JOIV : Int.
Inform.
Visualization, 8.
: IT for Global Goals: Building a Sustainable Tomorrow - November 2024 1545-1551
INTERNATIONAL JOURNAL
ON INFORMATICS VISUALIZATION
INTERNATIONAL
JOURNAL ON
INFORMATICS
VISUALIZATION
journal homepage : w.
org/index.
php/joiv Attributes Classification for Elaborating the Information of Digital and Imaging Mapping Riki Mukhaiyar a,*.
Utriweni Mukhaiyar b Department of Electrical Engineering.
Universitas Negeri Padang.
Air.
Tawar.
Padang.
Indonesia Institut Teknologi Bandung.
Coblong.
Bandung, 40132.
Indonesia Corresponding author: *riki.
mukhaiyar@ft.
AbstractAi The rapid development of information has made it possible for everyone to obtain the latest information, complete and accurate, in real time, anytime, anywhere, all over the world.
Any information is fine catching up by updated with the latest news and even detailed information on local conditions.
With the same analogy, detailed information regarding land utilization, land containing, landscape provision, earth surface contour, etc.
, are required to inform and elaborate any appropriate decision needed.
The Geographic information systems (GIS) is a recent technology commonly used by research in earth science to facilitate many layered detail information by one way to get up-to-date, detailed information.
In this research, the GIS utilizes several types of imaging data such remote sensing images and digitize images.
As the name suggests, this system captures detailed geographic information about a location or region.
By inputting classified images of remote sensing results into a GIS database at regular intervals .
djusted as necessary, such as every year, every two years, every three years, etc.
), the number of information sources that can be obtained increases.
There are several reasons for that.
First, remote sensing images are images that cover the entire surface of the Earth.
Next, remote sensing images are images that contain information about the state of the earth's surface.
Third, a variety of information can be obtained by performing appropriate image processing.
Furthermore, this research could be elaborated by implementing an artificial intelligent algorithm to create a robust outcome.
KeywordsAi Classification.
image processing.
imaging mapping.
digital mapping.
Manuscript received 11 Oct.
revised 22 Sep.
accepted 21 Oct.
Date of publication 30 Nov.
International Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.
0 International License.
Besides, the spatial data includes thematic information such as soil, water, land, forests, even spatially presented .
socioeconomic data: population density, distribution of economic activities, and so on .
, .
, .
INTRODUCTION
Some of the definitions of GIS have been put forward by In general.
GIS is a computer-based system to store and manipulate geographical information .
, .
, .
Geographic Information Systems are designed to collect, store, and analyses objects and phenomena in a geographical location that are important or critical to be analyzed .
, .
, .
Thus, a Geographic Information System is a computer system that has the following four capabilities in handling geographic reference data: inputting, managing .
ata storing and recallin.
, analyzing and manipulating, and outputting data .
, .
, .
GIS, as a computer-based system, must contain digital data .
, .
, .
More specifically, this digital GIS data is called spatial data.
Spatial data is data with spatial reference .
, .
, .
It means that the measured data has a Geo-referenced or a location reference, whether it works with a standard system .
oordinate system, reference area, or certain projection.
, or a local system .
, .
, .
II.
MATERIALS AND METHOD
There are two components of data taken for GIS: the graphic data component, and the non-graphic data component .
, .
, .
Both data components are in digital form.
The two data components have specific characteristics, and each requires different handling in storing efficiency, processing, and outputting .
, .
, .
Graphical data in GIS is a digital statement of map elements or elements being mapped.
Thus, the map principles defining the elements of cartography must be implemented correctly .
, .
, .
In GIS, graphic data is used in such a way that it can visualize maps or cartographic images on the monitor screen .
oft cop.
, on hard copy paper, or other select-able presenting media .
, .
, .
Graphic data of GIS consists of two digital data formats: vector data format and raster data format.
In the vector data format, the output of objects is in points or line Whereas in the raster data format, all outputs of the objects are in the form of cells called pixels .
, .
Non-graphic nature/characteristics, quality, or spatial relationship between map elements and geographic locations .
, .
Nongraphic data is also called textual data or attribute data .
This data is taken separately from the GIS management system or directly by the system managing the GIS .
The relationship between graphic data and non-graphic data must be maintained in a GIS.
A common way to link between the two is by giving identities (I.
and stored them in the system Identity (I.
is uniquely made, for example in the form of a systematic code number.
One of the GIS functions is to improve the ability of integrated spatial The analysis tools include aggregation, classification, measurement, overlays, buffering, networks, and map algebra .
The GIS analysis function is shown in Figure 1.
A-1 A-2
B-1 B-2
Aggregation Overlays Interme High Low Slope Erode Types of Fig.
2 Data Processing Flowchart in General Not Erode Description:
= 1: 50,000 scale maps that have not been digitized (**) = Table data such as population, pollutant concentration, etc.
(***)
= 1: 50,000 scale maps such as land use maps, river flow pattern maps, etc.
, which have not yet been digitized (*) Erosion Potential with Soil and Classification Measurement i.
RESULTS AND DISCUSSION
The editing process through three ways to edit digitally generated data:
Combining objects, with following stages:
Select an object from the layer as a target.
Select set target to define the target as the only edited Select other objects to be combined.
Both objects are combined with the combine command in the Objects menu bar.
Buffer .
Separating objects:
Select an object from the layer as a target.
Select set target.
Select another object as a reference for separation.
Choose to split from bar Objects menu.
Start Networks Fig.
1 Spatial Analysis in GIS .
Erasing Object:
Select an object from the layer as a target.
Select set target.
Select another object as a reference for separation.
Choose Erase from bar Objects menu.
Before digitizing, it is necessary to establish a coordinate system to be used.
In this study, all digital maps are in the UTM (Universal Transverse Mercato.
coordinate system.
The stages of digitizing using MapInfo software are as follows: Place the map sheet on the digitizing table.
Set the projection to UTM 48 south zone, because the study area uses that projection, inputting the four map control points .
, and digitize the features on the map.
In general, the data processing flowchart in GIS can be seen in Fig.
Inputting the Data Attribute Attribute data .
on-graphic dat.
is a tabular data file that is added for analysis and manipulation purposes.
Besides, it provides information about graphic data.
The types of TABLE i ATTRIBUTES CLASSIFICATION OF WATERSHED FOR ELABORATING THE attribute data are adjusted as the needs of the study.
In this study, the type of attribute data needed are as follows:
C Attribute data of land use.
C Attribute data of Subdistrict administration.
C Attribute data of river basin.
C Attribute data of pollutant measurement point.
C Attribute data of simulation point.
C Attribute data of river.
C Attribute data of the Ciliwung River Basin Spatial Plan Each attribute data classification above is adjusted to the classification in each of the graphic data.
The process of inputting attribute data is done interactively through a INFORMATION Watershed .
Attribute Data of Land Use:
The land use attribute database obtained from the digital land use map is in Table 1.
Explanation:
WATERSHED=Watershed number feature.
AREA = area feature .
Attribute data of pollutant measurement point:
Pollutant measurement point attribute database obtained from the digital map of measurement point 1 by entering temperature measurement data (TEMP) attributes.
BOD pollutant measurement data (BOD_PPM).
COD pollutant (COD_PPM), and NH4 pollutants (NH4_PPM) as in Table 4.
TABLE I
ATTRIBUTES CLASSIFICATION OF LAND USE FOR ELABORATING THE
INFORMATION
NID
L176
L177
L178
L142
L118
L042
L011
L043
L012
L119
L044
Area
Residences Residences Residences Residences Reeds Mixed garden
Residences Forest
Forest
Plantation Bushes TABLE iV
ATTRIBUTES CLASSIFICATION OF POLLUTANT MEASUREMENT FOR
ELABORATING THE INFORMATION
CODE
C-1
C-2
C-3
C-4
C-5
C-6
C-7
C-8
C-9
C-10
C-11
C-12
C-13
C-14
C-15
C-16
Explanation:
NID=feature classification number.
AREA=feature area .
ID=Land use.
Attribute data of subdistrict administration:
In the subdistrict administration attribute database, there is an addition on the population density attribute (KPDT_PDD_JIWA), and the number of population (JMLH_PDD_JIWA) administrative digital map attribute data.
The attributes of the addition can be seen in Table 2.
D-30
TABLE II
ATTRIBUTES CLASSIFICATION OF SUBDISTRICT FOR ELABORATING THE
D-29
INFORMATION
NID
A20
A61
A04
A13
A29
A34
A35
A36
A40
SD_1
SD_2
SD_3
SD_4
SD_5
SD_6
SD_7
SD_8
SD_9
Area
Density Area
24,8912
15,7066
22,0644
46,8946
34,9625
26,5904
6,6883
29,1183
44,9274
14,7069
40,2857
50,8741
D-3
Population D-2A
D-2
D-1
SEG
UPPER
UPPER
UPPER
UPPER
UPPER
HULU
UPPER
UPPER
UPPER
MID
MID
MID
MID
MID
MID
MID
DOWNSTREAM
DOWNSTREAM
DOWNSTREAM
DOWNSTREAM
DOWNSTREAM
DOWNSTREAM
TEMP
BIO
CHEM
AMON
Explanation:
CODE=feature code.
SEG=feature segment on Ciliwung River Basin.
TEMP=temperature measurement .
C).
BIO =BOD
ram/lite.
COD_PPM=COD load .
ram/lite.
AMON =NH4 load .
ram/lite.
Explanation:
NID=feature classification number.
ID=featureAos name.
AREA=feature area .
DENSITY=Population Density (Perso.
POPULATION=Population Number feature (Perso.
Attribute data of simulation point:
The database of the simulation points of the watershed is the attribute data from the digital map of the watershed boundary, as in Table 5.
Attribute data of River Basin:
Database of River Basin attributes, an attribute data from the digital map of watershed boundary, can be seen in Table 3.
TABLE V
ATTRIBUTES CLASSIFICATION OF SIMULATION POINT FOR ELABORATING THE
There are two spatial data in the intersection overlaying operations to obtain watershed information from each feature in the spatial data: land use and sub-district administration.
INFORMATION
Point
Length
P10
P11
TABLE VIIVI
ADDING LAND USE CLASSIFICATION FOR ELABORATING THE INFORMATION
Attribute Data of Rivers Database of river attributes obtained from the digital river map simulation is in Table 6.
NID
SUBD
Area
Percent
L176
Housing L177
Housing L178
Housing L142
Housing L118
Greenland
L042
Mixed Garden
L011
Housing L043 Forestry L012 Forestry L119 Planting L044 Grass Explanation:
NID=feature classification number.
SUB=Watershed number feature.
AREA=area feature .
PERCENT=Percentage of area feature on Watershed.
ID=Land Use.
TABLE VIV
ATTRIBUTES CLASSIFICATION OF RIVERS FOR ELABORATING THE
TABLE VIIX
ADDING ADMINISTRATIVE CLASSIFICATION ATTRIBUTES FOR ELABORATING
Explanation:
POINT=feature code.
LENGTH=feature distance from estuary .
INFORMATION
Cipari THE INFORMATION
Length
NID
Area
Density Population Watershed A20
SD_1
A61
SD_2
A04
SD_3
A13
SD_4
A29
SD_5
A34
SD_6
A35
SD_7
A36
SD_8
A40
SD_9
Explanation:
NID=feature classification number.
ID=feature name.
AREA=feature area .
DENSITY=Population Density (Perso.
POPULATION=Population Number feature (Perso.
WATERSHED=watershed number feature.
Explanation:
ID=feature name.
1-8= River orde.
LENGTH=River Length .
TABLE X
ADDITION OF RIVER CLASSIFICATION ATTRIBUTES FOR ELABORATING THE
Attribute data of the Ciliwung River basin spatial plan:
The database of the attributes of the Ciliwung Watershed spatial plan are from the general plan digital map as in Table INFORMATION Explanation:
ID=feature name.
SD= watershed LENGTH=river length .
TABLE VIV
ATTRIBUTES CLASSIFICATION OF LAND USE FOR ELABORATING THE
NID
Area Mixed Residences / Public Buildings 0,1726 Mixed Residences / Public Buildings 0,0851 Mixed Residences / Public Buildings 0,4189 Mixed Residences / Public Buildings 0,8840 Mixed Residences / Public Buildings 0,6009 Mixed Residences / Public Buildings 0,2998 Green with no buildings 0,7925 Mixed Residences / Public Buildings 0,1970 Mixed Residences / Public Buildings 0,0676 Mixed Residences / Public Buildings 0,0510 Explanation:
NID=feature number.
ID=feature identity.
AREA=feature area.
Length 1-8=river Obtaining watershed information on simulated river spatial data requires union operations.
Geographic Information System Data Analysis The analysis is overlaying and buffering.
Overlaying is done to get information about watershed from each data.
Buffering is done to get to the border of the river.
Operation Cipari INFORMATION Digital Map of Watershed Boundary Digital Map of Subdistrict Administration Intersection Digital Map of Subdistrict River Fig.
3 Overlay Analysis for Getting Information of watershed .
Buffer and union operations to obtain river border Because we want to get the river border information, the spatial data taken for the buffering operation is the simulation Besides, the analysis of river boundary considerations of the general spatial plan is using union operations.
Fig.
7 Digital Map General Plan and Digital Map on River Border Note: Clockwise: Up-Stream.
Middle Up-Stream.
Middle Down-Stream.
Down-Stream Fig.
5 Watershed Boundaries and Digital Map of Simulation River Note: Clockwise: Up-Stream.
Middle Up-Stream.
Middle Down-Stream.
Down-Stream.
Buffering operations for each river order are different, due to the different size of the river.
The larger a river, the greater the influence of the river on the environment damage on either side.
Fig.
8 Digital Map of Union Operation result between Digital Map of River Border and Digital Map of General Plan Note: Clockwise: Up-Stream.
Middle Up-Stream.
Middle Down-Stream.
Down-Stream.
IV.
CONCLUSION
In general, digital image processing and image classification in this study showed relatively very good results.
However, during the classification process, the process of overlaying digital maps with an image map when defining the training site has not been done.
Thus, we cannot decide which technique produces a better classification image.
For further research, other classification techniques need to be done.
Fig.
6 Digital Map of Simulation2 Map and Digital Map of River Border Results of Buffer Operations Note: Clockwise: Up-Stream.
Middle Up-Stream.
Middle Down-Stream.
Down-Stream Therefore, the results can be compared with the results of the techniques applied in this research.
REFERENCES