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point data represents the matured data of field that observed using water checker tool. For interpolation processes of all observed field
data, the kriging features in GIS functionalities were applied to create isohyets map, including salinity, dissolved oxygen, pH of water
characteristic and the rainfall intensity. The thematic maps, which were resulted from field data processing, can be shown in the Appendices 6
to 12. Both primary and secondary data sources and their edition, which
will be used in the GIS raster together with ANN and GIS vector map overlay analysis are described in the Table 3.1.
Table 3.1. Map and data used in the research
No. MapData Edition Sources
1. Water Salinity, DO
and pH 2005
Field survey 2.
Rainfall data 1994-
2004 Agricultural Office of
Kutai Kartanegara Regency
3. Topography
1991 National Coordinating
Agency for Surveys and Mapping Bakosurtanal
4. Soil texture, drainage 2000
Center of Soils and Agro Climate Research
Puslitanak 5.
Land Cover 2001
PT. Total Indonesia 6. Pipeline Distribution
2001 PT. Total Indonesia
7. Spatial Plan Land
Use Planning 2003
Regional Plan and Development Office of
Kutai Kartanegara Regency
3.3.3. Equipment
Equipment for this research includes field survey and laboratory tools. For field survey the global positioning system GPS and water
quality checker were used. GPS was used to determine the position on
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earth’s surface of sample point location to be plotteddrawn on the map. Six water parameters were observed using the ‘water quality
checker’ instrument, including pH, conductivity, turbidity, dissolved oxygen, temperature and salinity.
The equipment for data processing in laboratory includes GIS software such as ArcView GIS 3.3 and ArcGIS®9.0. ArcGIS®9.0 support
both raster and vector spatial data model. Facilities to data converting and map algebra are very adequate in this software package. Microsoft
Visual Basic 6 were used to develop computer program for ANN. The hardware used to process the spatial data is PC Intel Pentium
III with 256 MB of RAM. The storage device is 20 GB of hard disk and movable storage such 250 MB of flash disk. Another external storage
device is CD, flash disk and floppy disk drives.
3.4. Procedures 3.4.1. Spatial Database Preparation
Data preparation is started from scoring each attribute of criteria. Scoring aims at providing a quantity value of each class of attribute of
each layer. Each layer breaks down into certain number classes based on the attribute of data sources. All layer are broken down into four
scales of score. The score is ranked by providing the weight of each criterion and combining multiple sources of evidence. The assignment
of weights to maps is carried out either by analyzing the importance of evidence relatives to the experience or by using subjective judgment of
related corresponding scientist. Based on the criteria from blend of scientist and the availability of
the data and also considering the specific characteristic of the region to be assessed, defining the physical aspect of coastal-land aquaculture
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are presented in the Table 3.2. Table 3.2 describes the requirement of suitability of aquaculture that represents the GIS database layers within
its attributes and scores. Both vector and raster format will be used and applied those requirements. Spatial analysis is conducted with
involving these factors using spatial analysis features in GIS.
Table 3.2. Classification scheme on coastal aquaculture suitability, especially for shrimps and crabs
Suitability and Scores N o. RequirementFactors
HS 4 MoS 3
MaS 2 N S 1
A. 1.
2. 3.
Water Parameters Dissolved oxygen
mgl Salinity ppt
Water pH 5
10–20 7.5–8.5
3–5 20–30
8.5–10, 6–7.5
1–3 30–50,
10 10–11,
4-6 1
50 11,
4 B.
4. Infrastructure
Distance to rivers meter
250-500 500-700
700-900 900
C. 5.
6. Soil Parameters
Soil drainage Soil texture
Very poor 75 fine
Poor 75 medium,
50-75 medium Moderately
poor, good 50-75
coarse, 50 all
Very good 75
coarse
D. 7.
Pollution Risk Distance to pipe line
meter 500
300-400 200-300
100-200 E.
8. N atural Indicator
Mangrove Ecosystem Land Cover
Rhizopora, Avicenia,
Sonneratia Tambak,
Nypa+Rhizopora Mangrove,
Pure Nypa Tidal zone,
degraded forest
F. 9.
Spatial Plan Spatial plan map
Ponds zone -
- -
G. 10.
Climate Rain fall mmyear
2500-3000 2000-2500
1000-2000, 3000-3500
1000, 3500
Note: HS 4 = Highly Suitable, MoS 3 = Moderately Suitable, MaS 2 = Marginally Suitable, NS 1 = Not Suitable
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Source: This table was composed based on referenced of: Hardjowigeno 1996 and Kapetsky and Nath 1997, field survey, interview to petroleum company
authority, fisherman ponds and interview to aquaculture expert 2005.
3.4.2. Mapping Suitability by Vector Analysis
Map overlay for vector data format is conducted to obtain an aggregate of layers that determines the suitability. It includes ten map
layers with its score as described in Tables 3.2. In vector, the map overlay operation is done in pairs. For a more than two layers to be
overlaid, it will be taken several steps. For example, 10-layers amount will be taken 9 steps of ‘Union’ map overlays operation.
Each map layers contains the database and score as prepared in Table 3.2, then overlay step by step as illustrated in Figure 3.3. The
‘Union’ overlay operation will perform the new polygon features based on the origin layers. So, the final layer of Union will contain 10 layers
distinguish features. The final layer of overlay is a map containing the aggregate features of each composer layers, such as attributes and
score. The score are then summed to produce the total sum up of all score. The total score need to be classified into four suitability’s
classes: S1, S2, S3, N.
Layer-1 Layer-2
Layer-3
Layer-5 Layer-4
Layer-6
Layer-10 Layer-9
Layer-8 Layer-7
Union- 1
Unoni- 2
Union- 3
Union- 4
Union- 5
Union- 6
Union- 7
Union- 8
Union- 9
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Figure 3.3. Process of GIS map overlay of vector spatial analysis Suitability will be defined by implementing the parametric
approach, which classified the land based on the grades or value of distinguish certain land and combines it grades to obtain the suitability
Sitorus, 1998. Parametric approach is appropriate for evaluating the land that superimposes the isohyets or isotherm map including the
quantitative variable to produce the aggregate map. Classification of aggregate score is based on addition method
Sitorus, 1998 where each individual each layer will be considered as proportional weight, then classify its score according to the number of
class to be assigned. In this case, the algebraic addition methods will be implemented with the same weight. Summing of the total score and
classification as follows:
Consideration of class-range division and classification involve several factors, such as number of layers, maximum and minimum
layers score and total score. Based on these, the defined maximum and minimum score are 40 and 0, respectively. The most suitable of S1
should be faithfully by 40, S2 will be exactly 30 of total score, S3 should be 20 and N should be 10. However, the total score is very
immeasurable due to those scores that were performed by ten parameters as map overlay result. Because of the range of the total
Total_Score = Layer1_Score + Layer2_Score + Layer3_Score + Layer4_Score + Layer5_Score +
Layer6_Score + Layer7_Score + Layer8_Score +
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scores, four suitability classes can be defined by classifying them as shown in Table 3.3.
Table 3.3. Ranges of class suitability in aggregate layer of vector spatial analysis
No. Range of Total Score
Class Suitability
1. 30 – 40
