Abstract
The urban built-up area is of great significance for urban planning, construction, and management, and is an important index to measure the degree of urbanization. This study reviews the definitions of the urban built-up area in different countries and analyzes its basic characteristics. The rational and advantages of defining the urban built-up area using taxi trajectory data are described. First, we preprocessed these data and used the density-based spatial clustering of applications with noise (DBSCAN) algorithm to cluster the trajectory points. The urban built-up area was defined by the boundaries of each cluster of trajectory points, generated using the Delaunay triangulation method. A parameter decision model was assessed to determine the best-suited parameter for the clustering algorithm. The taxi trajectory data in Beijing were taken as the experimental data, and the urban built-up area generated according to the proposed method shows high consistency with remote sensing images. The results with the proposed method show that the urban built-up area of Beijing comprises a main area and several suburban areas. The main part appears in block form, with some bulges distributed along the city’s main roads. The suburban areas serve as special functional areas of the city and surrounding towns.
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Arsanjani JJ, Helbich M, Vaz EDN (2013) Spatiotemporal simulation of urban growth patterns using agent-based modeling: the case of Tehran. Cities 32:33–42
Edelkamp S, Schrdl S (2003) Route planning and map inference with global positioning traces. Com Sci Perspect Berlin: Springer:128–151
Ester M, Kriegel H, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining (KDD’96), Portland, OR, USA, 2–4 August 1996, pp 226–231
Fang C, Li G, Wang S (2016) Changing and differentiated urban landscape in China: spatio-temporal patterns and driving forces. Environ Sci Technol 50:2217
Fu Z, Li Q, Liu L et al (2017) Identification of urban network congested segments using GPS trajectories double-clustering method. Geomat Info Sci Wuhan Univ 42(9):1264–1270
Gu C, Shen J (2003) Transformation of urban socio-spatial structure in socialist market economies: the case of Beijing. Habitat Inter 27:107–122
Hagenauer J, Helbich M (2012) Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks[J]. Int J Geogr Inf Sci 26:963–982
He C, Okada N, Zhang Q et al (2006) Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China. Appl Geogr 26:323–345
Hey T, Tansley S, Tolle K (2009) The fourth paradigm: data-intensive scientific discovery. Proc IEEE 99:1334–1337
Hu Y, Wu Z, Xiong W et al (2008) Study on the method of defining urban built-up area: taking Wuhan as an example. City Plan Rev:88–91
Jia T, Jiang B (2010) Measuring urban sprawl based on massive street nodes and the novel concept of natural cities. http://arxiv.org/abs/1010.0541
Jia T, Jiang B, Carling K et al (2012) An empirical study on human mobility and its agent-based modeling. J Statistic Mechan Theo Exper 11:P11024
Jiang B (2015) Head/tail breaks for visualization of city structure and dynamics. Cities 43:69–77
Jiang B, Jia T (2011) Zipf’s law for all the natural cities in the United States: a geospatial perspective. Int J Geogr Inf Sci 25:1269–1281
Jiang B, Liu X (2010) Scaling of geographic space from the perspective of city and field blocks and using volunteered geographic information. Int J Geogr Inf Sci 26:215–229
Jiang B, Ma D (2018) How complex is a fractal? head/tail breaks and fractional hierarchy. J Geovis Spat Anal 2:6
Karagiorgou S, Pfoser D. On vehicle tracking databased road network generation. Proceedings of the 20th International Conference on Advances in Geographic Information Systems, Redondo Beach, California, ACM, 2012, 89–98
Karathanassi V, Iossifidis C, Rokos D (2000) A texture-based classification method for classifying built areas according to their density. Int J Remote Sens 21:1807–1823
Kumar N, Misra S, Rodrigues JJPC et al (2015) Coalition games for spatio-temporal big data in Internet of vehicles environment: a comparative analysis. IEEE Internet Things J 2:310–320
Li F, Wang R, Paulussen J, Liu X (2005) Comprehensive concept planning of urban greening based on ecological principles: a case study in Beijing, China. Landscape and Urban Planning 72:325–336
Li J, Qin Q, You L et al (2013a) Parking lot extraction method based on floating car data. Geomat Info Sci Wuhan Univ 38(5):599–603
Li X, Zhou W, Ouyang Z (2013b) Forty years of urban expansion in Beijing: what is the relative importance of physical, socioeconomic, and neighborhood factors. Appl Geogr 38:1–10
Liu X, Ban Y (2013) Uncovering spatio-temporal cluster patterns using massive floating car data. ISPRS Int J Geo-Inf 2:371–384
Long Y, Shen Y (2014) Mapping parcel-level urban areas for a large geographical area.
Long Y, Shen Y, Jin X (2015) Mapping block-level urban areas for all Chinese cities. Ann Assoc Am Geogr 106:1–18
Long Y, Zhai W, Shen Y, et al. Understanding uneven urban expansion with natural cities using open data. Landscape & Urban Planning, 2017
Masek JG, Lindsay FE, Goward SN (2000) Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations. Int J Remote Sens 21:3473–3486
Ministry of Construction of People’s Republic of China (1999) Basic terminology of urban planning standards. China Construction Industry Press, Beijing, China
Mu F, Zhang Z, Chi Y, Liu B, Zhou Q, Wang C et al (2007) Dynamic monitoring of built-up area in Beijing during 1973-2005 based on multi-original remote sensing images. J Remote Sens 11:257–268
Pandey B, Joshi PK, Seto KC (2013) Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data. Int J Appl Earth Obs Geoinf 23:49–61
Ramachandra TV, Aithal BH, Sanna DD (2012) Insights to urban dynamics through landscape spatial pattern analysis. Int J Appl Earth Obs Geoinf 18:329–343
Ranagalage M, Estoque RC, Zhang X et al (2018) Spatial changes of urban heat island formation in the Colombo District, Sri Lanka: implications for sustainability planning. Sustainability 10
Sahin Y, Teke M, Erdem A, Duzgun S (2012) Urban area and building detection on high resolution multispectral satellite images using spatial statistics. In: 20th signal processing and communications applications conference (SIU), 2012, pp 1–4
Schroedl S, Wagstaff K, Rogers S et al (2004) Mining GPS traces for map refinement. Data Min Knowl Disc 9:59–87
Seto KC, Fragkias M (2005) Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landsc Ecol 20:871–888
Shackelford AK, Davis CH (2003) A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Transactions on Geoscience & Remote Sensing 41:2354–2363
Tan M, Li X, Lv C (2003) An analysis of driving forces of urban land expansion in China. Econ Geogr 23:635–639
Tannier C, Thomas I, Vuidel G et al (2011) A fractal approach to identifying urban boundaries. Geogr Anal 43:211–227
United Nations (UN) (2015) World urbanization prospects: the 2014 revision: highlights, vol 12. United Nations, New York, NY, USA
Wang J (2017) Cartography in the age of spatio-temporal big data. Acta Geodaetica Et Cartographica Sinica 46(10):1226–1237
Wang J, Wu F, Guo J et al (2017) Challenges and opportunities of spatio-temporal big data. Sci Survey Map 42:1–7
Wei YD, Yu D (2006) State policy and the globalization of Beijing: emerging themes. Habitat Inter 30:377–395
Xia Y, Zhang X, Wang GY (209) Cluster-based congestion outlier detection method on trajectory data. In: Sixth international conference on fuzzy systems and knowledge discovery. IEEE explore, pp 243–248
Yang W, Ai T (2017) Refueling stop activity detection and gas station extraction using crowdsourcing vehicle trajectory data. Acta Geodaetica et Cartographica Sinica 46(7):918–927
Yao Y, Zhang Y, Guan Q et al (2019) Sensing multi-level urban functional structures by using time series taxi trajectory data. Geomat Info Sci Wuhan Univ 44(6):875–884
Ye X, She B, Benya S (2018) Exploring regionalization in the network urban space. J Geovis Spat Anal 2:4
Yu L, Kang C, Wang F (2014) Towards big data-driven human mobility patterns and models. Geomat Info Sci Wuhan Univ 39:660–666
Zhang Y, Yu J, Fan W (2008) Fractal features of urban morphology and simulation of urban boundary. Geo-spatial Info Sci 11:121–126
Zhang J, Li P, Wang J (2014) Urban built-up area extraction from Landsat TM/ETM+ images using spectral information and multivariate texture. Remote Sens 6:7339–7359
Zhou XL (2015) Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago. PLoS One 10:e0137922
Zhou Q (2016) Comparative study of approaches to delineating built-up areas using road network data. Trans GIS 19:848–876
Acknowledgments
We would like to express our gratitude to the National Natural Science Foundation of China for supporting our project.
Funding
This research was supported by the National Natural Science Foundation of China under grants 41571399, 41501446, and 41901397.
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The corresponding author, Yuanfu Li, proposed the topic and completed most work of the data processing and analysis, as well as the writing of the manuscript. Qun Sun guided the research process and writing of the manuscript and gave many useful advices. Xiaolin Ji, Li Xu, Chuanwei Lu, and Yunpeng Zhao helped in the design, research implementation and analysis, and writing of the manuscript.
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Li, Y., Sun, Q., Ji, X. et al. Defining the Boundaries of Urban Built-up Area Based on Taxi Trajectories: a Case Study of Beijing. J geovis spat anal 4, 8 (2020). https://doi.org/10.1007/s41651-020-00047-6
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DOI: https://doi.org/10.1007/s41651-020-00047-6