A Living fromLivestock

IGADLivestockPolicyInitiative

Joint FAO/WFP Workshop on Spatial Analysis Methodologies for Poverty and

Vulnerability Applications(in the Horn of Africa)

Meeting Report

28 February 2006 - FAO, Rome

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TABLE OF CONTENTS

Summary .............................................................................................................. ii Introduction .......................................................................................................... 1 Presentations and Discussion ...................................................................................... 4

Small Area Estimates ............................................................................................ 4 Socio-economic data and thematic maps for the Poverty Mapping in South-East Asia ............... 5 Poverty Mapping in Uganda..................................................................................... 6 Vulnerability Analysis and Mapping............................................................................ 7 GeoNetwork....................................................................................................... 8

Additional Discussion ............................................................................................... 9 Conclusions ..........................................................................................................10 Annex 1: Agenda....................................................................................................11 Annex 2: Participant List ..........................................................................................12

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SUMMARY

The workshop on spatial analysis methodologies for poverty and vulnerability applications, with an emphasis on the Horn of Africa, was co-organized by the Food and Agriculture Organisation’s Livestock Information, Sector Analysis and Policy Branch (FAO-AGAL), under the IGAD Livestock Policy Initiative1 (IGAD LPI), and the World Food Programme’s Vulnerability Analysis and Mapping unit (WFP-VAM) to address technical and data issues related to poverty and vulnerability mapping and to increase data and information sharing between and within the two agencies.

Participants at the workshop were mainly spatial information users, involved in sustainable development, food security, poverty alleviation, vulnerability and natural resources management within FAO and WFP. FAO’s involvement was very broad: with participation from the Livestock Information, Sector Analysis and Policy Branch (FAO-AGAL), which houses the IGAD Livestock Policy Initiative; the Global Perspectives Studies Unit (ESDG), which houses the Food Insecurity and Vulnerability Information and Mapping System (FIVIMS); the Agricultural Sector in Economic Development Service (ESAE); and the Environment and Natural Resources Service (SDRN).

The first part of the workshop focused on presentations of the methodologies used in different FAO poverty mapping and WFP vulnerability mapping activities, especially in Africa (and in particular the Horn of Africa). The presentations were followed by discussions on a number of issues associated with the different methodologies, such as data availability, underlying assumptions, accuracy of prediction models and validation of the results. The issues that represent major concerns in terms of implementation and use of poverty and vulnerability maps relate to the lack of validation of the maps and the results in the field, and the lack of appropriate communication or understanding between the technical staff, the decision-makers and the donor community.

The workshop provided a good opportunity for the different groups to share their expertise and exchange information on the tools and methodologies they are using in their poverty and vulnerability mapping activities. They all agreed that as much as data sharing is important, information sharing is also essential to provide timely information to decision-makers, avoid duplication of efforts, enhance capabilities and expertise and to provide access to analytical tools and approaches that could the be adopted more widely.

1 IGAD, the Intergovernmental Authority on Development, is a regional economic community covering the Horn of Africa: Djibouti, Eritrea, Ethiopia, Kenya, Somalia, Sudan and Uganda.

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INTRODUCTION

Poverty maps have been increasingly used in recent years to provide information on the spatial distribution of poverty and therefore to provide a tool to target interventions to reduce poverty. The use of maps and GIS-based poverty analysis makes it easier to integrate poverty data from various sources, and facilitates visualization and interpretation of the results also for a non-specialist audience. The assessment of poverty information comes from a variety of sources and can be presented at various levels (global, national and local). Indicators of income poverty (such as GDP per capita or daily subsistence levels), or of well-being (such as life expectancy, child mortality, or literacy) are most frequently used in poverty maps, and are derived from national census data or household surveys.

The most widely-used methodology to produce poverty maps has been developed by the World Bank (in the Development Economics Research Group, Poverty Cluster), using the so-called Small Area Estimate technique (see the first presentation); exploiting links between census (wide area) and survey (smaller area coverage) data. The detailed relationships found within the survey data are extended to the census data that must share some predictor variables with the survey data. Both census and survey data tend to be socio-economic in nature; the mapping thus exploits the internal correlations within potentially strongly correlated data sets – one ‘measure’ of poverty is often correlated with another. Despite a number of advantages and a wide applicability, the models are predictive, not causal: they do not seek to explain the determinants of poverty, but maximize precision in identifying the poor. In most developing countries, the lack of information on the determinants of poverty is one of the inhibiting factors for devising effective policies for poverty alleviation.

A number of more recent studies seeks to incorporate ancillary socio-economic data and environmental variables into their predictive models. They also seek to investigate the relative contribution of the different variables to poverty measures and therefore to provide a preliminary assessment of the major contributing factors to poverty in a country or a region. The presentations that followed illustrated the importance of the integration of environmental and socio-economic data for poverty and vulnerability assessments.

Poverty mapping and vulnerability assessments are in fact inter-linked and in a way complementary to each other. Many FAO and WFP activities have similar objectives: to provide support to interventions to target poverty reduction and food assistance for example. To do so, they both provide decision-makers with the results of geo-statistical analyses that aim to map vulnerable areas, as well as poor areas. Both organizations rely on similar datasets and tools to perform the analyses. Remote sensing and GIS for instance are now extensively used respectively to provide data on a number of environmental parameters and to support spatial analysis and assessments.

The motivation for this workshop was for the two organizations to exchange information on methodologies and data for spatial analysis in support of their activities of poverty mapping, food insecurity and vulnerability assessments. The focus was perhaps more on the characterization of the “drivers” of poverty, such as environmental constraints, food insecurity and livestock/agriculture characteristics (in turn dependent on the environment), rather than on the more measurable effects of poverty (health, sanitation, etc). In fact, most of the spatial analysis tools currently implemented by FAO and WFP to estimate the number and distribution of poor people rely more on the assessment of the factors that contribute to poverty, than on the measures of the resulting effects. Even though maps that represent the spatial distribution of poor people (usually by administrative units) are valuable to identify hotspots and priorities for intervention, the key aspect of the workshop was the

Introduction

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common interest to go one step further and examine the tools and methodologies available to analyze poverty and vulnerability distribution.

Another important objective of the workshop was to discuss methods to improve data access and sharing. GeoNetwork is one example of such a tool that could enhance collaboration and communication across sectors by allowing national institutions to access, manage, analyze and publish data.

Below we provide a short description of the main groups that attended the workshop.

The Livestock Information, Sector Analysis and Policy Branch (AGAL) of FAO’s Animal Production and Health Division (AGA) aims to explore linkages between livestock, poverty and the environment. Livestock is vital to the economies of many developing countries. Animals are a source of food, more specifically protein for human diets, income, employment and possibly foreign exchange. For low income producers, livestock can serve as a store of wealth, provide draught power and organic fertiliser for crop production and a means of transport. Consumption of livestock and livestock products in developing countries, though starting from a low base, is growing rapidly, and this growing demand for livestock products will stimulate livestock production which can, if well planned, contribute to poverty alleviation. The Pro-Poor Livestock Policy Initiative (PPLPI) is an initiative launched in 2001 to facilitate and support the formulation and implementation of policies and institutional changes that have a positive impact on the livelihoods of a large number of the world's poor. Given the critical role played by livestock in supporting and sustaining their livelihoods, the initiative has a distinct focus on livestock. Stemming from the PPLPI is the IGAD LPI, launched in 2005, with a similar objective to the PPLPI, though focussing on the Horn of Africa.

http://www.fao.org/ag/againfo/projects/en/pplpi/home.html

The Environment and Natural Resources Service (SDRN) of FAO provides advisory and technical services to promote sustainable agriculture and food security through protection of the environment and integrated management of natural resources. It assists countries in developing environmental policies, strategies and information systems and brings together a broad range of technical and policy skills to support member countries. The geographic information systems (GIS) group has been involved, together with CIAT (the International Center for Tropical Agriculture) and the United Nations Environment Programme’s Global Resource Information Database (UNEP-GRID), in a FIVIMS initiative to establish a web-based network of individuals and institutions mapping food insecurity, poverty and vulnerability, and to develop improved methods and tools for using map-based information to combat food insecurity and poverty at all levels (see more at www.povertymap.net) http://www.fao.org/sd/sdrn/index_en.htm

Within FAO’s Ecomonics and Social Department (ES) is the Agricultural Sector in Economic Development Service (ESAE), involved in poverty analysis and mapping, and the Global Perspectives Studies Unit (ESDG), which houses the Food Insecurity and Vulnerability Information and Mapping System (FIVIMS). FIVIMS defines systems that assemble, analyse and disseminate information on who the food-insecure are, where they are located, and why they are food-insecure, nutritionally vulnerable or at risk. FIVIMS is coordinated by an Inter-agency working group (AWG-FIVIMS) that oversees the development of FIVIMS and promotes the use of information and mapping systems for food insecurity and vulnerability assessments. The objectives of the FIVIMS are: i) to develop a consensus among donors and technical agencies on best practices in food security information systems, ii) to coordinate the donor and technical agencies on such food security systems, and iii) to link these information systems to remedial action programs and to evaluate the impact of these combined programs on real

Introduction

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reductions over time in the number of undernourished and the number of the poor and vulnerable. http://www.fivims.net

The World Food Programme’s (WFP) Vulnerability Analysis and Mapping (VAM) unit provides a systematic set of methods and tools to assess and map food security and vulnerability. It is an information/analytical tool, that provides timely, accurate and relevant information to WFP operations about the nature of food insecurity and vulnerability among the hungry poor. This information is explicitly intended to support WFP decision-making at key points in drawing up emergency and development programmes. http://www.wfp.org/operations/VAM/index.asp?section=5&sub_section=4

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PRESENTATIONS AND DISCUSSION

The slides of the presentations are available upon request.

Small Area Estimates

Benjamin Davis, FAO

The first presentation was made by Benjamin Davis (FAO-ESAE), who showed the different methodologies used in poverty mapping, with their advantages and disadvantages, and some results from case studies.

Poverty mapping usually involves techniques that permit sufficient disaggregation of a poverty measure to local administrative levels or small geographical units. Different methodologies include econometric models, statistical techniques, livelihood-systems analysis and participatory appraisals.

Small-area estimation (SAE) is a statistical technique that combines survey and census data to estimate welfare or other indicators for disaggregated geographical units such as municipalities or rural communities. In particular, it applies parameters from a predictive model to identical variables in a census or auxiliary database. The assumption of this methodology is that the relationship defined by the model holds for the larger population as well as the original sample. The models are predictive, not causal: they do not seek to explain the determinants of poverty, but maximize precision in identifying the location of the poor.

There are two main SAE methods: the ELL (from Elbers, Lanjouw, and Lanjouw) and the Bigman method. The ELL method requires a minimum of two sets of data: household-level census data and a representative household survey (Living Standards Measurement Surveys (LSMS), Demographic and Health Surveys (DHS), etc.) corresponding approximately to the same period as the census. The variable to be mapped can vary from a variety of poverty measures, to inequality, food security, etc. The main advantages of this method are the institutional backing of the World Bank, the fact that it is rigorous, researched and continually improved, and it is flexible (in terms of adding variables, as long as they can be linked in both datasets). On the other hand, the methodology is data and analytically intensive, access to census and survey data can be difficult, and it is static (does not respond to changing conditions, and difficult to go back in time due to the limited availability of census and survey in different years).

The Bigman method follows essentially the same steps as the ELL, but uses data aggregated over small geopolitical units (e.g. community-level) instead of household-level data. This has the additional advantage that data requirements are less stringent and data are more readily available.

The major drawback with all poverty mapping methods is that few poverty maps are systematically verified in the field. Davis reported of an example in Indonesia where Suharyo et al. (2005) systematically compared rankings from SAE and expert opinion from focus group discussions at the provincial, district, sub-district and village levels for a sample of regions, and found that the rankings down to sub-district level were consistent, while village level poverty estimates need to be used with caution. Davis also reported on some examples where the SAE maps were compared with maps derived using other methodologies (principal components, etc.).

Finally, an example of poverty mapping over time was presented. In Costa Rica, where census data were available for four points in time, poverty maps were constructed using the Bigman method and results compared with those of principal component analysis (Cavatassi, et al, 2004).

Presentations and Discussion

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These methods are widely used in different countries: in fact several examples are available on the poverty mapping website ( http://www.povertymap.net ).

Some issues were raised during the discussion that followed the presentation. In particular the participants discussed the problems of:

• Availability of a benchmark accurately to predict poverty values, and more generally the accuracy of the predictions. Davis pointed out that usually an R2 of 0.45-0.50 is considered a good level of statistical accuracy. The main issue here is that this is still a statistical benchmark, not necessarily a good measure for the real situation of poverty indicators in the country.

• Validation of the results of the poverty map. In this case too, even though the results might be statistically sound, there is usually very little effort to validate and verify the results on the ground. This leads to an increased level of uncertainty (besides the statistical significance) of the methodology.

• Comparability across countries. Since the input data are census and survey data, which normally differ from country to country, it is difficult to obtain identical indicators and thus compare the results for different countries.

Socio-Economic Data and Thematic Maps for the Poverty Mapping in South-East Asia

Mirella Salvatore and Mario Bloise, FAO

This presentation focused on an on-going effort within SDRN to map poverty indicators in South-East Asia. In particular they are now collecting data on a number of indicators, taken from the Millennium Development Goals (MDGs), in eight different thematic sections: living standards, demographic structure, education, economic activities, health, food security, agriculture, and natural disasters. The data are being collected at the province level for six countries (Cambodia, Lao PDR, Malaysia, Myanmar, Thailand and Viet Nam) and the Yunnan province of China (where data are collected by district) and, wherever available, for different time periods.

The database is developed as a relational database in Microsoft Access and available also as Excel tables (for example, indicators for the agriculture sector include: total land area, total forest area, total irrigated area, arable land, and rice production).

Then they illustrated the software they are developing that will accompany the database, which be part of a CD that will be made available by FAO. The software is developed as an ArcView application and allows visualization and query of the collected indicators, by country or region. Being a fully operational GIS, it allows the display to be customised (legend type, colour schemes etc.) and it also allows some level of analysis. The types of analysis supported in the current version include comparison in absolute and percentage values of different years for a given indicator, but the functionality will be expanded in the future to include spatial analysis.

One issue that was immediately raised was the problem of the defining the different indicators: there is a “general” definition, but there are also definitions that might vary from country to country, and within different institutions, so harmonization is essential before doing any regional analysis. One possibility is to weight the different indicators for better comparability. At this stage, some indicators have been collected according to the definition. For those not available, proxy variables are being identified. The main task is to harmonize all the indicators in terms of their temporal and spatial availability. Another issue relates to data gaps (i.e. missing data for certain provinces in a country). The solution may vary depending on the country and the available data: in some cases data from a different time periods, if available, are taken, or an average of the values from the neighbouring provinces is estimated, or it

Presentations and Discussion

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can be just left blank, etc. As with the problem of defining indicators, data gaps may also be treated differently among different organizations. Since this problem depends on data availability and country characteristics, harmonization of methods to fill data gaps is particularly challenging.

A note was made by Mark Smulders (FAO-FIVIMS) on the FIVIMS database, which includes several indicators and can be a useful complementary source of information. The country-level indicators are available via KIDS, the Key Indicators Data System developed by the World Agricultural Information Centre (WAICENT).

KIDS is an internationally comparable database of key indicators, structured as an interactive multi-functional Internet-based database that provides easy access to reliable food insecurity and vulnerability indicators, as well as functionalities that will allow better understanding of the causes and dimensions of food insecurity and vulnerability. KIDS will allow the combination of data from different agencies and examination of causal relationships between nutrition, health, demographic, economic, environmental, and other key indicator groups. More information on KIDS is available at http://kids.fao.org.

Once the SDRN database on South-East Asia is finished it will enable analysis to be conducted at regional and sub-regional scales and could be linked to other poverty and vulnerability mapping activities.

Ergin Ataman (FAO-SDRN) pointed out that once they conclude the data collection and implementation of the database and software for South-East Asia, they will prepare a number of CDs for distribution, and subsequently start setting up a similar database and software for other regions, with West Africa next in line.

Poverty Mapping in Uganda

Tim Robinson, FAO

Tim Robinson presented a recently completed poverty mapping study in Uganda undertaken by the Pro-Poor Livestock Policy Initiative in collaboration with the University of Oxford. The team has taken an approach varies from SAE method, in that it matches up household survey data with environmental data. The assumption is that agricultural activities, human and animal diseases, natural resources and other environmentally-determined factors are likely to be key ingredients in determining levels of poverty. Earth-observation satellite imagery can reveal the seasonal variations in environmental conditions, and has been used extensively, for example to map distribution of vector-borne diseases. Following these principles, the team used satellite derived and other environmental data to predict the poverty distribution based on the 2002 Uganda National Household Survey (UNHS).

Ground-based predictors used in the analysis included human population density, livestock density, ‘distance’ to markets (calculated as time to travel to towns of a certain size –), and the probability of occurrence of three important tsetse species.

Satellite-derived predictor variables included an elevation surface and data layers arising from temporal Fourier analysis of data from the Advanced Very High Resolution Radiometer (AVHRR) on-board the NOAA series of oceanographic satellites. These Fourier processed images include the mean, maximum and minimum of each satellite channel, the amplitude and phase (i.e. timing) of annual, bi-annual and tri-annual cycles that collectively define habitat seasonality of land-surface and air temperatures, Cold Cloud Duration (a proxy for rainfall), Vapour Pressure Deficit and vegetation growth, as estimated by the Normalised Difference Vegetation Index (NDVI).

Presentations and Discussion

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The predicted (household expenditure) and predictor (ground-based and satellite) variables are linked via discriminant analytical methods. In the case of the Ugandan data, expenditure was divided into ten categories and the predictor variables that best discriminated these categories were selected in a step-wise inclusive manner that maximised the fit of the model to the data (using kappa, the index of agreement between predicted and observed categories). Models were constructed at a variety of different spatial resolutions, from 0.01 to 1.0 degree (approximately 1.1 km and 110 km at the equator). Both predictor and predicted variables were averaged at each spatial resolution before the models were constructed.

One interesting result was that whilst the maps are perhaps more appealing at higher resolutions (due the smaller pixel size), the statistical accuracy (R2) increases with decreasing resolution (larger pixel sizes), reaching values of 0.9 at a resolution of about 40 km.

This raised the issues of the appropriate resolution at which to map poverty and the usefulness of these maps for regional, rather than local, applications, and more in general for targeting poverty reduction strategies. As with the SAE maps there has been no validation of the results, which clearly needs to be taken into account when interpreting the maps.

Other issues that arose during the discussion, and that should be addressed in the next, analytical phase of assessing and validating the results, were related to:

• The availability of other non-environmentally related factors influencing poverty distribution, such as conflicts, and ways of including them in the list of dependent variables.

• The comparability of expenditure as a measure of poverty between pastoralist societies and more market-oriented societies (though it was noted that the estimates of expenditure include consumption).

Vulnerability Analysis and Mapping

Paola De Salvo, WFP

The presentation started with an explanation of how the VAM unit fits into the Operation Department (ODA) at WFP, and then addressed VAM’s critical role for the effective and efficient targeting of WFP assistance. VAM’s role in fact is three-fold: information provision (who are the hungry poor and where they live?), ii) analysis, and iii) programme support (informing intervention decisions and improving targeting of food assistance).

An example from Nepal was presented, where implementation of the working plan involved:

• Data preparation and analysis of secondary data (environmental and socio-economic data layers) and creation of district clusters through Principal Component Analysis.

• Survey planning (using the above-defined clusters).

• Primary data collection (through use of tailored questionnaires at the village and household level).

• Analysis (the outputs are a set of quantitative statistics that are representative at the level of districts or development regions).

• Outputs (at various geographical levels), which are given to country offices and eventually are used by both local and international partners.

Presentations and Discussion

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De Salvo then reported on VAM’s Comprehensive Food Security and Vulnerability Analysis (CFSVA) for the 2004-2006 period. Since Uganda was among those completed, it was agreed that it could be used as a “case study” to compare the results from the WFP analysis and the FAO-AGAL poverty maps and to foster a collaboration between the two units.

Finally, it was shown how useful remote sensing can be in providing timely information that can be integrated within the Food Security and Vulnerability Analysis at different levels, and therefore extremely valuable for food security monitoring and early warning to address WFP activities in target areas. The VAM unit makes extensive use of spatial data to address issues related to food aid planning. Spatial data include demographic, health and nutrition, livestock, land use, climatic and meteorological, roads, markets, food security, early warning, and refugee data. The main issues that arise concern the origin of the data (from multiple sources, at different scales, obtained with different methodologies, etc.), the lack of information that comes with it, the lack of standards, the lack of communication between data providers and agencies and lack of appropriate data storage after emergencies.

GeoNetwork

Patrizia Monteduro and Roberto Giaccio, FAO

GeoNetwork is a web-based geographic information catalogue that integrates metadata search, metadata administration, data publication and distribution and interactive access to maps from distributed servers. One of its main objectives is to improve accessibility and sharing of a wide variety of geographic data at different scales and from multidisciplinary sources. For these reasons GeoNetwork has been adopted by FAO, WFP and a number of United Nations (UN) and non-UN agencies to distribute their geographic data. One of the principal aims of GeoNetwork is to increase collaboration between UN and other international organizations for collecting data and makeing them available to the wider community over the Internet.

GeoNetwork was originally started at FAO, within the SDRN division, in 2000 and has since then undergone a number of improvements and expansions. There are currently 16 decentralized nodes, each with separate ownership and responsibilities on data maintenance and upload. A new and improved version of GeoNetwork is in development and expected to be released in the near future. In addition to some differences in the technical characteristics (e.g. compliancy to the official ISO xml schema and an Open GIS Consortium (OGC) catalogue service for the web), the new GeoNetwork will feature a new interface, based on MapServer, that will have interactive capabilities to show/add layers and browse the datasets. This will allow users to display different datasets and to visualize data and the interactions among different parameters, instead of simply searching and downloading the data. GeoNetwork can be found at: http://www.fao.org/geonetwork .

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ADDITIONAL DISCUSSION

In a general discussion after the presentations the group addressed some broader issues regarding poverty measures and data sharing. With regards to poverty and vulnerability measures and mapping tools, the main lesson is perhaps that they both respond to different needs. It is therefore important to identify particular tools that might be more useful for specific purposes. A more thorough analysis of strengths and weaknesses of the different methodologies will help users to chose the most appropriate measure/tool for a particular task.

The FIVIMS team said they would be very happy to be involved in such initiatives and could contribute their expertise to help understand which models to use under particular circumstances. They also offered to write a short piece in their next Newsletter, to publicize the outcome of the workshop and help foster the collaboration between the different groups.

Another important issue that was raised was data harmonization. Ergin Ataman suggested the possibility of setting up a small working group on data standardization that would deal with socio-economic data; much like the UNGIWG (United Nations Geographic Information Working Group) does with respect to spatial data. As shown in the second presentation, in addition to the issues of dealing with different data types and sources, one major problem with the socio-economic data and indicators is the inconsistency in nomenclature and definitions between different countries.

Even though not the field of expertise of the people attending this workshop, the issue of standardization of socio-economic data is key to all their activities, so they agreed to contribute to the development of such a standardization group.

The discussion moved then to issues regarding the implementation of poverty and vulnerability maps. One perceived problem is that often the links between the output (maps and data for example) and the decision-makers, are either missing or poorly structured. It was suggested that at times this might be due to particularly complex level of details, or an excessive amount of information, making these outputs difficult for non-technical people to understand. It was suggested that an on-going discussion and a continuous flow of information between the technicians and the decision-makers would be essential to address these issues. Furthermore, as Mark Smulders pointed out, these issues of data standardization and linkage between technicians and decision-makers are very important for the donor communities. Naturally, they would want to make sure the maps and data are used wisely to help implement the poverty reduction strategies and food aid plans that they are supporting. FAO is conducting three different studies on early warning in Africa and the importance of their effects and results for decision-making processes.

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CONCLUSIONS

The workshop provided a good opportunity for the different groups to share their expertise and exchange information on the tools and methodologies they are using in their poverty and vulnerability mapping activities. They all agreed that as much as data sharing is important, information sharing is also essential to provide timely information to decision-makers, avoid duplication of efforts, enhance capabilities and expertise and to gain access to analytical tools and approaches that could be complementary to their respective activities.

The presentations offered an overview of the current methodologies used in poverty mapping (using census and survey data only, and incorporating other socio-economic and environmental data) and vulnerability assessments, and of the software developments to integrate various poverty indicators and to search and share data among the different agencies. The discussions that followed each presentation were in general more technical: examining the strengths and weaknesses of the different approaches; the methods to validate the accuracy of the results of the models and to assess how representative the maps were spatially; the techniques to “fill in” data gaps; the techniques to make data comparable across countries; and the importance of integrating socio-economic data (from censuses and surveys, as well as other georeferenced datasets) with environmental data, especially remotely-sensed parameters in poverty mapping and analysis.

The concluding discussion covered more general issues regarding poverty measures, and data sharing, as well the actual implementation and use of poverty and vulnerability maps. The major problems the agencies are facing is a lack of appropriate communication or understanding between the technical staff, who produces the maps, and the country representatives and the decision-makers who seek to implement the agencies’ recommendations. This issue is also very important for the donor community, who would like to ensure that the maximum use of the maps is made. The lack of validation of poverty maps is also a major concern. Decision-makers are expected to trust in data (poverty maps etc.) that, whilst accompanied by measures of statistical significance, have not been validated in the field. These maps should always be treated with some caution, and measures should be taken to evaluate them on the ground.

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ANNEX 1: AGENDA

10.00 – 11.30 Presentations

10.00 Introduction - Francesca Pozzi, FAO

Small Area Estimates - Benjamin Davis, FAO

10.30 Socio-economic data and thematic maps for the Poverty Mapping in

South-East Asia - Mirella Salvatore and Mario Bloise, FAO

10.50 Poverty Mapping in Uganda - Tim Robinson, FAO

11.10 Vulnerability Analysis and Mapping - Paola De Salvo, WFP

11.30 – 13.00 Discussion

o Comparison of methodologies:

• Small Area Estimation using census and detailed household surveys • Logistic regression with inclusion of environmental and other socio-economic

data

• Discriminant analysis based on remotely-sensed data • Spatial Analysis for Vulnerability Mapping

o Accuracy of prediction models o Methodologies to validate the results and the poverty/vulnerability maps o Issues of scale and spatial representativeness (i.e. at which scale are the

poverty/vulnerability maps accurate and useful, especially depending on the intended users)

o Issues of poverty indices and classes, and urban/rural differentiation in poverty and vulnerability mapping

o Availability of data needed for poverty evaluation and mapping in the IGAD countries (Djibouti, Eritrea, Ethiopia, Kenya, Somalia, Sudan, Uganda), especially related to:

• Census data • Surveys (socio-economic, health, etc) • Other socio-economic data related to livestock, transport, markets • Vulnerability assessment

o Available platforms for sharing/awareness of Spatial datasets

• Geonetwork

14.00 – 16.00 Continuation of Discussion and Joint Follow up activities/studies

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ANNEX 2: PARTICIPANT LIST

Ergin Ataman GIS Manager FAO-SDRN Ergin.Ataman@fao.org

Cristina Bassani Volunteer FAO-SDRN Cristina.Bassani@fao.org

Andrea Berardo Food Security Analyst WFP-VAM Andrea.Berardo@wfp.org

Mario Bloise GIS Consultant FAO-SDRN GIS-Consultant1@fao.org

Benjamin Davis Economist FAO-ESAE Benjamin.Davis@fao.org

Paola De Salvo GeoSpatial Analyst WFP-VAM Paola.Desalvo@wfp.org

Jan Delbaere Crisis Information Specialist WFP-VAM Jan.Delbaere@wfp.org

Fulvia Fiorenzi Programme and Liaison Officer IAWG-FIVIMS Fulvia.Fiorenzi@wfp.org

Tobias Fleming Programme Officer WFP-VAM Tobias.Fleming@wfp.org

Gianluca Franceschini GIS Consultant FAO-AGAL Gianluca.Franceschini@fao.org

Roberto Giaccio IT Consultant FAO-SDRN Roberto.Giaccio@fao.org

Cristina Lopriore Training and Liason Officer FAO-ESDG Cristina.Lopriore@fao.org

Patrizia Monteduro GIS Consultant FAO-SDRN Patrizia.Monteduro@fao.org

Daniela Ottaviani Statistician FAO-SDRN Daniela.Ottaviani@fao.org

Livia Peiser GIS Consultant WFP-VAM Livia.Peiser@wfp.org

Francesca Pozzi GIS Consultant FAO-AGAL Francesca.Pozzi@fao.org

Tim Robinson Livestock Information Officer FAO-AGAL Tim.Robinson@fao.org

Mirella Salvatore Statistician and GIS Expert FAO-SDRN Mirella.Salvatore@fao.org

Raffaella Sanna Volunteer FAO-SDRN Raffaella.Sanna@fao.org

Mark Smulders FIVIMS Coordinator FAO-FIVIMS Mark.Smulders@fao.org

Francoise Trine Food Security Analyst FAO-FIVIMS Francoise.Trine@fao.org