Javier Garcia-Bernardo
J. Garcia-Bernardo, H. Qi, J. M. Shultz, A. M. Cohen, N. F. Johnson, P. S. Dodds
http://arxiv.org/abs/1506.06305
Visualization: https://everytownresearch.org/school-shootings
Visualization: https://everytownresearch.org/school-shootings
Visualization: https://everytownresearch.org/school-shootings
N. F. Johnson, et al., Scientific reports 3, 3463 (2013).
N. F. Johnson, et al., Scientific reports 3, 3463 (2013).
Interevent time between attacks n-1 and n
N. F. Johnson, et al., Scientific reports 3, 3463 (2013).
Interevent time between attacks n-1 and n
Attack number
N. F. Johnson, et al., Scientific reports 3, 3463 (2013).
Interevent time between attacks n-1 and n
Escalation rate
Attack number
College
College
K12
D. Marsan, O. Lengliné, Science 319, 1076 (2008).
A. G. Hawkes, D. Oakes, Journal of Applied Probability pp. 493–503 (1974).
D. Marsan, O. Lengliné, Science 319, 1076 (2008).
A. G. Hawkes, D. Oakes, Journal of Applied Probability pp. 493–503 (1974).
Attack rate at space point x and time t
D. Marsan, O. Lengliné, Science 319, 1076 (2008).
A. G. Hawkes, D. Oakes, Journal of Applied Probability pp. 493–503 (1974).
Attack rate at space point x and time t
Base attack rate
D. Marsan, O. Lengliné, Science 319, 1076 (2008).
A. G. Hawkes, D. Oakes, Journal of Applied Probability pp. 493–503 (1974).
Attack rate at space point x and time t
Base attack rate
Influence of previous attacks
1. School shootings cluster in time, but not in space (except within-town attacks).
2. School shootings get media attention, which in turn affect the probability of new attacks (copycat effect).
3. We can model the relationship in several ways:
3.1. Blue vs Red model:
- We find distinct trends in school shootings (College vs K12)
- The outliers in the model have distinctive characteristics.
3.2. Hawkes process:
- The attacks are randomly distributed in space (except within-town).
- The attacks increase the probability of new attacks during months.
1. School shootings cluster in time, but not in space (except within-town attacks).
2. School shootings get media attention, which in turn affect the probability of new attacks (copycat effect).
3. We can model the relationship in several ways:
3.1. Blue vs Red model:
- We find distinct trends in school shootings (College vs K12)
- The outliers in the model have distinctive characteristics.
3.2. Hawkes process:
- The attacks are randomly distributed in space (except within-town).
- The attacks increase the probability of new attacks during months.
1. School shootings cluster in time, but not in space (except within-town attacks).
2. School shootings get media attention, which in turn affect the probability of new attacks (copycat effect).
3. We can model the relationship in several ways:
3.1. Blue vs Red model:
- We find distinct trends in school shootings (College vs K12)
- The outliers in the model have distinctive characteristics.
3.2. Hawkes process:
- The attacks are randomly distributed in space (except within-town).
- The attacks increase the probability of new attacks during months.
1. School shootings cluster in time, but not in space (except within-town attacks).
2. School shootings get media attention, which in turn affect the probability of new attacks (copycat effect).
3. We can model the relationship in several ways:
3.1. Blue vs Red model:
- We find distinct trends in school shootings (College vs K12)
- The outliers in the model have distinctive characteristics.
3.2. Hawkes process:
- The attacks are randomly distributed in space (except within-town).
- The attacks increase the probability of new attacks during months.
1. School shootings cluster in time, but not in space (except within-town attacks).
2. School shootings get media attention, which in turn affect the probability of new attacks (copycat effect).
3. We can model the relationship in several ways:
3.1. Blue vs Red model:
- We find distinct trends in school shootings (College vs K12)
- The outliers in the model have distinctive characteristics.
3.2. Hawkes process:
- The attacks are randomly distributed in space (except within-town).
- The attacks increase the probability of new attacks during months.
http://bit.ly/2d7N3Qr
arxiv: 1506.06305
@uvaCORPNET // @javiergb_com garcia@uva.nl