Key Publications

The list below contains a number of selected key publications. A full list of publications can be found at

Havas, C. and Resch, B. (2021) Portability of Semantic and Spatial-temporal Machine Learning Methods to Analyse Social Media for Near-real-time Disaster Monitoring. Natural Hazards (2021). DOI:

Stolerman, L.M., Clemente, L., Poirier, C., Parag, K.V., Majumder, A., Masyn, S., Resch, B. and Santillana, M. (2023) Using Digital Traces to Build Prospective and Real-time County-level Early Warning Systems to Anticipate COVID-19 Outbreaks in the United States. Science Advances,

Havas, C., Wendlinger, L., Stier, J., Julka, S., Krieger, V., Ferner, C., Petutschnig, A., Granitzer, M., Wegenkittl, S. and Resch, B. (2021) Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics. ISPRS International Journal for Geo-Information, 10(8), pp. 498, DOI:

Petutschnig, A., Resch, B., Lang, S. and Havas, C. (2021) Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data. ISPRS International Journal for Geo-Information, 10(5), pp. 323, DOI:

Ehrhart, M., Resch, B., Havas, C. and Niederseer, D. (2022) A Conditional GAN for Generating Time Series Data for Stress Detection in Wearable Physiological Sensor Data. Sensors, 22(16), 5969,

Crivellari, A. and Resch, B. (2022) Investigating Functional Consistency of Mobility-related Urban Zones via Motion-driven Embedding Vectors and Local POI-type Distributions. Computational Urban Science, 2(1), 19,

Petutschnig, A., Albrecht, J., Resch, B., Ramasubramanian, L. and Wright, A. (2022) Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES Data. International Journal for Geo-Information, 11(1), 15. DOI:

Kogan, N.E., Clemente, L., Liautaud, P., Kaashoek, J., Link, N.B., Nguyen, A.T., Lu, F.S., Huybers, P., Resch, B., Havas, C., Petutschnig, A., Davis, J., Chinazzi, M., Mustafa, B., Hanage, W.P., Vespignani, A. and Santillana, M. (2021) An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time. Science Advances, 7(10), eabd6989. DOI:

Petutschnig, A., Resch, B., Lang, S. and Havas, C. (2021) Evaluating the Representativeness of Socio-Demographic Variables over Time for Geo-Social Media Data. ISPRS International Journal for Geo-Information, 10(5), pp. 323, DOI:

Resch, B., Puetz, I., Bluemke, M., Kyriakou, K. and Miksch, J. (2020) An Interdisciplinary Mixed-methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability. International Journal of Environmental Research and Public Health, 17(19), 6994. DOI:

Pykett, J., Chrisinger, B., Kyriakou, K., Osborne, T., Resch, B., Stathi, B., Toth, E. and Whittaker, A. (2020): Developing a Citizen Social Science Approach to Understand Urban Stress and Promote Wellbeing in Urban Communities. Palgrave Communications 6(1), 85,

Pykett, J., Osborne, T. and Resch, B. (2020): From Urban Stress to Neurourbanism: How Should We Research City Wellbeing?. Annals of the Association of American Geographers DOI: 10.1080/24694452.2020.1736982.

Doerrzapf, L., Kovács-Gyori, A., Resch, B. and Zeile, P. (2019): Defining and Assessing Walkability: An Integrated Approach Using Surveys, Biosensors and Geospatial Analysis. Urban Development Issues 62, 5-15, DOI: 10.2478/udi-2019-0008.

Kyriakou, K. and Resch, B. (2019): Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data. Adavances in Cartography and GIScience of the ICA 2, 9, DOI: 10.5194/ica-adv-2-9-2019.

Kyriakou, K., Resch, B., Sagl, G., Petutschnig, A., Werner, C., Niederseer, D., Liedlgruber, M., Wilhelm, F.H., Osborne, T. and Pykett, J. (2019): Detecting Moments of Stress from Measurements of Wearable Physiological Sensors. Sensors 19(17), pp. 3805, DOI: 10.3390/s19173805.

Kinne, J. and Resch, B. (2018): Analyzing and Predicting Micro-Location Patterns of Software Firms. ISPRS International Journal of Geo-Information (IJGI) 7(1), pp. 1, DOI: 10.3390/ijgi7010001.

Kounadi, O. and Resch, B. (2018): A Geoprivacy by Design Guideline for Research Campaigns that use Participatory Sensing Data. Journal of Empirical Research on Human Research Ethics 13(3), 203-222, DOI: 10.1177/1556264618759877.

Kounadi, O., Resch, B. and Petutschnig, A. (2018): Privacy Threats and Protection Recommendations for the Usage of Geosocial Network Data in Research. Social Sciences 7(10), pp. 191, DOI: 10.3390/socsci7100191.

Bluemke, M., Resch, B., Lechner, C., Westerholt, R. and Kolb, J.-P. (2017): Integrating Geographic Information into Survey Research: Current Applications, Challenges and Future Avenues. Survey Research Methods 11(3), 307-327, DOI: 10.3390/ijgi7010001.

Resch, B. , Usländer, F. and Havas, C. (2017): Combining Machine-learning Topic Models and Spatio-temporal Analysis of Social Media Data for Disaster Footprint and Damage Assessment. Cartography and Geographic Information Science , 362-376, DOI: 10.1080/15230406.2017.1356242.

Resch, B., Summa, A., Zeile, P. and Strube, M. (2016): Citizen-centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm. Urban Planning 1(2), 114-127, DOI: 10.17645/up.v1i2.617.

Westerholt, R., Steiger, E., Resch, B. and Zipf, A. (2016): Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis. PLOS ONE 11(9), 1-31, doi:10.1371/journal.pone.0162360.

Resch, B., Summa, A., Sagl, G., Zeile, P. and Exner, J.-P. (2015): Urban Emotions – Geo-semantic Emotion Extraction from Technical Sensors, Human Sensors and Crowdsourced Data. In: Gartner, G. and Haosheng Huang (eds.) (2015) Progress in Location-Based Services 2014 , Springer International Publishing, Switzerland, 199-212.

Steiger, E., Resch, B. and Zipf, A. (2015): Exploration of Spatiotemporal and Semantic Clusters of Twitter Data Using Unsupervised Neural Networks. International Journal of Geographical Information Science 30, 1694-1716, DOI: 10.1080/13658816.2015.1099658.

Steiger, E., Westerholt, R., Resch, B. and Zipf, A. (2015): Twitter as an Indicator for Whereabouts of People? Correlating Twitter with UK Census Data. Computers, Environment and Urban Systems 54, 255-265.

Westerholt, R., Resch, B. and Zipf, A. (2015): A Local Scale-Sensitive Indicator of Spatial Autocorrelation for Assessing High/Low Value Clustering. International Journal of Geographical Information Science 29(5), 868-887, DOI:10.1080/13658816.2014.1002499.

Blaschke, T. and Merschdorf, H. (2014): Geographic Information Science as a Multidisciplinary and Multi-paradigmatic Field. Cartography and Geographic Information Science 41(3), 196-213.

Resch, B. (2013): People as Sensors and Collective Sensing – Contextual Observations Complementing Geo-Sensor Network Measurements. In: Krisp, J. (2013) Advances in Location-Based Services, ISBN 978-3-642-34202-8, Springer, Berlin Heidelberg, 391-406.