Author ORCID Identifier
0000-0001-8555-590X
0000-0002-2670-5864
DOI
10.22191/nejcs/vol6/iss1/3
Abstract
We propose an extension of a previously proposed method for lead-lag analysis of multivariate time series to include the analysis of spatial correlations. We applied the extended spatial and temporal method to CIRIGHTS, a large global human rights dataset, in order to determine the most influential and most influenced indicators of human rights, freedoms, and atrocities over time. We consider four target countries, each from a different continent. The previously proposed method used a weighted directed network with several lags of each variable as nodes and with edges weighted by transfer entropy. In this study, that method is extended to the spatial multivariate time series by investigating geographical correlations between each target country and its surrounding region to determine which variables best represent activity between the target country and the surrounding region. The nodes in this undirected network are variables in the target country and variables in the surrounding region, with edges weighted by mutual information. Eigenvector centrality is used to determine which variables best represent activity between the target country and the surrounding region. Key findings indicate that occupational and worker rights are the most influential and most influenced in the target countries over time and by region, and laws tend to influence future activity.
Recommended Citation
Goodrick, Amanda; Mark, Skip; Filippov, Mikhail G.; Cingranelli, David; and Sayama, Hiroki
(2024)
"Multivariate Analysis of the Temporal and Spatial Correlations of the Global Human Rights Dataset,"
Northeast Journal of Complex Systems (NEJCS): Vol. 6
:
No.
1
, Article 3.
DOI: 10.22191/nejcs/vol6/iss1/3
Available at:
https://orb.binghamton.edu/nejcs/vol6/iss1/3