Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Using Social Media to Enhance Emergency Situation Awareness
2012461 citationsJie Yin, Andrew Lampert et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of Robert Power'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 Robert Power with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Power more than expected).
This network shows the impact of papers produced by Robert Power. 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 Robert Power. The network helps show where Robert Power may publish in the future.
Co-authorship network of co-authors of Robert Power
This figure shows the co-authorship network connecting the top 25 collaborators of Robert Power.
A scholar is included among the top collaborators of Robert Power 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 Robert Power. Robert Power is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sparks, Ross, Bella Robinson, Robert Power, Mark Cameron, & Sam Woolford. (2016). An investigation into social media syndromic monitoring. Communications in Statistics - Simulation and Computation. 46(8). 5901–5923.8 indexed citations
Power, Robert, et al.. (2015). A Case Study for Monitoring Fires with Twitter.. ISCRAM.5 indexed citations
8.
Bai, Hua, et al.. (2015). Sina Weibo Incident Monitor and Chinese Disaster Microblogging Classification.6 indexed citations
9.
Yin, Jie, Sarvnaz Karimi, Andrew Lampert, et al.. (2015). Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract. 4234–4239.7 indexed citations
10.
Yin, Jie, Sarvnaz Karimi, Andrew Lampert, et al.. (2015). Using social media to enhance emergency situation awareness. 4234–4238.39 indexed citations
11.
Robinson, Bella, et al.. (2014). Developing a Sina Weibo Incident Monitor for Disasters. 59–68.5 indexed citations
12.
Power, Robert, et al.. (2013). The Pilot Impacts Portal: Experience in building an emergency management information sharing tool. Australian Journal of Emergency Management. 28(4). 20.1 indexed citations
Abel, David J., Volker Gaede, Robert Power, & Xiaofang Zhou. (1999). Caching Strategies for Spatial Joins. GeoInformatica. 3(1). 33–59.3 indexed citations
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.