Joon Jin Song
- Safety, Risk, Reliability and Quality top 0.5%
- Molecular Biology
- Transportation top 2%
- Statistics and Probability top 5%
- Public Health, Environmental and Occupational Health
- Co-authors
- Shaw‐Pin MiaouBani K. MallickMalay GhoshErin M. ConlonGyuWon LeeDeukwoo KwonWalter BottjeHo‐Jin Lee
- Topics
- Statistical Methods and Bayesian Inference (11 papers)Meteorological Phenomena and Simulations (8 papers)Soil Moisture and Remote Sensing (8 papers)
- Partner nations
- United StatesSouth KoreaChina
In The Last Decade
Joon Jin Song
56 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 148
- Safety, Risk, Reliability and Quality 432
- Molecular Biology 230
- Transportation 228
- Statistics and Probability 125
- Public Health, Environmental and Occupational Health 113
Countries citing papers authored by Joon Jin Song
This map shows the geographic impact of Joon Jin Song's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Joon Jin Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joon Jin Song more than expected).
Fields of papers citing papers by Joon Jin Song
This network shows the impact of papers produced by Joon Jin Song. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Joon Jin Song. The network helps show where Joon Jin Song may publish in the future.
Co-authorship network of co-authors of Joon Jin Song
This figure shows the co-authorship network connecting the top 25 collaborators of Joon Jin Song. A scholar is included among the top collaborators of Joon Jin Song based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Joon Jin Song. Joon Jin Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 0 | |
| 3 | 15 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 0 | |
| 7 | 4 | |
| 8 | 2 | |
| 9 | 13 | |
| 10 | 22 | |
| 11 | 29 | |
| 12 | 54 | |
| 13 | 33 | |
| 14 | 10 | |
| 15 | Bayesian analysis of simultaneous autoregressive models | 17 |
| 16 | 34 | |
| 17 | 34 | |
| 18 | 191 | |
| 19 | 120 | |
| 20 | ROADWAY TRAFFIC CRASH MAPPING: A SPACE-TIME MODELING APPROACH | 147 |
About Joon Jin Song
Joon Jin Song is a scholar working on Nuclear Energy and Engineering, Statistics and Probability and Environmental Engineering, having authored 61 papers that have together received 1.2k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (11 papers), Meteorological Phenomena and Simulations (8 papers) and Soil Moisture and Remote Sensing (8 papers). The work is most often cited by research in Safety, Risk, Reliability and Quality (432 citations), Transportation (228 citations) and Statistics and Probability (125 citations). Joon Jin Song has collaborated with scholars based in United States, South Korea and China. Frequent co-authors include Shaw‐Pin Miaou, Bani K. Mallick, Malay Ghosh, Erin M. Conlon, GyuWon Lee, Deukwoo Kwon, Walter Bottje, Ho‐Jin Lee, Anna Liu and Victor De Oliveira. Their work appears in journals such as Technometrics, Remote Sensing of Environment and Geophysical Research Letters.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.