Mapping Spatial Distribution Poverty by Asian Development (34 results)

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Paperback or Softback. Condition: New. Mapping the Spatial Distribution of Poverty Using Satellite Imagery in Thailand. Book.

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Paperback. Condition: New. This report presents the results of a feasibility study on high-quality poverty statistics in Thailand using satellite imagery, geospatial data, and advanced algorithmic techniques to complement conventional survey methods.The ""leave no one behind"" principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country's population. This entails detailed, granular data on population groups that extend beyond national trends and averages. ADB collaborated with the National Statistical Office of Thailand and the Word Data Lab for the feasibility study, which aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in Thailand.

Paperback. Condition: New. This report presents the results of a feasibility study on high-quality poverty statistics in Thailand using satellite imagery, geospatial data, and advanced algorithmic techniques to complement conventional survey methods.The ""leave no one behind"" principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country's population. This entails detailed, granular data on population groups that extend beyond national trends and averages. ADB collaborated with the National Statistical Office of Thailand and the Word Data Lab for the feasibility study, which aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in Thailand.

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Paperback. Condition: Brand New. 58 pages. 8.50x0.16x11.00 inches. In Stock.

Paperback. Condition: Brand New. 68 pages. 8.50x0.18x11.00 inches. In Stock.

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Paperback. Condition: New. This report presents the results of a feasibility study on high-quality poverty statistics in Thailand using satellite imagery, geospatial data, and advanced algorithmic techniques to complement conventional survey methods.The ""leave no one behind"" principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country's population. This entails detailed, granular data on population groups that extend beyond national trends and averages. ADB collaborated with the National Statistical Office of Thailand and the Word Data Lab for the feasibility study, which aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in Thailand.

Paperback. Condition: New. This report presents the results of a feasibility study on high-quality poverty statistics in Thailand using satellite imagery, geospatial data, and advanced algorithmic techniques to complement conventional survey methods.The ""leave no one behind"" principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country's population. This entails detailed, granular data on population groups that extend beyond national trends and averages. ADB collaborated with the National Statistical Office of Thailand and the Word Data Lab for the feasibility study, which aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in Thailand.

Condition: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This report presents the results of a feasibility study on generating high-quality poverty statistics in the Philippines using satellite imagery, geospatial data, and powerful machine-learning algorithms.The "leave no one behind" principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country's population. Conducted by ADB in collaboration with the Philippine Statistics Authority and the World Data Lab, the study aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in the Philippines.

Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This report presents the results of a feasibility study on generating high-quality poverty statistics in the Philippines using satellite imagery, geospatial data, and powerful machine-learning algorithms.The "leave no one behind" principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country's population. Conducted by ADB in collaboration with the Philippine Statistics Authority and the World Data Lab, the study aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in the Philippines.

Paperback. Condition: new. Paperback. This report shows how Indonesia can combine satellite imagery and geospatial data with machine learning to create maps showing areas of poverty, which could help shape targeted support for vulnerable communities. Based on a collaborative study with Statistics Indonesia (BPS) and World Data Lab, the report shows how Indonesia can develop a grid-like poverty map. Computer vision and machine learning can help identify roads, buildings, and vegetation, and that information can be harnessed to estimate poverty levels. The report details poverty reduction efforts in the diverse archipelago and shows how geospatial data can help improve social protection and resource allocation. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: 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.

Paperback. Condition: new. Paperback. This report shows how Indonesia can combine satellite imagery and geospatial data with machine learning to create maps showing areas of poverty, which could help shape targeted support for vulnerable communities. Based on a collaborative study with Statistics Indonesia (BPS) and World Data Lab, the report shows how Indonesia can develop a grid-like poverty map. Computer vision and machine learning can help identify roads, buildings, and vegetation, and that information can be harnessed to estimate poverty levels. The report details poverty reduction efforts in the diverse archipelago and shows how geospatial data can help improve social protection and resource allocation. 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.

Paperback. Condition: 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.

Paperback. Condition: new. Paperback. This report shows how Indonesia can combine satellite imagery and geospatial data with machine learning to create maps showing areas of poverty, which could help shape targeted support for vulnerable communities. Based on a collaborative study with Statistics Indonesia (BPS) and World Data Lab, the report shows how Indonesia can develop a grid-like poverty map. Computer vision and machine learning can help identify roads, buildings, and vegetation, and that information can be harnessed to estimate poverty levels. The report details poverty reduction efforts in the diverse archipelago and shows how geospatial data can help improve social protection and resource allocation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: 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.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This report presents the results of a feasibility study on high-quality poverty statistics in Thailand using satellite imagery, geospatial data, and advanced algorithmic techniques to complement conventional survey methods. The 'leave no one behind' principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country's population. This entails detailed, granular data on population groups that extend beyond national trends and averages. ADB collaborated with the National Statistical Office of Thailand and the Word Data Lab for the feasibility study, which aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in Thailand.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This report presents the results of a feasibility study on generating high-quality poverty statistics in the Philippines using satellite imagery, geospatial data, and powerful machine-learning algorithms. The 'leave no one behind' principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country's population. Conducted by ADB in collaboration with the Philippine Statistics Authority and the World Data Lab, the study aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in the Philippines.

Taschenbuch. Condition: Neu. Mapping the Spatial Distribution of Poverty Using Satellite Imagery in the Philippines | Asian Development Bank | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2021 | Asian Development Bank | EAN 9789292621315 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: Neu. Mapping the Spatial Distribution of Poverty Using Satellite Imagery in Thailand | Asian Development Bank | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2021 | Asian Development Bank | EAN 9789292627683 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.