Peer and neighborhood effects: Citation analysis using a spatial autoregressive model and pseudo-spatial data
作者：
Highlights：
• Citation analysis may benefit from the use of spatial autoregressive models.
• Authors, articles, citations and so forth are intrinsically non-spatial items.
• However, they are arranged in pseudo-spaces according to several proximity measures.
• The approach proves useful to delve into the citing behavior of the nearby scholars.
• Further developments are expected in the construction of proximity matrices.
摘要
•Citation analysis may benefit from the use of spatial autoregressive models.•Authors, articles, citations and so forth are intrinsically non-spatial items.•However, they are arranged in pseudo-spaces according to several proximity measures.•The approach proves useful to delve into the citing behavior of the nearby scholars.•Further developments are expected in the construction of proximity matrices.
论文关键词：Citation analysis,Spatial analysis,Spatial scientometrics,Autoregressive models,Peer effects,Neighborhood effects
论文评审过程：Received 15 February 2018, Revised 3 January 2019, Accepted 3 January 2019, Available online 17 January 2019, Version of Record 17 January 2019.
论文官网地址：https://doi.org/10.1016/j.joi.2019.01.002
