African Income Indicator
The Africa Income Indicator is a spatial dataset which provides the income ranges at either point (i.e. rooftop level); or a count of the points aggregated to suburb level in selected African destinations.· This product enables the end-user to make to-the-point decisions with regards to the household income profile of a micro catchment area.
Complimenting this dataset is the detailed Quality Spatial Platform (QSP) for Africa. QSP is the leading vector dataset for countries throughout Africa (the data source for which is MapIT / TomTom), and is sold as an optional add-on for the African Income Indicator dataset on a country by country basis.
Detailed maps indicating streets, suburbs, cities, railway lines, land use, points of interest etc are available and are sold on a “country by country” basis with a view to enhance the Africa Income Indicator offering.
This level of data can be used to answer questions such as:
• “Where is the nearest bush shelter in relation to an ATM in relation to low income earning households?”; or
• “Where are hubs located that contain a shopping centre, filling station, ATM, and quick service restaurant within 500m of one another – in relation to a medium household income concentration?”; or
• “How many competitors do I have within a 2 km radius of my stores - and where are my stores in relation to a concentration of high income earning households?”
Fernridge Consulting started counting households (“rooftops”) in 1999 / 2000 using limited available aerial imagery as it was very difficult to correlate actual retail performance data as measured in terms of store turnover with demographic data that was available at the time.
The result was that Fernridge Consulting developed a unique method of extracting demographics from aerial photography. Not only does this dataset employ the latest available aerial photography but it also utilizes property research, GPS fieldwork and feedback users to ensure the most up-to-date information available.
A spatial “point” is digitized with their Mapinfo GIS system on each rooftop. Each point (i.e. household) in this dataset represents a household or dwelling. These households are then classified in terms of ‘type of dwelling’ and ‘household income’.
The income ranges are derived from property values. To uphold ownership of a property does require some form of income to afford maintenance, rates and taxes, etc. ‘If you cannot pay, you cannot stay’ applies in respect to the allocation of Income Groups / Ranges.
Quality control is very important, and this dataset is constantly maintained, interrogated by consulting analysts, and industry-tested on a daily basis with our clients through expenditure models, potential estimates, forecasts (i.e. turnovers, trading densities, memberships, etc.) and comparisons with actual performance.
The dataset comprises 3 income ranges in a High / Medium / Low income scenario and currently shows the household income by suburb for 35 of the most important African cities. This number is shortly to be increased to 65 cities. The dataset will be offered in three tiers, based on the number of households in the city.
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