Nicholas Hamm

59 papers receiving 3.4k citations

Nicholas Hamm's Hit Papers

A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information 2018 · 486 citations
4860+4+8Years since publication4008001.2k

Peers

Nicholas Hamm
Comparison fields: 5 of 154
  • Ecological Modeling 745
  • Environmental Engineering 720
  • Health, Toxicology and Mutagenesis 670
  • Nature and Landscape Conservation 517
  • Global and Planetary Change 817
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Citations per field
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Citations per year

Countries citing papers authored by Nicholas Hamm

Since Specialization
Citations

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

Fields of papers citing papers by Nicholas Hamm

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 64 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Where is positional uncertainty a problem for species distribution modelling?
Hit paper breakdown →
20131414
2
A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information
Hit paper breakdown →
2018486
3 2017183
4 2021166
5 2012151
6 2010121
7 2011106
8 201689
9 201679
10 200550
11 201842
12 201742
13 201541
14 201437
15 201835
16 201432
17 202431
18 199930
19 200928
20 201325

About Nicholas Hamm

Nicholas Hamm is a scholar working on Environmental Engineering, Health, Toxicology and Mutagenesis, Global and Planetary Change, Media Technology and Ecology, having authored 64 papers that have together received 3.5k indexed citations. Recurring topics across this work include Air Quality and Health Impacts (9 papers), Remote Sensing in Agriculture (8 papers), Geochemistry and Geologic Mapping (7 papers), Land Use and Ecosystem Services (7 papers), Soil Geostatistics and Mapping (6 papers), Air Quality Monitoring and Forecasting (6 papers), Remote-Sensing Image Classification (5 papers) and Impact of Light on Environment and Health (4 papers). The work is most often cited by research in Ecological Modeling (745 citations), Environmental Engineering (720 citations), Health, Toxicology and Mutagenesis (670 citations), Nature and Landscape Conservation (517 citations) and Global and Planetary Change (817 citations). Nicholas Hamm has collaborated with scholars based in Netherlands, China and United Kingdom. Frequent co-authors include Thomas Groen, Andrew K. Skidmore, Babak Naimi, Albertus G. Toxopeus, Alfred A. Stein, Luke D. Knibbs, Jianping Guo, Yuming Guo, Hongyan Ren and Wei Cao. Their work appears in journals such as Remote Sensing, The Science of The Total Environment, International Journal of Applied Earth Observation and Geoinformation, PLoS neglected tropical diseases and International Journal of Remote Sensing.

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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