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Articles

Sensing Mixed Urban Land-Use Patterns Using Municipal Water Consumption Time Series

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Pages 68-86 | Received 24 Jan 2019, Accepted 26 Mar 2020, Published online: 21 Jul 2020

References

  • Akar, Ö. 2018. The rotation forest algorithm and object-based classification method for land use mapping through UAV images. Geocarto International 33 (5):538–53. doi: 10.1080/10106049.2016.1277273.
  • Bennett, D. A., W. Tang, and S. Wang. 2011. Toward an understanding of provenance in complex land use dynamics. Journal of Land Use Science 6 (2–3):211–30. doi: 10.1080/1747423X.2011.558598
  • Bingham, A., and D. Spradlin. 2011. The long tail of expertise. Santa Monica, CA: Pearson Education.
  • Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry & Remote Sensing 65 (1):2–16. doi: 10.1016/j.isprsjprs.2009.06.004.
  • Boarnet, M. G. 2011. A broader context for land use and travel behavior, and a research agenda. The Journal of the American Planning Association 77 (3):197–213. doi: 10.1080/01944363.2011.593483.
  • Bourne, L. S. 1976. Urban structure and land use decisions. Annals of the American Association of Geographers 66 (4):531–35. doi: 10.1111/j.1467-8306.1976.tb01108.x.
  • Bratasanu, D., I. Nedelcu, and M. Datcu. 2011. Bridging the semantic gap for satellite image annotation and automatic mapping applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4 (1):193–204. doi: 10.1109/JSTARS.2010.2081349.
  • Cao, G., and P. Zhang. 2010. The partial urbanization phenomenon in developed regions: Changshu example. Planners 4:16.
  • Chang, H., M. R. Bonnette, P. Stoker, B. Crow-Miller, and E. Wentz. 2017. Determinants of single family residential water use across scales in four western U.S. cities. The Science of the Total Environment 596–597:451–64. doi: 10.1016/j.scitotenv.2017.03.164.
  • Chen, Y., X. Liu, X. Li, X. Liu, Y. Yao, G. Hu, X. Xu, and F. Pei. 2017. Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method. Landscape & Urban Planning 160:48–60. doi: 10.1016/j.landurbplan.2016.12.001.
  • Chen, Y., L. Xia, S. Wang, X. Liu, and B. Ai. 2013. Simulating urban form and energy consumption in the Pearl River Delta under different development strategies. Annals of the Association of American Geographers 103 (6):1567–85. doi: 10.1080/00045608.2012.740360.
  • Dietterichl, T. G. 2002. Ensemble learning. In Handbook of brain theory & neural networks, ed. L. Yann and B. Yoshua, 125–42. Cambridge, MA: MIT Press.
  • Ding, Z. S., Y. Wang, Z. Y. Shang, L. I. Ya-Ru, X. Y. Song, and X. J. Chang. 2014. The spatial characteristics of producer service agglomeration in town: The case of Changshu in Jiangsu Province. Scientia Geographica Sinica 253 (9):122–37.
  • Dovey, K., and E. Pafka. 2017. What is functional mix? An assemblage approach. Planning Theory & Practice 18 (2):249–67. doi: 10.1080/14649357.2017.1281996.
  • Eck, J. R. V., and E. Koomen. 2008. Characterising urban concentration and land-use diversity in simulations of future land use. The Annals of Regional Science 42 (1):123–40. doi: 10.1007/s00168-007-0141-7.
  • Elwood, S., M. F. Goodchild, and D. Z. Sui. 2012. Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the Association of American Geographers 102 (3):571–90. doi: 10.1080/00045608.2011.595657.
  • Gui, C., C. Li, and K. Gao. 2016. A case study of water consumption change and water-use pattern for city industries. Journal of Shenzhen University Science & Engineering 33 (1):49–54. doi: 10.3724/SP.J.1249.2016.01049.
  • Horner, M. W., T. Zhao, and T. S. Chapin. 2011. Toward an integrated GIScience and energy research agenda. Annals of the Association of American Geographers 101 (4):764–74. doi: 10.1080/00045608.2011.567938.
  • Huang, X., Q. Lu, and L. Zhang. 2014. A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas. ISPRS Journal of Photogrammetry & Remote Sensing 90 (4):36–48. doi: 10.1016/j.isprsjprs.2014.01.008.
  • Jiang, L. I., and Q. S. Guo. 2002. Analysis of dynamic evolvement in urban land-use composition based on Shannon entropy. Resources and Environment in the Yangtze Basin 11 (5):393–97.
  • Jiang, Y., Z. Li, and S. L. Cutter. 2019. Social network, activity space, sentiment, and evacuation: What can social media tell us? Annals of the American Association of Geographers 109 (6):1795–810. doi: 10.1080/24694452.2019.1592660.
  • Kitamura, R., P. L. Mokhtarian, and L. Daidet. 1997. A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area. Transportation 24 (2):125–58. doi: 10.1023/A:1017959825565.
  • Kuemmerle, T., K. Erb, P. Meyfroidt, D. Müller, P. H. Verburg, S. Estel, H. Haberl, P. Hostert, M. R. Jepsen, T. Kastner, et al. 2013. Challenges and opportunities in mapping land use intensity globally. Current Opinion in Environmental Sustainability 5 (5):484–93. doi: 10.1016/j.cosust.2013.06.002.
  • Kwan, M. P. 2016. Algorithmic geographies: Big data, algorithmic uncertainty, and the production of geographic knowledge. Annals of the Association of American Geographers 106 (2):274–82.
  • Liu, M., and J. Zhang. 2011. Time series abnormality diagnosis method. Chinese Journal of Health Statistics 28 (4):478–80.
  • Liu, X., J. He, Y. Yao, J. Zhang, H. Liang, H. Wang, and Y. Hong. 2017. Classifying urban land use by integrating remote sensing and social media data. International Journal of Geographical Information Science 31 (8):1675–96. doi: 10.1080/13658816.2017.1324976.
  • Liu, Y., X. Liu, S. Gao, L. Gong, C. Kang, Y. Zhi, G. Chi, and L. Shi. 2015. Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers 105 (3):512–30. doi: 10.1080/00045608.2015.1018773.
  • Liu, Y., F. Wang, Y. Xiao, and S. Gao. 2012. Urban land uses and traffic “source-sink areas”: Evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning 106 (1):73–87. doi: 10.1016/j.landurbplan.2012.02.012.
  • Luo, Z. 2008. 2008 Jiangsu Provincial Government Work Report. Accessed June 4, 2020. http://www.gov.cn/test/2008-02/18/content_892000.htm
  • Margineantu, D. D., and T. G. Dietterich. 1997. Pruning adaptive boosting. Paper presented at International Conference on Machine Learning, Nashville, TN, July 8.
  • Meentemeyer, R. K., W. Tang, M. A. Dorning, J. B. Vogler, N. J. Cunniffe, and D. A. Shoemaker. 2013. Futures: Multilevel simulations of emerging urban–rural landscape structure using a stochastic patch-growing algorithm. Annals of the Association of American Geographers 103 (4):785–807. doi: 10.1080/00045608.2012.707591.
  • Moran, P. A. P., and M. G. Kendall. 1973. Rank correlation methods. Journal of the Royal Statistical Society 41 (3):399–400. doi: 10.2307/1402637.
  • Niu, N., X. Liu, H. Jin, X. Ye, Y. Liu, X. Li, Y. Chen, and S. Li. 2017. Characterizing mixed-use buildings based on multi-source big data. International Journal of Geographical Information Science 32 (4):738–56. doi: 10.1080/13658816.2017.1325489.
  • Portnov, B. A., and M. Zusman. 2014. Spatial data analysis using kernel density tools. In Encyclopedia of business analytics and optimization, ed. J. Wang, 2252–64. Hershey, PA: IGI Global.
  • Ribeiro, A. I., A. Pires, M. S. Carvalho, and M. F. Pina. 2015. Distance to parks and non-residential destinations influences physical activity of older people, but crime doesn’t: A cross-sectional study in a southern European city. BMC Public Health 15 (1):1–12. doi: 10.1186/s12889-015-1879-y.
  • Rodríguez, J. J., L. I. Kuncheva, and C. J. Alonso. 2006. Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (10):1619–30. doi: 10.1109/TPAMI.2006.211.
  • Seto, K. C., R. Sánchez-Rodríguez, and M. Fragkias. 2010. The new geography of contemporary urbanization and the environment. Annual Review of Environment and Resources 35 (1):167–94. doi: 10.1146/annurev-environ-100809-125336.
  • Shi, J. L. 2001. On the unity of the methods of calculating seasonal indexes. Journal of East China Shipbuilding Institute (Social Sciences) 1 (2):45–47.
  • Shi, L., P. Xu, C. Wang, T. Guan, Y. Zhang, and H. Xu. 2018. A review of applying spatial modelling and GIS in residential water use. IOP Conference Series Materials Science and Engineering 392 (6):062106.
  • Singleton, A. D., S. E. Spielman, and D. Folch. 2018. Urban analytics. Thousand Oaks, CA: Sage.
  • Soliman, A., K. Soltani, J. Yin, A. Padmanabhan, and S. Wang. 2017. Social sensing of urban land use based on analysis of Twitter users’ mobility patterns. PLoS ONE 12 (7):e0181657. doi: 10.1371/journal.pone.0181657.
  • Song, Y., and G.-J. Knaap. 2004. Measuring the effects of mixed land uses on housing values. Regional Science and Urban Economics 34 (6):663–80. doi: 10.1016/j.regsciurbeco.2004.02.003.
  • Tian, L., Y. Liang, and B. Zhang. 2017. Measuring residential and industrial land use mix in the peri-urban areas of China. Land Use Policy 69:427–38. doi: 10.1016/j.landusepol.2017.09.036.
  • Toole, J. L., M. Ulm, D. Bauer, and M. C. Gonzalez. 2012. Inferring land use from mobile phone activity. Paper presented at KDD ’12—The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, August 12.
  • van den Hoek, J. W. 2008. The MXI (Mixed-use Index) as tool for urban planning and analysis. Paper presented at Envisioning Corporate Real Estate in the Urban Future Conference, Brussels, May 26.
  • Villar-Navascués, R. A., and A. Pérez-Morales. 2018. Factors affecting domestic water consumption on the Spanish Mediterranean coastline. The Professional Geographer 64:1–13.
  • Wang, L., W. P. Sousa, and P. Gong. 2004. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing 25 (24):5655–68. doi: 10.1080/014311602331291215.
  • Wang, Y., W. Teng, T. Ming-Hsiang, L. Hao, J. Wei, and F. Guo. 2016. Mapping dynamic urban land use patterns with crowdsourced geo-tagged social media (Sina-Weibo) and commercial points of interest collections in Beijing, China. Sustainability 8 (11):1202. doi: 10.3390/su8111202.
  • Wen, D., X. Huang, L. Zhang, and J. A. Benediktsson. 2016. A novel automatic change detection method for urban high-resolution remotely sensed imagery based on multiindex scene representation. IEEE Transactions on Geoscience and Remote Sensing 54 (1):609–25. doi: 10.1109/TGRS.2015.2463075.
  • Wu, C., L. Zhang, and L. Zhang. 2016. A scene change detection framework for multi-temporal very high resolution remote sensing images. Signal Processing 124:184–97. doi: 10.1016/j.sigpro.2015.09.020.
  • Wu, S. S., X. Qiu, E. L. Usery, and L. Wang. 2009. Using geometrical, textural, and contextual information of land parcels for classification of detailed urban land use. Annals of the Association of American Geographers 99 (1):76–98. doi: 10.1080/00045600802459028.
  • Xia, J., P. Du, X. He, and J. Chanussot. 2014. Hyperspectral remote sensing image classification based on rotation forest. IEEE Geoscience and Remote Sensing Letters 11 (1):239–43. doi: 10.1109/LGRS.2013.2254108.
  • Yang, H., J. Song, and M. Choi. 2016. Measuring the externality effects of commercial land use on residential land value: A case study of Seoul. Sustainability 8 (5):432. doi: 10.3390/su8050432.
  • Yang, J., Y. Li, N. F. Zhang, J. F. Yang, K. Kuang, Y. H. Hu, and W. G. Qi. 2015. Analysis of urban residential water consumption based on smart meters and fuzzy clustering. Paper presented at the IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, UK, October 26.
  • Yao, Y., X. Li, X. Liu, P. Liu, Z. Liang, J. Zhang, and K. Mai. 2017. Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model. International Journal of Geographical Information Science 31 (4):825–48. doi: 10.1080/13658816.2016.1244608.
  • Yu, T., J. Zhang, and X. Luo. 2010. The research on urbanization quality of county-level cities in eastern developed area of China—A case study of Changshu City. Urban Studies 17 (11):7–12.
  • Yuan, J., Y. Zheng, and X. Xie. 2012. Discovering regions of different functions in a city using human mobility and POIs. KDD ’12—The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, August 12. doi: 10.1145/2339530.2339561.
  • Zelinsky, W., and D. F. Sly. 1984. Personal gasoline consumption, population patterns, and metropolitan structure: The United States, 1960–1970. Annals of the Association of American Geographers 74 (2):257–78. doi: 10.1111/j.1467-8306.1984.tb01452.x.
  • Zhang, X., and S. Du. 2015. A linear Dirichlet mixture model for decomposing scenes: Application to analyzing urban functional zonings. Remote Sensing of Environment 169:37–49. doi: 10.1016/j.rse.2015.07.017.
  • Zhang, X., S. Du, and Y.-C. Wang. 2015. Semantic classification of heterogeneous urban scenes using intrascene feature similarity and interscene semantic dependency. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (5):2005–14. doi: 10.1109/JSTARS.2015.2414178.
  • Zhao, G. 2013. Changshu Yushan creates a textile and clothing special service industry cluster. Textile and Clothing Weekly 46:98.
  • Zheng, Y., L. Capra, O. Wolfson, and H. Yang. 2014. Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology 5 (3):1–55. doi: 10.1145/2629592.
  • Zhu, J., X. Wang, L. Zhang, H. Cheng, and Z. Yang. 2015. System dynamics modeling of the influence of the TN/TP concentrations in socioeconomic water on NDVI in shallow lakes. Ecological Engineering 76 (5210):27–35. doi: 10.1016/j.ecoleng.2014.06.030.

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