Key Challenges and Potential Urban Modelling Opportunities in South Africa, with Specific Reference to the Gauteng City-Region

Wray, Chris; Cheruiyot, Kenneth

doi:10.4314/sajg.v4i1.2

Cited by 18 publications

(5 citation statements)

References 34 publications

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“…The analysis of such changes includes the study of a whole complex of interrelated issues: changing labor requirements, transformation of the education and training system, new approaches to the management system. The level of knowledge, competencies and skills of the population in the field of digital technologies are an important factor in the spread and development of these trends in various spheres of public life [12]. To analyze this factor of socioeconomic development, the category of "human capital" is used.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Digital economy in developing countries: problems and prospects

Semyachkov¹

2019

Proceedings of the 1st International Scientific Conference "Modern Management Trends and the Digital Economy: From Regional Dev

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The purpose of this study is to develop the principles for building a digital economy in developing countries. It is shown that the priority task of the socioeconomic development of these countries is the creation of a digital economy. The features of the development of the digital economy are highlighted, which include the presence of intellectual assets, the increasing importance of data, the network organization of business, the widespread adoption of the Internet and the global nature of data exchange. The tools for solving the problems of the digital society are defined, which include digital platforms for the development of a "share economy", as well as cloud technologies and methods for processing large databases. The principles of building skills development systems in terms of digitalization are formulated, among which are rational use of resources, combining short-term and long-term development goals, lifelong learning, systematic approach, a unified approach to management at different levels of government and involvement of all stakeholders in the digitalization process.

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Section: Discussion Of Resultsmentioning

confidence: 99%

Digital economy in developing countries: problems and prospects

Semyachkov¹

2019

Proceedings of the 1st International Scientific Conference "Modern Management Trends and the Digital Economy: From Regional Dev

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“…A grid of cells derived from remote sensing imageries is a CA modelling basis (C. and K., 2015;Musa et al, 2017). Musa et al stated that each cell of remote sensing image represents a certain class of land use and its future state is performed in CA according to the neighbourhood cells of it and its former state together in compliance with a function of transition rules (Musa et al, 2017).…”

Section: Study Materials and Methodsmentioning

confidence: 99%

Estimating Built-Up Area Change in Ulaanbaatar City, Mongolia

Myagmartseren¹,

Myagmarjav

Enkhtuya³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Long-term urban built-up area changes of the Ulaanbaatar city has accelerated since the 1950s and due to rapid urbanization most of the Mongolian population, or about 68%, live in urban areas. The systematic understanding of urban land expansion is a crucial clue for urban land use planning and sustainable land development. Therefore, in this paper, we used a Markov chain model and cellular automata (CA) to simulate and predict current and future built-up areas expansion is Ulaanbaatar. Landsat imageries (Landsat TM 5, Landsat ETM 7 and Landsat OLI 8) of 1988, 1998, 2008, and 2017 were used to derive main land use classes. Clark Lab’s (Clark University) Geospatial Monitoring and Model software had been used for the urban expansion prediction. The results are innovated to comparable to validate with other study results by using a different kind of methods. Built-up area expansion modeled and predicted 2028’s trends based on a historical expansion of the Ulaanbaatar city between 1988 and 2017, which are prepared according to input model requirements. The built-up area was 7282 hectares (ha) in 1988 and has expanded to 31144 ha in 2017. The built-up area growth of the Ulaanbaatar city has reached 4.3 times over the past 30 years, and from 2017 to 2028 the expansion of the built-up area will be 1.5 times. A comparison of urban expansion from 1988 to 2017 has revealed a rapid built-up invasion to the previous areas of agriculture, grassland, and forest. Simulation performance of Markov chain with the cellular automata model can be used for an improvement in the understanding of the urban expansion processes while allowing helpful for better planning of Ulaanbaatar city, as well as for other rapidly developing towns of Mongolia.

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“…Urbanisation is one of the major global challenges which is leading to change in the properties of land surface materials (Mathew et al, 2018). Urbanisation leads to urban sprawl which has an impact on the quality of the environments, which include air quality and land surface temperature (Abutaleb et al, 2014;Wray and Cheruiyot, 2015). Land Surface Temperature (LST) increases were reported in urban areas as a result of impervious surfaces and high concentration of pollutants leading to the establishment of urban heat islands (UHI) (Mallick et al, 2008;Sheng et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variations of land surface temperature using Landsat and MODIS: case study of the City of Tshwane, South Africa

Magidi¹,

Ahmed²

2022

SA J of Geomatics

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Urbanisation is accelerating urban land use dynamics and this has a significant impact on land surface temperature (LST). Impervious surfaces and increase in air pollution has led to the increase in land surface temperature. This study reports on the use of geospatial technologies to monitor and quantify changes in LST using remotely sensed data in the City of Tshwane. Land surface temperature was retrieved using the winter and summer Landsat datasets for 1997 and 2015 and the MODIS data from 2000 to 2015. Land surface temperature was extracted using emissivity and satellite temperature as input parameters. The spatial and temporal variations in the LST were retrieved to show the effects of land cover change on LST. Change in LST was also analysed on different land cover types using transects across the study area. The study revealed an increase in land surface temperature between the years. It also showed that impervious surfaces had a higher LST compared to the non-impervious surfaces. The results revealed variations in LST between non-cropped and cropped agricultural areas, where the former had higher LST than the latter. Temporal trends revealed a notable increase in LST in the urban areas and there were some seasonal variations in LST with high LST values in summer and low values in winter. Cross-section analysis along transects revealed spatio-temporal thermal variations across different land cover types.

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Key Challenges and Potential Urban Modelling Opportunities in South Africa, with Specific Reference to the Gauteng City-Region

Cited by 18 publications

References 34 publications

Digital economy in developing countries: problems and prospects

Digital economy in developing countries: problems and prospects

Estimating Built-Up Area Change in Ulaanbaatar City, Mongolia

Spatio-temporal variations of land surface temperature using Landsat and MODIS: case study of the City of Tshwane, South Africa

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