Daniel Chu

31 papers receiving 433 citations

Peers

Daniel Chu
Comparison fields: 5 of 60
  • Applied Psychology 27
  • Epidemiology 164
  • Toxicology 13
  • Public Health, Environmental and Occupational Health 101
  • Transportation 24
Replace Gillian C. Williams with:
Gillian C. Williams Canada
Kelsey A. Simpson United States
Catherine T. Baca United States
Lucía Moure-Rodríguez Spain
Keely Latham United States
Jimikaye B. Courtney United States
Megan K. Reed United States
M Gomel Australia
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Citations per field
00.5×10×14.5×
Gillian C. Williams · 1×
Citations per year

Countries citing papers authored by Daniel Chu

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Chu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Chu. 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 Daniel Chu. The network helps show where Daniel Chu may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Chu, 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 Daniel Chu Line = papers co-authored together Daniel Chu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201548
2 201932
3 201531
4 201928
5 202128
6 201528
7 201428
8 201426
9 201726
10 201619
11 202218
12 201416
13 201815
14 202113
15 202111
16 202110
17 202110
18 20249
19 20226
20 20246

About Daniel Chu

Daniel Chu is a scholar working on Epidemiology, Public Health, Environmental and Occupational Health, Clinical Psychology, Health, Toxicology and Mutagenesis and General Health Professions, having authored 31 papers that have together received 438 indexed citations. Recurring topics across this work include HIV, Drug Use, Sexual Risk (8 papers), Obesity, Physical Activity, Diet (3 papers), Eating Disorders and Behaviors (3 papers), Substance Abuse Treatment and Outcomes (2 papers), Urban Transport and Accessibility (2 papers), Air Quality and Health Impacts (2 papers), Intramuscular injections and effects (2 papers) and Psychological Well-being and Life Satisfaction (1 paper). The work is most often cited by research in Applied Psychology (27 citations), Epidemiology (164 citations), Toxicology (13 citations), Public Health, Environmental and Occupational Health (101 citations) and Transportation (24 citations). Daniel Chu has collaborated with scholars based in United States and Australia. Frequent co-authors include Ricky N. Bluthenthal, Lynn D. Wenger, Alex H. Kral, Genevieve F. Dunton, Tyler B. Mason, Brendan Quinn, Scott P. Novak, Sydney O’Connor, Li Yi and Philippe Bourgois. Their work appears in journals such as Drug and Alcohol Dependence, Journal of Physical Activity and Health, International Journal of Environmental Research and Public Health, BMC Public Health and Spatial and Spatio-temporal Epidemiology.

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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