Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model

By Yao Yao, Xia Li, Xiaoping Liu, Penghua Liu, Zhaotang Liang, Jinbao Zhang, Ke Mai. International Journal of Geographical Information Science, 2017

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Classifying urban land use by integrating remote sensing and social media data (2017, background): “2015b, Yao et al .”
Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017, background): “, trajectories from mobile phones), POI data, and social media feeds to study the spatial and social structure of urban environments (Hu et al., 2015; Jiang, Alves, Rodrigues, Ferreira, & Pereira, 2015; Liu et al., 2015; McKenzie et al., 2015; Pei et al., 2014; Steiger, Westerholt, & Zipf, 2016; Yao”
Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method (2018, background): “Urban sprawl, which arises from the rapid growth of the economy and population, has become a major challenge for sustainable urban development worldwide (Yao et al., 2016, 2017; Hashem & Balakrishnan, 2015; Liu et al., 2014).”
A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects (2017, background): “Rapid urban expansion and socio- economic development have increased the tension in human-environment interactions (Vitousek et al., 1997; Yao et al., 2016), as more than 50% of the world’s population lived in urban areas in 2007, a number that will likely reach approximately 70% by 2050 (Bloom,…”