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Figure 1b. Profile benchmarks
and sample points
3.2. Soil survey database design
Developing a database for soil survey results are required to define the data types. Data
types are playing an important role for mapping in Geographical Information System softwares. The
data types and the parameters of the soil survey are given both soil benchmark profile and laborato-
ry analyzes results in Table 1, 2, 3.
Table 1
Profile Attributes
Table 2
Sample Point Attributes
Parameter Name
Data type
Sample Point Number Integer
Sample Point X Coor. Double
Sample Point Y Coor. Double
Depth
Integer
pH
Double
Salt
Double
Lime
Double
Sand
Double
Silt
Double
Clay
Double
Structure
String
Ca
Double
Mg
Double
Na
Double
Parameter Name Data type
Profile Number
Integer
Profile X Coor.
Double
Profile Y Coor.
Double
Profile Location
String
Date
Date
Project Number
Integer
Alkalinity
Integer
Boron
Double
Drainage
Double
Stoniness
Double
Usage Status
Double
Rocky
Double
Slope
Double
Erosion
Double
Dry color
Double
Wet color
Double
Table 3
Profile Laboratory Attributes
Parameter Name Data type Parameter Name
Data
type
Profile Number
Integer
Cr
Double
Profile X Coor.
Double
Cu
Double
Profile Y Coor.
Double
Ni
Double
Profile Location
String
Zn
Double
Date
Date
Hg
Double
Project Number
Integer
Pb
Double
Laboratory number Integer
Cd
Double
Depth
Double
Cr
Double
Organik
Material Double
Water saturation
Double
Azote
Double
CACO
3
Double
Organic Carbon
Double
Salt
Double
Inorganic Carbon
Double
Sand
Double
K
Double
Clay
Double
P
Double
Silt
Double
Pb
Double
Ca+Mg
Double
Cd
Double
Active CACO
3
Double
Soil survey, database and mapping are related fields for assessment of land and planning of
land use. Thus, soil survey results are required a combination of database and mapping technolo-
gies. Geographical Information systems are providing effective tools to realize the combination of
data, database, analyze and mapping techniques. Using soil survey data in land consolidation re-
quires
feature type, attribute table and relationship between these data types. Relationships are
providing visualization of soil survey data and queries. In Figure 2, feature types, attribute tables
and relationships are given.
3.3. Visualizing soil survey data
Visualization of soil survey data are required a relationship between vector and attribute da-
ta. Because of soil survey data are being measured at specific points, the measured values are repre-
sented as point vector with a (X,Y) coordinate. The visualization of coordinates and
attribute table
is given in Fig. 3.
Fig. 3. Visualization of coordinates
At this point, soil survey data are representing the attribute data of the points. For mapping
purposes, data type of the attribute data is important to realize numerical analysis. Data types are
usually being specified when importing the soil survey data to prevent mistakes and data lose. Thus,
conversion process is a vital part of constituting databases. In GIS softwares, there are several tools
to convert attribute data to any format as like
dbf, xml, database table and
xls. Attribute data for-
mats can be used according to the software and database type.
X: 38,651 N
Y: 32,129 E
Attribute
Data
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77
Fig. 2. Sample database design and relationships between feature class and attribute table
Databases are the core object of the GIS systems thanks to the data storage, data manage-
ment, accessibility via web and supported wide range of data types. Databases are being called in
GIS concept as Geodatabase which is including geographic value both raster and vector format and
attribute data inside. Geodatabase constitution can be realized with GIS software tools or any data-
base management software as like Access, MySQL and Oracle. GIS softwares are including data-
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