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Understanding the spatial and temporal patterns of human pressures provides a foundation for understanding interactions between human and environment and managing human activities for a sustainable development. This study is the first attempt focused within China at calculating the spatial–temporal human footprint and its driving forces in a highly urbanized area with intensive human activities. Population, land use, night-time lights, and road impacts were used to generate human footprint maps of Jiangsu Province for 2000, 2010 and 2015 with a resolution of 1 km * 1 km. Five natural drivers and four anthropogenic drivers were employed to construct generalized additive models for explaining the spatial variation of human footprint and its change. It shows that a large difference is between the human footprint in northern and southern Jiangsu, and the pattern of human pressures conforms to the “Matthew effect”, with spatial aggregation of high human footprint areas accelerating. Slope, industrialization level are significant in explaining the spatial variation of human footprint in 2000, 2010 and 2015. The effect of natural drivers decreases for explaining the human footprint over time. Furthermore, annual precipitation, mean annual temperature and urban per capita disposable income are also significant drivers for human footprint in 2010 and 2015. And the increasing of human footprint slows with increasing of industrialization level. The difference of industrialization level and urban income between northern and southern Jiangsu mainly caused different driving pattern for human footprint and its change. Our study has generated new insights on the interaction pattern between human and nature in highly developed regions based on the human footprint concept, and can provide references for managing human activities in similar regions rapid socioeconomic development.

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Understanding the spatial–temporal variation of human footprint in Jiangsu Province, China, its anthropogenic and natural drivers and potential implications