From MFKP_wiki

Jump to: navigation, search


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

Claudio Bosco, Natalia Tejedor-Garavito, Daniele de Rigo, Andrew J. Tatem, Carla Pezzulo, Richard Wood, Heather Chamberlain, Tom Bird

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.


No. 49. (2018) 
Key: INRMM:14597431

Keywords

                                     


Available versions (may include free-access full text)

http://docs.aiddata.org/ad4/pdfs/wps49_Ge… http://aiddata.org/publications/geostatis…

Versions of the publication are also available in Google Scholar.
Google Scholar code: GScluster:17022476023956832068

Works citing this publication (including grey literature)

An updated list of who cited this publication is available in Google Scholar.
Google Scholar code: GScites:17022476023956832068

Further search for available versions

Search in ResearchGate (or try with a fuzzier search in ResearchGate)
Search in Mendeley (or try with a fuzzier search in Mendeley)

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core

Digital preservation of this INRMM-MiD record

Internet Archive

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.