Introduction to Volume 9, Issue 1 This issue contains 6 papers, written in Chinese with English abstracts. The papers cover four topics related to risk analysis, which are incomplete geospatial data, earthquake, meteorology and crop water requirement. The paper “Geospatial Information Diffusion Technology Supporting by Background Data” by Chongfu Huang, introduces well known information diffusion technology to supplement incomplete geospatial data to be complete. The diffusion is done with 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 suggested method has obvious advantages over the geographic weighted regression and the artificial neural network for inferring the observations in gap units. There are two papers in earthquake topic. The paper “Study on the Changes of Industrial Structure and Its Causes in Sichuan Province before and after the `5.12 Wenchuan Earthquake' ” by Dongdong Zhu, et al., uses the input-output table, the deviation share analysis method, structural decomposition method and industrial correlation analysis method to analyze the impact of the Wenchuan earthquake on various industries in Sichuan Province. The second paper “Rapid Assessment of Disaster Loss and Spatial Distribution of Intensity Residuals in Linfen Area” by Xiaofei Han, et al., concludes that the probability of earthquake occurrence of high-intensity impact in the Linfen basin is higher than that of the mountains on both sides of the basin, and the difference is significant. In calculating the seismic intensity, the difference in the seismic geological background and the complexity and homogeneity of the site conditions have been considered. There are also two papers related to meteorology. The paper “Study on Meteorological Service Policy for Agricultural Insurance in Hebei Province under the Background of Climate Change” by Kaicheng Xing and Shujun Guo, with the climate models under moderate and high emission scenarios, given the distribution characteristics of mean annual and seasonal air temperature in Hebei Province in the first 50 years of the 21st century, and suggest some countermeasures to optimize the agricultural insurance policy and improve the meteorological service effect of agricultural insurance. The second paper “Rainstorm Warning Information in Beijing: Exploring the Local Perceptions and Views” by Fangping Wang, et al., using the descriptive statistics and non-parametric test methods and the public's cognition, evaluates and expectation of rainstorm warning information in Beijing. The results show that more than 80% of the public can recognize the importance of rainstorm warning. The paper “The Study of Estimation on Maize Irrigation Water Requirement in the northwest of Liaoning Province” by Xiaojing Liu, et al., using Penman-Montes equation, effective coefficient method and Cropland Soil Moisture Index, estimated irrigation water requirement of maize at different growth stages of the northwest of Liaoning Province. We sincerely thank the referees for their strong support and kind help. Thanks to all the authors for their submissions. Particularly, thanks to Prof. Mu Zhang, Publication Chair of the Society for Risk Analysis – China, and thanks to Prof. Junxiang Zhang, Manager of Journal of Risk Analysis and Crisis Response, they devoted their time to overseeing the reviews. Editors-in-Chief Prof. Chongfu Huang Beijing Normal University No.19 Xinjiekouwai Street Beijing 100875, China Email: hchongfu@126.com Prof. Gordon Huang Faculty of Engineering and Applied Science University of Regina Regina, Sask S4S 0A2, Canada Email: gordon.huang@uregina.ca Journal of Risk Analysis and Crisis Response Vol. 9(1), March (2019), p. 1 DOI: https://doi.org/10.2991/jracr.b.190328.007; eISSN: 2210-8505, ISSN: 2210-8491 https://www.atlantis-press.com/journals/jracr © 2019, The Authors. Published by Atlantis Press SARL. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).