Spatiotemporal analysis of urban growth of five global cities using remote sensing, population estimates and GDP growth

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dc.contributor.authorStrydom, Jacobus
dc.date.accessioned2018-07-18T09:33:23Z
dc.date.available2018-07-18T09:33:23Z
dc.date.issued2017
dc.descriptionA research report submitted in partial fulfilment of the requirements for the degree of Master of Science. to the Faculty of Science, University of the Witwatersrand, Johannesburg, 2017.en_ZA
dc.description.abstractThis study used an object orientated image nearest neighbour classification method to quantify the urban area extent of five global cities over a period of 25 years. The cities analysed were Cairo (Egypt), Delhi (India), Dhaka (Bangladesh), Lagos (Nigeria) and Shanghai (China). The observations were made every 5 years from 1990 to 2015, six observations for each city using Landsat 5, 7 and 8 imagery. The accuracy assessment process resulted in a minimum average overall accuracy of 78.29% and a kappa coefficient of 72.88% and a maximum average overall accuracy of 83.4% and a maximum kappa coefficient of 80.46%. The classification results were used to quantify the size of the urban footprint of each of the cities. These results were used to analyse the relationship between UN population estimates and physical urban growth. A Pearson’s correlation and a regression analysis, for each city and its population estimates, were used to further analyse the relationship. All the regression models resulted in a correlation coefficient of above 0.95 except for Dhaka with 0.868. All the regression analysis models had an R2 above 0.90, except Dhaka at 0.77. The results showed that the population estimates do not follow the urban area growth trends and a weak relationship was identified. The quantified size of the urban footprint was also used to analyse the relationship between GDP growth, as estimated by the World Bank and physical urban growth. A Pearson’s correlation and a regression analysis was performed for each city and its population estimates, to further analyse the relationship. All the regression models resulted in a correlation coefficient of above 0.90. Regression analysis models all had an R2 greater than 0.90, except Cairo at 0.81 and India at 0.89. The analysis pointed to a weak positive relationship. The results also indicated a complex relationship between economic growth and urban growth for all five cities and their countries. A potential lag effect between economic growth and urban area growth was identified. These results showed that there is indeed the relationship between urban growth and economic growth, though in all five cases the relationship is weak. The fact that these results differ between the countries also indicate that the relationship is highly complex.en_ZA
dc.description.librarianLG2018en_ZA
dc.format.extentOnline resource (114 leaves)
dc.identifier.citationStrydom, Jacobus (2017) Spatiotemporal analysis of urban growth of five global cities using remote sensing, population estimates and GDP growth, University of the Witwatersrand, Johannesburg, https://hdl.handle.net/10539/25036
dc.identifier.urihttps://hdl.handle.net/10539/25036
dc.language.isoenen_ZA
dc.subject.lcshSustainable development
dc.subject.lcshCity planning--Environmental aspects
dc.titleSpatiotemporal analysis of urban growth of five global cities using remote sensing, population estimates and GDP growthen_ZA
dc.typeThesisen_ZA
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