Our Nodes of Growth need to be connected to people. The second task in Phase 1 is the collection of spatial datasets which answer three key questions:
(1) Who wants to grow improved drought-tolerant beans?
(2) Where can they get improved drought-tolerant bean seeds?
(3) How can they get to and from the places selling or loaning these seeds?
Who wants to grow improved drought-tolerant beans? – Population data
The ultimate beneficiaries of the Nodes of Growth project are small-holder bean producers in Kenya. We have reviewed existing spatial data on the location of the population within Kenya, their levels of poverty and the importance of beans for their livelihoods and diets.
Basic information on population is publicly available from numerous sources; the majority of these offer data specific to Kenya while other sources of population have been developed for the whole world.
Our criteria for choosing data sources are:
(i) level of aggregation – where we prefer data for the smallest possible administrative unit;
(ii) timeliness – where we prefer the most up to date information; and,
(iii) the depth of the information – where we prefer datasets which as much information about the population, for instance poverty levels.
The source chosen gives data at the sub-location level which is the fifth administrative unit (after the Province, District, Division and Location), and is based on the 1999 population census. This is linked to data on poverty incidence and the poverty gap which are available at the location level.
The importance of beans is based on the major bean producing areas produced as part of the CIAT Bean Atlas in 1998. Aggregated data on bean production were not available in Kenya. Our target population was further refined by excluding sub-locations in Nairobi district as well as national park areas (e.g. Mount Kenya).
Where can they get improved drought-tolerant bean seeds? – Seed outlet locations
At the start of the project we had expected that information on seed outlets would be generally in existence and would not require field-work to acquire. The reality is rather different and while the knowledge may exist in the minds and experience of numerous people it has proven difficult to locate the seed outlets. As a result the project partners have had to obtain GPS receivers and are collecting the location of seed outlets during their routine journeys to the field. Data are now coming in but there are some districts for which no data have been collected. We are trying to resolve this problem now, but will proceed with the modelling in Kirinyaga and Nzaui (formerly part of Makueni) districts.
How can they get to and from the places selling or loaning these seeds? – Road network
Roads, tracks and paths form the physical network that connects the Nodes of Growth. Having access to a good spatial dataset of roads is essential for modelling the physical access of our target population to the seed outlets.
Road data from three sources were investigated using the following criteria:
(i) positional accuracy – how accurate is the location of the road;
(ii) attribute accuracy – is the description accurate when the road was first surveyed and is it still accurate;
(iii) the proportion of the roads that are captured in the dataset, and;
(iv) internal consistency – are all parts of the country mapped equally and consistently.
The most suitable dataset was the result of an inventory exercise planned by the Kenyan government (GoK), implemented by Roy Jorgensen Associates and funded by the World Bank in 2004. The road data were collected using GPS mounted on vehicles. This data includes categories of the road that can be used to assign different speeds for modelling accessibility. The internal consistency was checked using high resolution imagery in different parts of Kenya.
For modelling the feeder road network (including paths and tracks) we found that the relationship between feeder road length and population is not linear and areas with larger populations do not always have a greater length of feeder road network. The relationship can be used to derive values for the simulated track and path density for each sub-location in Kenya and hence the time required to travel. The creation of a speed for these areas assumes that people will be walking to the nearest road (captured in the GoK data source).
The modelling framework will be developed in Phase 2.
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