Analyzing Cities with the Global Human Settlement Layer: A Methodology to Compare Urban Growth Using Remote Sensing Data
Data is a key resource to analyze and compare cities and to track urbanization processes and changes in cities. Nowadays, innovative procedures for acquiring and processing remote imagery provide analysts and policy makers with globally consistent multitemporal data on human settlements, which promise unprecedented advances in urban analysis. The Global Human Settlement Layer (GHSL) has been developed by the European Commission Joint Research Centre. It contains fine-scale global and multitemporal geospatial data on populations and built-up areas. The GHSL opens the possibility to study all cities in a globally consistent and comparative way. This study proposes a new methodology to work on the GHSL at city level and to monitor the process of urban expansion, both in terms of spatial footprint and population dynamics. The methodology introduces seven indicators: area [in km2], population [inhabitants], built-up [square kilometers], built-up to area ratio, population density [inhabitants per square kilometer], and built-up and area per capita [in square meters]. The changes across reference points in the years 1975–1990 to 2000–2015 are used to monitor and benchmark urban growth and expansion. This research paper illustrates the methodological procedure for data preparation, and it explains the GHSL-derived indicators for monitoring urban analysis. The second part of the research applies the methodology to the Chinese cities of Beijing and Guangzhou to analyze, by a comparative perspective, the two cases. The paper contains data analysis capturing the dynamics of growth of urban areas, population, and built-up surfaces between 1975 and 2015. Guangzhou and Beijing are representative examples of rapid urbanization: Beijing grew faster than Guangzhou in the period 1990–2015 in terms of population, while built-up growth over the same years has been faster in Guangzhou. The comparison of the two cities elicits interesting phenomena such as the different speeds of population and built-up growth in the period 1990–2015.
KeywordsUrbanization Remote sensing data Megacities GHSL
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