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Correction

Correction: Jiang et al. Assessing the Transformation of Armed Conflict Types: A Dynamic Approach. Big Data Cogn. Comput. 2025, 9, 123

1
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Big Data Cogn. Comput. 2025, 9(11), 269; https://doi.org/10.3390/bdcc9110269
Submission received: 10 September 2025 / Revised: 11 September 2025 / Accepted: 15 September 2025 / Published: 24 October 2025

Text Correction

There was an error in the original publication [1]. In the fourth paragraph of the Section 1 Introduction, the authors cite reference [19], noting that [19] highlights the limitations of other prior papers. However, in the Methods Section, the authors describe how Walther et al. proposed the SCDi indicator to address this limitation, without emphasizing it in the introduction.
A correction has been made to the fourth paragraph of the Introduction:
Recent research has begun to address the dynamic nature of armed conflicts by modeling their escalation and de-escalation. Mueller et al. [17] developed a predictive framework based on conflict history and textual features extracted from news reports to forecast violence escalation. Their model demonstrated moderate success in identifying short-term trends but struggled with long-term predictions due to its reliance on historical data alone. In a more advanced approach, Vestby et al. [18] evaluated gradient boosting algorithms to predict subnational violence escalation and de-escalation, achieving exceptional performance in capturing localized trends. Despite these advances, current models rarely consider the multifaceted attributes of conflicts, such as their intensity (the severity of violence) and concentration (the spatial clustering of events). These attributes are critical for understanding the magnitude and geographic scope of conflicts, yet their integration into dynamic models remains limited. Walther et al. [19] proposed the Spatial Conflict Dynamic Indicator to address this issue. Furthermore, few studies adopt a spatiotemporal perspective, which is essential for capturing the evolution of conflicts across time and space [20].
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

References

In the first paragraph of Section 2.2, “Construction of the SCDi and Its Transformation Types”, the first sentence of the first paragraph incorrectly cited reference [50]. The correct reference is [19]. The sentence has been corrected to read: “Walther et al. [19]…” With this correction, the order of some references has been adjusted accordingly.
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Jiang, D.; Zhuo, J.; Fan, P.; Ding, F.; Hao, M.; Chen, S.; Dong, J.; Wu, J. Assessing the Transformation of Armed Conflict Types: A Dynamic Approach. Big Data Cogn. Comput. 2025, 9, 123. [Google Scholar] [CrossRef]
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Share and Cite

MDPI and ACS Style

Jiang, D.; Zhuo, J.; Fan, P.; Ding, F.; Hao, M.; Chen, S.; Dong, J.; Wu, J. Correction: Jiang et al. Assessing the Transformation of Armed Conflict Types: A Dynamic Approach. Big Data Cogn. Comput. 2025, 9, 123. Big Data Cogn. Comput. 2025, 9, 269. https://doi.org/10.3390/bdcc9110269

AMA Style

Jiang D, Zhuo J, Fan P, Ding F, Hao M, Chen S, Dong J, Wu J. Correction: Jiang et al. Assessing the Transformation of Armed Conflict Types: A Dynamic Approach. Big Data Cogn. Comput. 2025, 9, 123. Big Data and Cognitive Computing. 2025; 9(11):269. https://doi.org/10.3390/bdcc9110269

Chicago/Turabian Style

Jiang, Dong, Jun Zhuo, Peiwei Fan, Fangyu Ding, Mengmeng Hao, Shuai Chen, Jiping Dong, and Jiajie Wu. 2025. "Correction: Jiang et al. Assessing the Transformation of Armed Conflict Types: A Dynamic Approach. Big Data Cogn. Comput. 2025, 9, 123" Big Data and Cognitive Computing 9, no. 11: 269. https://doi.org/10.3390/bdcc9110269

APA Style

Jiang, D., Zhuo, J., Fan, P., Ding, F., Hao, M., Chen, S., Dong, J., & Wu, J. (2025). Correction: Jiang et al. Assessing the Transformation of Armed Conflict Types: A Dynamic Approach. Big Data Cogn. Comput. 2025, 9, 123. Big Data and Cognitive Computing, 9(11), 269. https://doi.org/10.3390/bdcc9110269

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