A rule-based approach for generating urban footprint maps: from road network to urban footprint

Abstract

Decision and policymakers need urban footprint data for monitoring human impact on the urban ecosystem for politics and services. Deriving urban footprint is a challenging work since it has rapidly changing borders. The existing methods for deriving urban footprint map based on raster images have several steps such as determination of indicators and parameters of image classification. These steps limit the process by an operator since they require human decisions. This paper proposes a new rule-based approach for obtaining urban footprint based on Delaunay triangulation among selected centroids of roads and dead-end streets. The selection criterion is determined as maximum road length by using standard deviation operator. To produce urban footprints, this method needs no other data or information apart from road network geometry. This means that the proposed method uses only intrinsic indicators and measures. The experimental study was conducted with OpenStreetMap road data of Washington DC, Madrid, Stockholm, and Wellington. The comparisons with authority data prove that the proposed method is sufficient in many parts of urban and suburban lands.

Keywords

• OpenStreetMap
• Urban footprint
• TIN
• Road network analysis
• Land use

References

1. Akbulut, Z. Özdemir, S. Acar, H. Dihkan, M. and Karslı, F. (2018). Automatic extraction of building boundaries from high resolution images with active contour segmentation. International Journal of Engineering and Geosciences , 3(1) , 37-42. https://doi.org/10.26833/ijeg.373152
2. Angel, S., Parent, J. and Civco, D. (2007). Urban sprawl metrics: an analysis of global urban expansion using GIS. Proceedings of ASPRS 2007 Annual Conference, Tampa, Florida May 7–11. URL: http://clear.uconn.edu/publications/research/tech_papers/Angel_et_al_ASPRS2007.pdf
3. Bhatta, B. Saraswati, S. and Bandyopadhyay, D. (2010). Urban sprawl measurement from remote sensing data. Appl Geogr. 30(4), 731-740. https://doi.org/10.1016/j.apgeog.2010.02.002
4. Canaz Sevgen, S . (2019). Airborne lidar data classification in complex urban area using random forest: a case study of Bergama, Turkey. International Journal of Engineering and Geosciences , 4 (1) , 45-51. https://doi.org/10.26833/ijeg.440828
5. Corcoran, P. Mooney, P. and Bertolotto, M. (2013). Analysing the growth of OpenStreetMap networks. Spat Stat. 3, 21-32. https://doi.org/10.1016/j.spasta.2013.01.002
6. DCGIS. (2019). Existing Land Use. DC Office of Planning - Long Range Planning. [accessed on 20 September 2019]. http://opendata.dc.gov/datasets/245179183eee41e08852f f9d5dbd3bcb_4
7. EEA. (2019). Urban atlas. EU Open Data Portal. [accessed on 20 September 2019]. URL: https://data.europa.eu/euodp/data/dataset/data_urbanatlas
8. Esch, T. Marconcini, M. Felbier, A. Roth, A. Heldens, W. Huber, M. Schwinger, M. Taubenböck, H. Müller, A. and Dech, S. (2013). Urban footprint processor—Fully automated processing chain generating settlement masks from global data of the TanDEM-X mission. IEEE Geosci Remote S. 10(6), 1617-1621. https://doi.org/10.1109/LGRS.2013.2272953

9. ESRI. (2019). Imagery Basemap. Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community
10. GADM. (2019). GADM data. Database of Global Administrative Areas. [accessed on 20 September 2019]. URL: https://gadm.org/data.html
11. Gökgöz, T. (2005). Generalisation of contours using deviation angles and error bands. Cartogr J. 42(2), 145-156. https://doi.org/10.1179/000870405X61441
12. Hacar, M. and Gökgöz, T. (2019). A new, score-based multi-stage matching approach for road network conflation in different road patterns. ISPRS Int J Geo-Inf, 8(2), 81. https://doi.org/10.3390/ijgi8020081
13. Hacar, M. Kılıç, B. and Şahbaz, K. (2018). Analysing OpenStreetMap road data and characterising the behavior of contributors in Ankara, Turkey. ISPRS Int J Geo-Inf. 7(10), 400. https://doi.org/10.3390/ijgi7100400
14. Jiang, F. Liu, S. Yuan, H. and Zhang, Q. (2007). Measuring urban sprawl in Beijing with geo-spatial indices. J Geogr Sci. 17(4), 469-478. https://doi.org/10.1007/s11442-007-0469-z
15. Kang, M. Wang, M. and Du, Q. 2015. A method of DTM construction based on quadrangular irregular networks and related error analysis. PloS one, 10(5), e0127592. https://doi.org/10.1371/journal.pone.0127592
16. Karakuş, P. Karabork, H. and Kaya, S. (2017). A comparison of the classification accuracies in determining the land cover of Kadirli region of Turkey by using the pixel based and object based classification algorithms. International Journal of Engineering and Geosciences , 2(2) , 52-60. https://doi.org/10.26833/ijeg.298951
17. Koukoletsos, T. Haklay, M. and Ellul, C. (2012). Assessing data completeness of VGI through an automated matching procedure for linear data. Trans GIS. 16(4), 477-498. https://doi.org/10.1111/j.1467-9671.2012.01304.x
18. Kumar, J.A.V. Pathan, S.K. and Bhanderi, R.J. (2007). Spatio-temporal analysis for monitoring urban growth–a case study of Indore city. J Indian Soc Remot. 35(1), 11-20. https://doi.org/10.1007/BF02991829
19. Liu, X. and Jiang, B. (2011). A novel approach to the identification of urban sprawl patches based on the scaling of geographic space. Int J Geomat Geosci. 2(2), 415-429. URL: http://www.divaportal.org/smash/get/diva2:502047/FULLTEXT01.pdf
20. Musa, S.I. Hashim, M. Reba, M.N.M. (2017). A review of geospatial-based urban growth models and modelling initiatives. Geocarto Int. 32(8), 813-833. https://doi.org/10.1080/10106049.2016.1213891
21. Neis, P. and Zipf, A. (2012). Analysing the contributor activity of a volunteered geographic information project—The case of OpenStreetMap. ISPRS Int J GeoInf. 1(2), 146-165. https://doi.org/10.3390/ijgi1020146
22. Owen, K.K. and Wong, D.W. (2013). An approach to differentiate informal settlements using spectral, texture, geomorphology and road accessibility metrics. Appl Geogr. 38, 107-118. https://doi.org/10.1016/j.apgeog.2012.11.016
23. Polat, Z.A. Memduhoğlu, A. Hacar, M. and Duman, H. (2017). [in Turkish: Kentsel Büyüme İle Motorlu Araç Trafiği Yoğunluğu Arasindaki İlişkinin Belirlenmesi: İstanbul Örneği]. Omer Halisdemir University J Eng Sci, 6(2), 442-451. https://doi.org/10.28948/ngumuh.341275
24. Semboloni, F. (2000). The growth of an urban cluster into a dynamic self-modifying spatial pattern. Environ Plann B. 27(4), 549-564. https://doi.org/10.1068%2Fb2673
25. Triantakonstantis, D. and Stathakis, D. (2015). Examining urban sprawl in Europe using spatial metrics. Geocarto Int. 30(10), 1092-1112. http://dx.doi.org/10.1080/10106049.2015.1027289
26. Tsai, Y.H. (2005). Quantifying urban form: compactness versus 'sprawl'. Urban Stud. 42(1), 141-161. https://doi.org/10.1080%2F0042098042000309748 Turkish Statistical Institute (TUIK). (2019). Address based population registration system. [accessed on 20 September 2019]. URL:http://www.turkstat.gov.tr/Start.do
27. WCC. (2019). WCC District Plan Zones. Wellington City Council. [accessed on 20 September 2019]. URL: https://datawcc.opendata.arcgis.com/datasets/6c3aaccfdbbf470491f b688595cf5b7e_0
28. Yang, B. Li, Q. and Shi, W. (2005). Constructing multiresolution triangulated irregular network model for visualisation. Comput Geosci. 31(1), 77-86. https://doi.org/10.1016/j.cageo.2004.09.011
29. Zhao, P. Jia, T. Qin, K. Shan, J. and Jiao, C. (2015). Statistical analysis on the evolution of OpenStreetMap road networks in Beijing. Physica A. 420, 59-72. https://doi.org/10.1016/j.physa.2014.10.076