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Customizable process design for collaborative geographic analysis

delete2022-06-06
delete19
delete
OA
AI
Z
Zaiyang Ma
M
Min Chen *
S
Songshan Yue
Z
Zhiyi Zhu
B
Beichen Zhang
王
王进 (Jin Wang)
F
Feng‐Yuan Zhang
Y
Yongning Wen
G
Guonian Lü
DOI:10.1080/15481603.2022.2082751delete
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摘要

摘要

En 中文
Collaborative geographic analysis can lead to better outcomes but requires complicated interactions among participants, support resources and analytic tools. A process expression with explicit structure and content can help coordinate and guide these interactions. For different geographic problems, the structure and content of collaborative geographic analysis are generally distinct. Since the process structure embodies the pathway of problem-solving and the process content contains the information flow and internal interactions, both the structure and the content of the process expression must be clarified during process customization. However, relevant studies concerning the collaborative geographic analysis process mainly focus on the process structure, which remains a black box in terms of the process content, especially the internal interactions. Therefore, this article designs a customizable process expression model that takes both process structure and content into account and proposes a corresponding process customization method for collaborative geographic analysis. Additionally, a support method for geographic analysis process implementation is also provided. To verify the feasibility and capability, these methods were implemented in a prototype system, and a case study on traffic noise assessment was conducted. The results suggest that the proposed strategy can effectively improve geographic analysis by customizing processes, guiding participants, performing interactions, and recording operations throughout the process.
Keyword:
Geographic analysis
collaboration
process customization
urban traffic noise assessment
OpenGMS

期刊

GIScience and Remote Sensing 封面图
GIScience and Remote Sensing
IF:
6.9
论文数:
1.1K
被引数:
4.5K

机构

C
chiba university
学者数:
467
论文数: 173
被引数: 0
N
Nanjing Normal University
学者数:
1.3K
论文数: 439
被引数: 0
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