Assessing the Transformation of Armed Conflict Types: A Dynamic Approach
Armed conflict is a dynamic social phenomenon, yet existing research often overlooks its evolving nature. We propose a method to simulate the dynamic transformations of armed conflicts. First, we enhanced the Spatial Conflict Dynamic Indicator (SCDi) by integrating conflict intensity and clustering,...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/9/5/123 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850257360383115264 |
|---|---|
| author | Dong Jiang Jun Zhuo Peiwei Fan Fangyu Ding Mengmeng Hao Shuai Chen Jiping Dong Jiajie Wu |
| author_facet | Dong Jiang Jun Zhuo Peiwei Fan Fangyu Ding Mengmeng Hao Shuai Chen Jiping Dong Jiajie Wu |
| author_sort | Dong Jiang |
| collection | DOAJ |
| description | Armed conflict is a dynamic social phenomenon, yet existing research often overlooks its evolving nature. We propose a method to simulate the dynamic transformations of armed conflicts. First, we enhanced the Spatial Conflict Dynamic Indicator (SCDi) by integrating conflict intensity and clustering, which allowed for the distinction of various conflict types. Second, we established transformation rules for the SCDi, quantifying five types of transformations: outbreak, stabilization, escalation, de-escalation, and maintaining peace. Using the random forest algorithm with multiple covariates, we simulated these transformations and analyzed the driving factors. The results reveal a global trend of polarization in armed conflicts over the past 20 years, with an increase in clustered/high-intensity (CH) and dispersed/low-intensity (DL) conflicts. Stable regions of ongoing conflict have emerged, notably in areas like Syria, the border of Afghanistan, and Nepal’s border region. New conflicts are more likely to arise near these zones. Various driving forces shape conflict transformations, with neighboring conflict scenarios acting as key catalysts. The capacity of a region to maintain peace largely depends on neighboring conflict dynamics, while local factors are more influential in other types of transformations. This study quantifies the dynamic process of conflict transformations and reveals detailed changes. |
| format | Article |
| id | doaj-art-d749f2139e7f49d59e7fcaf1cf14921c |
| institution | OA Journals |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-d749f2139e7f49d59e7fcaf1cf14921c2025-08-20T01:56:25ZengMDPI AGBig Data and Cognitive Computing2504-22892025-05-019512310.3390/bdcc9050123Assessing the Transformation of Armed Conflict Types: A Dynamic ApproachDong Jiang0Jun Zhuo1Peiwei Fan2Fangyu Ding3Mengmeng Hao4Shuai Chen5Jiping Dong6Jiajie Wu7Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaArmed conflict is a dynamic social phenomenon, yet existing research often overlooks its evolving nature. We propose a method to simulate the dynamic transformations of armed conflicts. First, we enhanced the Spatial Conflict Dynamic Indicator (SCDi) by integrating conflict intensity and clustering, which allowed for the distinction of various conflict types. Second, we established transformation rules for the SCDi, quantifying five types of transformations: outbreak, stabilization, escalation, de-escalation, and maintaining peace. Using the random forest algorithm with multiple covariates, we simulated these transformations and analyzed the driving factors. The results reveal a global trend of polarization in armed conflicts over the past 20 years, with an increase in clustered/high-intensity (CH) and dispersed/low-intensity (DL) conflicts. Stable regions of ongoing conflict have emerged, notably in areas like Syria, the border of Afghanistan, and Nepal’s border region. New conflicts are more likely to arise near these zones. Various driving forces shape conflict transformations, with neighboring conflict scenarios acting as key catalysts. The capacity of a region to maintain peace largely depends on neighboring conflict dynamics, while local factors are more influential in other types of transformations. This study quantifies the dynamic process of conflict transformations and reveals detailed changes.https://www.mdpi.com/2504-2289/9/5/123armed conflictconflict dynamicsconflict intensityconflict clusteringconflict transformation |
| spellingShingle | Dong Jiang Jun Zhuo Peiwei Fan Fangyu Ding Mengmeng Hao Shuai Chen Jiping Dong Jiajie Wu Assessing the Transformation of Armed Conflict Types: A Dynamic Approach Big Data and Cognitive Computing armed conflict conflict dynamics conflict intensity conflict clustering conflict transformation |
| title | Assessing the Transformation of Armed Conflict Types: A Dynamic Approach |
| title_full | Assessing the Transformation of Armed Conflict Types: A Dynamic Approach |
| title_fullStr | Assessing the Transformation of Armed Conflict Types: A Dynamic Approach |
| title_full_unstemmed | Assessing the Transformation of Armed Conflict Types: A Dynamic Approach |
| title_short | Assessing the Transformation of Armed Conflict Types: A Dynamic Approach |
| title_sort | assessing the transformation of armed conflict types a dynamic approach |
| topic | armed conflict conflict dynamics conflict intensity conflict clustering conflict transformation |
| url | https://www.mdpi.com/2504-2289/9/5/123 |
| work_keys_str_mv | AT dongjiang assessingthetransformationofarmedconflicttypesadynamicapproach AT junzhuo assessingthetransformationofarmedconflicttypesadynamicapproach AT peiweifan assessingthetransformationofarmedconflicttypesadynamicapproach AT fangyuding assessingthetransformationofarmedconflicttypesadynamicapproach AT mengmenghao assessingthetransformationofarmedconflicttypesadynamicapproach AT shuaichen assessingthetransformationofarmedconflicttypesadynamicapproach AT jipingdong assessingthetransformationofarmedconflicttypesadynamicapproach AT jiajiewu assessingthetransformationofarmedconflicttypesadynamicapproach |