# A Cellular Automata Model of the Relationship between Adverse Events and Regional Infrastructure Development in an Active War Theater

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Background

#### Study Area

## 3. Methods

#### 3.1. Cellular Automata Model

#### 3.2. Research Data

#### 3.3. Neighborhood Definition

_{d}, defined as a set of any first-order neighbors having a common border with the center district d. Each cell in the Voronoi diagram represents one district. To improve visual analysis, the size and location of the Voronoi cells was held to be similar to that of the political map of Afghanistan.

#### 3.4. State of Cells

#### 3.5. Transition Rules

#### 3.5.1. Transition State

#### 3.5.2. Adverse Event Change Ratio

#### 3.5.3. Weighted Euclidean Distance

- ${T}^{*}(j)=T(j)$ with the minimum $E\left(j\right)$ that have the same province as ${\tau}_{d}^{*}\left(t\right)$;
- ${T}^{*}(j)={T}^{*}(j)$ with the same district as ${\tau}_{d}^{*}\left(t\right)$;
- ${T}^{*}(j)={T}^{*}(j)$ with either the Islamic months of 9 or 12 (Ramadan and Dhul-Hijjah, respectively);
- Calculate the ratio of change in adverse events, ${r}^{a}$, for all a=k, w, h, and c as the mean of ${\tau}_{{d}^{\prime}}^{a}\left({t}^{\prime}\right)$ in ${T}^{*}(j)$.

#### 3.6. Weight of Impact Calculation

## 4. Results and Discussion

#### 4.1. Model Capability

#### 4.2. Performance Comparison of Models

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Collins, J.J. Understanding War in Afghanistan. DTIC Document; DTIC: Fort Belvoir, VA, USA, 2011.
- Tarnoff, C. Afghanistan: US Foreign Assistance. DTIC Document; DTIC: Fort Belvoir, VA, USA, 2010.
- Child, T.B. Reconstruction and Insurgency: The Importance of Sector in Afghanistan. Available online: http://www.europeanpeacescientists.org/T.B.Child_Stuart%20Bremer%20Winner%202014.pdf (accessed on 4 April 2017).
- Reveley, M.S.; Briggs, J.L.; Evans, J.K.; Jones, S.M.; Kurtoglu, T.; Leone, K.M.; Sandifer, C.E. Causal Factors and Adverse Events of Aviation Accidents and Incidents Related to Integrated Vehicle Health Management. NASA/TM—2011-216967. Available online: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20110009984.pdf (accessed on 2 April 2017).
- Rafter, N.; Hickey, A.; Condell, S.; Conroy, R.; O’Connor, P.; Vaughan, D.; Williams, D. Adverse events in healthcare: Learning from mistakes. QJM Int. J. Med.
**2015**, 108, 273–277. [Google Scholar] [CrossRef] [PubMed] - Rochefort, C.M.; Verma, A.D.; Eguale, T.; Lee, T.C.; Buckeridge, D.L. A novel method of adverse event detection can accurately identify venous thromboembolisms (vtes) from narrative electronic health record data. J. Am. Med. Inform. Assoc.
**2015**, 22, 155–165. [Google Scholar] [CrossRef] [PubMed] - Armitage, G.; Knapman, H. Adverse events in drug administration: A literature review April 2003. J. Nurs. Manag.
**2003**, 11, 130–140. [Google Scholar] [CrossRef] [PubMed] - Casillas, A.; Pérez, A.; Oronoz, M.; Gojenola, K.; Santiso, S. Learning to extract adverse drug reaction events from electronic health records in Spanish. Expert Syst. Appl.
**2016**, 61, 235–245. [Google Scholar] [CrossRef] - Çakıt, E.; Karwowski, W. A fuzzy overlay model for mapping adverse event risk in an active war theatre. J. Exp. Theor. Artif. Intell.
**2018**, 30, 691–701. [Google Scholar] [CrossRef] - Numrich, S.; Tolk, A. Challenges for Human, Social, Cultural, and Behavioral Modeling. SCS M S Mag.
**2010**, 4, 1–9. [Google Scholar] [CrossRef] - Bhattacharjee, Y. Pentagon Asks Academics for Help in Understanding its Enemies. Science
**2007**, 316, 534–535. [Google Scholar] [CrossRef] - HSCB Modeling Program. Available online: http://www.dtic.mil/biosys/docs/HSCB newsspring-2009.pdf (accessed on 12 June 2018).
- Federation of American Scientists. A Geoint Analysis of Terrorism in Afghanistan. Secrecy News; Federation of American Scientists: Washington, DC, USA, 2009.
- Van Hemel, S.B.; MacMillan, J.; Zacharias, G.L. Behavioral Modeling and Simulation: From Individuals to Societies; National Academies Press: Washington, DC, USA, 2008. [Google Scholar]
- LaFree, G.; Dugan, L.; Xie, M.; Singh, P. Spatial and temporal patterns of terrorist attacks by ETA 1970 to 2007. J. Quant. Criminol.
**2012**, 28, 7–29. [Google Scholar] [CrossRef] - Berrebi, C.; Lakdawalla, D. How does terrorism risk vary across space and time? An analysis based on the Israeli experience. Def. Peace Econ.
**2007**, 18, 113–131. [Google Scholar] [CrossRef] - Siebeneck, L.K.; Medina, R.M.; Yamada, L.; Hepner, G.F. Spatial and temporal analyses of terrorist incidents in Iraq, 2004–2006. Stud. Confl. Terror.
**2009**, 32, 591–610. [Google Scholar] [CrossRef] - Python, A.; Illian, J.B.; Jones-Todd, C.M.; Blangiardo, M. A Bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010–2016. J. R. Stat. Soc. Ser. A
**2019**, 182, 323–344. [Google Scholar] [CrossRef] - Marchment, Z.; Gill, P. Modelling the spatial decision making of terrorists: The discrete choice approach. Appl. Geogr.
**2019**, 104, 21–31. [Google Scholar] [CrossRef] - Hudak, D.; Baez, F. Cultural Geography Modeling and Analysis in Helmand Province. DTIC Document; DTIC: Fort Belvoir, VA, USA, 2010.
- Malleson, N.; Heppenstall, A.; See, L. Crime reduction through simulation: An agent-based model of burglary. Comput. Environ. Urban Syst.
**2010**, 34, 236–250. [Google Scholar] [CrossRef] - Schmorrow, D.; Nicholson, D. Advances in Cross-Cultural Decision Making; CRC Press: Boca Raton, FL, USA, 2010. [Google Scholar]
- Tutun, S.; Wang, H.; Liu, Z.; Yıldırım, M.F.; Khanmohammadi, S. An Agent Based Approach for Understanding Complex Terrorism Behaviors. In Industrial & Systems Engineering Research Conference (ISERC); Yang, H., Kong, Z., Sarder, M.D., Eds.; Institute of Industrial Engineers (IIE): Peachtree Corners, GA, USA, 2016. [Google Scholar]
- Kari, J. Theory of cellular automata: A survey. Theor. Comput. Sci.
**2005**, 334, 3–33. [Google Scholar] [CrossRef] [Green Version] - Spicer, V.; Reid, A.A.; Ginther, J.; Seifi, H.; Dabbaghian, V. Bars on blocks: A cellular automata model of crime and liquor-licensed establishment density. Comput. Environ. Urban Syst.
**2012**, 36, 412–422. [Google Scholar] [CrossRef] - Vaz, E.D.N.; Nijkamp, P.; Painho, M.; Caetano, M. A multi-scenario forecast of urban change: A study on urban growth in the Algarve. Landsc. Urban Plan.
**2012**, 104, 201–211. [Google Scholar] [CrossRef] [Green Version] - Wu, B.S.; Sui, D.Z. CA-based Simulation of Asian Urban Dynamics: A Case Study of Taipei Metropolitan Area, Taiwan. In Proceedings of the IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, 7–11 July 2008; Volume 5, pp. V-13–V-16. [Google Scholar]
- Wu, D.; Liu, J.; Wang, S.; Wang, R. Simulating urban expansion by coupling a stochastic cellular automata model and socioeconomic indicators. Stoch. Environ. Res. Risk Assess.
**2009**, 24, 235–245. [Google Scholar] [CrossRef] - Lauf, S.; Haase, D.; Hostert, P.; Lakes, T.; Kleinschmit, B. Uncovering land-use dynamics driven by human decision-making–A combined model approach using cellular automata and system dynamics. Environ. Model. Softw.
**2012**, 27, 71–82. [Google Scholar] [CrossRef] - Mago, V.K.; Bakker, L.; Papageorgiou, E.I.; Alimadad, A.; Borwein, P.; Dabbaghian, V. Fuzzy cognitive maps and cellular automata: An evolutionary approach for social systems modeling. Appl. Soft Comput.
**2012**, 12, 3771–3784. [Google Scholar] [CrossRef] - Çakıt, E.; Karwowski, W.; Bozkurt, H.; Ahram, T.; Thompson, W.; Mikusinski, P.; Lee, G. Investigating the relationship between adverse events and infrastructure development in an active war theater using soft computing techniques. Appl. Soft Comput.
**2014**, 25, 204–214. [Google Scholar] [CrossRef] [Green Version] - Çakit, E.; Karwowski, W. Assessing the Relationship between Economic Factors and Adverse Incidents in an Active War Theater Using Fuzzy Inference System Approach. Int. J. Mach. Learn. Comput.
**2015**, 5, 252–257. [Google Scholar] [CrossRef] - Çakit, E.; Karwowski, W. Fuzzy Inference Modelling with the Help of Fuzzy Clustering for Predicting the Occurrence of Adverse Incidents in an Active Theater of War. Appl. Artif. Intell.
**2015**, 29, 945–961. [Google Scholar] [CrossRef] - Çakit, E.; Karwowski, W. Gaining insight by applying geographical modeling. In Modeling Sociocultural Influences on Decision Making: Understanding Conflict, Enabling Stability; Cohn, J.V., Schatz, S., Freeman, H., Combs, D.J.Y., Eds.; CRC Press: Boca Raton, FL, USA, 2016; pp. 243–266. [Google Scholar]
- Çakıt, E.; Karwowski, W. Predicting the occurrence of adverse events using an adaptive neuro-fuzzy inference system (ANFIS) approach with the help of ANFIS input selection. Artif. Intell. Rev.
**2017**, 48, 139–155. [Google Scholar] [CrossRef] - Çakıt, E.; Karwowski, W. Understanding the Social and Economic Factors Affecting Adverse Incidents in an Active Theater of War: A Neural Network Approach. In International Conference on Applied Human Factors and Ergonomics; Çakıt, E., Karwowski, W., Eds.; Springer: Berlin, Germany, 2017; pp. 215–223. [Google Scholar]
- Shi, D.; Zurada, J.; Karwowski, W.; Guan, J.; Çakıt, E. Batch and data streaming classification models for detecting adverse events and understanding the influencing factors. Eng. Appl. Artif. Intell.
**2019**, 85, 72–84. [Google Scholar] [CrossRef] - Flache, A.; Hegselmann, R. Do irregular grids make a difference? Relaxing the spatial regularity assumption in cellular models of social dynamics. J. Artif. Soc. Soc. Simul.
**2001**, 4, 6. [Google Scholar] - Hegselmann, R.; Flache, A. Understanding complex social dynamics: A plea for cellular automata based modeling. J. Artif. Soc. Soc. Simul.
**1998**, 1, 1. [Google Scholar] - Willmott, C.J.; Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res.
**2005**, 30, 79. [Google Scholar] [CrossRef]

**Figure 4.**Visualization of irregular cellular automata neighborhoods for Afghanistan’s 400 districts.

$\mathbf{Impact}\mathbf{Factors}\mathbf{on}\mathbf{Sec}\mathbf{urity}\mathbf{Incidents}\mathbf{at}\mathbf{Time}{\mathit{t}}_{1}$ | ||||
---|---|---|---|---|

$\mathbf{State}\mathbf{of}\mathbf{a}\mathbf{Centering}\mathbf{District}\mathbf{at}\mathbf{Time}{\mathit{t}}_{0}$ | Killed | Wounded | Hijacked | Count |

Sum and count of infrastructure development (ω_{δγ}) | 0.40 | 0.10 | 0.10 | 0.10 |

Population (ω_{ϖ}) | 0.10 | 0.10 | 0.40 | 0.90 |

Total killed (ω_{k}) | 0.90 | 0.10 | 0.10 | 0.10 |

Total wounded (ω_{w}) | 0.40 | 0.90 | 0.90 | 0.10 |

Total hijacked (ω_{h}) | 0.10 | 0.10 | 0.90 | 0.10 |

Total of all security incidents (ω_{c}) | 0.40 | 0.40 | 0.10 | 0.40 |

$\mathbf{Impact}\mathbf{Factors}\mathbf{on}\mathbf{Security}\mathbf{Incidents}\mathbf{at}\mathbf{Time}{\mathit{t}}_{1}$ | ||||
---|---|---|---|---|

$\mathbf{State}\mathbf{of}\mathbf{a}\mathbf{Centering}\mathbf{District}\mathbf{at}\mathbf{Time}{\mathit{t}}_{0}$ | Killed | Wounded | Hijacked | Count |

Sum and count of infrastructure development (ω_{δγ}) | 0.13 | 0.03 | 0.10 | 0.05 |

Population (ω_{ϖ}) | 0.03 | 0.03 | 0.40 | 0.45 |

Total killed (ω_{k}) | 0.45 | 0.03 | 0.05 | 0.03 |

Total wounded (ω_{w}) | 0.20 | 0.30 | 0.45 | 0.03 |

Total hijacked (ω_{h}) | 0.05 | 0.03 | 0.45 | 0.03 |

Total of all security incidents (ω_{c}) | 0.20 | 0.13 | 0.05 | 0.13 |

Model Variation | Performance Measure | Killed | Wounded | Hijacked |
---|---|---|---|---|

CA with standardized Euclidean distances | MAE | 0.92 | 1.8 | 0.35 |

RMSE | 3.81 | 29.32 | 1.98 | |

CA with weighted Euclidean distances | MAE | 0.75 | 1.33 | 0.28 |

RMSE | 3.07 | 6.88 | 1.7 |

Killed | Wounded | Hijacked | |
---|---|---|---|

Exact Prediction (%) | 83.29 | 81.27 | 93.29 |

One-Away (%) | 88.90 | 85.22 | 95.10 |

Three-Away (%) | 93.90 | 91.54 | 97.33 |

Real Case | What-If Scenario | |||
---|---|---|---|---|

USAID Sector | Amount Spent | Count of Aid | Amount Spent | Count of Aid |

Agriculture | $220 | 0.01 | $440 | 0.01 |

Capacity Building | $31 | 0.01 | $62 | 0.01 |

Commerce & Industry | $10,305 | 0.14 | $20,611 | 0.28 |

Community Development | $438 | 0.00 | $876 | 0.01 |

Education | $705,451 | 9.56 | $1,410,902 | 19.12 |

Emergency Assistance | $0 | 0.00 | $0 | 0.00 |

Energy | $16,547 | 0.02 | $33,095 | 0.04 |

Environment | $10,270 | 0.02 | $20,539 | 0.05 |

Gender | $0 | 0.00 | $0 | 0.00 |

Governance | $2,132,035 | 0.24 | $4,264,071 | 0.48 |

Health | $12,064 | 0.03 | $24,129 | 0.07 |

Security | $0 | 0.00 | $0 | 0.00 |

Transport | $2,062,176 | 0.09 | $4,124,351 | 0.17 |

Water & Sanitation | $20,009 | 0.04 | $40,018 | 0.08 |

Grand Total ≥ | $4,969,547 | 10.15 | $9,939,093 | 20.30 |

Dependent Variable | Methods | MAE |
---|---|---|

Number of people killed | Çakıt and Karwowski (2015) [33] | 2.17 |

Proposed CA Model Variation 1 | 0.92 | |

Proposed CA Model Variation 2 | 0.75 | |

Number of people wounded | Çakıt and Karwowski (2015) | 4.30 |

Proposed CA Model Variation 1 | 1.81 | |

Proposed CA Model Variation 2 | 1.33 | |

Number of people hijacked | Çakıt and Karwowski (2015) | 0.50 |

Proposed CA Model Variation 1 | 0.35 | |

Proposed CA Model Variation 2 | 0.28 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Bozkurt, H.; Karwowski, W.; Çakıt, E.; Ahram, T.
A Cellular Automata Model of the Relationship between Adverse Events and Regional Infrastructure Development in an Active War Theater. *Technologies* **2019**, *7*, 54.
https://doi.org/10.3390/technologies7030054

**AMA Style**

Bozkurt H, Karwowski W, Çakıt E, Ahram T.
A Cellular Automata Model of the Relationship between Adverse Events and Regional Infrastructure Development in an Active War Theater. *Technologies*. 2019; 7(3):54.
https://doi.org/10.3390/technologies7030054

**Chicago/Turabian Style**

Bozkurt, Halil, Waldemar Karwowski, Erman Çakıt, and Tareq Ahram.
2019. "A Cellular Automata Model of the Relationship between Adverse Events and Regional Infrastructure Development in an Active War Theater" *Technologies* 7, no. 3: 54.
https://doi.org/10.3390/technologies7030054