IEEE International Conference on (pp. 122-129). IEEE.

Year 2016 : 4 citations

 Lin, Chow-Sing, and Shang-Hsuan Hsu. "Effective self-adjustment places of interest discovery in public places." International Journal of Ad Hoc and Ubiquitous Computing 22.4 (2016): 226-235.

 Yao, Yao, et al. "Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model." International Journal of Geographical Information Science (2016): 1-24.

 Chen, Chenru, et al. "Land use classification in construction areas based on volunteered geographic information." Agro-Geoinformatics (Agro-Geoinformatics), 2016 Fifth International Conference on. IEEE, 2016.

 Montini, Lara. Extraction of transportation information from combined position and accelerometer tracks. PhD Thesis Diss. Eidgenössische Technische Hochschule Zürich. 2016.

Year 2014 : 3 citations

 Montini, L., Rieser-Schüssler, N., Horni, A., Axhausen, K., Trip Purpose Identification from GPS Tracks. Transportation Research Board, pp 16–23, 2014.

 Montini, L., & Rieser, N. Implementation and pretest of the trip purpose detection. 2014

 Bakillah, M., Liang, S., Mobasheri, A., Jokar Arsanjani, J., & Zipf, A.. Fine-resolution population mapping using OpenStreetMap points-of-interest. International Journal of Geographical Information Science, (ahead-of-print), 1-24. 2014.

Year 2013 : 1 citations

 Küster, T., Lützenberger, M., Freund, D., & Albayrak, S. Distributed evolutionary optimisation for electricity price responsive manufacturing using multi-agent system technology. International Journal On Advances in Intelligent Systems, 6(1 and 2), 27-40, 2013.