Modeling the Drivers of Urban Land Use Change in the Pearl River Delta, China: Integrating Remote Sensing with Socioeconomic Data

By Karen C. Seto, Robert K. Kaufmann. Land Economics, 2003

490 citations recorded by the source corpus.

Subfields: Atmospheric Science, Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/964b3d10ddf55134b869a1a2a8c7f2ec2e9516b1

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Citations in the corpus

Land and Urban Economic Growth in China (2011, unknown): “Empirical studies conducted to date have focused on demonstrating the association between economic growth and spatial expansion in urban areas with an emphasis on characterizing urban expansion as an outcome of economic growth (Seto and Kaufman, 2003; Deng et al., 2008).”
Mapping global urban boundaries from the global artificial impervious area (GAIA) data (2020, methodology): “Each image is composited by Landsat data using a natural color (R: Band 3, G: Band 2; B: Band 1). boundaries between urban and rural settlements of cities in Asia are more challenging to identify due to the close interactions between urban and rural areas in these cities (Seto and Kaufmann 2003).”
Driving forces of tropical deforestation: The role of remote sensing and spatial models (2006, background): “…the existence of protected reserves effectively blocking land access and use, and policies such as infrastructure development, colonization programmes, market integration, structural adjustment and agricultural subsidies (Cropper et al. , 1999; Seto & Kaufmann, 2003; Walker & Solecki, 2004).”
Local officials as land developers: Urban spatial expansion in China (2009, background): “Urban expansion is, of course, a normal concomitant of economic growth, and empirical studies of China find a strong association between economic growth and urban spatial expansion (Seto and Kaufman, 2003; Ho and Lin, 2004; Deng et al., 2008).”
Land pricing and its impact on land use efficiency in post-land-reform China: A case study of Beijing (2016, background): “However, studies of land use dynamics have provided a de facto economic and/or demographic rationale for land development and urban expansion in various regions (Cheng and Masser, 2003; Ho and Lin, 2004; Seto and Kaufmann, 2003; Xie, Mei, Tian, and Xing, 2005).”
Comparison of empirical methods for building agent-based models in land use science (2007, methodology): “A number of analytical methods are available for pursuing this approach, including panel data analysis (Hsiao 1986, Seto and Kaufmann 2003), survival analysis (An and Brown, in review), and multivariate limited dependent variable models (Mertens and Lambin 2000, Parker and Munroe 2006).”
Exploring Complexity in a Human–Environment System: An Agent-Based Spatial Model for Multidisciplinary and Multiscale Integration (2005, methodology): “These methods include multi-variate spatial models (e.g., Seto and Kaufmann 2003), Markov chain analysis (e.g., Brown, Pijanowski, and Duh 2000; Lo´pez et al. 2001), and cellular automata (e.g., Batty, Xie, and Sun 1994; Li and Yeh 2002; Malanson 2002).”
Modeling spatial variations of urban growth patterns in Chinese cities: The case of Nanjing (2009, unknown): “Last, we selected three more variables to reflect the influence of existing socio-economic factors, especially urban agglomerations; for urban land expansion we chose: distance to major city centers, distance to suburban centers, and distance to industrial centers, which have also been used by…”
Monitoring and predicting land use change in Beijing using remote sensing and GIS (2006, background): “In China, the rapid urbanization process caused an unprecedented scale and rate of urban expansion over the last two decades (Seto and Kaufmann, 2003).”