PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2019 | 6 | nr 53 | 151--163
Tytuł artykułu

Google Street View Image Predicts Car Accident Risk

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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. (original abstract)
Rocznik
Tom
6
Numer
Strony
151--163
Opis fizyczny
Twórcy
  • Faculty of Economic Sciences, University of Warsaw, Poland
  • Department of Bioengineering, Stanford University, Stanford, CA, USA
Bibliografia
  • [1] Andersson, V. O., Birck, M. A. F., & Araujo, R. M. (2017). Investigating crime rate prediction using street-level images and Siamese convolutional neural networks. In E. Teles & C. Brackmann (Eds.), Computational neuroscience (pp. 81-93). Cham, Switzerland: Springer International Publishing.
  • [2] Anguelov, D., Dulong, C., Filip, D., Frueh, C., Lafon, S., Lyon, R., ... Weaver, J. (2010). Google street view: Capturing the world at street level. Computer, 43(6), 32-38.
  • [3] Bingham, C. R., Shope, J. T., & Zhu, J. (2008). Substance-involved driving: Predicting driving after using alcohol, marijuana, and other drugs. Traffic Injury Prevention, 9(6), 515-526.
  • [4] Blitz, M. J. (2012). The right to map (and avoid being mapped): Reconceiving first amendment protection for information-gathering in the age of Google Earth. The Columbia Science and Technology Law Review, 14, 115.
  • [5] Braver, E. R. (2003). Race, Hispanic origin, and socioeconomic status in relation to motor vehicle occupant death rates and risk factors among adults. Accident; Analysis and Prevention, 35(3), 295-309.
  • [6] Cizek, P., Härdle, W. K., & Weron, R. (2005). Statistical tools for finance and insurance. Berlin, German: Springer Science & Business Media.
  • [7] Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologistlevel classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
  • [8] Finer, M., Novoa, S., Weisse, M. J., Petersen, R., Mascaro, J., Souto, T., ... Martinez, R. G. (2018). Combating deforestation: From satellite to intervention. Science, 360(6395), 1303-1305.
  • [9] Frees, E. W., Meyers, G., & Cummings, A. D. (2011). Summarizing insurance scores using a Gini Index. Journal of the American Statistical Association, 106(495), 1085-1098.
  • [10] Gaulding, J. (1994). Race sex and genetic discrimination in insurance: What's fair. Cornell Law Review, 80, 1646.
  • [11] Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017). Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences of the United States of America, 114(50), 13108-13113.
  • [12] Gogol, F. (1993). The Value of Information in Insurance Pricing. The Journal of Risk and Insurance, 60(1), 119-128.
  • [13] Gillis, A. R. (1974). Population density and social pathology: The case of building type, social allowance and juvenile delinquency. Social Forces; a Scientific Medium of Social Study and Interpretation, 53(2), 306-314.
  • [14] Gini, C. (1921). Measurement of inequality of incomes. The Economic Journal of Nepal, 31(121), 124-126.
  • [15] Goel, R., Garcia, L. M. T., Goodman, A., Johnson, R., Aldred, R., Murugesan, M., ... Woodcock, J. (2018). Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain. PloS One, 13(5), e0196521.
  • [16] Goldburd, M., Khare, A., & Tevet, C. D. (2016). Generalized linear models for insurance rating. In Casualty Actuarial Society. Retrieved from https:// www.casact.org/pubs/monographs/papers/05- Goldburd-Khare-Tevet.pdf.
  • [17] Golden, L. L., Brockett, P. L., Ai, J., & Kellison, B. (2016). Empirical evidence on the use of credit scoring for predicting insurance losses with psycho-social and biochemical explanations. North American Actuarial Journal: NAAJ, 20(3), 233-251.
  • [18] Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794.
  • [19] Karlaftis, M. G., & Golias, I. (2002). Effects of road geometry and traffic volumes on rural roadway accident rates. Accident; Analysis and Prevention, 34(3), 357-365.
  • [20] Kolyshkina, I., Wong, S., & Lim, S. (2004). Enhancing generalised linear models with data mining. In Casualty Actuarial Society (pp. 279-290).
  • [21] Lakhani, P., & Sundaram, B. (2017). Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology, 284(2), 574-582.
  • [22] Levenson, R. M., Krupinski, E. A., Navarro, V. M., & Wasserman, E. A. (2015). Pigeons (Columba livia) as trainable observers of pathology and radiology breast cancer images. PloS One, 10(11), e0141357.
  • [23] Lorenz, M. O. (1905). Methods of measuring the concentration of wealth. Publications of the American Statistical Association, 9(70), 209-219.
  • [24] McCartt, A. T., Shabanova, V. I., & Leaf, W. A. (2003). Driving experience, crashes and traffic citations of teenage beginning drivers. Accident; Analysis and Prevention, 35(3), 311-320.
  • [25] Rolison, J. J., Hanoch, Y., Wood, S., & Liu, P.-J. (2014). Risk-taking differences across the adult life span: A question of age and domain. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 69(6), 870-880.
  • [26] Shankar, V., Mannering, F., & Barfield, W. (1995). Effect of roadway geometrics and environmental factors on rural freeway accident frequencies. Accident; Analysis and Prevention, 27(3), 371-389.
  • [27] Spedicato, G. A., Dutang, C., & Petrini, L. (2018). Machine learning methods to perform pricing optimization. A comparison with standard GLMs. Variance: Advancing the Science of Risk, 111(2), 69-89.
  • [28] Spilkova, J., Dzúrova, D., & Pitonak, M. (2014). Perception of neighborhood environment and health risk behaviors in Prague's teenagers: A pilot study in a post-communist city. International Journal of Health Geographics, 13, 41.
  • [29] Strayer, D. L., Drews, F. A., & Crouch, D. J. (2003). Fatal distraction? A comparison of the cellphone driver and the drunk driver. In Driving Assessment Conference (Vol. 2). University of Iowa. doi:10.17077/drivingassessment.1085.
  • [30] Taylor, G. (2001). Geographic premium rating by whittaker spatial smoothing. ASTIN Bulletin: The Journal of the IAA, 31(1), 147-160.
  • [31] Tran-Thanh, L., Stein, S., Rogers, A., & Jennings, N. R. (2014). Efficient crowdsourcing of unknown experts using bounded multi-armed bandits. Artificial Intelligence, 214, 89-111.
  • [32] Werner, G., & Modlin, C. (2016). Basic ratemaking (5 ed.). Casualty Actuarial Society.
  • [33] Yan, J., Guszcza, J., Flynn, M., & Wu, C.-S. P. (2009). Applications of the offset in property-casualty predictive modeling. In Casualty Actuarial Society E-Forum, Winter 2009 (p. 366).
  • [34] Yao, J. (2008). Clustering in ratemaking: Applications in territories clustering. Casualty Actuarial Society Discussion Paper Program Casualty Actuarial Society-Arlington, Virginia, 170-192.
  • [35] Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., & Oliva, A. (2014). Learning deep features for scene recognition using places database. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in neural information processing systems 27 (pp. 487- 495). Red Hook, NY: Curran Associates.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171603095

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.