Multidimensional measurement of poverty and its spatio-temporal dynamics in China from the perspective of development geography

Exploring the spatio-temporal dynamics of poverty is important for research on sustainable reduction in China. Based perspective development geography, this paper proposes a panel vector autoregressive (PVAR) model that combines human approach with global indicator framework Sustainable Development Goals (SDGs) to identify poverty-causing and poverty-reducing factors The aim measure multidimensional index (MPI) China’s provinces from 2007 2017, use exploratory data analysis (ESTDA) method reveal characteristics poverty. results show following: (1) China include high social gross dependency ratio crop-to-disaster ratio, per capita GDP, security expenditure, public health number hospitals 10,000 people, rate participation new rural cooperative medical scheme, vegetation coverage, education universities, (R&D) funding cultural undertakings. (2) From provincial income (IP), (HP), (CP), have been significantly reduced China, overall national has dropped by 5.67% annually. there differentiation along different dimensions certain provinces. (3) During study period, local spatial pattern between showed strong dynamics, trend increase eastern central western regions was noted. MPI among exhibited dependence over time form decrease northwestern northeastern surrounding areas. (4) networks adjacent were mainly negatively correlated, only Shaanxi Henan, Ningxia, Qinghai Gansu, Hubei Anhui, Sichuan Guizhou, Hainan Guangdong forming spatially relationships. These reference value implementation alleviation strategy.