Centro Internacional de Agricultura Tropical Análisis Espacial y Sistemas de Información Geográfica para establecer prioridades Regionales en la Investigación y Desarrollo Agropecuario Glenn Hyman Centro Internacional de Agricultura Tropical

OUTLINE Mapping, GIS and Spatial Analysis for Targeting Political and Agroecological Units New Information for Targeting and Priority-setting Some directions for the Future

Mapping, GIS and Spatial Analysis for Targeting

Thematic Mapping of “Necesidades Basicas Insatisfechas” at different scales in Honduras

Village-level mapping based on multivariate statistical analysis in the Central Peruvian Amazon

Boolean Analysis Preliminary overlay of areas of high population density [shown in lavender, > 25 people/km2] and low rice yields [shown in yellow < 1.5 tons/ha ] Purple colors show areas meeting both conditions

Spatial Clustering Methods based on village-level census data in Honduras

Fungicide Applications Model based on data Source: Hijmans et al. 2000

Political and Agroecological Units Most interventions are carried out in countries, departments, municipios, villages Biological, soil and climate processes occur in agroecological zones Priority setting exercises should analyze conditions according to both political and agroecological units Significant improvements can be made by using information at higher spatial resolution (e.g. TAC priority setting exercises)

As a general principal, geographical targeting Most interventions occur according to political units 10,400 units most are municipios Municipio (canton) level Parroquia level As a general principal, geographical targeting improves with greater spatial resolution (e.g. leakage)

CIAT Agroecological Zones from climate classificiation (cluster analysis method)

Source: Jeff White, CIMMYT Classification based on length of growing season

Modifiable Areal Unit Problem Population from Census Improving socioeconomic data for further analysis Modifiable Areal Unit Problem - estimation techniques improve spatial resolution Accessibility Model of Population

Sum of errors when modeled population is compared to Modeled population counts compared to actual census data for Peru Modeled estimates reduced overall error by one half in Peru Sum of errors when modeled population is compared to actual census data PERU

Population and Vegetation Types in Honduras - with better resolution maps, we can more easily estimate population in vegetation zones CCAD Vegetation Map

Potential Agricultural Productivity Population summed by zones Note: Areas en black are cities. Zones in Purple are higher rural population densities. Zones in green are low rural population densities. Raster Population Surface (digital map)

Agricultural potential in calories/ha/yr From a spatial overlay, we can estimate the number of rural people living in different classes of agricultural potential.

Overlay of Land Degradation and Population Maps Sources: GLASSOD, CIAT Population Data

New Information for Targeting and Priority-setting

Global Land Cover from 1 km AVHRR Source: USGS

Source: WRI, IFPRI

New Climate Data and Application Tools, Including IWMI’s World Water Atlas and CIAT’s MarkSim Example:Weather Stations used in calibration set for Markov climatic models

Global Population Data Set At 1 km spatial resolution Source: LandScan

Global Nighttime Lights Database From U. S Global Nighttime Lights Database From U.S. National Geophysical Data Center

Collection Dates from Last Census Round Year of Census La RESOLUCION TEMPORAL DE LA INFORMACION SEGUN PAIS ESTA MOSTRADO EN ESTE MAPA.

Most countries plan to conduct their next census within the next few years.

NATIONAL SPATIAL DATA INFRASTRUCTURES GIS is moving to the Internet Survey for 21 countries Leadership + Participation Core Data Components  Pricing Legal restrictions Challenges http://www.procig.org http://www.gsdi.org

Gateways to Geographic Information NSDI Clearinghouse Growth 1999 Source: United States Geological Survey 200 180 160 140 120 100 80 60 40 20 Jun-95 Jun-96 Feb-97 Dec-97 Sep-98 Mar-99 Jun-99 Sep-99 Nov-99 Intn'l Domestic Gateway Total

Source: United States Geological Survey

PROCIG http://www.procig.org 26 instituciones participantes Instituto Nacional de Estadística y Censos Ministerio de Agricultura y Ganadería Ministerio de Ambiente y Energía Instituto Geográfico Nacional CATIE Dirección de Estadística y Censos Ministerio de Desarrollo Agropecuario Autoridad Nacional de Ambiente Instituto Nacional de Estadística Ministerio de Agricultura, Ganadería y Alimentación Comisión Nacional de Medio Ambiente CIAT Dirección General de Estadística y Censos Ministerio de Medio Ambiente y Recursos Naturales Viceministerio de Vivienda y Desarrollo Urbano Ministerio Agropecuario y Forestal Ministerio de Ambiente y Recursos Naturales INETER Secretaría de Agricultura y Ganadería Secretaría de Recursos Naturales y Ambiente

Some directions for the Future Computational Geography Better data, and power to analyze it Combining Census and Survey Methods LSMS DHS Better Integration between the Social, Economic, Biological and Geographical

A relatively small investment in improved geographical targeting could yield large gains in poverty reduction