Geostatistical Techniques for Settlement Pattern Analysis of Adamawa and Taraba States, North East Nigeria

Abstract

In this paper, the use of geospatial and geostatistical techniques for determination of settlement patterns in Adamawa and Taraba states were demonstrated. The actual locations of the settlements and thier attributes in both states were generated on digital maps to derive the settlement distribution pattern in the states. The analyzing pattern of the spatial statistical tool of ArcGIS 10.5 was used to calculate the average nearest neighbour of the settlements in the two States, the GetisOrd Gi* method of hot spots was adopted to assess the clustering of settlement based on relief. The results revealed that though Taraba State is far larger in land area than Adamawa State, but Adamawa State has a total settlement of 586, with a land area of 38006.41, with 64.86 settlement density, while Taraba State with 579 settlements and 58378.51 land area has settlement density of 100.83. The nearest neighbour index which suggests the average distance among the settlements in Adamawa State is approximately 0.92 meters, while Taraba State has average of 0.88 meters. Since the index of the two states are less than one (1), the pattern exhibits clustering. The generated z-scores and the p-values in the two States are both negative and small respectively which further confirms the tendency towards clustering of the settlement pattern. About 6% of the land area in each of the two States are protected areas, that is, Gashaka-Gumti National Park. The study also revealed that the settlements in the entire Benue valley in Adamawa State are clustered, while only some areas within the Benue valley in Taraba State are clustered. In Adamawa State, an average of every 143.72 kilometers of protected land area has one settlement, while Taraba State has one settlement at every 160.27 kilometers. It was recommended that settlement pattern analysis should be seen as an important aspect of even developmental planning and hence, be integrated in regional planning processes while geospatial and geostatistical techniques which is easier to process with reliable results should be encouraged.