24 Papers
67 Citations
Jun Xu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Spatial analysis & Spatial relation. The author has an hindex of 11, co-authored 22 publications.
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Papers
COVID-19: Challenges to GIS with Big Data
Chenghu Zhou,Fenzhen Su,Tao Pei,Tao Pei,An Zhang,Yunyan Du,Bin Luo,Zhidong Cao,Juanle Wang,Wen Yuan,Yunqiang Zhu,Ci Song,Jie Chen,Jun Xu,Fujia Li,Ting Ma,Lili Jiang,Fengqin Yan,Jiawei Yi,Yunfeng Hu,Yilan Liao,Han Xiao +21 more
- 01 Mar 2020
TL;DR: The development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GISShould be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.
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Finding community structure in spatially constrained complex networks
Yu Chen,Jun Xu,Minzheng Xu +2 more
TL;DR: This article proposes a geo-distance-based method of detecting communities in spatially constrained networks to identify communities that are both highly topologically connected and spatially clustered, based on the fast modularity maximisation (CNM) algorithm.
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Formalizing natural-language spatial relations between linear objects with topological and metric properties
TL;DR: A series of quantitative indices that are related to natural‐language spatial relation terms are defined, and this paper uses these indices to formalize the ambiguous natural‐ language representation with a decision‐tree algorithm.
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GIScience and remote sensing in natural resource and environmental research: Status quo and future perspectives
Tao Pei,Jun Xu,Yu Liu,Xin Huang,Liqiang Zhang,Weihua Dong,Cheng-Zhi Qin,Ci Song,Jianya Gong,Chenghu Zhou +9 more
- 01 Sep 2021
TL;DR: In this article, the role of remote sensing and GIScience in the fields of natural resources and environmental science in this new information era is discussed and the authors provide forecasts of ten future directions for GIS, and eight future direction for remote sensing, which aim to solve issues related to natural resource and the environment.
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Sensing multiple semantics of urban space from crowdsourcing positioning data
TL;DR: The dynamic semantics of urban spaces are extracted through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data using a high-order decomposition method, tensor factorization, to explore the crowdsourcing positioning data.
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