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Mapping the Spatial Distribution of Poverty
Mapping the Spatial Distribution of Poverty
Knygos.lt klubas Knygos.lt nariams
39,75 €
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56,79 €
  • Išsiųsime per 12–18 d.d.
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 estim…
  • Leidėjas:
  • ISBN-10: 9292775642
  • ISBN-13: 9789292775643
  • Formatas: 21.6 x 27.9 x 0.4 cm, minkšti viršeliai
  • Kalba: Anglų

Mapping the Spatial Distribution of Poverty (el. knyga) (skaityta knyga) | knygos.lt

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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.

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  • Leidėjas:
  • ISBN-10: 9292775642
  • ISBN-13: 9789292775643
  • Formatas: 21.6 x 27.9 x 0.4 cm, minkšti viršeliai
  • Kalba: Anglų

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.

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