Population Density Social Economic Characteristics

48 Village Buffaloes Cattle Horse Goat Sheep Pork Chicken Duck Bauro 1028 736 103 711 911 602 Muapitine 115 56 43 15 126 704 Mehara 1784 620 157 185 10 1985 1987 Tutuala 359 538 38 288 1166 2062 Com 318 279 70 1892 25 115 270 Lore 1 180 375 167 479 2225 1377 45 Horses are mainly used in farmer’s activity such for transportation and Buffalos are used as tools in land preparation. Livestock distribution in table 30 showed that all villages are having animals that feed grass and lower plants. This means that a management planning also required allocating a grassing zone to avoid the negative impacts to the conservation areas. Its future need to have deeply study on transition areas within NKSNP since the function of transition areas here is to eliminate the pressure that might potential occurred and destroy the Cora Areas and Buffer Zones.

4.2 Social Economic Characteristics

Technically, evaluation of social economic pressure is done in raster- vector processes by combine population density data layer and forests activities with priorities conservation areas. Product of evaluation to priorities conservation areas would provide information of social-economic pressure level to priorities conservation areas. This information is one of the factors that would be used as base of ecosystem approach to defined proper management zoning for national park with social-economic characteristics in each village.

4.2.1 Population Density

Evaluation of population density and priority conservation areas done by summed values of population density as social data layer and priorities conservation areas map as others data layer, both are in raster datasets. Aggregation of population density layer and conservation areas layer conducted in order to produce information of the level of pressures by population density by scoring. Aggregation procedure is following ArcGIS Model Builder in weighted sum processes. This process would overlays both raster multiplying each by their 49 given weight and summing them together and this would produce new raster dataset as aggregation of population density values and zonation values. Population pressure to each zones of is presented in Table 31 and Figure 15 below, while the data processing is showing in figure 14. The first raster is PCAs layer, consists of PCA-1, Non PCA-1 and Non PCA. Through normalization weight 0-1 then the PCA raster has normalized based on its attributes. PCA-1 the raster values is 1.0000, Non PCA-1 raster values is 0.66666 and Non PCA raster values is 0.33333. The second raster is population density, which is have cells values 0.3681906 very low, 0.597687 low, 0.664339 medium, 0.691494 high and 1.0000 very high. These values were generated through normalized the values of population density of each village. The sum weighted processes done using ArcGIS tools by multiplying each other by their given weighted using default 1. The cells values of raster sum results of PCAs- Population density thence re-classes into five level, such as 0.701439 very low, 1.032191 low, 1.367737 medium, 1.593031 high and 1.928577 very high. In raster processes, the first output is in floating data format. Required next step is by reclassify the number of classes thence saved it as integer data format. Method of re-classified is using natural break method called also Jenks. Number of re-classes using here is five classes same as scoring basis that used in characterization of social economic values as stated in methodology of this research. The new raster that produced as output of weighted processes thence converted into vector format for vectorization processes such as calculating distribution areas per PCA of each village. Table 31 Population pressure scores in each village PCAVillage Bauro Com Lore I Tutuala Mehara Muapitine PCA1 - - 5 5 3 5 Non PCA1 3 5 4 4 2 5 Non PCAs 2 5 3 3 1 4 50 Figure 14 Rasterization processes in analyzing population pressure to the conservation areas Figure 15 Spatial distributions pressure of population density to PCAs per village

4.2.2 Household Forests Activities