F.N. Karanja

Market segmentation using geographic information systems (GIS): A case study of the soft drink industry in Kenya

Purpose – To show how the analytical and visualization capabilities of geographic information systems (GIS) can enhance the communication, understanding and utility of data and information to be used in marketing planning, as compared with their conventional presentation as text and tables.Design/methodology/approach – A digital map of the study area was generated and a set of market zones. A multiple regression model for predicting sale of the product under study was then developed, taking into account sales figures from specific distribution outlets and the demographic and socio‐economic characteristics of the population served by the distribution outlets in the identified market zones. Optimum routes and times between the manufacturing plant and the distribution outlets were derived.Findings – Combining geospatial methods with conventional marketing techniques enables users to visualize the spatial distribution of data in maps, complemented by various statistical graphs and diagrams. This form of prese...

Monitoring the trend of unplanned developments in an urban area

Although remotely sensed data has been used extensively for urban studies, additional collateral information is usually a pre-requisite for the correct automatic identification and localization of objects found within a particular scene. Hence the ability to categorize an urban area into informational classes namely formal or informal developments requires information about the town model which has in principle some legal backing. In this study, remotely sensed data are used as a source for current information. Specifically, spatially enhanced 1998 SPOT XS (10 m) resolution for Dar-es-Salaam, Tanzania is automatically interpreted and the classes aggregated into developed (built) and reserved (non-built). The 1992 land use map is used to mask out the newly developed areas. Weighted indicators based on compatibility of land uses, infrastructure network, and hydrological sources are used to establish their relationship on the new developed areas. Results show that existing land uses influence highly new developed areas. A combination of the weighted indicators is also employed to constrain the new developed areas thus resulting in a stratification of the new areas into fuzzy blocks ranging from those which are likely extensions of unplanned developments to those which are unlikely. Such information could facilitate planners in prioritizing areas that require urgent reaction planning.

The contribution of gis and remote sensing in urban land use negotiation in developing countries

Most of the studies related to urban environments in developing countries focus mainly on particular aspects e.g. communication, residential, vegetated areas, etc. However, planners who are concerned about urban space management are also interested in the distribution and the change extent of the three main spatial domains namely developed, transition and reserved areas. This information facilitates subsequent land use negotiation processes. In this paper, standard classification procedures have been employed to extract this information from remotely sensed imagery such as the ones delivered by the new high resolution satellites, specifically from colour aerial photographs from 1995 and 1998 resampled to a ground resolution of 5m and Landsat TM from 1995. Postclassification analysis revealed comparable results for the 1995 datasets, whereas land use changes were evaluated by comparing the 1995 and 1998 classified images.

Using Geoinformatics to Identify Suitable Middle to Long Distance Athletics' Training Sites in Kenya

Kenyan athletes have continued to dominate middle and long distance running in the global arena for a very long time now. In this study the use of geoinformation in selecting suitable training sites for endurance running was investigated. The study area chosen was Keiyo district within the Rift Valley province in Kenya, where a considerable number of the top Kenyan athletes reside and train. Different geospatial data sources were used and relevant criteria selected. Geographic Information Systems (GIS) was employed as the basic tool for modeling and analysis. For each of the 11 regions within the study area, weights were allocated to each of the multiple criteria identified. The Analytical Hierarchy Process (AHP) was used to determine the overall suitability-ranking index. From the results and analysis performed the regions of Iten, Tambach, Kaptarakwa and Kapkenda respectively were ranked as suitable areas for High-Low training. Kamwosor, Chepkorio, Iten and Kaptarakwa respectively were identified as ideal regions for altitude training. Nyaru and Metkei were determined to be unsuitable for both High-Low and altitude training regimens. The approach formulated in this study can be applied to other areas to enable athletes and coaches to identify suitable training sites.