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Geostatistical tools to map the interaction between development aid and indices of need

Geostatistical tools to map the interaction between development aid and indices of need
Geostatistical tools to map the interaction between development aid and indices of need
In order to meet and assess progress towards global sustainable development goals (SDGs), an improved understanding of geographic variation in population wellbeing indicators such as health status, wealth and access to resources is crucial, as the equitable and efficient allocation of international aid relies on knowing where funds are needed most. Unfortunately, in many low-income countries, detailed, reliable and timely information on the spatial distribution and characteristics of intended aid recipients are rarely available. Furthermore, lack of information on the past distribution of aid relative to need also hinders assessments of the impacts of aid. High-resolution data on key social and health indicators, as well as how aid distribution relates to these indicators are therefore fundamental for targeting limited resources and building on past efforts.

In this study, we show how modern statistical approaches combined with a new geographic database of aid distribution can be used to map the distribution of indicators with a level of detail that can support geographically stratified decision-making. Based on geo-located survey data from Demographic and Health Surveys (DHS) in Nigeria (2008 - 2013) and Nepal (2006 - 2011), Bayesian geostatistical models and machine learning approaches were used in combination with a suite of spatial data layers to create high-resolution predictive maps for (i) the rates of stunting in children under the age of five and (ii) the household wealth index. An ensemble model was also exploited for aggregating different modelling results to improve the modelling prediction capacity in Nigeria (for stunting 2008). By combining these maps with information on the disbursement of aid for increasing food security and alleviating poverty (AidData database - http://aiddata.org/), we quantified both the reported spatial distribution of aid relative to stunting and poverty, as well as how changes in these indices overtime related to aid disbursement. While many cases of aid disbursement lacked detailed spatial information, the results here demonstrate the potential of this approach and highlight the value of spatially disaggregated data on the distribution of aid.
49
AidData
Bosco, Claudio
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Tejedor Garavito, Natalia
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de Rigo, Daniele
20a45c8d-8580-4fe6-936b-5688e65cdfda
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Pezzulo, Carla
876a5393-ffbd-479a-9edf-f72a59ca2cb5
Wood, Richard
fa173eb1-9e26-4d9f-8d90-0c48d5586f94
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Bird, Tomas J
b491394a-2b91-42d5-8262-d1c0e9ff17cd
Bosco, Claudio
9bf27082-5f4c-4b9f-8f12-6c4159f556f5
Tejedor Garavito, Natalia
26fd242c-c882-4210-a74d-af2bb6753ee3
de Rigo, Daniele
20a45c8d-8580-4fe6-936b-5688e65cdfda
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Pezzulo, Carla
876a5393-ffbd-479a-9edf-f72a59ca2cb5
Wood, Richard
fa173eb1-9e26-4d9f-8d90-0c48d5586f94
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Bird, Tomas J
b491394a-2b91-42d5-8262-d1c0e9ff17cd

Bosco, Claudio, Tejedor Garavito, Natalia, de Rigo, Daniele, Tatem, Andrew, Pezzulo, Carla, Wood, Richard, Chamberlain, Heather and Bird, Tomas J (2018) Geostatistical tools to map the interaction between development aid and indices of need (AidData Working Paper, 49) AidData 43pp.

Record type: Monograph (Working Paper)

Abstract

In order to meet and assess progress towards global sustainable development goals (SDGs), an improved understanding of geographic variation in population wellbeing indicators such as health status, wealth and access to resources is crucial, as the equitable and efficient allocation of international aid relies on knowing where funds are needed most. Unfortunately, in many low-income countries, detailed, reliable and timely information on the spatial distribution and characteristics of intended aid recipients are rarely available. Furthermore, lack of information on the past distribution of aid relative to need also hinders assessments of the impacts of aid. High-resolution data on key social and health indicators, as well as how aid distribution relates to these indicators are therefore fundamental for targeting limited resources and building on past efforts.

In this study, we show how modern statistical approaches combined with a new geographic database of aid distribution can be used to map the distribution of indicators with a level of detail that can support geographically stratified decision-making. Based on geo-located survey data from Demographic and Health Surveys (DHS) in Nigeria (2008 - 2013) and Nepal (2006 - 2011), Bayesian geostatistical models and machine learning approaches were used in combination with a suite of spatial data layers to create high-resolution predictive maps for (i) the rates of stunting in children under the age of five and (ii) the household wealth index. An ensemble model was also exploited for aggregating different modelling results to improve the modelling prediction capacity in Nigeria (for stunting 2008). By combining these maps with information on the disbursement of aid for increasing food security and alleviating poverty (AidData database - http://aiddata.org/), we quantified both the reported spatial distribution of aid relative to stunting and poverty, as well as how changes in these indices overtime related to aid disbursement. While many cases of aid disbursement lacked detailed spatial information, the results here demonstrate the potential of this approach and highlight the value of spatially disaggregated data on the distribution of aid.

Full text not available from this repository.

More information

Published date: May 2018

Identifiers

Local EPrints ID: 421134
URI: https://eprints.soton.ac.uk/id/eprint/421134
PURE UUID: b937bdce-e7f4-447b-b9b0-ad70dd50c565
ORCID for Natalia Tejedor Garavito: ORCID iD orcid.org/0000-0002-1140-6263
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 23 May 2018 16:30
Last modified: 14 Mar 2019 01:35

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Contributors

Author: Claudio Bosco
Author: Daniele de Rigo
Author: Andrew Tatem ORCID iD
Author: Carla Pezzulo
Author: Richard Wood
Author: Tomas J Bird

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