Research Article
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Year 2020, Volume: 5 Issue: 2, 100 - 108, 01.06.2020
https://doi.org/10.26833/ijeg.623592

Abstract

References

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • EEA. (2019). Urban atlas. EU Open Data Portal. [accessed on 20 September 2019]. URL: https://data.europa.eu/euodp/data/dataset/data_urbanatlas
  • 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
  • ESRI. (2019). Imagery Basemap. Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community
  • GADM. (2019). GADM data. Database of Global Administrative Areas. [accessed on 20 September 2019]. URL: https://gadm.org/data.html
  • Gökgöz, T. (2005). Generalisation of contours using deviation angles and error bands. Cartogr J. 42(2), 145-156. https://doi.org/10.1179/000870405X61441
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • WCC. (2019). WCC District Plan Zones. Wellington City Council. [accessed on 20 September 2019]. URL: https://datawcc.opendata.arcgis.com/datasets/6c3aaccfdbbf470491f b688595cf5b7e_0
  • 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
  • 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

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

Year 2020, Volume: 5 Issue: 2, 100 - 108, 01.06.2020
https://doi.org/10.26833/ijeg.623592

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.

References

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • EEA. (2019). Urban atlas. EU Open Data Portal. [accessed on 20 September 2019]. URL: https://data.europa.eu/euodp/data/dataset/data_urbanatlas
  • 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
  • ESRI. (2019). Imagery Basemap. Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community
  • GADM. (2019). GADM data. Database of Global Administrative Areas. [accessed on 20 September 2019]. URL: https://gadm.org/data.html
  • Gökgöz, T. (2005). Generalisation of contours using deviation angles and error bands. Cartogr J. 42(2), 145-156. https://doi.org/10.1179/000870405X61441
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • WCC. (2019). WCC District Plan Zones. Wellington City Council. [accessed on 20 September 2019]. URL: https://datawcc.opendata.arcgis.com/datasets/6c3aaccfdbbf470491f b688595cf5b7e_0
  • 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
  • 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
There are 29 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Müslüm Hacar 0000-0002-8737-8262

Publication Date June 1, 2020
Published in Issue Year 2020 Volume: 5 Issue: 2

Cite

APA Hacar, M. (2020). A rule-based approach for generating urban footprint maps: from road network to urban footprint. International Journal of Engineering and Geosciences, 5(2), 100-108. https://doi.org/10.26833/ijeg.623592
AMA Hacar M. A rule-based approach for generating urban footprint maps: from road network to urban footprint. IJEG. June 2020;5(2):100-108. doi:10.26833/ijeg.623592
Chicago Hacar, Müslüm. “A Rule-Based Approach for Generating Urban Footprint Maps: From Road Network to Urban Footprint”. International Journal of Engineering and Geosciences 5, no. 2 (June 2020): 100-108. https://doi.org/10.26833/ijeg.623592.
EndNote Hacar M (June 1, 2020) A rule-based approach for generating urban footprint maps: from road network to urban footprint. International Journal of Engineering and Geosciences 5 2 100–108.
IEEE M. Hacar, “A rule-based approach for generating urban footprint maps: from road network to urban footprint”, IJEG, vol. 5, no. 2, pp. 100–108, 2020, doi: 10.26833/ijeg.623592.
ISNAD Hacar, Müslüm. “A Rule-Based Approach for Generating Urban Footprint Maps: From Road Network to Urban Footprint”. International Journal of Engineering and Geosciences 5/2 (June 2020), 100-108. https://doi.org/10.26833/ijeg.623592.
JAMA Hacar M. A rule-based approach for generating urban footprint maps: from road network to urban footprint. IJEG. 2020;5:100–108.
MLA Hacar, Müslüm. “A Rule-Based Approach for Generating Urban Footprint Maps: From Road Network to Urban Footprint”. International Journal of Engineering and Geosciences, vol. 5, no. 2, 2020, pp. 100-8, doi:10.26833/ijeg.623592.
Vancouver Hacar M. A rule-based approach for generating urban footprint maps: from road network to urban footprint. IJEG. 2020;5(2):100-8.