数据驱动的贫困识别、分类与预测方法研究

doi:10.16381/j.cnki.issn1003-207x.2024.1770

Abstract

Abstract:

Poverty refers to individuals or groups who are unable to obtain the resources and services necessary to meet basic living requirements， and it has long been a major global social issue. Traditional methods of poverty identification and measurement mainly rely on statistical data and sample surveys， which are limited by high costs， low efficiency， poor timeliness， and data scarcity， making it difficult to reflect dynamic socioeconomic conditions in a timely manner. With the advent of the digital era， data resources in fields such as population， geography， and economy are increasingly abundant， providing new opportunities for the use of artificial intelligence （AI） and data-driven models to tackle poverty in more precise and timely ways. It systematically reviews the key poverty concepts and measurements in this paper， focusing on the application of data-driven models and algorithms in poverty mapping， poverty trend prediction， and socioeconomic status assessment. It is organized as follows： Section 2 provides a succinct overview of the diverse definitions of poverty， and summarizes both unidimensional and multidimensional measurements of poverty through the lenses of education， health， and the living environment， among other perspectives. Section 3 to 7 enumerates the data-driven models found in the existing literature， categorizing them systematically based on the various types of methodologies， including regression analysis， machine learning， neural networks， complex network theory， and natural language processing （NLP）. To conclude， the potential implications and opportunities for utilizing big data and AI technologies in achieving poverty reduction goals are discussed in section 8， and the forefront is pointed out as well as critical challenges of the field， such as more precise spatial analysis， real-time monitoring capabilities， and trend prediction. At the same time， key challenges are highlighted such as data representativeness， data quality， and model interpretability， while also pointing out possible future directions.

Key words: SDG, poverty measurement, big data, artificial intelligence

CLC Number:

Suoyi Tan, Mengning Wang, Ye Tian, Jianguo Liu, Xin Lu. Data-Driven Models and Applications on Poverty Identification, Classification, and Prediction[J]. Chinese Journal of Management Science, 2025, 33(5): 124-137.

Figures/Tables 6

指标名称	计算方法
贫困人口指数	P₀= $1 N ∑ i = 1 N I (y i < z)$
贫困深度指数	$P 1 = 1 N ∑ i = 1 N G i z G i = (z - y i) × I (y i < z)$
贫困强度指数	$P 2 = 1 N ∑ i = 1 N G i z 2 G i = (z - y i) × I (y i < z)$
基尼系数	G=1-2 $∫ 01 L (X) d X$
森指数	P_S =P₀G^P +P $1 P$ (1-G^P )
SST指数	P_SST =P₀P $1 P$ （1+ $G ̂$ _P ）

References 101

1	汪三贵. 在发展中战胜贫困——对中国30年大规模减贫经验的总结与评价［J］. 管理世界， 2008（11）： 78-88.
	Wang S G. Overcoming poverty in development： A summary and evaluation of China’s 30 years of experience in large-scale poverty reduction［J］. Journal of Management World， 2008（11）： 78-88.
2	习近平. 携手消除贫困促进共同发展［N］.人民日报， 2015-10-17（2）.
	Xi J P. Work together to eliminate poverty and promote common development［N］.People’s Daily 2015-10-17（2）.
3	Department of Economic and Social Affairs. The sustainable development goals report 2023： Special edition ［R］. Working Paper， United Nations， 2023.
4	李玉恒，武文豪，宋传垚，等. 世界贫困的时空演化格局及关键问题研究［J］. 中国科学院院刊， 2019， 34（1）： 42-50.
	Li Y H， Wu W H， Song C Y， et al. Spatial-temporal pattern of world poverty reduction and key problems analysis［J］. Bulletin of the Chinese Academy of Sciences， 2019， 34（1）： 42-50.
5	易成栋，樊正德，刘小奇，等. 全球减贫成效的变化趋势与影响因素研究［J］. 中国人口科学， 2021（6）： 12-26+126.
	Yi C D， Fan Z D， Liu X Q， et al. Research on changing trends and influencing factors of global poverty reduction effectiveness［J］. Chinese Journal of Population Science， 2021（6）： 12-26+126.
6	中华人民共和国国务院新闻办公室. 《人类减贫的中国实践》白皮书［EB/OL］. （2021-04-06）［2024-09-30］. .
	The State Council Information Office of the People's Republic of China. White Paper： Poverty Alleviation： China's Experience and Contribution［EB/OL］. （2021-04-06）［2024-09-30］. .
7	叶兴庆，殷浩栋. 从消除绝对贫困到缓解相对贫困：中国减贫历程与2020年后的减贫战略［J］. 改革， 2019（12）： 5-15.
	Ye X Q， Yin H D. From eliminating absolute poverty to alleviating relative poverty：China’s history of poverty reduction and poverty reduction strategies after 2020 ［J］ Reform， 2019（12）： 5-15.
8	王小林，张晓颖. 中国消除绝对贫困的经验解释与2020年后相对贫困治理取向［J］. 中国农村经济， 2021（2）： 2-18.
	Wang X L， Zhang X Y. An explanation of China’s experience in eliminating absolute poverty and the orientation of relative poverty governance in the post-2020 era［J］. Chinese Rural Economy， 2021（2）： 2-18.
9	United Nations Development Programme. 2023 global multidimensional poverty index （MPI）［R］. Working Paper， United Nations Development Programme，2013.
10	Ibrahim M， Alola A A， Ferreira D. Assessing sustainable development goals attainment through energy-environmental efficiency： The case of Latin American and Caribbean countries ［J］. Sustainable Energy Technologies and Assessments， 2023， 57： 103219.
11	Galal S. Number of people living in extreme poverty in Africa 2016-2030［EB/OL］（2025-01-27）［2025-05-18］..
12	Guerriero C， Haines A， Pagano M. Health and sustainability in post-pandemic economic policies［J］. Nature Sustainability， 2020， 3（7）： 494-6.
13	Laborde D， Martin W， Swinnen J， et al. COVID-19 risks to global food security［J］. Science， 2020， 369（6503）： 500-502.
14	Ge Y， Liu M， Hu S， et al. Who and which regions are at high risk of returning to poverty during the COVID-19 pandemic？［J］. Humanities and Social Sciences Communications， 2022， 9（1）： 183.
15	Brady D. Poverty， not the poor［J］. Science Advances， 2023， 9（34）： eadg1469.
16	Ravallion M， Chen S H. Welfare-consistent global poverty measures［R］.Working Paper， National bureau of economic research， 2017.
17	Ferreira F H， Ravallion M. Global poverty and inequality： A review of the evidence ［R］. Working Paper， The World Bank，2008.
18	Cobham A. Benefits and costs of the IFF targets for the Post-2015 development agenda［M］//Lomborg B. Prioritizing Development： A Cost Benefit Analysis of the United Nations' Sustainable Development Goals. Cambridge： Cambridge University Press，2018： 171-191.
19	Serajuddin U， Uematsu H， Wieser C， et al. Data deprivation： Another deprivation to end［R］. Working Paper，The world Bank， 2015.
20	Blumenstock J， Cadamuro G， On R. Predicting poverty and wealth from mobile phone metadata［J］. Science， 2015， 350（6264）： 1073-1076.
21	Liu J H， Wang J， Shao J， et al. Online social activity reflects economic status［J］. Physica A： Statistical Mechanics and Its Applications， 2016， 457： 581-589.
22	Marty R， Duhaut A. Global poverty estimation using private and public sector big data sources［J］. Scientific Reports， 2024， 14： 3160.
23	Zhao X， Yu B， Liu Y， et al. Estimation of poverty using random forest regression with multi-source data： A case study in Bangladesh［J］. Remote Sensing， 2019， 11（4）： 375-392.
24	Niu T， Chen Y， Yuan Y. Measuring urban poverty using multi-source data and a random forest algorithm： A case study in Guangzhou［J］. Sustainable Cities and Society， 2020， 54： 102014.
25	Smythe I S， Blumenstock J E. Geographic microtargeting of social assistance with high-resolution poverty maps ［J］. Proceedings of the National Academy of Sciences， 2022， 119（32）： e2120025119.
26	Rotondi V， Kashyap R， Pesando L M， et al. Leveraging mobile phones to attain sustainable development［J］. Proceedings of the National Academy of Sciences， 2020， 117（24）： 13413-13420.
27	Rentschler J， Salhab M， Jafino B A. Flood exposure and poverty in 188 countries［J］. Nature Communications， 2022， 13（1）： 3527-3537.
28	Deville P， Linard C， Martin S， et al. Dynamic population mapping using mobile phone data ［J］. Proceedings of the National Academy of Sciences， 2014， 111（45）： 15888-15893.
29	Blumenstock J. Don’t forget people in the use of big data for development［J］. Nature， 2018， 561（7722）： 170-172.
30	高见，周涛. 大数据揭示经济发展状况［J］. 电子科技大学学报， 2016， 45（4）： 625-633.
	Gao J， Zhou T. Big data reveal the status of economic development［J］. Journal of University of Electronic Science and Technology of China， 2016， 45（4）： 625-633.
31	廖建新. 大数据技术的应用现状与展望［J］. 电信科学， 2015， 31（7）： 7-18.
	Liao J X. Big data technology： Current applications and prospects［J］. Telecommunications Science， 2015， 31（7）： 7-18.
32	Atkinson A B. On the measurement of poverty［J］. Econometrica： Journal of the Econometric Society， 1987，55（4）： 749-764.
33	Ravallion M. Poverty comparisons： A guide to concepts and methods［R］. Working Paper， The World Bank， 1992.
34	Sen A. Commodities and capabilities［M］. Amsterdam：North-Holland， 1985.
35	Levitas R， Pantazis C， Fahmy E， et al. The multi-dimensional analysis of social exclusion［R］. Working Paper，DERA （Digital Education Resource Archive）， 2007.
36	Brock K， Norton A， Bird B，et al. A rough guide to PPAS： Participatory poverty assessment： An introduction to theory and practice［M］. London： Overseas Development，2001.
37	Deon F， Haishan F， Carolina S-P. An adjustment to global poverty lines［R］. Working Paper， World Bank Blogs， 2022.
38	Eurostat. The measurement of poverty and social inclusion in the EU： Achievements and further improvements［R］.Working paper，United Nations Economic Commission For Europe， 2013.
39	Foster J， Greer J， Thorbecke E. A class of decomposable poverty measures［J］. Econometrica： Journal of the Econometric Society， 1984，52（3）： 761-766.
40	Gini C. Measurement of inequality of incomes［J］. The Economic Journal， 1921， 31（121）： 124-126.
41	Sen A. Poverty： An ordinal approach to measurement［J］. Econometrica： Journal of the Econometric Society， 1976，44（2）： 219-231.
42	Shorrocks A F. Revisiting the sen poverty index［J］. Econometrica： Journal of the Econometric Society， 1995， 63（5）： 1225-1230.
43	Townsend P， Phillimore P， Beattie A. Health and deprivation： Inequality and the North［M］.London： Routledge， 1988.
44	Townsend P. Poverty in the United Kingdom： A survey of household resources and standards of living［M］. Berkeley：University of California Press， 1979.
45	Tank Planning.Human poverty index （HPI）［EB/OL］．（2015-06-11）［2025-05-18］. .
46	Alkire S， Santos M E. Measuring acute poverty in the developing world： Robustness and scope of the multidimensional poverty index［J］. World Development， 2014， 59： 251-274.
47	Elvidge C D， Baugh K E， Kihn E A， et al. Relation between satellite observed visible-near infrared emissions， population， economic activity and electric power consumption［J］. International Journal of Remote Sensing， 1997， 18（6）： 1373-1379.
48	Henderson J V， Storeygard A， Weil D N. Measuring economic growth from outer space［J］. American Economic Review， 2012， 102（2）： 994-1028.
49	Mellander C， Lobo J， Stolarick K， et al. Night-time light data： A good proxy measure for economic activity？［J］. PLoS ONE， 2015， 10（10）： e0139779.
50	Sutton P C， Costanza R. Global estimates of market and non-market values derived from nighttime satellite imagery， land cover， and ecosystem service valuation［J］. Ecological Economics， 2002， 41（3）： 509-527.
51	Frias-Martinez V， Virseda J. On the relationship between socio-economic factors and cell phone usage ［C］//Proceedings of the 15th International Conference on Information and Communication Technologies and Development， Atlanta， Georgia， USA， March 12-15，Association for Computing Machinery，2012：76-84.
52	Pappalardo L， Pedreschi D， Smoreda Z， et al. Using big data to study the link between human mobility and socio-economic development［C］//Proceedings of the 2015 IEEE International Conference on Big Data， Santa Clara， CA，USA，October 29-November l，IEEE， 2015： 871-878.
53	Xu Y， Belyi A， Bojic I， et al. Human mobility and socioeconomic status： Analysis of singapore and boston ［J］. Computers， Environment and Urban Systems， 2018， 72： 51-67.
54	Fatehkia M， Del Villar Z， Koebe T， et al. Using facebook advertising data to describe the socio-economic situation of syrian refugees in Lebanon［J］. Frontiers in big Data， 2022， 5： 1033530.
55	Fackbook. Facebook ads API［EB/OL］．（2025-01-21）［2025-05-18］. .
56	McCallum I， Kyba C C M， Bayas J C L， et al. Estimating global economic well-being with unlit settlements［J］. Nature Communications， 2022， 13（1）： 2459-2466.
57	Watmough G R， Marcinko C L J， Sullivan C， et al. Socioecologically informed use of remote sensing data to predict rural household poverty［J］.Proceedings of the National Academy of Sciences，2019，116（4）： 1213-1218.
58	Aiken E， Bellue S， Karlan D， et al. Machine learning and phone data can improve targeting of humanitarian aid［J］. Nature， 2022， 603（7903）： 864-870.
59	Aiken E L， Bedoya G， Blumenstock J E， et al. Program targeting with machine learning and mobile phone data： Evidence from an anti-poverty intervention in Afghanistan［J］. Journal of Development Economics， 2023， 161： 103016.
60	Steele J E， Sundsøy P R， Pezzulo C， et al. Mapping poverty using mobile phone and satellite data［J］. Journal of The Royal Society Interface， 2017， 14（127）： 20160690.
61	Pokhriyal N， Jacques D C. Combining disparate data sources for improved poverty prediction and mapping［J］. Proceedings of the National Academy of Sciences， 2017， 114（46）： E9783-E9792.
62	Kondmann L， Haeberle M， Zhu X X. Combining twitter and earth observation data for local poverty mapping ［C］// Proceedings of NeuRIPS Machine Learning for the Developing World Workshop， Vancouver， Canada， December 12 ， NeuRIPS Machine Learning for the Developing World Workshop， 2020： 1-5.
63	Chi G， Fang H， Chatterjee S， et al. Microestimates of wealth for all low- and middle-income countries［J］. Proceedings of the National Academy of Sciences， 2022， 119（3）： e2113658119.
64	Blumenstock J E. Fighting poverty with data［J］. Science， 2016， 353（6301）： 753-754.
65	Pan S J， Yang Q. A survey on transfer learning［J］. IEEE Transactions on knowledge and Data Engineering， 2009， 22（10）： 1345-1359.
66	Jean N， Burke M， Xie M， et al. Combining satellite imagery and machine learning to predict poverty［J］. Science， 2016， 353（6301）： 790-794.
67	Head A， Manguin M， Tran N， et al. Can human development be measured with satellite imagery［C］// Proceedings of the Ninth International Conference on Information and Communication Technologies and Development， Lahore Pakistan， November 16 ， Association for Computing Machinery， 2017， 8： 1-11.
68	Xie M， Jean N， Burke M， et al. Transfer learning from deep features for remote sensing and poverty mapping［C］//Proceedings of the AAAI Conference on Artificial Intelligence，Phoenix， Arizona USA， February 12–17 ， AAAI Press，2016：3929-3935.
69	Yeh C， Perez A， Driscoll A， et al. Using publicly available satellite imagery and deep learning to understand economic well-being in Africa［J］. Nature Communications， 2020， 11（1）： 1-11.
70	Espín-Noboa L， Kertész J， Karsai M. Interpreting wealth distribution via poverty map inference using multimodal data［C］//Proceedings of the ACM Web Conference 2023， Austin TX USA， April 30 ， Association for Computing Machinery，2023： 4029-4040.
71	Ratledge N， Cadamuro G， de la Cuesta B， et al. Using machine learning to assess the livelihood impact of electricity access［J］.Nature， 2022， 611： 491-495.
72	Onnela J P， Saramäki J， Hyvönen J， et al. Structure and tie strengths in mobile communication networks［J］. Proceedings of the National Academy of Sciences， 2007， 104（18）： 7332-7336.
73	Filho R M， Borges G R， Almeida J M， et al. Inferring user social class in online social networks［C］//Proceedings of the 8th workshop on social network mining and analysis， New York， United States， August 24 ， Association for Computing Machinery，2014： 1-5.
74	Dunbar R I， Arnaboldi V， Conti M， et al. The structure of online social networks mirrors those in the offline world［J］. Social Networks， 2015， 43： 39-47.
75	Steele J E， Pezzulo C， Albert M， et al. Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings［J］. Humanities and Social Sciences Communications， 2021， 8：288-299.
76	Deville P， Song C， Eagle N， et al. Scaling identity connects human mobility and social interactions［J］. Proceedings of the National Academy of Sciences， 2016， 113（26）： 7047-7052.
77	Smith-Clarke C， Mashhadi A， Capra L. Poverty on the cheap： Estimating poverty maps using aggregated mobile communication networks［C］//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems， Toronto， Ontario， Canada， April 27，Association for Computing Machinery.2014： 511-520.
78	Wang X， Pei T， Song C， et al. How does socioeconomic status influence social relations？ A perspective from mobile phone data［J］. Physica A： Statistical Mechanics and Its Applications， 2023， 615： 128612.
79	Eagle N， Macy M， Claxton R. Network diversity and economic development［J］.Science， 2010， 328（5981）： 1029-1031.
80	Gutierrez T， Krings G， Blondel V D. Evaluating socio-economic state of a country analyzing airtime credit and mobile phone datasets［J］. ArXiv Preprint ArXiv，2013：13094496.
81	Mao H， Shuai X， Ahn Y Y， et al. Quantifying socio-economic indicators in developing countries from mobile phone communication data： Applications to Côte d’Ivoire［J］. EPJ Data Science， 2015， 4（1）： 15-30.
82	Vaca Ruiz C， Quercia D， Aiello L M， et al. Taking Brazil's pulse： Tracking growing urban economies from online attention［C］//Proceedings of the 23rd International Conference on World Wide Web，Seoul， Korea， April 7 ， Association for Computing Machinery，2014： 451-456.
83	Norbutas L， Corten R. Network structure and economic prosperity in municipalities： A large-scale test of social capital theory using social media data［J］. Social Networks， 2018， 52： 120-134.
84	Tóth G， Wachs J， Di Clemente R， et al. Inequality is rising where social network segregation interacts with urban topology［J］. Nature Communications， 2021， 12（1）： 1143-1151.
85	Preoţiuc-Pietro D， Volkova S， Lampos V， et al. Studying user income through language， behaviour and affect in social media［J］. PLoS ONE， 2015， 10（9）： e0138717.
86	Mikolov T， Chen K， Corrado G S， et al. Efficient estimation of word representations in vector space［C］//Proceedings of the International Conference on Learning Representations，ICLR 2013，Scottsdale， Arizona， USA， May 2-4， 2013.
87	Lampos V， Aletras N， Geyti J K， et al. Inferring the socioeconomic status of social media users based on behaviour and language［C］//Proceedings of the Advances in Information Retrieval： 38th European Conference on IR Research， ECIR 2016， Padua， Italy， March 20-23 ， Springer International Publishing， 2016： 689-695.
88	Flekova L， Preoţiuc-Pietro D， Ungar L. Exploring stylistic variation with age and income on twitter［C］//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics， Berlin， Germany， August 7-12 ，Association for Computational Linguistics，2016： 313-319.
89	Gimpel K， Schneider N， O’connor B， et al. Part-of-speech tagging for twitter： Annotation， features， and experiments［C］//Proceedings of the 49th annual meeting of the Association for Computational Linguistics： Human Language Technologies， Portland， Oregon， USA， June 19-24 ， Association for Computational Linguistics，2011： 42-47.
90	Coleman M， Liau T L. A computer readability formula designed for machine scoring［J］. Journal of Applied Psychology， 1975， 60（2）： 283-284.
91	Flesch R. A new readability yardstick［J］. Journal of Applied Psychology， 1948， 32（3）： 221-233.
92	Anderson J. Lix and rix： Variations on a little-known readability index［J］. Journal of Reading， 1983， 26（6）： 490-496.
93	Heylighen F， Dewaele J M. Variation in the contextuality of language： An empirical measure［J］. Foundations of science， 2002， 7： 293-340.
94	Beinborn L， Zesch T， Gurevych I. Towards fine-grained readability measures for self-directed language learning ［C］// Proceedings of Electronic Conference Proceedings， Lund， Sweden， October 25 ， Proceedings of the SLTC workshop on NLP for CALL， 2012， 80： 11-19.
95	Quercia D， Ellis J， Capra L， et al. Tracking “gross community happiness” from tweets［C］//Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work， Seattle Washington， USA， February 11 ， Association for Computing Machinery，2012： 965-968.
96	Lerman K， Arora M， Gallegos L， et al. Emotions， demographics and sociability in twitter interactions［C］//Proceedings of the International AAAI Conference on Web and Social Media， Cologne， Germany，May 17-20，2016： 201-210.
97	Thelwall M， Buckley K， Paltoglou G. Sentiment strength detection for the social web［J］. Journal of the American Society for Information Science and Technology， 2012， 63（1）： 163-173.
98	Warriner A B， Kuperman V， Brysbaert M. Norms of valence， arousal， and dominance for 13，915 English lemmas［J］. Behavior Research Methods， 2013， 45： 1191-1207.
99	Wesolowski A， Eagle N， Noor A M， et al. The impact of biases in mobile phone ownership on estimates of human mobility［J］. Journal of The Royal Society Interface， 2013， 10（81）： 20120986.
100	王璐，孟小峰. 位置大数据隐私保护研究综述［J］. 软件学报， 2014， 25（4）： 693-712.
	Wang L， Meng X F. Location privacy preservation in big data era： A survey［J］. Journal of Software， 2014， 25（4）： 693-712.
101	Munoz Boudet A M， Buitrago P， Leroy De La Briere B， et al. Gender differences in poverty and household composition through the life-cycle： A global perspective ［R］. Working Paper，The World Bank， 2018.

领域	HPI-1	HPI-2
寿命	预期寿命不足40岁的人口比例	预期寿命不足60岁的人口比例
教育	成年人文盲率	缺乏基本读写能力的成年人百分比
生活质量	缺乏清洁饮水、医疗服务的人口比例，儿童（5岁以下）营养不良的人口比例	生活在收入贫困线以下的人口比例
社会排斥	—	长期（≥12个月）失业的人口比例

[1]	Wei Gu, Yajin Liu, Feng Susan Lu, Xiangbin Yan. AI-Driven Decision Sciences: Application, Perception and Bias [J]. Chinese Journal of Management Science, 2025, 33(5): 99-112.
[2]	Wei Ge, Han Xiao. Artificial Intelligence, Household Consumption and Economic Singularity: Based on the Perspective of Optimizing Redistribution Policy [J]. Chinese Journal of Management Science, 2025, 33(3): 93-106.
[3]	Feng Shi, Yang Yang, Yun Yuan, Jianmin Jia. Marketing Transformation in the Age of Artificial Intelligence [J]. Chinese Journal of Management Science, 2025, 33(1): 111-123.
[4]	Haiyan Wang, Zhaohan Sheng. Modeling of Complex System Management Scenarios Driven by Big Data: Techniques and Processes [J]. Chinese Journal of Management Science, 2025, 33(1): 22-33.
[5]	Xiangpei Hu, Yaxian Zhou. Review of Research on Economics and Management Based on Generative Artificial Intelligence [J]. Chinese Journal of Management Science, 2025, 33(1): 76-97.
[6]	Xiuli Meng,Jing Yang,Bo Liu. Competition Strategy of Crowdsourcing Logistics Service Quality Based on Big Data Technology [J]. Chinese Journal of Management Science, 2024, 32(4): 130-140.
[7]	YAN Yu-meng, XIONG Xiong, LU Lei, ZHANG Wei, ZHANG Yong-jie. From “A Peek” to “A Strategic View”: Individual Investor Behaviors in the Age of Big Data [J]. Chinese Journal of Management Science, 2023, 31(9): 244-254.
[8]	ZHAO Hui, ZHANG Tao, TANG Ke. How does Agglomeration Affect Entrepreneurial Activities in the North and South? Evidence from High Frequency Full Enterprise Big Data [J]. Chinese Journal of Management Science, 2023, 31(6): 231-240.
[9]	YANG Shan-lin, LI Xiao-jian, ZHANG Qiang, JIAO Jian-ling, YANG Chang-hui. Artificial Intelligence and Management Transformation [J]. Chinese Journal of Management Science, 2023, 31(6): 1-11.
[10]	SONG Jin-bo, ZHOU Yu-shan, HE Qiu-ying. The Impact of Emotional Load on Service Quality and Operational Efficiency of Online Political Inquiry Platform—Evidence from Textual Data [J]. Chinese Journal of Management Science, 2023, 31(3): 133-142.
[11]	LUO Jian, TANG Jia-fu, YU Qing-ya, WU Zhi-qiao. The Division of O2O Takeaway Business Zone and Discovery of Customer Demand Distribution Law [J]. Chinese Journal of Management Science, 2023, 31(3): 58-68.
[12]	QIU Ruo-zhen, WU Xu, SUN Yue, ZHU Zhu. Robust Optimization and Coordination Model for Supply Chain with Big Data Investment under Uncertain Demand [J]. Chinese Journal of Management Science, 2023, 31(1): 128-141.
[13]	XU Xuan-hua, MA Zhi-peng, CHEN Xiao-hong. Dynamic Evolution Research on Emergency Decision Quality of Large Group Based on the Public Preferences Big Data [J]. Chinese Journal of Management Science, 2022, 30(7): 140-149.
[14]	YUAN Xianzhi, ZHOU Yun-peng, YAN Cheng-xing, LIU Hai-yang, QIAN Guo-qi, WANG Fan, WEI Li-jian, LI Zhi-yong, LI Bo, David Li, ZENGTu. The Framework for the Risk Feature Extraction Method on Corporate Financial Fraud George [J]. Chinese Journal of Management Science, 2022, 30(3): 43-54.
[15]	XING Gen-shang, LU Fang, ZHOU Zhong-bao, YE Jin-long. Can the Right of Data Portability Govern Big Data-based Price Discrimination? [J]. Chinese Journal of Management Science, 2022, 30(3): 85-95.

Data-Driven Models and Applications on Poverty Identification, Classification, and Prediction

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 101

Related Articles 15

Metrics

Comments

Recommended 0