An area merging method in map generalization considering typical characteristics of structured geographic objects

2021 ◽  
Vol 48 (3) ◽  
pp. 210-224
Author(s):  
Chengming Li ◽  
Yong Yin ◽  
Pengda Wu ◽  
Wei Wu
Author(s):  
P. D. Wu ◽  
Y. Yin ◽  
C. M. Li

Abstract. Merging is an important operation for the generalization of land-cover data. However, current research often entails merging on a global perspective, which is not conducive to capturing the spatial characteristics of geographic objects with significant spatial structures, i.e., structured geographic objects. As such, this paper proposes an area merging method that can maintain the boundary characteristics of the structured geographic objects. First, we identify the structured geographic objects based on the description parameters of the spatial structure. Second, a Miter-type buffer transformation is introduced to extract the boundary of each structured geographic object, and area elements inside the boundary are processed with corresponding merging operations. Finally, the boundary of the structured geographic objects and the merging result of the area elements are inserted back into the aggregated result of the original land-cover data using the NOT operation. The proposed approach is experimentally validated using geographical condition census data for a city in southern China. The experimental validation indicates that the proposed approach not only reasonably identify the typical characteristics of structured geographic objects but also effectively maintains the boundary characteristics of these objects.


2013 ◽  
Vol 15 (5) ◽  
pp. 649
Author(s):  
Changbin WU ◽  
Zaihong SUN ◽  
Weifeng QIAO ◽  
Guonian LV
Keyword(s):  
Land Use ◽  

Author(s):  
Man Tianxing ◽  
Nataly Zhukova ◽  
Alexander Vodyaho ◽  
Tin Tun Aung

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.


1964 ◽  
Author(s):  
W. E. Grabau ◽  
E. E. Addor
Keyword(s):  

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