• Journal of Korea Spatial Information System Society
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• v.6 no.2 s.12
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• pp.15-22
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• 2004

Map services for cellular phone have problem for implementation, which are the limitation of a screen size. To effectively represent map data on screen of celluar phone, it need a process which translate a detailed map data into less detailed data using map generalization, and it should manipulate zoom in out quickly by leveling the generalized data. However, current spatial indexing methods supporting map generalization do not support all map generalization operations. In this paper, We propose a leveled spatial indexing method, LMG-tree, supporting map generalization and presents the results of performance evaluation.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.19-31
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• 2022

In Machine learning, we usually divide the entire data into training data and test data, train the model using training data, and use test data to determine the accuracy and generalization performance of the model. In the case of models with low generalization performance, the prediction accuracy of newly data is significantly reduced, and the model is said to be overfit. This study is about a method of generating training data based on central limit theorem and combining it with existed training data to increase normality and using this data to train models and increase generalization performance. To this, data were generated using sample mean and standard deviation for each feature of the data by utilizing the characteristic of central limit theorem, and new training data was constructed by combining them with existed training data. To determine the degree of increase in normality, the Kolmogorov-Smirnov normality test was conducted, and it was confirmed that the new training data showed increased normality compared to the existed data. Generalization performance was measured through differences in prediction accuracy for training data and test data. As a result of measuring the degree of increase in generalization performance by applying this to K-Nearest Neighbors (KNN), Logistic Regression, and Linear Discriminant Analysis (LDA), it was confirmed that generalization performance was improved for KNN, a non-parametric technique, and LDA, which assumes normality between model building.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.367-376
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• 2001

For the electronic map service by wireless, the existing map database cannot be used directly. This is because, the data volume of a map is too big to transfer by wireless and although the map is transferred successfully, the devices to display the map usually don’t have enough resources as the ones for desktop computers. It is also not acceptable to construct map database for the exclusive use of wireless service because of the vast cost. We propose new technique to generate a map for wireless service dynamically, from the existing map database. This technique includes the generalization method to reduce the map data volume and filtering method to guarantee that the data volume don’t exceed the limit of bandwidth. The generalization is performed in 3 steps :ㅁ step of merging the layers, a step of reducing the size of spatial objects, and a step of processing user interface. The filtering is performed by 2 module, counter and selector module. The counter module checks whether the data blume of generated map by generalization, exceeds the bandwidth limit. The selector module eliminates the excess objects and selects the rest, on the basis of distance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.91-97
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• 2019

In automatic map generalization, the formalization of cartographic principles is important. This study proposes and evaluates the selection method for road network generalization that analyzes existing maps using reverse engineering and formalizes the selection rules for the road network. Existing maps with a 1:5,000 scale and a 1:25,000 scale are compared, and the criteria for selection of the road network data and the relative importance of each network object are determined and analyzed using $T{\ddot{o}}pfer^{\prime}s$ Radical Law as well as the logistic regression model. The selection model derived from the analysis result is applied to the test data, and road network data for the 1:25,000 scale map are generated from the digital topographic map on a 1:5,000 scale. The selected road network is compared with the existing road network data on the 1:25,000 scale for a qualitative and quantitative evaluation. The result indicates that more than 80% of road objects are matched to existing data.

• Journal of Korea Spatial Information System Society
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• v.4 no.1 s.7
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• pp.5-14
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• 2002

The generalization of geospatial data is an important way in deriving a new database from an original one. The generalization of a geospatial object changes not only its geometric and aspatial attributes but also results in propagation to other objects along their relationship. We call it generalization propagation of geospatial databases. Without proper handling of the propagation, it brings about an inconsistent database or loss of semantics. Nevertheless, previous studies in the generalization have focused on the derivation of an object by isolating it from others. And they have proposed a set of generalization operators, which were intended to change the geometric and aspatial attributes of an object. In this paper we extend the definition of generalization operators to cover the propagation from an object to others. In order to capture the propagation, we discover a set of rules or constraints that must be taken into account during generalization procedure. Each generalization operator with constraints is expressed in relational algebra and it can be converted to SQL statements with ease. A prototype system was developed to verify the correctness of extended operators.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2786-2796
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• 1998

Extraction of interesting and general knowledge from large spatial database is an important task in the development of geographical information system and knowledge-base systems. In this paper, we propose a spatial data mining system using generalization method; In this system, we extend an existing generalization mining and design a rulebase to support deriving new spatial knowledge. For this purpose, we propose an interleaved method which integrates spatial data dominated and nonspatial data dominated mining and construct a rulebase to extract topological relationship between spatial objects.

• Spatial Information Research
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• v.6 no.1
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• pp.11-23
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• 1998

Map generalization functions to visualize the spatial data or to change their scale by changing the level of details of data. Until recently, the studies on map generalization have concentrated more on line features than on point features. However, point features are one of the essential components of digital maps and cannnot be ignored because of the great amount of information they carry. This study, therefore, aimed to find out a detailed procedure of point features' generalization. Particularly, this work chose the distribution pattern of point features as the most important factor in the point generalization in investigating the geometric characteristics of source data. First, it attempted to find out the characteristics of distribution pattern of point features through quadrat analysis with Grieg-Smith method and nearest-neighbour analysis. It then generalized point features through the generalization threshold which did not alter the characteristics of distribution pattern and the removal of redudant point feautres. Therefore, the generalization procedure of point features provided by this work maintained the geometric characteristics as much as possible.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.543-554
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• 2002

GIS (Geographic Information Systems) need faster access and better visualization. For faster access and better visualization in GIS, map generalization and levels of detail are needed. Existing spatial indexing methods do not support map generalization. Also, a few existing spatial indexing methods supporting map generalization do not support ail map generalization operations. We propose a new index structure, i.e. the LR-tree, supporting ail map generalization operations. This paper presents algorithms for the searching and updating the LR-tree and the results of performance evaluation. Our index structure works better than other spatial indexing methods for map generalization.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.225-237
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• 2012

This study proposes a quantification algorithm for a PLS method with several sets of variables. We called the quantification method for PLS with more than 2 sets of data a generalization. The basis of the quantification for PLS method is singular value decomposition. To derive the form of singular value decomposition in the data with more than 2 sets more easily, we used the constraint, $a^ta+b^tb+c^tc=3$ not $a^ta=1$, $b^tb=1$, and $c^tc=1$, for instance, in the case of 3 data sets. However, to prove that there is no difference, we showed it by the use of 2 data sets case because it is very complicate to prove with 3 data sets. The keys of the study are how to form the singular value decomposition and how to get the coordinates for the plots of variables and observations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.14 no.1
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• pp.27-38
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• 1996

Non-scale digital map have important role in the field of GIS and other application area which using geographical data in recently against conventional map restricted by scale and information. The main objective of this study is to develope the automated map production system for small scale map in conjuction with generalization techniques in a digital environment. We will intend to develope algorithms and programs for each generalization operators based on specific terrain feature with vector data. This study will be performed aspects related to an data model development of generalization process, focussing on priority for processing sequency with maintaining vector topology, and error analysis for generalized digital data.