IDEAS home Printed from https://ideas.repec.org/a/vrs/ceuecj/v6y2019i53p151-163n9.html
   My bibliography  Save this article

Google Street View image predicts car accident risk

Author

Listed:
  • Kita-Wojciechowska Kinga

    (Faculty of Economic Sciences, University of Warsaw, Poland)

  • Kidziński Łukasz

    (Department of Bioengineering, Stanford University, Stanford, CA, USA)

Abstract

Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for the appropriate allocation of resources in healthcare, insurance, civil engineering and other industries. We show how images of houses are predictive of car accidents. We analyse 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View and annotate house features such as age, type and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, the public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.

Suggested Citation

  • Kita-Wojciechowska Kinga & Kidziński Łukasz, 2019. "Google Street View image predicts car accident risk," Central European Economic Journal, Sciendo, vol. 6(53), pages 151-163, January.
    • Handle: RePEc:vrs:ceuecj:v:6:y:2019:i:53:p:151-163:n:9
    DOI: 10.2478/ceej-2019-0011
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/ceej-2019-0011
    Download Restriction: no
    File URL: https://libkey.io/10.2478/ceej-2019-0011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Frees, Edward W. & Meyers, Glenn & Cummings, A. David, 2011. "Summarizing Insurance Scores Using a Gini Index," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1085-1098.
    2. Pavel Cizek & Wolfgang Karl Härdle & Rafal Weron, 2005. "Statistical Tools for Finance and Insurance," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0501.
    3. Linda L. Golden & Patrick L. Brockett & Jing Ai & Bruce Kellison, 2016. "Empirical Evidence on the Use of Credit Scoring for Predicting Insurance Losses with Psycho-social and Biochemical Explanations," North American Actuarial Journal, Taylor & Francis Journals, vol. 20(3), pages 233-251, July.
    4. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    5. Taylor, Greg, 2001. "Geographic Premium Rating by Whittaker Spatial Smoothing," ASTIN Bulletin, Cambridge University Press, vol. 31(1), pages 147-160, May.
    6. Jonathan J. Rolison & Yaniv Hanoch & Stacey Wood & Pi-Ju Liu, 2014. "Risk-Taking Differences Across the Adult Life Span: A Question of Age and Domain," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 69(6), pages 870-880.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cui, Yiran & del Baño Rollin, Sebastian & Germano, Guido, 2017. "Full and fast calibration of the Heston stochastic volatility model," European Journal of Operational Research, Elsevier, vol. 263(2), pages 625-638.
    2. Rafał Weron, 2009. "Heavy-tails and regime-switching in electricity prices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 457-473, July.
    3. Wookjae Heo & John E. Grable & Barbara O’Neill, 2017. "Wealth Accumulation Inequality: Does Investment Risk Tolerance and Equity Ownership Drive Wealth Accumulation?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 209-225, August.
    4. Henrik Serup Christensen & Lauri Rapeli, 2021. "Immediate rewards or delayed gratification? A conjoint survey experiment of the public’s policy preferences," Policy Sciences, Springer;Society of Policy Sciences, vol. 54(1), pages 63-94, March.
    5. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    6. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    7. Greg Hannsgen, 2011. "Infinite-variance, Alpha-stable Shocks in Monetary SVAR: Final Working Paper Version," Economics Working Paper Archive wp_682, Levy Economics Institute.
    8. Freddy Gabbay & Rotem Lev Aharoni & Ori Schweitzer, 2022. "Deep Neural Network Memory Performance and Throughput Modeling and Simulation Framework," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    9. Eudald Romo & Luis Ortiz-Gracia, 2021. "SWIFT calibration of the Heston model," Papers 2103.01570, arXiv.org.
    10. Michal Benko & Alois Kneip, 2005. "Common functional component modelling," SFB 649 Discussion Papers SFB649DP2005-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    12. Yue Sun & Songmin Dai & Jide Li & Yin Zhang & Xiaoqiang Li, 2019. "Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps," Future Internet, MDPI, vol. 11(2), pages 1-12, February.
    13. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    14. Wenjuan Fan & Jingnan Liu & Shuwan Zhu & Panos M. Pardalos, 2020. "Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)," Annals of Operations Research, Springer, vol. 294(1), pages 567-592, November.
    15. Young Jae Kim & Seung Seog Han & Hee Joo Yang & Sung Eun Chang, 2020. "Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-9, June.
    16. Weron, Rafał, 2004. "Computationally intensive Value at Risk calculations," Papers 2004,32, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    17. Dario Sipari & Betsy D. M. Chaparro-Rico & Daniele Cafolla, 2022. "SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis," IJERPH, MDPI, vol. 19(16), pages 1-27, August.
    18. Shambhavi Tiwari & Morten Moshagen & Benjamin E. Hilbig & Ingo Zettler, 2021. "The Dark Factor of Personality and Risk-Taking," IJERPH, MDPI, vol. 18(16), pages 1-18, August.
    19. Mara Giavina-Bianchi & Raquel Machado de Sousa & Vitor Zago de Almeida Paciello & William Gois Vitor & Aline Lissa Okita & Renata Prôa & Gian Lucca dos Santos Severino & Anderson Alves Schinaid & Rafa, 2021. "Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-13, September.
    20. Janczura, Joanna & Weron, Rafal, 2010. "Goodness-of-fit testing for regime-switching models," MPRA Paper 22871, University Library of Munich, Germany.

    More about this item

    Keywords

    Generalized Linear Model; risk modelling; insurance pricing; satellite imagery; Google Street View;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:ceuecj:v:6:y:2019:i:53:p:151-163:n:9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.