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Research Article

Spatiotemporal evolution analysis of OpenStreetMap buildings in the Yangtze River Delta of China based on Tree-like model

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Article: 2364727 | Received 18 Dec 2023, Accepted 31 May 2024, Published online: 01 Jul 2024

References

  • Ahmouda A, Hochmair HH, Cvetojevic S. 2018. Analyzing the effect of earthquakes on OpenStreetMap contribution patterns and tweeting activities. Geo-Spatial Informat Sci. 21(3):195–212. doi: 10.1080/10095020.2018.1498666.
  • Anderson J, Sarkar D, Palen L. 2019. Corporate editors in the evolving landscape of OpenStreetMap. IJGI. 8(5):232. doi: 10.3390/ijgi8050232.
  • Arsanjani JJ, Bakillah M. 2015. Understanding the potential relationship between the socio-economic variables and contributions to OpenStreetMap. Int J Digital Earth. 8(11):861–876. doi: 10.1080/17538947.2014.951081.
  • Barranquero M, Olmedo A, Gómez J, Tayebi A, Hellín CJ, de Adana FS. 2023. Automatic 3D building reconstruction from OpenStreetMap and LiDAR using convolutional neural networks. Sensors (Basel). 23(5):2444. doi: 10.3390/s23052444.
  • Bhatt, Deepika, Minal, 2022. ‘GIS and gravity model-based accessibility measure for Delhi metro’. Iran J Sci Technol Trans Civ Eng46(4):3411–3428. doi: 10.1007/s40996-021-00795-5.
  • Boulos MNK, Resch B, Crowley DN, Breslin JG, Sohn G, Burtner R, Pike WA, Jezierski E, Chuang KYS. 2011. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int J Health Geogr. 10:67. doi: 10.1186/1476-072X-10-67.
  • Bright J, De Sabbata S, Lee S. 2018. Geodemographic biases in Crowdsourced knowledge websites: do neighbours fill in the blanks? GeoJournal. 83(3):427–440. doi: 10.1007/s10708-017-9778-7.
  • Bshouty E, Shafir A, Dalyot S. 2020. Towards the generation of 3D OpenStreetMap building models from single contributed photographs. Comput Environ Urban Syst. 79:101421. doi: 10.1016/j.compenvurbsys.2019.101421.
  • Corcoran P, Mooney P. 2013. Characterising the metric and topological evolution of OpenStreetMap network representations. Eur Phys J Spec Top. 215(1):109–122. doi: 10.1140/epjst/e2013-01718-2.
  • Corcoran P, Mooney P, Bertolotto M. 2013. Analysing the growth of OpenStreetMap networks. Spatial Stat. 3:21–32. doi: 10.1016/j.spasta.2013.01.002.
  • Dock JP, Song W, Lu J. 2015. Evaluation of dine-in restaurant location and competitiveness: applications of gravity modeling in Jefferson County, Kentucky. Appl Geogr. 60:204–209. doi: 10.1016/j.apgeog.2014.11.008.
  • Duan CS, Zuo SD, Wu ZF, Qiu Y, Wang JF, Lei YH, Liao H, Ren Y. 2021. A review of research hotspots and trends in biogenic volatile organic compounds (BVOCs) emissions combining bibliometrics with evolution tree methods. Environ Res Lett. 16(1):013003. doi: 10.1088/1748-9326/abcee9.
  • Fan Y, Yu GM, He ZY. 2017. Origin, spatial pattern, and evolution of urban system: testing a hypothesis of “urban tree.” Habitat Int. 59:60–70. doi: 10.1016/j.habitatint.2016.11.012.
  • Fritz S, See L, Carlson T, Haklay M, Oliver JL, Fraisl D, Mondardini R, Brocklehurst M, Shanley LA, Schade S, et al. 2019. Citizen science and the united nations sustainable development goals. Nat Sustain. 2(10):922–930. doi: 10.1038/s41893-019-0390-3.
  • Girres JF, Touya G. 2010. Quality assessment of the French OpenStreetMap Dataset. Trans GIS. 14(4):435–459. doi: 10.1111/j.1467-9671.2010.01203.x.
  • Goetz M, Zipf A. 2012. Towards defining a framework for the automatic derivation of 3D CityGML models from volunteered geographic information. Int J 3-D Informat Model. 1(2):1–16. doi: 10.4018/ij3dim.2012040101.
  • Goldblatt R, Jones N, Mannix J. 2020. Assessing OpenStreetMap completeness for management of natural disaster by means of remote sensing: a case study of three small island states (Haiti, Dominica and St. Lucia). Remote Sens. 12(1):118. doi: 10.3390/rs12010118.
  • Golovko A, Sahin H. 2021. Analysis of International Trade Integration of Eurasian Countries: gravity Model Approach. Eurasian Econ Rev. 11(3):519–548. doi: 10.1007/s40822-021-00168-3.
  • Goodchild MF. 2007. Citizens as sensors: the world of volunteered geography. GeoJournal. 69(4):211–221. doi: 10.1007/s10708-007-9111-y.
  • Hagenauer J, Helbich M. 2012. Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks. Int J Geogr Informat Sci. 26(6):963–982. doi: 10.1080/13658816.2011.619501.
  • Haklay M, Basiouka S, Antoniou V, Ather A. 2010. How many volunteers does it take to map an area well? The validity of Linus’ law to volunteered geographic information. Cartographic J. 47(4):315–322. doi: 10.1179/000870410X12911304958827.
  • Hecht R, Kunze C, Hahmann S. 2013. Measuring completeness of building footprints in OpenStreetMap over space and time. IJGI. 2(4):1066–1091. doi: 10.3390/ijgi2041066.
  • Herfort B, Lautenbach S, Albuquerque JPd, Anderson J, Zipf A. 2021. The evolution of humanitarian mapping within the OpenStreetMap community. Sci Rep. 11(1):3037. doi: 10.1038/s41598-021-82404-z.
  • Herfort B, Lautenbach S, Albuquerque JPd, Anderson J, Zipf A. 2023. A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap. Nat Commun. 14(1):3985. doi: 10.1038/s41467-023-39698-6.
  • Hoek JVD, Friedrich HKK, Ballasiotes A, Peters LER, Wrathall D. 2021. Development after displacement: evaluating the utility of OpenStreetMap data for monitoring sustainable development goal progress in refugee settlements. Int J Geo-Informat. 10(3):153.
  • Jing SQ, Wang JF, Xu CD, Yang JT. 2022. Tree-like evolution pathways of global urban land expansion. J Clean Product. 378:134562. doi: 10.1016/j.jclepro.2022.134562.
  • Jokar Arsanjani J, Helbich M, Bakillah M, Loos L. 2015. The emergence and evolution of OpenStreetMap: a cellular automata approach. Int J Digital Earth. 8(1):76–90. doi: 10.1080/17538947.2013.847125.
  • Kilsedar CE, Oxoli D, Frassinelli F, Montani M, Minghini M. 2017. Humanitarian Mapping within a Student Association: poliMappers. In FOSS4G-IT 2017
  • Le Guilcher A, Olteanu-Raimond AM, Balde MB. 2022. Analysis of massive imports of open data in Openstreetmap Database: a study case for France. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. VV-4-2022–2022:99–106. doi: 10.5194/isprs-annals-V-4-2022-99-2022.
  • Lei YH, Wang JF, Wang Y, Xu CD. 2023. Geographical evolutionary pathway of global tuberculosis incidence trends. Bmc Public Health. 23(1):755. doi: 10.1186/s12889-023-15553-7.
  • Liang YT, Xu CD. 2023. Knowledge diffusion of geodetector: a perspective of the literature review and Geotree. Heliyon. 9(9):e19651. doi: 10.1016/j.heliyon.2023.e19651.
  • Liu B, Shi Y, Li D-J, Wang Y-D, Fernandez G, Tsou M-H. 2020. An economic development evaluation based on the OpenStreetMap road network density: the case study of 85 cities in China. IJGI. 9(9):517. doi: 10.3390/ijgi9090517.
  • Liu LJ, Fu ZY, Xia Y, Lin H, Ding XH, Liao KT. 2023. A building polygonal object matching method based on minimum bounding rectangle combinatorial optimization and relaxation labeling. Trans Gis. 27(2):541–563. doi: 10.1111/tgis.13039.
  • Mann D, Rankavat S, Joshi PK. 2022. Road network drives urban ecosystems-a longitudinal analysis of impact of roads in the Central Himalaya. Geocarto Int. 37(4):1100–1125. doi: 10.1080/10106049.2020.1750064.
  • Minaei M. 2020. Evolution, density and completeness of OpenStreetMap Road networks in developing countries: the case of Iran. Appl Geogr. 119:102246. doi: 10.1016/j.apgeog.2020.102246.
  • Minghini M, Coetzee S, Grinberger AY, Yeboah G, Juhász L, Mooney P. 2020. Editorial : OpenStreetMap research in the COVID-19 Era. In Proceedings of the Academic Track at the State of the Map 2020 Online Conference; 4-5 July. doi: 10.5281/zenodo.3922054.
  • Mohammadi N, Sedaghat A, Khademi M. 2022. Mining spatiotemporal growth pattern of volunteered data using a contributor-based approach. Geocarto Int. 37(16):4805–4822. doi: 10.1080/10106049.2021.1899304.
  • Mooney P, Corcoran P, Ciepluch B. 2013. The potential for using volunteered geographic information in pervasive health computing applications. J Ambient Intell Human Comput. 4(6):731–745. doi: 10.1007/s12652-012-0149-4.
  • Morley C, Rosselló J, Santana-Gallego M. 2014. Gravity models for tourism demand: theory and use. Ann Tourism Res. 48:1–10. doi: 10.1016/j.annals.2014.05.008.
  • Neis P, Zipf A. 2012. Analyzing the contributor activity of a volunteered geographic information project - the case of OpenStreetMap. IJGI. 1(2):146–165. doi: 10.3390/ijgi1020146.
  • Neis P, Zielstra D, Zipf A. 2011. The street network evolution of crowdsourced maps: OpenStreetMap in Germany 2007–2011. Future Internet. 4(1):1–21. doi: 10.3390/fi4010001.
  • Neis P, Zielstra D, Zipf A. 2013. Comparison of volunteered geographic information data contributions and community development for selected world regions. Future Internet. 5(2):282–300. doi: 10.3390/fi5020282.
  • Nejad RG, Abbaspour RA, Chehreghan A. 2022. Spatiotemporal VGI contributor reputation system based on implicit evaluation relations. Geocarto Int. 37(26):12014–12041. doi: 10.1080/10106049.2022.2063406.
  • Pourabdollah A, Morley J, Feldman S, Jackson M. 2013. Towards an authoritative OpenStreetMap: conflating OSM and OS OpenData national maps’ road network. IJGI. 2(3):704–728. doi: 10.3390/ijgi2030704.
  • Quinn S. 2017. Using small cities to understand the crowd behind OpenStreetMap. GeoJournal. 82(3):455–473. doi: 10.1007/s10708-015-9695-6.
  • Javanmardi S, Ganjisaffar Y, Lopes C, Baldi P. 2009. User contribution and trust in Wikipedia. In Proceedings of the 2009 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing; Washington, DC, USA pp. 1–6, 11-14 November. doi: 10.4108/ICST.COLLABORATECOM2009.8376.
  • Salvucci G, Salvati L. 2021. Official statistics, building censuses, and OpenStreetMap completeness in Italy. IJGI. 11(1):29. doi: 10.3390/ijgi11010029.
  • Sarkar D, Anderson JT. 2022. Corporate editors in OpenStreetMap: investigating Co-editing patterns. Trans GIS. 26(4):1879–1897. doi: 10.1111/tgis.12910.
  • Scholz S, Knight P, Eckle M, Marx S, Zipf A. 2018. Volunteered geographic information for disaster risk reductionthe missing maps approach and its potential within the red cross and red crescent movement. Remote Sens. 10(8):1239. doi: 10.3390/rs10081239.
  • Senaratne H, Mobasheri A, Ali AL, Capineri C, Haklay M. 2017. A review of volunteered geographic information quality assessment methods. Int J Geogr Inform Sci. 31(1):139–167. doi: 10.1080/13658816.2016.1189556.
  • Silva LSL, Camboim SP. 2021. Authoritative cartography in brazil and collaborative mapping platforms: challenges and proposals for data integration. Boletim DE Ciencias Geodesicas. 27. doi: 10.1590/s1982-21702021000100003.
  • Su SL, Lei CR, Li AY, Pi JH, Cai ZL. 2017. Coverage inequality and quality of volunteered geographic features in Chinese cities: analyzing the associated local characteristics using geographically weighted regression. Appl Geogr. 78:78–93. doi: 10.1016/j.apgeog.2016.11.002.
  • Sun Y, Wang Y, Zhou X, Chen W. 2023. Are Shrinking Populations Stifling Urban Resilience? Evidence from 111 Resource-Based Cities in China. Cities. 141:104458. doi: 10.1016/j.cities.2023.104458.
  • Tan J, Peng L, Wu WX, Huang Q. 2023. Mapping the evolution patterns of urbanization, ecosystem service supply-demand, and human well-being: a tree-like landscape perspective. Ecol Indicators. 154:110591. doi: 10.1016/j.ecolind.2023.110591.
  • Tian YJ, Zhou Q, Fu XL. 2019. An analysis of the evolution, completeness and spatial patterns of OpenStreetMap building data in China. IJGI. 8(1):35. doi: 10.3390/ijgi8010035.
  • Van den Hoek J, Friedrich HK, Ballasiotes A, Peters LER, Wrathall D. 2021. Development after displacement: evaluating the utility of OpenStreetMap data for monitoring sustainable development goal progress in refugee settlements. IJGI. 10(3):153. doi: 10.3390/ijgi10030153.
  • Song W, Sun G. The role of mobile volunteered geographic information in urban management. In Proceedings of the 2010 18th International Conference on Geoinformatics, Beijing, China, 18–20 June 2010, pp. 1–5, doi: 10.1109/GEOINFORMATICS.2010.5567728.
  • Wang JF, Liu XH, Peng L, Chen HY, Driskell L, Zheng XY. 2012. Cities evolution tree and applications to predicting urban growth. Popul Environ. 33(2-3):186–201. doi: 10.1007/s11111-011-0142-4.
  • Wang J, Yong GE, Lianfa LI, Bin M, Jilei W, Yanchen B, Shihong DU, Yilan L, Maogui H, Chengdong X\. 2014. Spatiotemporal data analysis in geography. Acta Geograph Sin. 69(9):1326–1345. doi: 10.11821/dlxb201409007.
  • Wang Y, Wang JF. 2020. Modelling and prediction of global non-communicable diseases. BMC Public Health. 20(1):822. doi: 10.1186/s12889-020-08890-4.
  • Wu C, Ye XY, Ren F, Du QY. 2018. Check-in Behaviour and spatio-temporal vibrancy: an exploratory analysis in shenzhen, China. Cities. 77:104–116. doi: 10.1016/j.cities.2018.01.017.
  • Xin R, Ai TH, Ding LF, Zhu RX, Meng LQ. 2022. Impact of the COVID-19 pandemic on urban human mobility-a multiscale geospatial network analysis using New York bike-sharing data. Cities. 126:103677. doi: 10.1016/j.cities.2022.103677.
  • Xu J, Zhou Q. 2019. Temporal-spatial analysis of contributors’ mapping behavior for building data in OpenStreetMap. Proc Int Cartogr Assoc. 2:1–10. doi: 10.5194/ica-proc-2-149-2019.
  • Anran YANG. 2022. Investigating the mechanisms of VGI data quality assurance based on history data. Acta Geodaetica et Cartograph Sin. 51(9):1979–1979.
  • Yang AR, Fan HC, Jing N, Sun YR, Zipf A. 2016. Temporal analysis on contribution inequality in OpenStreetMap: a comparative study for four countries. IJGI. 5(1):5. doi: 10.3390/ijgi5010005.
  • Ye S. 2014. The application of volunteered geographic information on natural disaster emergency management. Application. 281:40–41.
  • Zhang Y, Li X, Wang A, Bao T, Tian S. 2015. Density and diversity of OpenStreetMap road networks in China. Big/Open Data for Urban Manag. 4(2):135–146. doi: 10.1016/j.jum.2015.10.001.
  • Zhao PX, Jia T, Qin K, Shan J, Jiao CJ. 2015. Statistical analysis on the evolution of OpenStreetMap road networks in Beijing. Physica A. 420:59–72. doi: 10.1016/j.physa.2014.10.076.
  • Zhou YN, Yang Y, Xia SY. 2022. A novel geographic evolution tree based on econometrics for analyzing regional differences in determinants of Chinese CO2 emission intensity. J Environ Manage. 305:114402. doi: 10.1016/j.jenvman.2021.114402.
  • Zia M, Cakir Z, Seker DZ. 2019. Turkey OpenStreetMap dataset –- spatial analysis of development and growth proxies. Open Geosciences. 11(1):140–151. doi: 10.1515/geo-2019-0012.
  • Zielstra D, Zipf A. 2010. A comparative study of proprietary geodata and volunteered geographic information for Germany. Berlin, Heidelberg.
  • Zook M, Graham M, Shelton T, Gorman S. 2010. Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Med Health Policy. 2(2):7–33. doi: 10.2202/1948-4682.1069.