James A. Shine

407 citations
14 papers · 269 · h-index 9

Impact in

Papers in

James A. Shine

14 papers receiving 257 citations

Peers

James A. Shine
Comparison fields: 5 of 58
  • Signal Processing 183
  • Information Systems 148
  • Geography, Planning and Development 31
  • Transportation 34
  • Computer Networks and Communications 46
Replace Zarine P. Kemp with:
Zarine P. Kemp United Kingdom
Jesús M. Almendros-Jiménez Spain
Thomas Bernecker Germany
Esther Galbrun Finland
Udo W. Lipeck Germany
Xiaoxuan Wang China
Maria A. Cobb United States
Ahmed R. Mahmood United States
Tamas Abraham Australia
John G. Stell United Kingdom
James A. Shine relative to Zarine P. Kemp United Kingdom Zarine P. Kemp's profile →
Citations per field
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Citations per year

Countries citing papers authored by James A. Shine

Since Specialization
Citations

This map shows the geographic impact of James A. Shine'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 A. Shine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James A. Shine more than expected).

Fields of papers citing papers by James A. Shine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James A. Shine. 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 A. Shine. The network helps show where James A. Shine may publish in the future.

Co-authors

The 14 scholars most cited alongside James A. Shine, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with James A. Shine Line = papers co-authored together James A. Shine links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 200851
2 201149
3 201146
4 200638
5 201016
6 200516
7 200714
8 201212
9 200610
10 20117
11
Discovering and Quantifying Mean Streets: A Summary of Results
20075
12 20112
13 20032
14
Bayesian, Evidence, Fuzzy: Which Theory Works Best When Reasoning with Uncertain Knowledge?,
19851

About James A. Shine

James A. Shine is a scholar working on Signal Processing, Information Systems, Computer Networks and Communications, Artificial Intelligence and Media Technology, having authored 14 papers that have together received 269 indexed citations. Recurring topics across this work include Data Management and Algorithms (7 papers), Data Mining Algorithms and Applications (7 papers), Advanced Database Systems and Queries (3 papers), Remote-Sensing Image Classification (2 papers), Time Series Analysis and Forecasting (2 papers), Geochemistry and Geologic Mapping (2 papers), Automated Road and Building Extraction (1 paper) and Robotics and Sensor-Based Localization (1 paper). The work is most often cited by research in Signal Processing (183 citations), Information Systems (148 citations), Geography, Planning and Development (31 citations), Transportation (34 citations) and Computer Networks and Communications (46 citations). James A. Shine has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include James P. Rogers, Shashi Shekhar, Mete Çelik, Pradeep Mohan, Zhe Jiang, James E. Gentle, Margaret A. Oliver, James M. Kang, Shashi Shekhar and Arnold P. Boedihardjo. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Journal of Social History, Wiley Interdisciplinary Reviews Computational Statistics, International Journal of Remote Sensing and University of Minnesota Digital Conservancy (University of Minnesota).

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.

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