LogR_SL logistic Regression of Shoreline. LogR_SL will be
valued 0 for inside area of buffering and 1 for outside area Figure 4.13.
Shore line was buffered also 1,000 meters towards inside the land, there is no deal with the area toward the sea.
Un-scale Vector Map
Figure 4.13.
The result of assAgning data by location spatial join of shore
line LogR_SL 1,000 m, where yellow cells is shoreline buffered 1,000 m 1 and light blue is
≥ 1,000 m 0
4.2.6. Data Extracting of Population Center Buffer Area Process.
Population center was determined by the position point of Subdistrict Office of Ciracap and Ciemas Subdistrict. Marking the
point was done by GPS Global Position System Etrex-Vista of Garmin.
The two points was buffered at a specified distance 10,000 meters or 10 kilometers. It is assumed that apart of deforestation
area would be covered by buffering 10 km. Position of population center point and shape of study area is also considered to define 10
km buffer. 46
Attribute table has been added two fields LogR_CP Figure 4.14.
Un-scale Vector Map
a b
Figure 4.14.
The result of assigning data by location spatial join of shore line
LogR_CP
1,000 m, where yellow cells is center population buffered 10,000 m 1 and light blue is
≥ 10,000 m 0 LogR_CP Logistic Regression of Center Population is
valued 0 for inside buffer area and 1 for outside. Figure 4.14a depicts the position of center population where
being represented by sub-district office. So Figure 4.14a is a map of two subdistrict that cover entire the study area, and 10 km rings of
buffering process. The category variable of 10 km distance from center
population, is accorded to Sitorus, Rustiadi, and Ardiansyah 2001 research that between 1992 to 2000, decreasing pattern of population
density is seen until to distance 10 km from Monas. In this research point of Ciemas and Ciracap Subdistrict Office is based point to
buffer 10 km.
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4.2.7. Data Extracting of Aspect Area Process.
The result process of sub title 2.2 can be used in this process. It is a shapefile in line form with 50 meters interval of contour data.
This process uses 3D Analyst extension for creating TIN from Features. In the creating process of TIN from topographic line
features was needed a boundary polygon study area as a cookies cutter.This process will produce a temporary file, and this file must
be saved into grid with 90 meters resolution, and then this grid will used as a raw material to produce aspect grid and shapefile.
The next process that uses grid TIN is using ArcView ModelBuilder. ModelBuilder will identify input automatically of
grid theme, in this case the result from gridding TIN file. Extracting aspect from elevation grid file is provide default
by ArcView ModelBuilder. It will produce classification of aspect in raster or grid format that also as thematic or discrete grid theme; with
defined resolution 90 meters. Since process produced a grid file, and it needs to convert to
shapefiles. The process to extract the aspect data can be continued, with first step to edit the table attribute. Converting shapefile from
aspect grid only produce ID and Gridcode that means the aspect.
Those are: • 1 = Flat
• 2 = North • 3 =
Northeast • 4 = East
• 5 = Southeast • 6 = South
• 7 = Southwest • 8 = West
• 9 = Northwest
All of the aspectcompass, will be input in PVC attribute data table. by editing table, and adding field “Compass”, and change the
number into string by using Query Builder and Field Calculator, for example Gridcode = 4 means Compass = East. Finding Gridcode = 4
uses Query Builder and continue to use Field Calculator to change Gridcode = 4 into Compass = East.
According to Saadi and Abolfazl 2003 that the slope aspect has an important role to deforestation in their study area. To define
the logistic regression value that means binary 0 or 1 for each point of compass, is based on coming direction of sun light. Flat is the
neutral condition, it is assumed that flat area tends to be deforested, so flat area is 1, so does West and East. The remaining points of
compass are 0. Only West, East and flat aspect will be focused and concern area to observe deforestation, since Indonesia is equatorial
country where sunlight more intensive in the east and west
Un-scale Vector Map
Figure 4.15. The result of assigning data by location spatial join of aspect
LogR_COM
, where yellow cells is East, West,, and flat area and remaining compass is the light blue.
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4.2.8. Data Extracting of Slope Area Process.
