Driving mechanisms of urbanization: Evidence from geographical, climatic, social-economic and nighttime light data

Urbanization induced changes have attracted widespread attention. Key challenges arise from the inherent uncertainties in attribution models diagnosing the driving mechanisms and the interrelationships of the attributes given by the complexity of interactions within a city. Here, we investigate urbanization dynamics from nighttime light signals before analyzing their driving mechanisms from 2014 to 2020 on both provincial and regional scale and a flat versus mountainous urbanization comparison. Model uncertainties are discussed comparing the contribution results from Geodetector and the Gini importance from Random Forest analyses. The method is applied to Shaanxi Province, where flat urban land is located mainly in its center and mountainous urban land is situated in the North and South. The following results are noted: i) Employing the Geodetector based maximum contribution method for urban region extraction of night time light reveals a notable accuracy improvement in flat urban land compared with the closest area method. ii) Geographical factors attain high contribution for mountainous urban land of Shannan, while for flat urbanization land dynamics, economic factors and community factors prevail. iii) The most obvious driving mechanisms are economic factors which, associated with local urban development strategies, show highest contribution values in 2014 (2018) over the flat (mountainous) urban land of Guanzhong Plain (Northern Shaanxi Plateau or Shanbei region) linked with an early (late) development. iv) Population factors achieve high contribution values in the initially low populated urban land of the northern mountainous land initiating huge migration. v) The contributions resulting from Geodetector are in agreement with the Gini importance from Random Forest in agriculture, geographical and population factors (R2 > 0.5) but not in economy, community and climatic factors (R2 < 0.5). The dynamics of driving mechanisms for urbanization provides insights in connecting urban geographical expansion with multifactors and thus to assist municipal governments and city stakeholders to design a city with geographical, climatic and social-economic changes and interactions in mind.

Automated summary

This study examines the dynamics and driving mechanisms of urbanization in China by analyzing nighttime light signals. The findings reveal that geographical and economic factors are the main driving mechanisms in flat urban areas, while geographical and community factors play a more important role in mountainous urban areas. Additionally, population factors also have a significant impact on urbanization. These results have important implications for urban planning and decision-making.