A Poverty Measurement Method Incorporating Spatial Correlation: A Case Study in Yangtze River Economic Belt, China

Qianqian Zhou,Siwei Lin,Nan Chen

doi:10.3390/ijgi11010050

Qianqian Zhou, Siwei Lin + Show 1 more

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https://doi.org/10.3390/ijgi11010050

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Abstract

The UN 2030 Agenda sets poverty eradication as the primary goal of sustainable development. An accurate measurement of poverty is a critical input to the quality and efficiency of poverty alleviation in rural areas. However, poverty, as a geographical phenomenon, inevitably has a spatial correlation. Neglecting the spatial correlation between areas in poverty measurements will hamper efforts to improve the accuracy of poverty identification and to design policies in truly poor areas. To capture this spatial correlation, this paper proposes a new poverty measurement model based on a neural network, namely, the spatial vector deep neural network (SVDNN), which combines the spatial vector neural network model (SVNN) and the deep neural network (DNN). The SVNN was applied to measure spatial correlation, while the DNN used the SVNN output vector and explanatory variables dataset to measure the multidimensional poverty index (MPI). To determine the optimal spatial correlation structure of SVDNN, this paper compares the model performance of the spatial distance matrix, spatial adjacent matrix and spatial weighted adjacent matrix, selecting the optimal performing spatial distance matrix as the input data set of SVNN. Then, the SVDNN model was used for the MPI measurement of the Yangtze River Economic Belt, after which the results were compared with three baseline models of DNN, the back propagation neural network (BPNN), and artificial neural network (ANN). Experiments demonstrate that the SVDNN model can obtain spatial correlation from the spatial distance dataset between counties and its poverty identification accuracy is better than other baseline models. The spatio-temporal characteristics of MPI measured by SVDNN were also highly consistent with the distribution of urban aggregations and national-level poverty counties in the Yangtze River Economic Belt. The SVDNN model proposed in this paper could effectively improve the accuracy of poverty identification, thus reducing the misallocation of resources in tracking and targeting poverty in developing countries.

Highlights

Experiments demonstrate that the spatial vector deep neural network (SVDNN) model can obtain spatial correlation from the spatial distance dataset between counties and its poverty identification accuracy is better than other baseline models
This paper proposes a new multidimensional poverty measurement method, the SVDNN model, which incorporates spatial correlation between counties, and further compared the model performance of this model with the three baseline models of deep neural network (DNN), back propagation neural network (BPNN) and artificial neural network (ANN)
The SVDNN model was used to calculate the multidimensional poverty index (MPI) of the YREB in 2000, 2005, 2010 and 2015

Summary

Introduction

The 2030 Agenda determined that eradicating extreme poverty is still the largest challenge in the world and placed “No poverty” at the top of the 17 Sustainable Development Goals, which demonstrated the determination to eradicate poverty and hunger in all forms and manifestations [1,2]. Poverty alleviation and eradication critically influence the development of the world economy [3,4,5]. The scarcity of reliable explanatory variables and poverty measurement methods represents a major challenge to poverty identification [8]. Under the influence of the concept of absolute poverty, several researchers indicated that poverty is an inequality [9,10]. The essential explanatory variable for measuring this inequality was basic living materials, such as income and food.

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