Spatial data needs for poverty management. Akinyemi, F. O Research and theory in advancing spatial data infrastructure concepts, 5:261--277, 2007. abstract bibtex As spatial determinants are increasingly considered essential in understanding poverty, the use of consistent spatial datasets in developing poverty reduction strategies is a growing requirement. This is because the success of poverty reduction programs (PRPs) depends largely on the use of quality data to help determine the nature and extent of poverty and to properly design and implement strategies for alleviating poverty in a particular context. The growing importance of spatial data in addressing complex social, environmental, and economic issues facing communities around the globe necessitates the establishment of spatial data infrastructures (SDIs) to support the sharing and use of this data locally, nationally, and internationally. SDIs facilitate access to data, data consistency, data sharing, and multinational decision making. They also facilitate the use of spatial data for poverty assessment and mapping and the development of poverty reduction applications that are integrative in nature and substantially generic. This article examines the growing need for spatial data in poverty mapping, particularly for understanding the importance of spatial factors in poverty and food security outcomes. By surveying different poverty mapping studies, the types of spatial data in use, the modes of usage, and data sources were identified. The objective was to identify spatial datasets essential for poverty mapping. The relevance of SDIs to poverty reduction is discussed. The survey reveals that the types of spatial data used for addressing poverty are diverse and the spatial datasets needed vary between programs, depending on the type of poverty measure adopted. Spatial data use is fast becoming a best practice for poverty assessment. The diversity of spatial datasets in use, the huge costs associated with their use, the need for consistency and accuracy in data, and access to spatial data at disaggregated scales are all issues pertinent to poverty reduction that SDIs can help resolve. abstract This article from Research and Theory in Advancing Spatial Data Infrastructure Concepts (ed. Harlan Onsrud; Redlands, CA: ESRI Press, 2007) is made available under a Creative Commons License, Attribution 2.5 The selection, coordination, arrangement, layout, and design of the compilation are the exclusive property of ESRI and are protected under United States copyright law and the copyright laws of the given countries of origin and applicable international laws, treaties, and/or conventions. Any use of the text contained in the individual articles in contradiction of the Creative Commons License, Attribution 2.5, requires express permission in writing by the authors of the
@article{Akinyemi2007,
abstract = {As spatial determinants are increasingly considered essential in understanding poverty, the use of consistent spatial datasets in developing poverty reduction strategies is a growing requirement. This is because the success of poverty reduction programs (PRPs) depends largely on the use of quality data to help determine the nature and extent of poverty and to properly design and implement strategies for alleviating poverty in a particular context. The growing importance of spatial data in addressing complex social, environmental, and economic issues facing communities around the globe necessitates the establishment of spatial data infrastructures (SDIs) to support the sharing and use of this data locally, nationally, and internationally. SDIs facilitate access to data, data consistency, data sharing, and multinational decision making. They also facilitate the use of spatial data for poverty assessment and mapping and the development of poverty reduction applications that are integrative in nature and substantially generic. This article examines the growing need for spatial data in poverty mapping, particularly for understanding the importance of spatial factors in poverty and food security outcomes. By surveying different poverty mapping studies, the types of spatial data in use, the modes of usage, and data sources were identified. The objective was to identify spatial datasets essential for poverty mapping. The relevance of SDIs to poverty reduction is discussed. The survey reveals that the types of spatial data used for addressing poverty are diverse and the spatial datasets needed vary between programs, depending on the type of poverty measure adopted. Spatial data use is fast becoming a best practice for poverty assessment. The diversity of spatial datasets in use, the huge costs associated with their use, the need for consistency and accuracy in data, and access to spatial data at disaggregated scales are all issues pertinent to poverty reduction that SDIs can help resolve. abstract This article from Research and Theory in Advancing Spatial Data Infrastructure Concepts (ed. Harlan Onsrud; Redlands, CA: ESRI Press, 2007) is made available under a Creative Commons License, Attribution 2.5 The selection, coordination, arrangement, layout, and design of the compilation are the exclusive property of ESRI and are protected under United States copyright law and the copyright laws of the given countries of origin and applicable international laws, treaties, and/or conventions. Any use of the text contained in the individual articles in contradiction of the Creative Commons License, Attribution 2.5, requires express permission in writing by the authors of the},
author = {Akinyemi, Felicia O},
file = {:Users/glennvancauwen/Library/Application Support/Mendeley Desktop/Downloaded/Akinyemi - 2007 - Spatial data needs for poverty management.pdf:pdf},
isbn = {978158948162},
journal = {Research and theory in advancing spatial data infrastructure concepts},
pages = {261--277},
title = {{Spatial data needs for poverty management}},
volume = {5},
year = {2007}
}
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The growing importance of spatial data in addressing complex social, environmental, and economic issues facing communities around the globe necessitates the establishment of spatial data infrastructures (SDIs) to support the sharing and use of this data locally, nationally, and internationally. SDIs facilitate access to data, data consistency, data sharing, and multinational decision making. They also facilitate the use of spatial data for poverty assessment and mapping and the development of poverty reduction applications that are integrative in nature and substantially generic. This article examines the growing need for spatial data in poverty mapping, particularly for understanding the importance of spatial factors in poverty and food security outcomes. By surveying different poverty mapping studies, the types of spatial data in use, the modes of usage, and data sources were identified. The objective was to identify spatial datasets essential for poverty mapping. 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