Military Interstate Dispute Spatial RelationshipsCold War Space-Time Cluster Analysis

Tyler Gill, University of Missouri, Department of GeographyBackground

Average Nearest Neighbor• Z-score = -52.451803• Z-score under -2.58 is clustered with a significates of 0.01Moran’s I • Z-score = 23.1137789948• Z-score greater than 2.58 is clustered with a significates

of 0.01• The results verified clustering at the global levelGetis-Ord Gi*• Map 1 is an overall map of hot spots from 1945-1992• Map 2 – 6 are time interval maps with red points

representing hot spots with 95% confidence• As time grew so did the extent of the clustering of

MIDLOC in the Middle East• The time interval of 1985-1992 saw conflict hot spots

expand across the world- This could be attributed to the breakup of the SovietUnion

• Factors of the results include:- Countries basemap used- Construction of the spatial weights matrix

Project Overview

• This project will examine MID for the Cold War period (1945-1992)

• The dataset is for the onset of MID, not the incidents that occur within the MID

Objectives:(1): Investigate clustering of MIDLOC at a global level (2): Examine where clustering is located at a local level using a space-time analysis(3): Observe changes in clustering over time with the space-time analysis• My hypothesis is there is clustering at the global level • It is concentrated in the Middle East and Southeast

Europe• Also the clustering of hotspots has remained stationary

over time• Important to identify clusters for future research for

analysis of MIDLOC clusters• Most research focuses on the contextual factors of MID

but not MID locations

• First, I check statistically for clustering of MIDLOC at a global level• Average Nearest Neighbor tool

- Calculates a nearest neighbor index based on the average distance from each feature to itsnearest neighboring feature

- Important first step because MIDLOC data is a point dataset• Next, I generated a spatial weights matrix to conceptualize the space-time window

- Neighborhood distance interval = 400 miles- Time interval = 10 years

• The space-time window breaks down spatial relationships into five year groups• Moran’s I tool

- Examines spatial autocorrelation and global level clustering- Uses space-time window as conceptualization of the spatial relationship - If clustering at the global level then the next step is to find where the clustering occurs at

the local level• I used Getis-Ord Gi* hot spot analysis to observe local clustering for the different time

intervals because of the results of Moran’s I statistic • Getis-Ord Gi*

- Identifies hot spot clusters for the dataset using space-time window; set at 95% confidence- A high Z-score for a feature indicates its neighbors have high attribute values - Compare different time-intervals against each other to find trends in the hot spots

• Militarized interstate disputes (MID) are international conflicts that never reach the level of war

• Interactions include:(1): Threat of Force(2): Display of force(3): Actual Use of Force

• The Militarized Interstate Dispute Location (MIDLOC) dataset developed by Alex Braithwaite (2010)

• MIDLOC dataset has three objectives- Examine patterns of participation in conflicts- Examine the influence that many geopolitical

factors- Identify possible ‘problem-areas’

• This study builds on the third point to identify hot spots and find statistical ‘problem-areas’

• Space-time clustering is a relatively new approach to conceptualize spatial relationships

• I was able to reject the null hypothesis for the first objective that clustering would be present at the global level for the dataset

• Nearest neighbor and Moran’s I both statistically significate at 99% confidence level

• The second objective comes with mixed results but presents a trend for the third objective

• While hot spots were located in the Middle East, they were not bound to that region

• I was not able to reject the null hypothesis for the third objective that clustering remained stationary over time

• I accept an alternative hypothesis that MIDLOC hotspots have expanded over time

• Future research can be done to determine why hotspots have their locations

- A geographic weighted regression is one approach

Research Methods Results Discussion

Braithwaite, A. (2010). MIDLOC: Introducing the Militarized Interstate Dispute Location dataset. Journal of Peace Studies. 47(1), 91-98.

Chen, J., Shaw, S.H., Yu, H., Lu, F., Chai, Y., Jia, Q. (2011). Exploring data analysis of activitydiary data: a space-time GIS approach. Journal of Transport Geography. 19, 394-404.

Quackenbush, S. (2015). International Conflict: Logic and Evidence. Los Angeles: Sage.

References

Conclusions

Map 2 Map 3 Map 4 Map 5 Map 6

Nearest Neighbor Statistic Moran’s I StatisticMap 1