Jason Goetz

17 papers receiving 1.3k citations

Jason Goetz's Hit Papers

Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling 2015 · 626 citations
6260+3+7Years since publication200400600

Peers

Jason Goetz
Comparison fields: 5 of 73
  • Management, Monitoring, Policy and Law 1.0k
  • Global and Planetary Change 708
  • Atmospheric Science 472
  • Safety, Risk, Reliability and Quality 190
  • Soil Science 101
Replace Helene Petschko with:
Helene Petschko Germany
Daniela Lagomarsino Italy
Rainer Bell Austria
Paola Salvati Italy
Francesco Sdao Italy
C.J. van Westen Netherlands
Kang-Tsung Chang Taiwan
Yonggang Ge China
K.S. Sajinkumar India
Marko Komac Slovenia
Jason Goetz relative to Helene Petschko Germany Helene Petschko's profile →
Citations per field
00.5×1.5×
Helene Petschko · 1×
Citations per year

Countries citing papers authored by Jason Goetz

Since Specialization
Citations

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

Fields of papers citing papers by Jason Goetz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1
Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling
Hit paper breakdown →
2015626
2 2011213
3 2014176
4 201954
5 201849
6 201647
7 201543
8 202333
9 201925
10 202220
11 201414
12 20148
13 20216
14 20226
15 20195
16 20184
17 20191

About Jason Goetz

Jason Goetz is a scholar working on Management, Monitoring, Policy and Law, Atmospheric Science, Global and Planetary Change, Environmental Engineering and Geology, having authored 17 papers that have together received 1.3k indexed citations. Recurring topics across this work include Landslides and related hazards (12 papers), Cryospheric studies and observations (10 papers), Flood Risk Assessment and Management (4 papers), Climate change and permafrost (4 papers), 3D Surveying and Cultural Heritage (3 papers), Remote Sensing and LiDAR Applications (3 papers), Fire effects on ecosystems (3 papers) and Tree Root and Stability Studies (2 papers). The work is most often cited by research in Management, Monitoring, Policy and Law (1.0k citations), Global and Planetary Change (708 citations), Atmospheric Science (472 citations), Safety, Risk, Reliability and Quality (190 citations) and Soil Science (101 citations). Jason Goetz has collaborated with scholars based in Germany, Canada and Austria. Frequent co-authors include Alexander Brenning, Helene Petschko, Philip L. Leopold, Richard Guthrie, Rainer Bell, Thomas Glade, Xavier Bodín, Marco Marcer, Yanjun Shen and Yanjun Shen. Their work appears in journals such as Natural hazards and earth system sciences, Remote Sensing of Environment, Computers & Geosciences, Journal of Geophysical Research Atmospheres and Isotopes in Environmental and Health Studies.

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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