Bernd Resch
researcher ORCID ID = 0000-0002-2233-6926
Facts
8 properties- Instance of
- human
- Occupation
- researcher, geographer
- Employer
- Harvard University, TU Wien, Heidelberg University, Massachusetts Institute of Technology[3][4]
- Student
- Rene Westerholt[5]
🏷️ Also known as
en Bernd Resch
🔖 Identifiers · 12
- ISNI
- 0000000376101564
- ORCID iD
- 0000-0002-2233-6926
- VIAF cluster ID
- 313241344
- Library of Congress authority ID
- no2010146487
- GND ID
- 1033522686
- DBLP author ID
- 31/2852
- DDB person (GND) ID
- 1033522686
- Dimensions author ID
- 0675505371.09
- Google Knowledge Graph ID
- /g/11f0mhr_rg
- Google Scholar author ID
- TlkErZoAAAAJ
- NUKAT ID
- n2019104319
- Scopus author ID
- 24528725900
🔗 Connections
Author of 28
Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis,
A Conditional GAN for Generating Time Series Data for Stress Detection in Wearable Physiological Sensor Data,
A Function-based model of Place,
Analyzing and Predicting Micro-Location Patterns of Software Firms,
An eDiary App Approach for Collecting Physiological Sensor Data from Wearables together with Subjective Observations and Emotions,
An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements,
An Interdisciplinary Mixed-Methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability,
Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data
+20 more
← Authored by 28
Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis,
A Conditional GAN for Generating Time Series Data for Stress Detection in Wearable Physiological Sensor Data,
A Function-based model of Place,
Analyzing and Predicting Micro-Location Patterns of Software Firms,
An eDiary App Approach for Collecting Physiological Sensor Data from Wearables together with Subjective Observations and Emotions,
An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements,
An Interdisciplinary Mixed-Methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability,
Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data
+20 more
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