Combining night time lights in prediction of poverty incidence at the county level

Xu, Jianbin; Song, Jie; Li, Baochao; Liu, Dan; Cao, Xiaoshu

doi:10.1016/j.apgeog.2021.102552

Cited by 42 publications

(20 citation statements)

References 44 publications

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“…The aim of the paper is to show that machine learning can be applied in identifying and predicting poverty at the county level [103]. In measuring poverty levels, a poverty incidence indicator was applied [104].…”

Section: Combining Night Time Lights In Prediction Of Poverty Inciden...mentioning

confidence: 99%

Utilities of Artificial Intelligence in Poverty Prediction: A Review

et al. 2022

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Artificial Intelligence (AI) is generating new horizons in one of the biggest challenges in the world’s society—poverty. Our goal is to investigate utilities of AI in poverty prediction via finding answers to the following research questions: (1) How many papers on utilities of AI in poverty prediction were published up until March, 2022? (2) Which approach to poverty was applied when AI was used for poverty prediction? (3) Which AI methods were applied for predicting poverty? (4) What data were used for poverty prediction via AI? (5) What are the advantages and disadvantages of the created AI models for poverty prediction? In order to answer these questions, we selected twenty-two papers using appropriate keywords and the exclusion criteria and analyzed their content. The selection process identified that, since 2016, publications on AI applications in poverty prediction began. Results of our research illustrate that, during this relatively short period, the application of AI in predicting poverty experienced a significant progress. Overall, fifty-seven AI methods were applied during the analyzed span, among which the most popular one was random forest. It was revealed that with the adoption of AI tools, the process of poverty prediction has become, from one side, quicker and more accurate and, from another side, more advanced due to the creation and possibility of using different datasets. The originality of this work is that this is the first sophisticated survey of AI applications in poverty prediction.

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Section: Combining Night Time Lights In Prediction Of Poverty Inciden...mentioning

confidence: 99%

Utilities of Artificial Intelligence in Poverty Prediction: A Review

et al. 2022

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“…In recent years, combining geospatial information and machine learning technology has become ever increasing interest for research on poverty area identification [6,8,13]. Geospatial information, such as nighttime lights, day-time satellite imagery, and crowd-sourced map data, can assist in capturing poverty and socioeconomic conditions on a coarse scale [1,3,30]. Machine learning technology allows researchers to effectively and efficiently utilize geospatial information [9,10,14].…”

Section: Introductionmentioning

confidence: 99%

Graph-based Village Level Poverty Identification

Ma,

Yang,

Feng

et al. 2023

Preprint

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Poverty status identification is the first obstacle to eradicating poverty. Village-level poverty identification is very challenging due to the arduous field investigation and insufficient information. The development of the Web infrastructure and its modeling tools provides fresh approaches to identifying poor villages. Upon those techniques, we build a village graph for village poverty status identification. By modeling the village connections as a graph through the geographic distance, we show the correlation between village poverty status and its graph topological position and identify two key factors (Centrality, Homophily Decaying effect) for identifying villages. We further propose the first graph-based method to identify poor villages. It includes a global Centrality2Vec module to embed village centrality into the dense vector and a local graph distance convolution module that captures the decaying effect. In this paper, we make the first attempt to interpret and identify village-level poverty from a graph perspective. CCS CONCEPTS• Information systems → Web mining; Web applications.

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“…Census data is the most reliable source of data for poverty mapping, but it has the problems of high cost, long cycle, and low efficiency (Minot and Baulch 2005 ; Niu et al 2020 ). With the advancement of technology, multi-source big data have been used to assist poverty mapping, such as night lights (Jean et al 2016 ; Andreano et al 2021 ; Xu et al 2021 ), remote sensing images (Elvidge et al 2009 ), mobile phones (Pokhriyal and Jacques 2017 ), and livelihood assets (Erenstein et al 2010 ). Poverty mapping based on multi-source data has been widely used in anti-poverty practices in regions such as Africa (Jean et al 2016 ), Southeast Asia (Erenstein et al 2010 ; Olivia et al 2011 ; Xu et al 2021 ), and Latin American and Caribbean (Andreano et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…With the advancement of technology, multi-source big data have been used to assist poverty mapping, such as night lights (Jean et al 2016 ; Andreano et al 2021 ; Xu et al 2021 ), remote sensing images (Elvidge et al 2009 ), mobile phones (Pokhriyal and Jacques 2017 ), and livelihood assets (Erenstein et al 2010 ). Poverty mapping based on multi-source data has been widely used in anti-poverty practices in regions such as Africa (Jean et al 2016 ), Southeast Asia (Erenstein et al 2010 ; Olivia et al 2011 ; Xu et al 2021 ), and Latin American and Caribbean (Andreano et al 2021 ). Focus on the spatial scale of this theme has gone from global (Elvidge et al 2009 ; Zhou and Liu 2022 ) and national (Okwi et al 2007 ; Wang and Alkire, 2009 ) to sub-national (Alejandro et al 2015 ), county (Liu et al 2017 ; Wang and Qi 2021 ), district (Minot and Baulch 2005 ; Erenstein et al 2010 ), village (Wang et al, 2018 ), and even household level (Blumenstock et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Geo-environmental and socioeconomic determinants of poverty in China: an empirical analysis based on stratified poverty theory

Zhou

Huang

2022

Environ Sci Pollut Res

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According to the stratified poverty theory, poverty includes individual (people) and regional (place) poverty. Understanding the interaction mechanism between individual poverty and regional poverty is crucial to achieving the UN goal of poverty eradication by 2030. However, at present, the relevant empirical research is still limited by the availability of data. To fill this important gap, based on the multi-source data of poverty census, geo-environmental and socio-economic data of China’s 1587 counties in 2013, we used exploratory spatial data analysis (ESDA) and spatial econometric models (spatial-lag and spatial-error model) to identify determinants of individual poverty and regional poverty in this county. Results show that the spatial distribution of the rural poor in China had strong spatial dependence (Global Moran’s I = 0.574). There was a high degree of spatial overlap between individual poverty and regional poverty. The poverty-causing factors were complex and varied across regions and individuals. Disease of family members was the leading factor driving rural areas in Northeast, Central, and Southwest China. Northeast China was mainly affected by the illness and lack of labor skills of family members. The complex terrain conditions were the determinants driving rural poverty in most areas of China. Improved transportation can greatly reduce rural poverty. Geographical isolation or lack of geographical capital caused by complex terrain conditions, backward transportation, and regional closure promoted regional poverty. In turn, regional poverty-causing factors further restricted the improvement of rural residents’ self-development ability and aggravated individual poverty. Our findings indicate that individual poverty and regional poverty have different poverty-causing mechanisms and poverty reduction priorities. Effective poverty reduction strategies require the coordinated promotion of individual and regional poverty reduction. The reduction of individual poverty should focus on enhancing the livelihood capital of the poor through differentiated policy intervention, while regional poverty alleviation should focus on creating a favorable development environment by increasing infrastructure investment and public service supply.

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Combining night time lights in prediction of poverty incidence at the county level

Cited by 42 publications

References 44 publications

Utilities of Artificial Intelligence in Poverty Prediction: A Review

Utilities of Artificial Intelligence in Poverty Prediction: A Review

Graph-based Village Level Poverty Identification

Geo-environmental and socioeconomic determinants of poverty in China: an empirical analysis based on stratified poverty theory

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