Dr Diana Contreras

School of Engineering

Please describe your current research interests/projects. 

I work for the project Learning from Earthquakes: Building Resilient Communities Through Earthquake Reconnaissance, Response and Recovery. I am interested in post-disaster recovery and resilience.

Why did you choose this field? 

I decided to work on the topic of post-disaster recovery to discover the determinants that make some societies more resilient than others.  

Can you tell me how you got into research? 

13 years ago, my father died and my family and I had to make a big effort to recover from this big loss.  

 What are the main methods you use, how and why? 

So far I have monitored changes in building conditions and building use through the time after the earthquake combining remote sensing (RS), observations on fieldwork and geographic information system (GIS). 

Which methods excite you? 

Currently I am using sentiment analysis for post-disaster recovery assesment. 

Are there any methods you’d like to explore further? 

Yes, sentiment analysis, natural language processing (NLP), artificial intelligence (AI) and big data. 

Who inspires you methodologically? 

Yuka Karatani 

Haruo Hayashi 

Sarah Jane Hogg 

Stephany Chang 

Henry Burton 

Ragini, J. Rexiline 

What, about methods, did you wish your younger self had known? 

RS, GIS and sentiment analysis. 

Have you had any memorable methodological blunders? 

Yes, I confused categorical with numerical variables. 

If you could recommend students read just one text on methods (book or journal article), what would it be? 

Regarding Post-disaster recovery assessment, I would recommend two of my own publications:  

  • Contreras, D.; Forino, G.; Blaschke, T., Measuring the progress of a recovery process after an earthquake: The case of L'Aquila, Italy. International Journal of Disaster Risk Reduction 2018, 28, 450-464. 

  • Contreras, D.; Blaschke, T.; Tiede, D.; Jilge, M., Monitoring recovery after earthquakes through the integration of remote sensing, GIS, and ground observations: the case of L’Aquila (Italy). Cartography and Geographic Information Science 2016, 43 (2), 115-133. 

Regarding sentiment analysis I recommend:  

  • Zucco, C.; Calabrese, B.; Agapito, G.; Guzzi, P. H.; Cannataro, M., Sentiment analysis for mining texts and social networks data: Methods and tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery n/a (n/a), e1333.