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@article{1421878,
author = {Zhou, Jiemin and Shen, Jie and Yang, Shuai and Yu, Zhenguo and Staněk, Karel and Štampach, Radim},
article_location = {Basel},
article_number = {7},
doi = {http://dx.doi.org/10.3390/ijgi7070235},
keywords = {cloud platform; point generalization; generalization constraints},
language = {eng},
issn = {2220-9964},
journal = {ISPRS International Journal of Geo-Information},
title = {Method of Constructing Point Generalization Constraints Based on the Cloud Platform},
url = {http://www.mdpi.com/2220-9964/7/7/235},
volume = {7},
year = {2018}
}
TY - JOUR
ID - 1421878
AU - Zhou, Jiemin - Shen, Jie - Yang, Shuai - Yu, Zhenguo - Staněk, Karel - Štampach, Radim
PY - 2018
TI - Method of Constructing Point Generalization Constraints Based on the Cloud Platform
JF - ISPRS International Journal of Geo-Information
VL - 7
IS - 7
SP - 1-18
EP - 1-18
PB - MDPI
SN - 22209964
KW - cloud platform
KW - point generalization
KW - generalization constraints
UR - http://www.mdpi.com/2220-9964/7/7/235
L2 - http://www.mdpi.com/2220-9964/7/7/235
N2 - As an important part of spatial data, the point feature has always been an essential element in web maps and navigation maps. With the development of location-based services and the rapid increase in volunteered geographic information and social media data, the amount of point data is increasing day by day, resulting in inevitable problems of overlay and congestion during visualization. Map generalization provides multiple algorithms that can be used to select, aggregate and make typification of points or point clusters. For the generalization of point data, however, the traditional stand-alone computing environment has difficulty with real-time realization. Currently, the rapid development of cloud computing technology provides a powerful support for improving the efficiency of map generalization. However, compared with the stand-alone environment, the data decomposition and the real-time display of point generalization in the cloud platform imposes higher requirements on the point generalization constraints, which play an important role in point-generalized process control. Based on the computational characteristics of the cloud platform, this paper analyzes the changes in point generalization constraints. In addition, our work proposes the constraints of point generalization based on the cloud platform and its construction method, builds a prototype system based on the Hadoop cloud platform. Our prototype system is tested using typical experimental data. Its efficiency and the quality of its results is examined. The results show that the efficiency and quality of point selection can be significantly improved by controlling the point generalization process with the generalization constraints in the cloud computing environment proposed in this paper. This paper provides a possible way for the realization of map generalization in the cloud computing environment. Its usability with real data and with many users accessing it will be the focus of further research.
ER -
ZHOU, Jiemin, Jie SHEN, Shuai YANG, Zhenguo YU, Karel STANĚK and Radim ŠTAMPACH. Method of Constructing Point Generalization Constraints Based on the Cloud Platform. \textit{ISPRS International Journal of Geo-Information}. Basel: MDPI, 2018, vol.~7, No~7, p.~1-18. ISSN~2220-9964. Available from: https://dx.doi.org/10.3390/ijgi7070235.
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