Publications

Miller, S., Preis, T., Mizzi, G., Bastos, L. S., Gomes, M. F. C., Coelho, F. C., Codeço, C. T., & Moat, H. S. (2022). Faster indicators of chikungunya incidence using Google searches. PLOS Neglected Tropical Diseases, 16, e0010441.

McKenna, R., Weinand, J. M., Mulalic, I., Petrović, S., Mainzer, K., Preis, T., & Moat. H. S. (2021). Scenicness assessment of onshore wind sites with geotagged photographs and impacts on approval and cost-efficiency. Nature Energy, 6, 663-672.

Botta, F., Preis, T., & Moat, H. S. (2020). In search of art: rapid estimates of gallery and museum visits using Google Trends. EPJ Data Science, 9, 14.

Botta, F., Moat, H. S., & Preis, T. (2020). Measuring the size of a crowd using Instagram. Environment and Planning B: Urban Analytics and City Science, 47, 1690-1703.

Miller, S., Moat, H. S., & Preis, T. (2020). Using aircraft location data to estimate current economic activity. Scientific Reports, 10, 7576.

Preis, T., Botta, F., & Moat, H. S. (2020). Sensing global tourism numbers with millions of publicly shared online photographs. Environment and Planning A: Economy and Space, 52, 471-477.

Seresinhe, C. I., Preis, T., MacKerron, G., & Moat, H. S. (2019). Happiness is Greater in More Scenic Locations. Scientific Reports, 9, 4498.

Olivola, C. Y., Moat, H. S., & Preis, T. (2019). Using big data to map the relationship between time perspectives and economic outputs. Behavioral and Brain Sciences, 42, e206.

Seresinhe, C. I., Moat, H. S., & Preis, T. (2018). Quantifying scenic areas using crowdsourced data. Environment and Planning B: Urban Analytics and City Science, 45, 567-582.

Seresinhe, C. I., Preis, T., & Moat, H. S. (2017). Using deep learning to quantify the beauty of outdoor places. Royal Society Open Science, 4, 170170.

Curme, C., Zhuo, Y. D., Moat, H. S., & Preis, T. (2017). Quantifying the diversity of news around stock market moves. Journal of Network Theory in Finance, 3, 1-20.

Kristoufek, L., Moat, H. S., & Preis, T. (2016). Estimating suicide occurrence statistics using Google Trends. EPJ Data Science, 5, 32.

Moat, H. S., Olivola, C. Y., Chater, N., & Preis, T. (2016). Searching choices: quantifying decision-making processes using search engine data. Topics in Cognitive Science, 8, 685-696.

Seresinhe, C. I., Preis, T., & Moat, H. S. (2016). Quantifying the link between art and property prices in urban neighbourhoods. Royal Society Open Science, 3, 160146.

Alanyali, M., Preis, T., & Moat, H. S. (2016). Tracking protests using geotagged Flickr photographs. PLOS ONE, 11, e0150466.

Letchford, A., Preis, T., & Moat, H. S. (2016). The advantage of simple paper abstracts. Journal of Informetrics, 10, 1-8.

Letchford, A., Preis, T., & Moat, H. S. (2016). Quantifying the search behaviour of different demographics using Google Correlate . PLOS ONE, 11, e0149025.

Botta, F., Moat, H. S., Stanley, H. E., & Preis, T. (2015). Quantifying stock return distributions in financial markets. PLOS ONE, 10, e0135600.

Letchford, A., Preis, T., & Moat, H. S. (2015). The advantage of short paper titles. Royal Society Open Science, 2, 150266

Barchiesi, D., Preis, T., Bishop, S., & Moat, H. S. (2015). Modelling human mobility patterns using photographic data shared online. Royal Society Open Science, 2, 150046.

Barchiesi, D., Moat, H. S., Alis, C. M., Bishop, S. R., & Preis, T. (2015). Quantifying international travel flows using Flickr. PLOS ONE, 10, e0128470.

Preis, T., & Moat, H. S. (2015). Early signs of financial market moves reflected by Google Searches. In B. Gonçalves & N. Perra (Eds.), Social Phenomena: From Data Analysis to Models, pp. 89-102. Amsterdam: Springer.

Alis, C. M., Letchford, A., Moat, H. S., & Preis, T. (2015). Estimating tourism statistics with Wikipedia page views. In ACM Web Science 2015.

Botta, F., Moat, H. S., & Preis, T. (2015). Quantifying crowd size with mobile phone and Twitter data. Royal Society Open Science, 2, 150162.

Alis, C. M., Lim, M. T., Moat, H. S., Barchiesi, D., Preis, T., & Bishop, S. R. (2015). Quantifying regional differences in the length of Twitter messages. PLOS ONE, 10, e0122278.

Seresinhe, C., Preis, T., & Moat, H. S. (2015). Quantifying the impact of scenic environments on health. Scientific Reports, 5, 16899.

Preis, T., & Moat, H. S. (2014). Adaptive nowcasting of influenza outbreaks using Google searches. Royal Society Open Science, 1, 140095.

Noguchi, T., Stewart, N., Olivola, C. Y., Moat, H. S., & Preis, T. (2014). Characterizing the time-perspective of nations with search engine query data. PLOS ONE, 9, e95209.

Curme, C., Preis, T., Stanley, H.E., & Moat, H. S. (2014). Quantifying the semantics of search behavior before stock market moves. Proceedings of the National Academy of Sciences, 111, 11600 – 11605.

Moat, H. S., Curme, C., Stanley, H. E., & Preis, T. (2014). Anticipating stock market movements with Google and Wikipedia. In D. Matrasulov, & H. E. Stanley (Eds.), Nonlinear Phenomena in Complex Systems: From Nano to Macro Scale, pp. 47-59. Amsterdam: Springer.

Moat, H. S., Preis, T., Olivola, C.Y., Liu, C., & Chater, N. (2014). Using big data to predict collective behavior in the real world. Behavioral and Brain Sciences, 37, 92-93.

Alanyali, M., Moat, H. S., & Preis, T. (2013). Quantifying the relationship between financial news and the stock market. Scientific Reports, 3, 3578.

Preis, T., Moat, H. S., Bishop, S.R., Treleaven, P., & Stanley, H. E. (2013). Quantifying the digital traces of Hurricane Sandy on Flickr. Scientific Reports, 3, 3141.

Moat, H. S., Curme, C., Avakian, A., Kenett, D.Y., Stanley, H. E., & Preis, T. (2013). Quantifying Wikipedia usage patterns before stock market moves. Scientific Reports, 3, 1801.

Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3, 1684.

Preis, T., Moat, H. S., Stanley, H. E., & Bishop, S.R. (2012). Quantifying the advantage of looking forward. Scientific Reports, 2, 350.

PhD Theses

Alanyali, M. (2018). Quantifying human behaviour with online images. PhD thesis, University of Warwick.

Botta, F. (2016). Quantifying human behaviour using complex social datasets. PhD thesis, University of Warwick.

Chahal, B. K. (2022). Using deep learning to infer house prices from street view imagery. PhD thesis, University of Warwick.

Lochanachit, S. (2020). Estimating socioeconomic indicators using online data. PhD thesis, University of Warwick.

Miller, S. (2022). Faster socieoeconomic indicators using novel data sources. PhD thesis, University of Warwick.

Mizzi, G. (2019). Improving dengue fever surveillance with online data. PhD thesis, University of Warwick.

Seresinhe, C. I. (2018). From landscapes to cityscapes : quantifying the connection between scenic beauty and human wellbeing. PhD thesis, University of Warwick.