Using land cover, population, and night light data to assess urban expansion in Kimberley, South Africa

Kabanda, Tabaro

doi:10.1080/03736245.2022.2028667

Cited by 19 publications

(14 citation statements)

References 31 publications

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“…These results indicate that, as the night-time light increases, the likelihood of deterioration in the performance of sales and demand is reduced. Thus, our results are consistent with the existing literature showing that night-time light can be used as a proxy for a number of variables, including infrastructure, urbanization, density, and economic growth (Mellander et al 2015;Li et al 2017;Kabanda 2022). However, the impact of NTL on the export share and working hours is not significant.…”

Section: Resultssupporting

confidence: 91%

“…The smallest gender gap of firm owners and managers among the four countries is in Mongolia. Night-time light data can be used as a proxy for a number of variables, including infrastructure, urbanization, density, and economic growth (Mellander 2015;Kabanda 2022). We use the data from Google Earth on night-time light (NTL) intensity.…”

Section: Figure 8: Average Foreign Ownership (Wave 1 and Wave 2)mentioning

confidence: 99%

“…The paper provides important results, which are summarized in Figure 14 Firstly, our results show that, as night-time light increases, firms' sales and demand are more likely to increase. Thus, night-time light can be used as a proxy for a number of variables, including infrastructure, urbanization, density, and economic growth (Mellander et al 2015;Kabanda 2022). Secondly, the strictness of "lockdown-style" government policies, i.e., the stringency index, does not harm firm performance on average.…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

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Evaluating COVID-19’s Impact on Firm Performance in the CAREC Region Using Night-Time Light Data: Azerbaijan, Georgia, Kazakhstan, and Mongolia

et al. 2022

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Section: Resultssupporting

confidence: 91%

Section: Figure 8: Average Foreign Ownership (Wave 1 and Wave 2)mentioning

confidence: 99%

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating COVID-19’s Impact on Firm Performance in the CAREC Region Using Night-Time Light Data: Azerbaijan, Georgia, Kazakhstan, and Mongolia

et al. 2022

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“…The coupling model was used to assess the decoupling of the spatial distribution of rural settlements and the rural population in different years and under different scenarios. Combined with the situation of the study area and previous research results [49,50], 18 natural and socioeconomic factors were selected as the driving factors for the data (Figure 3). Table 1 describes the driving factors, including natural, climatic, socioeconomic, and environmental factors.…”

Section: Research Frameworkmentioning

confidence: 99%

“…The OOB RMSE for all the land use components was less than 0.05, indicating that the RFR was well-trained and capable of capturing the relationships between the LUCC and the associated driving factors. Combined with the situation of the study area and previous research results [49,50], 18 natural and socioeconomic factors were selected as the driving factors for the data (Figure 3). Table 1 describes the driving factors, including natural, climatic, socioeconomic, and environmental factors.…”

Section: Research Frameworkmentioning

confidence: 99%

Simulating the Coupling of Rural Settlement Expansion and Population Growth in Deqing, Zhejiang Province, Based on MCCA Modeling

Zhao

Fan

Zhou

et al. 2022

Land

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Analyzing the relationship between rural settlements and rural population change under different policy scenarios is key in the sustainable development of China’s urban and rural areas. We proposed a framework that comprised the mixed land use structure simulation (MCCA) model and the human–land coupling development model to assess the spatiotemporal dynamic changes in rural settlements and its’ coupling relationship with the rural population in the economically developed region of Deqing, Zhejiang Province. The results showed that rural settlements and urban land increased by 14.36 and 29.07 km2, respectively, over the last 20 years. The expansion of some rural settlements and urban land occurred at the cost of cropland occupation. Rural settlements showed an expansion trend from 2000 to 2020, increasing from 42.69 km2 in 2000 to 57.05 km2 in 2020. In 2035, under the natural development scenario, the cropland protection scenario, and the rural development scenario, rural settlements are projected to show an expansion trend and Wukang and Leidian are the key regions with rural settlement expansion. The distance to Hangzhou, nighttime light data, distance to rivers, and precipitation are important factors influencing the expansion of rural settlements. The coupling relationship between rural settlements and the rural population developed in a coordinated manner from 2000 to 2020. For 2035, under different scenarios, the coupling relationship between rural settlements and the rural population showed different trends. In the rural development scenario, the highest number of towns with coordinated development between rural settlements and the rural population is in Deqing, predominantly with Type I coupling. Overall, an important recommendation from this study is that the sustainable development of regional land use can be promoted by controlling the occupation of cropland for urban and rural construction, balancing rural settlement expansion and rural population growth, and formulating land use policies that are more suitable for rural development.

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KNN-GCN: A Deep Learning Approach for Slope-Unit-Based Landslide Susceptibility Mapping Incorporating Spatial Correlations

Xia,

Tang,

Glade

et al. 2024

Math Geosci

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Using land cover, population, and night light data to assess urban expansion in Kimberley, South Africa

Cited by 19 publications

References 31 publications

Evaluating COVID-19’s Impact on Firm Performance in the CAREC Region Using Night-Time Light Data: Azerbaijan, Georgia, Kazakhstan, and Mongolia

Evaluating COVID-19’s Impact on Firm Performance in the CAREC Region Using Night-Time Light Data: Azerbaijan, Georgia, Kazakhstan, and Mongolia

Simulating the Coupling of Rural Settlement Expansion and Population Growth in Deqing, Zhejiang Province, Based on MCCA Modeling

KNN-GCN: A Deep Learning Approach for Slope-Unit-Based Landslide Susceptibility Mapping Incorporating Spatial Correlations

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