#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Gang confrontation: The case of Medellin (Colombia)


Autoři: Juan D. Botero aff001;  Weisi Guo aff002;  Guillem Mosquera aff002;  Alan Wilson aff003;  Samuel Johnson aff004;  Gicela A. Aguirre-Garcia aff005;  Leonardo A. Pachon aff001
Působiště autorů: Universidad de Antioquia, Instituto de Física, Medellin, Colombia aff001;  University of Warwick, Coventry, United Kingdom aff002;  Alan Turing Institute, London, United Kingdom aff003;  University of Birmingham, Birmingham, United Kingdom aff004;  Centro Nacional de Memoria Histórica, Bogotá, Colombia aff005;  Freie Universität Berlin, Lateinamerika-Institut, Berlin, Germany aff006;  guane Enterprises, Medellin, Colombia aff007
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0225689

Souhrn

Protracted conflict is one of the largest human challenges that have persistently undermined economic and social progress. In recent years, there has been increased emphasis on using statistical and physical science models to better understand both the universal patterns and the underlying mechanics of conflict. Whilst macroscopic power-law fractal patterns have been shown for death-toll in wars and self-excitation models have been shown for roadside ambush attacks, very few works deal with the challenge of complex dynamics between gangs at the intra-city scale. Here, based on contributions to the historical memory of the conflict in Colombia, Medellin’s gang-confrontation-network is presented. It is shown that socio-economic and violence indexes are moderate to highly correlated to the structure of the network. Specifically, the death-toll of conflict is strongly influenced by the leading eigenvalues of the gangs’ conflict adjacency matrix, which serves a proxy for unstable self-excitation from revenge attacks. The distribution of links based on the geographic distance between gangs in confrontation leads to the confirmation that territorial control is a main catalyst of violence and retaliation among gangs. As a first attempt to explore the time evolution of the confrontation network, the Boltzmann-Lotka-Volterra (BLV) dynamic interaction network analysis is applied to quantify the spatial embeddedness of the dynamic relationship between conflicting gangs in Medellin. However, the non-stationary character of the violence in Medellin during the observation period restricts the application of the BLV model and results suggest that more involved and comprehensive models are needed to described the dynamics of Medellin’s armed conflict.

Klíčová slova:

Centrality – Colombia – Eigenvalues – Homicide – Human rights – Network analysis – Unemployment rates – War and civil unrest


Zdroje

1. Guo W., Gleditsch K., Wilson A. Retool AI to Forecast and Limit Wars. Nature. 2018; 562: 331–333. doi: 10.1038/d41586-018-07026-4 30323227

2. Melguizo RC, Cronshaw F. The evolution of armed conflict in Medellin: An analysis of the major actors. Latin American Perspectives. 2001;28(1):110–131. doi: 10.1177/0094582X0102800107

3. Richardson LF. Frequency of Occurrence of Wars and other Fatal Quarrels. Nature. 1941;148. doi: 10.1038/148598a0

4. Zammit-Mangion, A., Dewar, M., Kadirkamanathan, V., Sanguinetti, G. Point process modeling of the Afghan War Diary. Proceedings of the National Academy of Sciences (PNAS). 2012;.

5. D’Orazio V, Kenwick M, Lane M, Palmer G, Reitter D. Crowdsourcing the Measurement of Interstate Conflict. PloS one. 2016;11(6):e0156527. doi: 10.1371/journal.pone.0156527 27310427

6. Van Holt T, Johnson JC, Moates S, Carley KM. The role of datasets on scientific influence within conflict research. PloS one. 2016;11(4):e0154148. doi: 10.1371/journal.pone.0154148 27124569

7. Bohorquez JC, Gourley S, Dixon AR, Spagat M, Johnson NF. Common ecology quantifies human insurgency. Nature. 2009;462. doi: 10.1038/nature08631 20016600

8. Johnson N, Medina P, Zhao G, Messinger D, Horgan J, Gill P, et al. Simple mathematical law benchmarks human confrontations. Nature Scientific Reports. 2013;.

9. D’Orazio V, Yonamine JE. Kickoff to conflict: A sequence analysis of intra-state conflict-preceding event structures. PloS one. 2015;10(5):e0122472. doi: 10.1371/journal.pone.0122472 25951105

10. Clauset A, Woodward R. Estimating the historical and future probabilities of large terrorist events. The Annals of Applied Statistics. 2013;7. doi: 10.1214/13-AOAS614R

11. Tench S, Fry H, Gill P. Spatio-temporal patterns of IED usage by the Provisional Irish Republican Army. European Journal of Applied Mathematics. 2016;27. doi: 10.1017/S0956792515000686

12. D. MacKa Information Theory, Inference, and Learning Algorithms. Cambridge University Press. 2004.

13. Godet M. The Art of Scenarios and Strategic Planning: Tools and PitfallsTechnological. Forecasting and Social Change. 2000; 65:3–22.

14. Lim M, Metzler R, Bar-Yam Y. Global Pattern Formation and Ethnic/Cultural Violence. Science. 2007;317. doi: 10.1126/science.1142734

15. Turchin P, Currie T, Turner E, Gavrilets S. War, space, and the evolution of Old World complex societies. Proceedings of the National Academy of Sciences (PNAS). 2013;.

16. Kutuzov, A., Velldal, E., Ovrelid, L. Tracing armed conflicts with diachronic word embedding models. ACL Proc. Events & Stories in News. 2017.

17. Hegre, H., Allansson, M., Basedau, M, Colaresi, M., Croicu, M., Fjelde, H. et al. ViEWS: a political violence early-warning system. J. of Peace Research. 2019.

18. Wilson A. Boltzmann, Lotka and Volterra and spatial structural evolution: an integrated methodology for some dynamical systems. Journal of The Royal Society Interface. 2008;5(25):865–871. doi: 10.1098/rsif.2007.1288

19. Baudains P, Zamazalova S, Altaweel M, Wilson A. Modeling Strategic Decisions in the Formation of the Early Neo-Assyrian Empire. J of Quantitative History and Cultural Evolution. 2015;.

20. Guo, W., Lu, X., Mosquera, G., Johnson, S. The Spatial Ecology of War and Peace. arXiv. 2017;.

21. Stouffer SA. Intervening Opportunities: A Theory Relating Mobility and Distance. American Sociological Review. 1940; 5: 845–867.

22. Zipf GK. Human Behavior and the Principle of Least Effort. Addison-Wesley, Reading, MA.

23. Isard W. Location theory and trade theory: short-run analysis. Quarterly Journal of Economics. 1954; 68: 305–322. doi: 10.2307/1884452

24. Zipf GK. The P1P2/D Hypothesis: On the Intercity Movement of Persons. American Sociological Review. 1946; 11: 677–686.

25. Braha D Global Civil Unrest: Contagion, Self-Organization, and Prediction. PLoS ONE. 2012; 7(10): e48596. doi: 10.1371/journal.pone.0048596 23119067

26. Braha D, Stacey B, Bar-Yam Y Corporate competition: A self-organized network. Social Networks. 2011; 33(3):219–30. doi: 10.1016/j.socnet.2011.05.004

27. Bonnasse-Gahot L, Berestycki H, Depuiset MA, Gordon MB, Roché S, Rodriguez N, Nadal JP. Epidemiological modelling of the 2005 French riots: a spreading wave and the role of contagion. Scientific Reports. 2018; 8(1): 107. doi: 10.1038/s41598-017-18093-4 29311553

28. Lee SH, Kim PJ, Jeong H. Statistical properties of sampled networks. Physical Review E. 2006; 73: 016102-1–016102-7. doi: 10.1103/PhysRevE.73.016102

29. Jirsa VK, Ding M. Will a large complex system with time delays be stable? Physical Review Letters. 2004;93(7):070602. doi: 10.1103/PhysRevLett.93.070602 15324222

30. Berman A, Plemmons RJ. Nonnegative matrices in the mathematical sciences. SIAM; 1994.

31. Baudains P, Wilson A. Conflict Modelling: Spatial Interaction as Threat. Global Dynamics: Approaches from Complexity Science. 2016; p. 143–158. doi: 10.1002/9781118937464.ch8

32. Wilson A. Global Dynamics: Approaches from Complexity Science. Wiley Series in Computational and Quantitative Social Science. Wiley; 2016. Available from: https://books.google.com/books?id=cxqJCgAAQBAJ.

33. Wilson AG. Approaches to Geo-mathematical Modelling: New Tools for Complexity Science. Wiley Series in Computational and Quantitative Social Science. Wiley; 2016. Available from: https://books.google.com/books?id=ThqJCgAAQBAJ.

34. Hsiang SM, Burke M, Miguel E. Quantifying the influence of climate on human conflict. Science. 2013.

35. Echeverría Ramírez MC, Rincón Patiño A, González Gómez LM. Ciudad de territorialidades: polémicas de Medellín. 22. Universidad Nacional de Colombia, Sede Medellín, Centro de Estudios del Hábitat Popular-CEHAP; 2000.

36. Sanín FG, Jaramillo AM. Crime,(counter-) insurgency and the privatization of security-the case of Medellín, Colombia. Environment and Urbanization. 2004;16(2):17–30. doi: 10.1177/095624780401600209

37. Alcalá PR. Jóvenes, memoria y violencia en Medellín: una antropología del recuerdo y el olvido. Universidad de Antioquia; 2006.

38. Restrepo JR. Medellín: Fronteras de discriminación y espacios de guerra. La Sociologia en sus escenarios. 2010;(14).

39. Barthélemy M. Spatial networks. Physics Reports. 2011;499(1):1–101.


Článek vyšel v časopise

PLOS One


2019 Číslo 12
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Svět praktické medicíny 1/2024 (znalostní test z časopisu)
nový kurz

Koncepce osteologické péče pro gynekology a praktické lékaře
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Význam metforminu pro „udržitelnou“ terapii diabetu
Autoři: prof. MUDr. Milan Kvapil, CSc., MBA

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#