Spatial Data for Development Domain Analysis in East and Central Africa

GIS dataset for constructing three-dimensional Development Domain for ASARECA's operation area in 12 East and Central Africa countries. Data layers of market accessibility, agricultural potential, and population density of 2010 at 5 arc-minute resolution were compiled from HarvestChoice.IFPRI1; HarvestChoiceEPTD; PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM

HCID: Global grid cell identification system at multiple spatial resolutions

HCID is a global grid identification system offering users to refer the location and boundary of a grid cell, available at multiple spatial resolutions, using a single integer number. Instead of using the coordinates (latitude and longitude) of two corners of the grid cell bounding box (i.e., upper-left and lower-right), we assign each grid cell with a sequential integer number, or a grid cell ID, unique to each spatial resolution. This system was developed by HarvestChoice (http://harvestchoice.org) and is being widely used to facilitate analysis of spatial data layers, including the visualization, domain analysis, spatial aggregation/dis-aggregation, and general exchange of spatially-explicit data across disciplines - without needing to use a GIS software and spatial analysis skills. For the five arc-minute resolution of grids, we call the ID system as "CELL5M", whereas ones for 30 arc-second, 30-minute and 1 degree are called CELL30S, CELL30M and CELL1D, respectively. Assigning 0 starting at the upper-left corner (longitude: -180.0, latitude: 90.0) with a geographic projection, for example, CELL5M ranges up to 9,331,199 at the lower-right corner (longitude: 180.0, latitude: -90.0). The grid cell ID at a specific location can be easily computed mathematically, and this can be also easily converted to different resolutions.HarvestChoice; IFPRI2EPTDCGIAR Research Program on Policies, Institutions, and Markets (PIM

CELL5M: A geospatial data and analytics platform of harmonized multi-disciplinary data layers for Africa South of the Sahara

Spatially-explicit data is increasingly becoming available across disciplines, yet they are often limited to a specific domain. In order to use such datasets in a coherent analysis, such as to decide where to target specific types of agricultural investment, there should be an effort to make such datasets harmonized and interoperable. For Africa South of the Sahara (SSA) region, the HarvestChoice CELL5M Database was developed in this spirit of moving multidisciplinary data into one harmonized, geospatial database. The database includes over 750 biophysical and socio-economic indicators, many of which can be easily expanded to global scale. The CELL5M database provides a platform for cross-cutting spatial analyses and fine-grain visualization of the mix of farming systems and populations across SSA. It was created as the central core to support a decision-making platform that would enable development practitioners and researchers to explore multi-faceted spatial relationships at the nexus of poverty, health and nutrition, farming systems, innovation, and environment. The database is a matrix populated by over 350,000 grid cells covering SSA at five arc-minute spatial resolution. Users of the database, including those conduct researches on agricultural policy, research, and development issues, can also easily overlay their own indicators. Numerical aggregation of the gridded data by specific geographical domains, either at subnational level or across country borders for more regional analysis, is also readily possible without needing to use any specific GIS software. See the HCID database ( http://dx.doi.org/10.7910/DVN/MZLXVQ ) for the geometry of each grid cell. The database also provides standard-compliant data API that currently powers several web-based data visualization and analytics tools.IFPRI2; HarvestChoiceEPTDCGIAR Research Program on Policies, Institutions, and Markets (PIM

Five-year yield growth rates of major crops in the CGIAR CRP II priority countries

Yield growth rate (%) of major crop commodities in CGIAR CRP II priority countries were computed using the FAOSTAT-retrieved national crop production statistics data for five most recent years (2009-2013 in most countries; where available, 2014 data was also included).IFPRI1; HarvestChoiceEPTD; PIMCGIAR Research Program on Policies, Institutions, and Markets (PIM

Rapid yield gap assessment: African Development Bank's priority commodities

Yield gap of nine priority commodities of the African Development Bank was assessed and aggregated at two levels across the Africa continent: 1) agro-ecological zones and 2) agro-ecological zones by country. In this rapid assessment, the yield gap was defined as the percentage difference between the actual yield estimated from spatially-disaggregated crop production statistics database and the potential yield retrieved from the FAO's Global Agro-Ecological Zones database, both at the 10-km pixel level.HarvestChoice; IFPRI1EPTDCGIAR Research Program on Policies, Institutions, and Markets (PIM

Urban Extent of Africa 2010

Accurate delineation of the urban and rural areas has a broad range of implications on the quality and reliability of agricultural production and socio-economic statistics, design of household survey, establishment of agricultural development strategies and policies, and effective resource allocation. Two most widely-used urban/rural mapping dataset across Africa, GRUMP (Global Rural and Urban Mapping Project; http://sedac.ciesin.columbia.edu/data/collection/grump-v1) and SAGE Urban Extents (https://nelson.wisc.edu/sage/data-and-models/schneider.php), uses the underlying datasets of 2000-2002. There are various pilot studies attempting to update the dataset in major metropolitan areas or specific countries, but no African continent-wide effort has been made to date. To address this, using the GRUMP 2000 data as the baseline, we used a set of recently-published datasets to identify the newly extended urban areas across Africa. Three main data sources were the nightlights data from Defense Meteorological Satellite Program (DMSP) 2010-2013, WorldPop 2010, and the MODIS Global Land Cover 2010-2013. Country-level urban population headcounts and their share of total population were acquired from the World Bank for 2010-2013 and used to control the total size of the urban population from the analysis is consistent with the statistics data at 1 km resolution

Travel time to markets in Africa South of the Sahara

Reliable market accessibility data is critical to develop agricultural policies and investment plans for ensuring smallholder farmers’ market participation and their profitable farming, yet this data is less frequently updated. Most of publicly available data benchmarks around the year 2000, not reflecting rapid development of transportation infrastructure in Africa South of the Sahara (SSA) in last decade. For this, using a newly available accessibility model input datasets, such as new land cover data from satellites, crowdsourced road network data, and updated population of major human settlements across SSA, HarvestChoice Project updated the existing market accessibility data and provides new market accessibility data layers benchmarking around the year 2010. The dataset includes five data layers representing travel time to the nearest market of five sizes (population of 20K, 50K, 100K, 250K, and 500K), respectively, on 5 arc-minute grids across SSA.HarvestChoice; IFPRI1EPTDCGIAR Research Program on Policies, Institutions, and Markets (PIM

Agro-ecological zones for Africa South of the Sahara

Agro-Ecological Zones (AEZ) for Africa South of the Sahara (SSA) were developed based on the methodology developed by FAO and IIASA. The dataset includes three classification schemes: 5, 8, and 16 classes, referred to as the AEZ5, AEZ8, and AEZ16, respectively.HarvestChoice; IFPRI1EPTDCGIAR Research Program on Policies, Institutions, and Markets (PIM

Replication data for: Subnational socio-economic dataset availability

This is replication data used in the analysis for the correspondence article published at Nature Climate Change: "Subnational socio-economic dataset availability."HarvestChoice; IFPRI1EPTDCGIAR Research Program on Policies, Institutions, and Markets (PIM

Urban extent of Africa 2010

Accurate delineation of the urban and rural areas has a broad range of implications on the quality and reliability of agricultural production and socio-economic statistics, design of household survey, establishment of agricultural development strategies and policies, and effective resource allocation. Two most widely-used urban/rural mapping dataset across Africa, GRUMP (Global Rural and Urban Mapping Project; http://sedac.ciesin.columbia.edu/data/collection/grump-v1) and SAGE Urban Extents (https://nelson.wisc.edu/sage/data-and-models/schneider.php), uses the underlying datasets of 2000-2002. There are various pilot studies attempting to update the dataset in major metropolitan areas or specific countries, but no African continent-wide effort has been made to date. To address this, using the GRUMP 2000 data as the baseline, we used a set of recently-published datasets to identify the newly extended urban areas across Africa. Three main data sources were the nightlights data from Defense Meteorological Satellite Program (DMSP) 2010-2013, WorldPop 2010, and the MODIS Global Land Cover 2010-2013. Country-level urban population headcounts and their share of total population were acquired from the World Bank for 2010-2013 and used to control the total size of the urban population from the analysis is consistent with the statistics data at 1 km resolution.HarvestChoice; IFPRI1EPTDCGIAR Research Program on Policies, Institutions, and Markets (PIM