Volume 9, Issue 1, March 2019, Pages 2 - 10
Geospatial Information Diffusion Technology Supporting by Background Data
Authors
Chongfu Huang1, 2, 3
1Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
2State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
3Faculty of Geographical Science, Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
Received 20 December 2018, Accepted 10 January 2019, Available Online 29 March 2019.
- DOI
- 10.2991/jracr.b.190328.001How to use a DOI?
- Keywords
- geographic unit; background data; information diffusion; normal diffusion; self-learning discrete regression
- Abstract
In this paper, we express the initial concept of geospatial information diffusion supporting by background data, which plays a role as a bridge to diffuse the information carried by the observations, obtained from observed units, to gap units. The self-learning discrete regression, based on the multivariate normal diffusion, is suggested to supplement incomplete geospatial data to be complete. The suggested method has obvious advantages over the geographic weighted regression and the artificial neural network for inferring the observations in gap units
- Copyright
- © 2019, The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Chongfu Huang PY - 2019 DA - 2019/03/29 TI - Geospatial Information Diffusion Technology Supporting by Background Data JO - Journal of Risk Analysis and Crisis Response SP - 2 EP - 10 VL - 9 IS - 1 SN - 2210-8505 UR - https://doi.org/10.2991/jracr.b.190328.001 DO - 10.2991/jracr.b.190328.001 ID - Huang2019 ER -