Synopsis
This report analyzes the potential benefits and challenges of machine learning and small area estimation (SAE) to monitor poverty and help tackle inequality in Maldives.
It explains how SAE can generate granular poverty estimates using household survey and census data, and how machine learning including convolutional neural networks were developed using a model trained on Indonesian data. The report highlights inconsistencies and challenges such as limited sample sizes and shows how developing localized models and synchronizing data collection could help Maldives improve its poverty mapping to drive equitable and targeted interventions.
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Paperback. Etat : new. Paperback. This report analyzes the potential benefits and challenges of machine learning and small area estimation (SAE) to monitor poverty and help tackle inequality in Maldives.It explains how SAE can generate granular poverty estimates using household survey and census data, and how machine learning including convolutional neural networks were developed using a model trained on Indonesian data. The report highlights inconsistencies and challenges such as limited sample sizes and shows how developing localized models and synchronizing data collection could help Maldives improve its poverty mapping to drive equitable and targeted interventions. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9789292775674
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Paperback. Etat : new. Paperback. This report analyzes the potential benefits and challenges of machine learning and small area estimation (SAE) to monitor poverty and help tackle inequality in Maldives.It explains how SAE can generate granular poverty estimates using household survey and census data, and how machine learning including convolutional neural networks were developed using a model trained on Indonesian data. The report highlights inconsistencies and challenges such as limited sample sizes and shows how developing localized models and synchronizing data collection could help Maldives improve its poverty mapping to drive equitable and targeted interventions. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9789292775674
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Paperback. Etat : new. Paperback. This report analyzes the potential benefits and challenges of machine learning and small area estimation (SAE) to monitor poverty and help tackle inequality in Maldives.It explains how SAE can generate granular poverty estimates using household survey and census data, and how machine learning including convolutional neural networks were developed using a model trained on Indonesian data. The report highlights inconsistencies and challenges such as limited sample sizes and shows how developing localized models and synchronizing data collection could help Maldives improve its poverty mapping to drive equitable and targeted interventions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9789292775674
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Mapping the Spatial Distribution of Poverty in Maldives
Edité par
Asian Development Bank, 2025
ISBN 10 : 9292775677
ISBN 13 : 9789292775674
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