Identification of secondary functional areas and functional structure analysis based on multisource geographic data

References

• Agirbas A. 2020. Characteristics of social formations and space syntax application to quantify spatial configurations of urban regeneration in Levent, Istanbul. J Hous Built Environ. 35:171–189.
   Web of Science ®Google Scholar
• Alalouch C, Al-Hajri S, Naser A, Al Hinai A. 2019. The impact of space syntax spatial attributes on urban land use in Muscat: implications for urban sustainability. Sustain Citi Soc. 46.
   Web of Science ®Google Scholar
• Andrade R, Alves A, Bento C. 2020. POI mining for land use classification: a case study. ISPRS Int J Geo-Inf. 9.
   Web of Science ®Google Scholar
• Bao HQ, Ming DP, Guo Y, Zhang K, Zhou KQ, Du SG. 2020. DFCNN-based semantic recognition of urban functional zones by integrating remote sensing data and POI data. Remote Sens. 12(7):1088.
   Google Scholar
• Cao K, Guo H, Zhang Y. 2019. Comparison of approaches for urban functional zones classification based on multi-source geospatial data: a case study in Yuzhong District, Chongqing, China. Sustainabil. 11(3):660.
   Web of Science ®Google Scholar
• Cao R, Tu W, Yang CX, Li Q, Liu J, Zhu JS, Qiu GP. 2020. Deep learning-based remote and social sensing data fusion for urban region function recognition. ISPRS J Photogramm Remote Sens. 163:82–97.
   Web of Science ®Google Scholar
• Dong XY, Xu Y, Huang LP, Liu ZG, Xu Y, Zhang KY, Wu GF. 2020. Exploring impact of spatial unit on urban land use mapping with multisource data. Remote Sens. 12(21):3597.
   Google Scholar
• Dragut L, Tiede D, Levick SR. 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int J Geograph Inform Sci. 24(6):859–871.
   Web of Science ®Google Scholar
• Du S, Du S, Liu B, Zhang X, Zheng Z. 2020. Large-scale urban functional zone mapping by integrating remote sensing images and open social data. Gisci Remote Sens. 57(3):411–430.
   Web of Science ®Google Scholar
• Du SJ, Du SH, Liu B, Zhang XY. 2019. Context-enabled extraction of large-scale urban functional zones from very-high-resolution images: a multiscale segmentation approach. Remote Sens. 11(16):1902.
   Google Scholar
• Esposito D, Santoro S, Camarda D. 2020. Agent-based analysis of urban spaces using space syntax and spatial cognition approaches: a case study in Bari, Italy. Sustainabil. 12(11):4625.
   Web of Science ®Google Scholar
• Hillier B, Hanson J. 1984. The social logic of space. Cambridge; New York: Cambridge University Press.
   Google Scholar
• Hinneburg A. 1998. An efficient approach to clustering in large multimedia databases with noise. Know Discov Data Minin. 98:58–65.
   Google Scholar
• Hu TY, Yang J, Li XC, Gong P. 2016. Mapping urban land use by using landsat images and open social data. Remote Sens. 8(2):151.
   Google Scholar
• Hu YF, Han YQ. 2019. Identification of urban functional areas based on POI data: a case study of the Guangzhou economic and technological development zone. Sustainabil. 11(5):1385.
   Web of Science ®Google Scholar
• Jiang S, Alves A, Rodrigues F, Ferreira J, Pereira FC. 2015. Mining point-of-interest data from social networks for urban land use classification and disaggregation. Comput Environ Urban Syst. 53:36–46.
   Web of Science ®Google Scholar
• Liu XP, He JL, Yao Y, Zhang JB, Liang HL, Wang H, Hong Y. 2017. Classifying urban land use by integrating remote sensing and social media data. Int J Geograph Inform Sci. 31(8):1675–1696.
   Web of Science ®Google Scholar
• Long Y, Liu X. 2013. Automated identification and characterization of parcels (AICP) with OpenStreetMap and Points of Interest.
   Google Scholar
• Obaco M, Royuela V, Vitores X. 2020. Identifying functional urban areas in ecuador using a varying travel time approach: FUAs in Ecuador: a varying travel time approach. Geog Anal. 52:107–124.. https://onlinelibrary.wiley.com/doi/10.1111/gean.12190.
   Web of Science ®Google Scholar
• Silverman BW. 1986. Density estimation for statistics and data analysis. London.
   Google Scholar
• Song JC, Lin T, Li XH, Prishchepov AV. 2018. Mapping urban functional zones by integrating very high spatial resolution remote sensing imagery and points of interest: a case study of Xiamen, China. Remote Sens. 10(11):1737.
   Google Scholar
• Tian GJ, Sha MQ. 2010. The spatial pattern of guangzhou metropolitan area industry based on point data and GIS. Progress Geograph. 29(4):387–395.
   Google Scholar
• Wang XY, Zhong YF, Zhang LP, Xu YY. 2019. Blind hyperspectral unmixing considering the adjacency effect. IEEE Trans Geosci Remote Sens. 57(9):6633–6649.
   Web of Science ®Google Scholar
• Wang YD, Wang T, Tsou MH, Li H, Jiang W, Guo FQ. 2016. Mapping dynamic urban land use patterns with crowdsourced geo-tagged social media (Sina-Weibo) and commercial points of interest collections in Beijing, China. Sustainabil. 8(11):1201.
   PubMed Web of Science ®Google Scholar
• Xing HF, Meng Y. 2018. Integrating landscape metrics and socioeconomic features for urban functional region classification. Comput Environ Urban Syst. 72:134–145.
   Web of Science ®Google Scholar
• Xu SY, Qing LB, Han LM, Liu M, Peng YH, Shen LF. 2020. A new remote sensing images and point-of-interest fused (RPF) model for sensing urban functional regions. Remote Sens. 12(6):1032.
   Google Scholar
• Xu ZT, Liang JZ, Xu WX. 2021. Research on correlation between Fuzhou road network form and retail commercial space layout based on spatial syntax. J Geomat. 46(S1):5–10.
   Google Scholar
• Yao Y, Li X, Liu XP, Liu PH, Liang ZT, Zhang JB, Mai K. 2017. Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model. Int J Geograph Inform Sci. 31(4):825–848.
   Web of Science ®Google Scholar
• Yi DS, Yang J, Liu JJ, Liu YS, Zhang J. 2019. Quantitative identification of urban functions with fishers’ exact test and POI data applied in classifying urban districts: a case study within the sixth ring road in Beijing. ISPRS Int J Geo-Inf. 8(12):555.
   Web of Science ®Google Scholar
• Yu BB, Wang ZH, Mu HW, Sun L, Hu FN. 2019. Identification of urban functional regions based on floating car track data and POI data. Sustainability. 11(23):6541.
   Web of Science ®Google Scholar
• Zhan XY, Ukkusuri SV, Zhu F. 2014. Inferring urban land use using large-scale social media check-in data. Network Spatial Econ. 14(3–4):647–667.
   Web of Science ®Google Scholar
• Zhang C, Sargent I, Pan X, Li HP, Gardiner A, Hare J, Atitinson PM. 2018. An object-based convolutional neural network (OCNN) for urban land use classification. Remote Sens Environ. 216:57–70.
   Web of Science ®Google Scholar
• Zhang XY, Du SH, Wang Q. 2017a. Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data. ISPRS J Photogramm Remote Sens. 132:170–184.
   Web of Science ®Google Scholar
• Zhang XY, Du SH, Zheng ZJ. 2020. Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data. ISPRS J Photogramm Remote Sens. 161:1–12.
   Web of Science ®Google Scholar
• Zhang Y, Li QZ, Huang HP, Wu W, Du X, Wang HY. 2017b. The combined use of remote sensing and social sensing data in fine-grained urban land use mapping: a case study in Beijing, China. Remote Sensing. 9(9):865.
   Web of Science ®Google Scholar
• Zhao HB, Yu DF, Miao CH, Li GH, Feng YB, Bie QL. 2018. The location distribution characteristics and influencing factors of culturai facilities in Zhengzhou based on POI data. Sci Geograph Sinica. 38(9):1525–1534.
   Google Scholar
• Zhong YF, Su Y, Wu SQ, Zheng ZD, Zhao J, Ma AL, Zhang LP. 2020. Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: a case study of Chinese cities. Remote Sens Environ. 247.
   Web of Science ®Google Scholar
• Zhou W, Ming DP, Lv XW, Zhou KQ, Bao HQ, Hong ZL. 2020. SO-CNN based urban functional zone fine division with VHR remote sensing image. Remote Sens Environ. 236.
   Web of Science ®Google Scholar