The geography of poverty: Review and research prospects

 
 The geography of poverty: Review and research prospects
Yang Zhou a,b,c , Yansui Liu a,b,c,*
           Geography of poverty (GOP) or poverty geography is a branch of human geography, which studies the geographical patterns, distribution characteristics, areal types and evolution mechanism of poverty and the relationship with geographical environment as well as antipoverty measures. Based on the systematical analysis on the significance of GOP research, this study firstly put forward the impoverished areal system (IAS), and then elaborated the main contents, research progresses and existing problems in GOP research, and finally proposed the possible key areas in the future. Results show that the IAS is an open system with structure and function and has its life-cycle law, which is composed of natural endowments, location conditions, human capital and geographical capital within a certain geographical area. The subsystem of human, land and industry is the core of the IAS. Poverty geography studies both regional (place) poverty and individual (people) poverty. Regional poverty is an external manifestation of the coupling maladjustment of human, land and industry elements in a particular area. There are 5W + H (What, Where, Why, When, Who and How) models in GOP research. Key areas of future GOP research include: 1) IASs life cycle evolution law; 2) regional multidimensional poverty measurement; 3) geographical identification of poverty and its areal type; 4) dynamic simulation of impoverished and its mechanism; 5) poverty mapping; 6) antipoverty measure; and 7) poverty reduction effectiveness evaluation. Facing the UNs goal of eradicating poverty by 2030, poverty geography research in the new period should focus on the complexity, spatial heterogeneity and mechanism of poverty, and designs anti-poverty paths
and models suitable for different countries. To adapt to the trend of globalization and informationization, poverty geographers should make use of modern technologies such as data platform, cloud computing, remote sensing and artificial intelligence to focus on the spatio-temporal pattern of poverty and its driving mechanism as well as antipoverty path, and to solve the global poverty problem and promote the internationalization, basification and engineering of geography.