27 a
Habitat suitability is the affordability of an area indicated by the availability of resources and environmental conditions necessary for relatively successful
species survival and reproduction. b
Habitat factor is spatial representation of the resources and environmental condition needed by the species for its survival.
c Estimated land is a spatial representation of an area which has suitability value
to be estimated. It is actually a collection of small arbitrary land unit. Each unit of area has suitability score. As well as observation unit data below, it is
represented by vector-based grid or similar to adjacent isometric cell Hirzel, 2001.
d Observation unit is an area where measurement to ecogeographic variable was
conducted. It is represented by uniform polygon such as rectangle or circle. The observation and estimated land feature are categorized as evaluated
feature. e
Ecogeographic variable is spatial properties of a unit of area based on the arrangement of corresponding habitat factor.
f Species distribution is a collection of species position in certain space related
to their survival.
4.1.2.1. Data Preparation Utility
All processes built in the system are developed by exploiting basic geometric function of MapObjects considerably. Even the design of process is
narrow in application, means that is specific for MapObjects application, the process is described here consider to the importance of documentation for better
development in the future.
4.1.2.1.1. Ecogeographical Data Generation
According to the definition ecogeographical variable, the generation of ecogeographical variable means to measure the arrangement structure of
correspond habitat factor. There are three basic types of spatial feature, i.e. point, line, and polygon. Therefore, the analysis of spatial features arrangement of an
area considers these spatial features.
28 There are two type of spatial analysis developed for SUITSTAT, i.e.:
content and proximity analysis. Content analysis is intended to know the structure of certain feature in the area. Proximity analysis is used to obtain the short
distance to certain feature which elucidates the contiguity relation of an area to its surrounding. These analyses were further developed for obtaining the attribute
information of feature which satisfied the analysis. A list of analysis available in SUISTAT is provided in the Table 4.
Table 4. Several Types of Analysis to Spatial Features Features Type
Analysis Type Detail analysis
The existence of point The number of point
The aggregation level of points Content Analysis
The attribute value of point The short distance value to a point
Point Proximity Analysis
The attribute value of nearest point The length of line feature
The number of segments Content Analysis
The attribute value of line feature The short distance value to a line
Line Proximity Analysis
The attribute value of nearest line The area of polygon feature
The number of polygon Content Analysis
The attribute value of line feature The short distance value to a polygon
Polygon Proximity Analysis
The attribute value of nearest polygon Basically, any type of content analysis is using the same procedure or
algorithm. Specifically, content analysis is used to find the existence, number of features, size of feature dimension such as area for polygon, length for line, and
also the attribute value of considered feature inside or belonged to the evaluated feature. The algorithm of content analysis is illustrated by Figure 8.
The algorithms above were developed using the SearchShape, SearchByDistance
, and other geometric operation methods, which available built-in methods for shape and layer object of MapObject. Further description
on these methods is available in the Appendix 3, 4, and 5.
29 Figure 8. Flowchart of Content Analysis
In contrast to content analysis, neighbor or proximity analysis uses distance function, such as SearchByDistance and DistanceTo. Searching process begin
with gradual distance to the searched layer. When records that containing shape was found, the process to determine the shortest distance among those shapes
begins. Figure 9 shows the algorithm of proximity analysis. Among of all types of content analysis, the exception is given to analysis of
vector-based point aggregation which has different algorithm. The method adopts He et al. 2000 or aggregation index AI, which applied for raster data.
The very basic of AI idea is the relation between the number shared edges of cells in i-th class patch’s cells to its aggregation appearance over the area. The
more clump the cells, the higher the shared edges among the cells. According to He et al. 2000, the maximum aggregation level is reached when the area clumps
into one patch that has the largest e
i;i
it does not have to be a square. Formally, it could be defined as the proportion of shared edges between the patch’s cells
e
i,i
with the maximum possible shared edges max_e
i,i
. The equation of AI of
class i is given by
i i
i i
i
e e
AI
, ,
max_ =
He et al., 2000.
Sum the area of found shape
Search shape inside Evaluated
Shapes ES on variable layer
Search shape crossing ES on
variable layer
Search shape contained by ES
on variable layer Intersect ES with
found shapes Sum the area of
found shapes NO
NO
YES YES
END START
30 Figure 9. Flowchart of Proximity Analysis
Given a class i is composed by A
i
cells and n is the side of largest integer square smaller than A, then the disparity between A and n or m, is equal with A –
n
2
. Afterward, the maximum shared edges in A
i
will take one of the three forms He et al., 2000:
d = initial distance searched
layer Cell grid
distance tolerance
initial distance
d distance tolerance ?
Search records in searched layer by ‘d’
distance upon cell
Get shape from records
Records count 0 ?
Return dshort
Get the distance from shape to cell
d
cell-shape
Next Record
End of Records ?
If dshort d
cell-shape
No Yes
dshort = d
cell-shape
Yes No
Yes No
d = d 2 No
31 1
2 max_
,
− =
n n
e
i i
, when m = 0, or 4.2
1 2
1 2
max_
,
− +
− =
m n
n e
i i
, when m n, or
4.3 2
2 1
2 max_
,
− +
− =
m n
n e
i i
, when m ≥
4.4 Method adjustment is needed for vector-based point aggregation
measurement, since AI is originally developed for raster data. The attention is especially given to the shared edges measurement. It must be noticed that the use
of shared edges in the clumping measurement method obviously needs the determination of cell size. In the vector-based, the virtual cell size is determined
based on the shortest distance among points d
min
. Each point is assumed placed on the center of imaginary squared grid as illusrated in the Figure 10. The grid
has maximum of four eligible adjacent grids, i.e. shared-edge adjacent grids with a point inside white grids, marked by roman capital number. It does not have a
shared edge with the ineligible adjacent grid shaded grid. Hence, the main problem in measuring shared-edge is how to identify that a point is inside the
eligible white grid and ineligible virtual grid. The solution is given by knowing the domain of inelegible and eligible grid. The definition of the domain is simple
since the points are laid in the same coordinate system.
Figure 10. The Illustration of Neighboring Grid of a Point Any point of Zx
z
, y
z
to the center point Ox
o
, y
o
has horizontal and vertical distance as defined as d
x
= | x
o
– x
z
| and d
y
= | y
o
– y
z
|, respectively. Every points which placed surround the center point Figure 10 have distance d
x
≤ 1.5d
min
and
d
min
P x
, y
0.5 d
min
d
min
d
min
IV I
II
III
32 d
y
≤ 1.5 d
min
, where d
min
is the shortest distance among the entire points. The point beyond these distance are overlooked. Any point in the center grid has
distance d
x
≤ 0.5 d
min
and d
y
≤ 0.5 d
min
, whereas every neighboring point to the center grid has a distance d
x
0.5 d
min
and d
y
0.5 d
min
. Hence, points that placed on the eligible grids, i.e. for quadrant I and III have distance:
D
x
= {d
x
| 0 ≤ d
x
≤ 0.5d
min
}
and
D
y
= {d
y
| 0.5d
min
d
y
≤ 1.5d
min
}
4.5 or it can be written as:
D
x
= {d
x
| 0 ≤ | x
o
– x
z
| ≤ 0.5d
min
}
and
D
y
= {d
y
| 0.5d
min
| y
o
– y
z
| ≤ 1.5d
min
}
4.6 Respectively, the distance of points that placed on quadrant II and IV
satisfies:
D
x
= {d
x
| 0.5d
min
d
x
≤ 1.5d
min
}
and
D
y
= {d
y
| 0 ≤ d
y
≤ 0.5d
min
}
4.7 or it can be written as:
D
x
= {d
x
| 0.5d
min
| x
o
– x
z
| ≤ 1.5d
min
}
and
D
y
= {d
y
| 0 ≤ | y
o
– y
z
| ≤ 0.5d
min
}
4.8 The points which laid beyond these distances are overlooked. And to
prevent double counting, after the measurement was taken place, the point is removed. The procedure of point aggregation measurement is described in the
Figure 11.
33 Figure 11. Aggregation Analysis Method Notes: Further Description is
Available in the Text
m n
for i=1 to Ai No
START
n=Int √ Ai
m = Ai – n
2
Max_e
i,i
= 2nn-1 m = 0
m ≥ 0
Max_ei,i = 2nn-1 + 2m - 1 Max_ei,i = 2nn-1 + 2m - 2
Get Points Number Ai
Set Point Collection
Point collection = 0?
next point
satisfied in D
x
and D
y
?
remove point P in
Point Collection Shared_edge =
Shared_edge + 1 Set point
P Set point
Z Get d
x
and d
y
END Ai
i
= Shared_edge Max_ei,i
No
No Yes
Yes Yes
Yes
No Yes
