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Year of publication
Subject
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SDG 4 big data 4 computer vision 4 data for development 4 machine learning algorithm 4 official statistics 4 poverty 4 Armut 2 Artificial intelligence 2 Big Data 2 Big data 2 Künstliche Intelligenz 2 Poverty 2 Statistical method 2 Statistische Methode 2 Thailand 2 multidimensional poverty 2 Algorithm 1 Algorithmus 1 Applied statistics 1 Data collection 1 Datenerhebung 1 Measurement 1 Messung 1 Statistical data 1 Statistik 1 Statistische Daten 1
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Online availability
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Free 4 CC license 2
Type of publication
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Book / Working Paper 4
Type of publication (narrower categories)
All
Working Paper 4 Arbeitspapier 2 Graue Literatur 2 Non-commercial literature 2
Language
All
English 4
Author
All
Addawe, Mildred 4 Bulan, Joseph 4 Durante, Ron Lester 4 Martillan, Marymell 4 Hofer, Martin 2 Martinez, Arturo 2 Martinez, Arturo M. 2 Puttanapong, Nattapong 2 Sako, Tomas 2
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Published in...
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ADB Economics Working Paper Series 2 ADB economics working paper series 2
Source
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ECONIS (ZBW) 2 EconStor 2
Showing 1 - 4 of 4
Cover Image
Applying artificial intelligence on satellite imagery to compile granular poverty statistics
Hofer, Martin; Sako, Tomas; Martinez, Arturo; Addawe, … - 2020
The spatial granularity of poverty statistics can have a significant impact on the efficiency of targeting resources meant to improve the living conditions of the poor. However, achieving granularity typically requires increasing the sample sizes of surveys on household income and expenditure or...
Persistent link: https://www.econbiz.de/10012665059
Saved in:
Cover Image
Predicting poverty using geospatial data in Thailand
Puttanapong, Nattapong; Martinez, Arturo M.; Addawe, Mildred - 2020
Poverty statistics are conventionally compiled using data from household income and expenditure survey or living standards survey. This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial...
Persistent link: https://www.econbiz.de/10012665060
Saved in:
Cover Image
Predicting poverty using geospatial data in Thailand
Puttanapong, Nattapong; Martinez, Arturo M.; Addawe, Mildred - 2020
Poverty statistics are conventionally compiled using data from household income and expenditure survey or living standards survey. This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial...
Persistent link: https://www.econbiz.de/10012403931
Saved in:
Cover Image
Applying artificial intelligence on satellite imagery to compile granular poverty statistics
Hofer, Martin; Sako, Tomas; Martinez, Arturo; Addawe, … - 2020
The spatial granularity of poverty statistics can have a significant impact on the efficiency of targeting resources meant to improve the living conditions of the poor. However, achieving granularity typically requires increasing the sample sizes of surveys on household income and expenditure or...
Persistent link: https://www.econbiz.de/10012403950
Saved in:
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