Sebuah Kajian Pustaka:


IT Journal Research and Development (ITJRD) 

Vol.7, No.2, March 2023, E-ISSN : 2528-4053 | P-ISSN : 2528-4061 

DOI : 10.25299/itjrd.2022.10878      209 
  

Journal homepage: http://journal.uir.ac.id/index/php/ITJRD 

The Comparison of Vector and Raster Data for The 

Calculation of Landscape Environment Using a 

Geographic Information System Approach 
 

 

Eggy Arya Giofandi1, Khursatul Munibah2, Kraugusteeliana3, Amira Novalinda4, Cipta Estri 

Sekarrini5 

Master Program of Regional Planning Science, IPB University1  

Department of Soil and Land Resources, IPB University2 

Departement of Information Systems, Universitas Pembangunan Nasional Veteran Jakarta3 

Master Program of Mathematics Science, Institut Teknologi Bandung4 

Doctoral Program of Geography Education, Universitas Negeri Malang5 

eggyarya@apps.ipb.ac.id1, munibah@apps.ipb.ac.id2, kraugusteeliana@upnvj.ac.id3, 

miranovalia@gmil.com4, ciptaputri123123@gmail.com5 
 

Article Info  ABSTRACT 

Article history: 

Received Nov 04, 2022 

Revised Jan 25, 2023 

Accepted Feb 8, 2023 

 

 GIS-based measurements can combine vector and raster data to 

produce thematic data obtained from remote sensing data. The data 

used in this study uses data on land use in the city of Pekanbaru. 

After the data is obtained, the pixel calculation process is carried out 

using three methods: the cell center method, the maximum area, and 

the maximum combined area. This data describes information with 

multiple raster data resolutions and then interprets the level of 

distortion in the data. The research findings found that in the process 

of raster data from 8 different resolution levels for the 5x5 meter 

category, it is able to provide results that are closest to the area of 

vector data, where PL 1 code produces 404229 pixels, PL 2 code 

ranges from 225717 pixels, PL 3 code ranges from 160323 pixels, 

code PL 4 ranges from 92268 pixels, PL 5 code reaches 73384 pixels, 

PL 6 code reaches 57237 pixels, and PL 7 code reaches 48315 pixels. 

Meanwhile, of the 3 methods that were compared to determine 

distortion with vector data, the cell center approach was the closest to 

raster data by calculation through eight levels of raster resolution 

compared to the other two methods. In choosing the right pixel 

resolution for further use in mathematical modeling, it is necessary to 

pay attention to the level of resolution by generalizing the resolution 

of satellite imagery data so that the data can have the same resolution. 

The weakness of the three methods lies in increasing the resolution 

the greater it will make the data coarser. This research is expected to 

be used as a consideration in future research to add a more precise 

process and be able to produce less storage capacity. 

Keyword: 

Comparison 

Multi-resolution 

GIS approach 

 

© This work is licensed under a Creative Commons Attribution-

ShareAlike 4.0 International License. 

Corresponding Author: 

Eggy Arya Giofandi 

Master Program of Regional Planning Science, Graduate School 

IPB University 

IPB Darmaga Campus, Bogor, West Java, Indonesia 

Email: eggyarya@apps.ipb.ac.id 



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The Comparison of Vector and Raster Data for the Calculation of Landscape Environment Using a 

Geographic Information System Approach, Eggy Arya Giofandi 

210 

 

1. INTRODUCTION 

Vector data is what most people think of when they think about spatial data[1][2]. Data in 

this format consists of points, lines, or polygons [3]. At the simplest level, vector data consists of 

individual points stored as coordinate pairs that represent physical locations in the world [4][5][6]. 

The points can be connected in any order to form a line, or combined into a closed area to form a 

polygon [7][8]. Vector data is useful for storing and representing data with discrete boundaries, 

such as building boundaries or footprints, roads and other traffic routes, and point locations [9]. 

Whereas, raster data has represented the world as a surface which is divided into squares or regular 

grid cells, where each cell has an associated value [10]. Dealing with each cell, it is mentioned as a 

pixel, corresponds to a specific color value [11]. When transferred into a GIS setting, cells in a 

raster grid can represent other data values, such as temperature, precipitation, or altitude  [12][13]. 

The main difference between a digital photo and a GIS representation is that in a GIS, the 

following data details where the earth's cells can be found and the size that has been found [14]. 

One of the acquisitions of raster data is by satellite imagery which provides a perfect 

background example of this GIS image and setup [15]. Simply put, a satellite image can be thought 
of as a digital photograph of the world's surface, with each cell showing a specific color [16]. 

However, when it emerges geographic context, we can set it correctly on Earth and then analyze 

cell values which can be thought of as color wavelength bands [17]. Raster datasets can be used to 

store a continuous representation of data, such as weather events and climate or surface elevation 

forecasts [18]. In this context, the grid representation is more closely related to the representation 

of physical properties or phenomena found in the world [19]. 

In the two forms of data comparison, there are certain advantages and disadvantages to use. 

Vector data is considered a more traditional method of cartographic representation, which provides 

sharp, clear and scalable representations [20]. It is also more interesting visually. Data is stored 

without loss or generalization and retains accurate geolocation information [21]. Besides, some 

attributes and data fields can be stored in feature vectors. However, the processing and storage of 

vector data can be intensive [22]. Since vector data is stored as a series of dots, each item needs to 

be stored and registered. In addition, each point or set of points can have associated data that can be 

added to the vector storage and processing time [23]. Topological representations can be useful in 

vector data, but can often be processed intensively. Depending on the size and number of features, 

editing and processing of vector data in topological or other formats. 

 

2. RESEARCH METHOD 

 

 
Figure 1. Research location 

 



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The Comparison of Vector and Raster Data for the Calculation of Landscape Environment Using a 

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The calculation of vector - raster - vector values used land use data of Pekanbaru in 2021. 

This data was obtained from object-based analysis calculations that took into account the values 

between pixels. SPOT 7 imagery was used to create land use data with retangle boundaries, this 

was used to produce more detailed information with a total area of 2569 hectares smaller than the 

administrative boundary of Pekanbaru City. Whereas, the total area of Pekanbaru city has an area 

of 402.32 Km2, with a total population of 983,356 people in the 2020 population census [24]. After 

the remote sensing data was processed, the data entered the vector analysis section into three 

stages, namely analysis of points, lines, and areas. 

 
Figure 2. The Calculation model of pixel polygon method level 

Source: ESRI, 2022 [25] 

 

The area analysis stage used three methods (cell center, maximum area, and maximum 

combined area) with a resolution range of 5 meters, 10 meters, 30 meters, 50 meters, 70 meters, 

100 meters, 130 meters, and 150 meters. The land use classes used in this study were categorized as 

(1) built up land, (2) shrubs, (3) mixed gardens, (4) open land, (5) plantations, (6) fields/moorlands, 

and (7) forest. If it is assumed that the coordinates of each polygon (P) are (x1,y1), (x2, 

y2),…,(xn,yn). Then the polygon for the use of the three methods can be calculated using the 

formula [26] as follows. 

 

             (1) 

 

 

3. RESULTS  

One of the approaches used in polygon to raster analysis at this stage is by using the cell 

center method, where the cell center implies information in the middle of the polygon and if the 

land use is around the pixel cell center, then what is categorized in one land use unit is the one 

whose position is located in the middle side. The next method is the maximum area method, in 

which it produces information that dominates the polygon and if the land use does not refer to the 

polygon that hits the center of the pixel, then what is categorized in one land use unit is the position 

where more information appears in one pixel, and the last method is the maximum combined area 

method and it is obtained from the combined process between the dominance of polygon 

information and the middle of the data. This method is a combination of the maximum area and cell 

center methods, so what is categorized in one land use unit is the one whose position appears more 

and also the center of the information is in one pixel. The results of the calculation of the cell center 

method, maximum area, and maximum combined area at a resolution of 5 meters to 150 meters are 

shown in the following figure. 

 



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The Comparison of Vector and Raster Data for the Calculation of Landscape Environment Using a 

Geographic Information System Approach, Eggy Arya Giofandi 

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(a) Polygon to raster of cell center method 

 
(b) Polygon to raster of  maximum area method 

 
(c) Polygon to raster of maximum combined area method 

Figure 3. The pixel change of (a) cell center, (b) maximum area, and (c) maximum combined area 

method 

 

The value of the change detection indicator for all pixels is calculated by the formula [27], 

further, the results can be created a map of the magnitude of change and a statistical histogram as 



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The Comparison of Vector and Raster Data for the Calculation of Landscape Environment Using a 

Geographic Information System Approach, Eggy Arya Giofandi 

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shown in Figure 3. produces values that undergo generalization of vector information. The vector 

land use class that has a more dominant area will have an influence on other land use classes. 

Where land use codes 6 and 7 are eliminated in the data change section to raster at a smaller 

resolution level (100, 130, 150 meters). Furthermore, there is no significant difference between the 

raster methods for all types of land cover at the 5 meter resolution level. 

This condition can be seen between the vector and the three conversion methods to raster 

so as to produce a difference in value close to the value contained in the vector data, although there 

is no significant difference, but in terms of the value of the cell center method, it produces an area 

value that is closest to the vector data. When the conversion algorithm is different, the important 

point for implementing spatial operations lies in the level of output used in both fine and coarse. In 

appearance, Figure 3 has a fairly small difference from the three methods where the application of 

the algorithm becomes the main key determinant in causing quite different results. These area and 

pixel changes values shown in (table 1) with the assignment distance changes applied by the rules 

in vector to raster conversion can result in large differences in the output data set, even with the 

same input vector data. 

 

Table 1. Pixel values and changes in resolution level area with three methods 

Number of Pixel and Large based on the pixel resolution level of call center method 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
 PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 404229 1010.57   1 2030 994.7 

  2 225717 564.29   2 1172 574.28 

Resolution 3 160323 400.8 Resolution 3 827 405.23 

5 meters 4 92268 230.67 70 4 469 229.81 

  5 73384 183.46 meters 5 379 185.71 

  6 57237 143.09   6 298 146.02 

  7 48315 120.78   7 242 118.58 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
 PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 100980 1009.8   1 1025 1025 

  2 56419 564.19   2 567 567 

Resolution 3 40100 401 Resolution 3 398 398 

10 meters 4 23056 230.56 100 4 228 228 

  5 18378 183.78 meters 5 188 188 

  6 14302 143.02   6 149 149 

  7 12107 121.07   7 120 120 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
 PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 11298 1016.82   1 613 1035.97 

  2 6257 563.13   2 322 544.18 

Resolution 

of  
3 4444 399.96 

Resolution 

of  
3 239 403.91 

30 4 2537 228.33 130 4 144 243.36 

meters 5 2047 184.23 meters 5 101 170.69 

  6 1597 143.73   6 89 150.41 

  7 1354 121.86   7 76 128.44 



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The Comparison of Vector and Raster Data for the Calculation of Landscape Environment Using a 

Geographic Information System Approach, Eggy Arya Giofandi 

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PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
 PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 4102 1025.5   1 479 1077.75 

Resolution 2 2226 556.5 Resolution 2 236 531 

50 3 1597 399.25 150 3 178 400.5 

meters 4 905 226.25 meters 4 102 229.5 

  5 748 187   5 72 162 

  6 566 141.5   6 66 148.5 

  7 488 122   7 49 110.25 

Number of Pixel and Large based on pixel resolution level of maximum area method 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
 PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 404143 1010.36  1 2026 992.74 

  2 225730 564.33  2 1180 578.2 

Resolution 3 160347 400.87 Resolution 3 824 403.76 

5 meters 4 92288 230.72 70 4 434 212.66 

  5 73387 183.47 meters 5 378 185.22 

  6 57242 143.11  6 299 146.51 

  7 48319 120.8   7 265 129.85 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
 PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 100856 1008.56  1 1028 1028 

  2 56451 564.51  2 565 565 

Resolution 3 40118 401.18 Resolution 3 392 392 

10 4 23076 230.76 100 4 207 207 

 meters 5 18382 183.82 meters 5 182 182 

  6 14312 143.12  6 150 150 

  7 12113 121.13   7 134 134 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
 PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 11174 1005.66  1 617 1042.73 

  2 6323 569.07  2 334 564.46 

Resolution 

of  
3 4471 402.39 

Resolution 

of  
3 230 388.7 

30 4 2549 229.41 130 4 105 177.45 

meters 5 2054 184.86 meters 5 94 158.86 

  6 1602 144.18  6 86 145.34 

  7 1367 123.03  7 86 145.34 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
 PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 3980 995   1 468 1053 

Resolution 2 2335 583.75 Resolution 2 254 571.5 

50 3 1612 403 150 3 181 407.25 



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The Comparison of Vector and Raster Data for the Calculation of Landscape Environment Using a 

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meters 4 886 221.5 meters 4 76 171 

  5 754 188.5   5 80 180 

  6 580 145   6 68 153 

  7 491 122.75   7 57 128.25 

Number of Pixel and Large based on pixel resolution level of maximum combined area 

method 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
  

PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 404141 1010.35   1 2049 1004.01 

  2 225732 564.33   2 1185 580.65 

Resolution 3 160350 400.88 Resolution 3 819 401.31 

5 meters 4 92289 230.72 70 4 413 202.37 

  5 73386 183.47 meters 5 376 184.24 

  6 57241 143.1   6 298 146.02 

  7 48318 120.8   7 267 130.83 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
  

PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 100859 1008.59   1 1032 1032 

  2 56458 564.58   2 566 566 

Resolution 3 40116 401.16 Resolution 3 394 394 

10 4 23077 230.77 100 4 203 203 

 meters 5 18382 183.82 meters 5 178 178 

  6 14311 143.11   6 150 150 

  7 12113 121.13   7 135 135 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
 PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 11191 1007.19   1 624 1054.56 

  2 6319 568.71   2 334 564.46 

Resolution 

of  
3 4469 402.21 

Resolution 

of  
3 230 388.7 

30 4 2540 228.6 130 4 120 202.8 

meters 5 2053 184.77 meters 5 106 179.14 

  6 1601 144.09   6 94 158.86 

  7 1367 123.03   7 83 140.27 

  
PL 

Code 

Number 

of Pixel 

Large 

(Ha) 
 PL 

Code 

Number 

of Pixel 

Large 

(Ha) 

  1 3991 997.75   1 470 1057.5 

Resolution 2 2339 584.75 Resolution 2 251 564.75 

50 3 1607 401.75 150 3 180 405 

meters 4 877 219.25 meters 4 79 177.75 

  5 753 188.25   5 78 175.5 

  6 579 144.75   6 69 155.25 

  7 492 123   7 57 128.25 

 



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It often raises doubts in the selection of the best method priority. The empirical tests of 

previous research are useful guides for determining the best method with spatial data regarding the 

problem of data sets or data conversion. Where (Table 1) groups the various levels of pixel 

resolution from the use of three different methods. From these findings, it is found that the 

application of raster resolution at 5x5 meters is a value that better describes information on the 

earth's surface, conditions in the use of geographic spatial data cannot be separated from remote 

sensing data. Therefore, in selecting the raster data resolution level, it is necessary to adjust the 

value of the remote sensing pixel resolution. If the use of geographic data uses medium resolution 

satellite imagery, the vector data used in additional information requires the data conversion 

operation process to use a resolution of 30 to 50 meters. Even though in table 1 the distortion level 

of the raster area value is greater, it is often a tolerant value in modeling geographic phenomena. 

The study of the discussion of converting vector data into raster data is still a consideration 

in selecting the right data conversion method to express land use at a representative level in more 

detail. The appearance of changes in the area of vector data in raster data format in this study can 

be seen in (Figure 4), where from the three methods used, the distortion values differ in reaching 
the wide limits of vector data. Conversion to vector polygons usually identifies continuously 

connected cells forming a grid of cells that will become polygons. The grid area value based on 

land use class obtained from multiple raster resolutions with different methods (figure 4) informs 

that the maximum area method produces the highest level of distortion compared to other methods, 

this can be seen at a resolution level of 50 meters. And for the conversion method that is closer or 

the level of distortion is small, it lies in the cell center method with the overall appearance of the 

various resolution levels leaning closer to the line graph of the vector data. The value of the 

distortion level of the three methods (figure 4) can be part of developing a method for converting 

data to raster by looking at the size of the data and understanding the significant changes that occur 

when the grid cell size is converted to a larger one. 

 

 
(a)            (b)          (c) 

Figure 4. The graph of large change based on pixel resolution level of (a) cell center, (b) maximum 

area, and (c) maximum combined area method 
 

4. DISCUSSION 

The findings indicated that the use of polygon has been converted to raster data results in a 
more visually interesting interpretation, however, in selecting the level of resolution one must still 

consider the lowest value of distortion so that data becomes more interesting and still pay attention 

to the true area of the vector data. When it is viewed from data storage, vector data is relatively 

more economical in terms of file size and precision in location, however, it is very difficult to use 

in mathematical computations. In contrast, raster data usually requires larger file storage space and 

lower location precision, but is easier to use mathematically [28]. 

Raster data is well suitable for mathematical modeling and analysis. It is due to a raster 

surface has represented a single attribute or value, calculations, algorithms, and quantitative 

processing can be performed very quickly and easily. Grid areas are great for displaying and 

storing continuous values [29]. Raster memory can also be small because of the way geographic 



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The Comparison of Vector and Raster Data for the Calculation of Landscape Environment Using a 

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locations are processed and stored [30]. The coordinates of each cell are taken from their position 

on the grid, it also requires at least a starting point such as the top left corner, grid cell size, number 

of rows and columns, and more. This condition places the grid surface into a map or globe 

projection and provides the correct size and appearance [31].  

Each data format has advantages and disadvantages. The choice of the data format used is 

highly dependent on the intended use, the available data, the volume of data generated, the desired 

accuracy, and the ease of analysis. The smaller the size of the earth's surface represented by a 

single cell, the higher the resolution. Raster data is very good for representing gradually changing 

boundaries, such as soil type, soil moisture, vegetation, soil temperature, and so on [32]. The main 

drawback of raster data is the large file size; The higher the grid resolution, the bigger the file size.  

 

5. CONCLUSION  

Changing or converting vector data into a GIS-based raster is carried out using the cell 

center, maximum area, and maximum combined area methods to produce area values that are 

different from each other. A comparison of distortion between vector data and the results in raster 

data results in the cell center method having the smallest difference compared to other conversion 

methods to raster data. This difference has a significant influence in the next stage of mathematical 

modeling, so as to reduce the bias for modeling landscape planning in the future. While the 

difference in area using data comparison through five different pixel resolution levels explains that 

the five-meter resolution level is able to produce a hectare area value close to the area in vector 

data, This can be used to better represent spatial data and produce accuracy close to that of data 

collected in the field. 

 

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The Comparison of Vector and Raster Data for the Calculation of Landscape Environment Using a 

Geographic Information System Approach, Eggy Arya Giofandi 

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