James Haworth
About
In The Last Decade
James Haworth
35 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 120
- Transportation 630
- Building and Construction 400
- Global and Planetary Change 221
- Signal Processing 126
- Computer Vision and Pattern Recognition 122
Countries citing papers authored by James Haworth
This map shows the geographic impact of James Haworth'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 James Haworth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Haworth more than expected).
Fields of papers citing papers by James Haworth
This network shows the impact of papers produced by James Haworth. 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 James Haworth. The network helps show where James Haworth may publish in the future.
Co-authorship network of co-authors of James Haworth
This figure shows the co-authorship network connecting the top 25 collaborators of James Haworth. A scholar is included among the top collaborators of James Haworth 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 James Haworth. James Haworth is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 10 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 8 | |
| 7 | 1 | |
| 8 | 2 | |
| 9 | CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning | 11 |
| 10 | 26 | |
| 11 | 11 | |
| 12 | Weathernet: Recognising weather and visual conditions from street-level images using deep residual learning | 52 |
| 13 | 4 | |
| 14 | 6 | |
| 15 | 61 | |
| 16 | 52 | |
| 17 | 5 | |
| 18 | 137 | |
| 19 | Using a moving window SVM classification to infer travel mode from GPS data | 1 |
| 20 | 17 |
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.