Urban spatial structure analysis: quantitative identification of urban social functions using building footprints

doi:10.1007/s11707-021-0904-y

Front. Earth Sci.

2021, Vol. 15

Issue (3) : 507-525 https://doi.org/10.1007/s11707-021-0904-y

RESEARCH ARTICLE

Urban spatial structure analysis: quantitative identification of urban social functions using building footprints

Zhiyao ZHAO¹, Xianwei ZHENG²(

), Hongchao FAN³, Mengqi SUN¹

¹. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
². State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
³. Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim 7491, Norway

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Abstract

Analysis of urban spatial structures is an effective way to explain and solve increasingly serious urban problems. However, many of the existing methods are limited because of data quality and availability, and usually yield inaccurate results due to the unclear description of urban social functions. In this paper, we present an investigation on urban social function based spatial structure analysis using building footprint data. An improved turning function (TF) algorithm and a self-organizing clustering method are presented to generate the variable area units (VAUs) of high-homogeneity from building footprints as the basic research units. Based on the generated VAUs, five spatial metrics are then developed for measuring the morphological characteristics and the spatial distribution patterns of buildings in an urban block. Within these spatial metrics, three models are formulated for calculating the social function likelihoods of each urban block to describe mixed social functions in an urban block, quantitatively. Consequently, the urban structures can be clearly observed by an analysis of the spatial distribution patterns, the development trends, and the hierarchy of different social functions. The results of a case study conducted for Munich validate the effectiveness of the proposed method.

Keywords urban spatial structure variable area unit (VAU) spatial metric social function likelihood OpenStreetMap

Corresponding Author(s): Xianwei ZHENG

Online First Date: 25 November 2021 Issue Date: 17 January 2022

Cite this article:

Zhiyao ZHAO,Xianwei ZHENG,Hongchao FAN, et al. Urban spatial structure analysis: quantitative identification of urban social functions using building footprints[J]. Front. Earth Sci., 2021, 15(3): 507-525.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0904-y
https://academic.hep.com.cn/fesci/EN/Y2021/V15/I3/507

Fig.1 The pipeline of the proposed approach.

Fig.2 The transformation from Euclidean space to TF space. (a) Calculation of the accumulated tangential angle and the normalized accumulated length for each vertex of a polygon shape; (b) transformation of the original polygon shape into TF space.

Fig.3 The effect of polygon rotation on the initial tangential angle. (a) Before rotation; (b) after rotation. With the rotation of the shape, the TF representation is also changed.

Fig.4 An invariable initial tangential angle by the improved TF method. (a) TF representation before rotation. (b) TF representation after rotation.

Fig.5 The envelope area of the TF curve of polygons (a)

A

and (b)

B

Tab.1 Pseudocode of the algorithm

Fig.6 The self-organizing clustering process for building footprints. The search center is highlighted by the orange border. The green edges represent the proximity relationship. The red border depicts the clustered polygons, while the black represents the unclustered polygons. The orange edges determine the subsequent search center.

Fig.7 VAU generation by the vertices of MCP. (a) The clustered building footprints connected by blue lines. (b) The vertices (blue points) of the polygons. (c) The MCP of the vertices set.

Fig.8 Examples of HBO and CTI measurement for VAUs with different building orientations and densities.

Fig.9 Examples of VAU-based spatial metrics measurement for VAUs with different spatial morphological characteristics.

Tab.2 Characteristics of each functional zone

Fig.10 Spatial distribution of the calculated five VAU-based spatial metric results for the city blocks in Munich: (a) homogeneity of block, (b) closeness of block, (c) coverage of VAU, (d) regularity index, and (e) building area entropy.

Tab.3 The three kinds of social function likelihood segmentation statistics

Fig.11 The distribution of the number of blocks with regard to the three social function likelihood types.

Tab.4 Confusion matrix

Fig.12 The DMIST of the social functions. The blue polylines represent the expansion direction, and the yellow and red polylines indicate significant differences in social function: (a) the DMIST of the residential function; (b) the DMIST of the commercial function; (c) the DMIST of the industrial function.

Fig.13 The DMAST of the social functions. Blue edges represent the restricting effect of the local minimum node to the neighboring blocks; red edges reflect the complementary effect of the local maximum node to the neighboring blocks. (a) The DMAST of the residential function. (b) The DMAST of the commercial function. (c) The DMAST of the industrial function.

Fig.14 Spatial metrics designed for three kinds of different object scenarios in the same region. For scenario (a), an individual building is regarded as the research unit. For scenario (b), an VAU is regarded as the research unit. For scenario (c), a patch is regarded as the research unit.

1	A Anas, R Arnott, K A Small (1998). Urban spatial structure. J Econ Lit, 36(3): 1426–1464
2	E M Arkin, L P Chew, D P Huttenlocher, K Kedem, J S Mitchell (1991). An efficiently computable metric for comparing polygonal shapes. IEEE T PATTERN ANAL, 13(3): 209–216 https://doi.org/10.1109/34.75509
3	R Arnott (1998). Congestion tolling and urban spatial structure. J Reg Sci, 38(3): 495–504 https://doi.org/10.1111/0022-4146.00105
4	M G Boarnet, A Hong, R Santiago-Bartolomei (2017). Urban spatial structure, employment subcenters, and freight travel. J Transp Geogr, 60: 267–276 https://doi.org/10.1016/j.jtrangeo.2017.03.007
5	D Burgalassi, T Luzzati (2015). Urban spatial structure and environmental emissions: a survey of the literature and some empirical evidence for Italian NUTS 3 regions. Cities, 49: 134–148 https://doi.org/10.1016/j.cities.2015.07.008
6	G Caruso, M Hilal, I Thomas (2017). Measuring urban forms from inter-building distances: combining MST graphs with a local index of spatial association. Landsc Urban Plan, 163: 80–89 https://doi.org/10.1016/j.landurbplan.2017.03.003
7	Y Chen, X Liu, X Li, X Liu, Y Yao, G Hu, X Xu, F Pei (2017a). Delineating urban functional areas with building-level social media data: a dynamic time warping (DTW) distance based k-medoids method. Landsc Urban Plan, 160: 48–60 https://doi.org/10.1016/j.landurbplan.2016.12.001
8	Z Chen, B Yu, W Song, H Liu, Q Wu, K Shi, J Wu (2017b). A new approach for detecting urban centers and their spatial structure with nighttime light remote sensing. IEEE Trans Geosci Remote Sens, 55(11): 6305–6319 https://doi.org/10.1109/TGRS.2017.2725917
9	C Ding, X Zhao (2014). Land market, land development and urban spatial structure in Beijing. Land Use Policy, 40: 83–90 https://doi.org/10.1016/j.landusepol.2013.10.019
10	H Fan, A Zipf, Q Fu, P Neis (2014). Quality assessment for building footprints data on OpenStreetMap. Int J Geogr Inf Sci, 28(4): 700–719 https://doi.org/10.1080/13658816.2013.867495
11	G Galster, R Hanson, M R Ratcliffe, H Wolman, S Coleman, J Freihage (2001). Wrestling sprawl to the ground: defining and measuring an elusive concept. Hous Policy Debate, 12(4): 681–717 https://doi.org/10.1080/10511482.2001.9521426
12	W M Getz, C C Wilmers (2004). A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. Ecography, 27(4): 489–505 https://doi.org/10.1111/j.0906-7590.2004.03835.x
13	P Gordon, A Kumar, H W Richardson (1989). The influence of metropolitan spatial structure on commuting time. J Urban Econ, 26(2): 138–151 https://doi.org/10.1016/0094-1190(89)90013-2
14	T Grippa, S Georganos, S Zarougui, P Bognounou, E Diboulo, Y Forget, M Lennert, S Vanhuysse, N Mboga, E Wolff (2018). Mapping urban land use at street block level using OpenStreetMap, remote sensing data, and spatial metrics. ISPRS Int J Geoinf, 7(7): 246 https://doi.org/10.3390/ijgi7070246
15	X He, X Zhang, Q Xin (2018). Recognition of building group patterns in topographic maps based on graph partitioning and random forest. ISPRS J Photogramm Remote Sens, 136: 26–40 https://doi.org/10.1016/j.isprsjprs.2017.12.001
16	U Heiden, W Heldens, S Roessner, K Segl, T Esch, A Mueller (2012). Urban structure type characterization using hyperspectral remote sensing and height information. Landsc Urban Plan, 105(4): 361–375 https://doi.org/10.1016/j.landurbplan.2012.01.001
17	M Herold, N C Goldstein, K C Clarke (2003a). The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ, 86(3): 286–302 https://doi.org/10.1016/S0034-4257(03)00075-0
18	M Herold, X Liu, K C Clarke (2003b). Spatial metrics and image texture for mapping urban land use. Photogramm Eng Remote Sensing, 69(9): 991–1001 https://doi.org/10.14358/PERS.69.9.991
19	T Hermosilla , J Palomar-Vázquez , Á Balaguer-Beser , J Balsa-Barreiro , & L A Ruiz (2014). Using street based metrics to characterize urban typologies. Comput Environ Urban Syst, 44: 68–79 https://doi.org/doi.org/10.1016/j.compenvurbsys.2013.12.002
20	B Huang, B Zhao, Y Song (2018). Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sens Environ, 214: 73–86 https://doi.org/10.1016/j.rse.2018.04.050
21	J Huang, X X Lu, J M Sellers (2007). A global comparative analysis of urban form: applying spatial metrics and remote sensing. Landsc Urban Plan, 82(4): 184–197 https://doi.org/10.1016/j.landurbplan.2007.02.010
22	C H Joh, C A Hwang (2010). A time-geographic analysis of trip trajectories and land use characteristics in Seoul metropolitan area by using multidimensional sequence alignment and spatial analysis. In Washington, DC: AAG Annual Meeting
23	F Le Néchet (2012). Urban spatial structure, daily mobility and energy consuption: a study of 34 European cities. Cybergeo https://doi.org/10.4000/cybergeo.24966
24	Y Liu, F Wang, Y Xiao, S Gao (2012). Urban land uses and traffic ‘source-sink areas’: evidence from GPS-enabled taxi data in Shanghai. Landsc Urban Plan, 106(1): 73–87 https://doi.org/10.1016/j.landurbplan.2012.02.012
25	Y Long, Z Shen (2015). Discovering functional zones using bus smart card data and points of interest in Beijing. In: Long Y, Shen Z, eds. Geospatial Analysis to Support Urban Planning in Beijing. Springer, 193–217
26	J Louw (2011). Context based detection of urban land use zones. Dissertation for the Doctor Degree. Cape Town: University of Cape Town
27	Z Q Lv, F Q Dai, C Sun (2012). Evaluation of urban sprawl and urban landscape pattern in a rapidly developing region. Environ Monit Assess, 184(10): 6437–6448 https://doi.org/10.1007/s10661-011-2431-x pmid: 22095203
28	G Pan, G Qi, Z Wu, D Zhang, S Li (2012). Land-use classification using taxi GPS traces. IEEE Trans Intell Transp Syst, 14(1): 113–123 https://doi.org/10.1109/TITS.2012.2209201
29	G Qi, X Li, S Li, G Pan, Z Wang, D Zhang (2011). Measuring social functions of city regions from large-scale taxi behaviors. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). IEEE, 384–388
30	W Simpson (1992). Urban Structure and the Labour Market: Worker Mobility, Commuting and Underemployment in Cities. Oxford: Clarendon Press, 1–198
31	S V Stehman (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ, 62(1): 77–89 https://doi.org/10.1016/S0034-4257(97)00083-7
32	J Sohn (2005). Are commuting patterns a good indicator of urban spatial structure? J Transp Geogr, 13(4): 306–317 https://doi.org/10.1016/j.jtrangeo.2004.07.005
33	S Steiniger, T Lange, D Burghardt, R Weibel (2008). An approach for the classification of urban building structures based on discriminant analysis techniques. Trans GIS, 12(1): 31–59 https://doi.org/10.1111/j.1467-9671.2008.01085.x
34	S Vanderhaegen, F Canters (2017). Mapping urban form and function at city block level using spatial metrics. Landsc Urban Plan, 167: 399–409 https://doi.org/10.1016/j.landurbplan.2017.05.023
35	H Xing, Y Meng (2018). Integrating landscape metrics and socioeconomic features for urban functional region classification. Comput Environ Urban Syst, 72: 134–145 https://doi.org/10.1016/j.compenvurbsys.2018.06.005
36	H Xing, Y Meng (2020). Measuring urban landscapes for urban function classification using spatial metrics. Ecol Indic, 108: 105722 https://doi.org/10.1016/j.ecolind.2019.105722
37	I Walde, S Hese, C Berger, C Schmullius (2014). From land cover-graphs to urban structure types. Int J Geogr Inf Sci, 28(3): 584–609 https://doi.org/10.1080/13658816.2013.865189
38	X Yang, Z Fang, L Yin, J Li, Y Zhou, S Lu (2018). Understanding the spatial structure of urban commuting using mobile phone location data: a case study of Shenzhen, China. Sustainability-basel. 10(5):1435 https://doi.org/doi.org/10.3390/su10051435
39	T Yoshida, K Tanaka (2005). Land-use diversity index: a new means of detecting diversity at landscape level. Landscape Ecol Eng, 1(2), 201–206 https://doi.org/doi.org/10.1007/s11355-005-0022-0
40	J Zhang, M F Goodchild (2002). Uncertainty in Geographical Information. London: CRC Press
41	C Zhang (2008). An analysis of urban spatial structure using comprehensive prominence of irregular areas. Int J Geogr Inf Sci, 22(6): 675–686 https://doi.org/10.1080/13658810701602245
42	X Zhang, S Du, 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
43	C Zhang, I Sargent, X Pan, H Li, A Gardiner, J Hare, P M Atkinson (2019a). Joint deep learning for land cover and land use classification. Remote Sens Environ, 221: 173–187 https://doi.org/10.1016/j.rse.2018.11.014
44	S Zhang, X Liu, J Tang, S Cheng, Y Wang (2019b). Urban spatial structure and travel patterns: analysis of workday and holiday travel using inhomogeneous Poisson point process models. Comput Environ Urban Syst, 73: 68–84 https://doi.org/10.1016/j.compenvurbsys.2018.08.005
45	C Zhong, S M Arisona, X Huang, M Batty, G Schmitt (2014). Detecting the dynamics of urban structure through spatial network analysis. Int J Geogr Inf Sci, 28(11): 2178–2199 https://doi.org/10.1080/13658816.2014.914521
46	C Zhong, X Huang, S M Arisona, G Schmitt (2013). Identifying spatial structure of urban functional centers using travel survey data: a case study of Singapore. In: Proceedings of the First ACM SIGSPATIAL International Workshop on Computational Models of Place, Orlando, FL, USA, 2013, 28–33

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