{"metadata":{"id":"0062f9675369b3fa722eaa9f3a87b413","source":"gardian_index","url":"https://cgspace.cgiar.org/rest/bitstreams/3edea7f6-4239-4076-8e09-00b77cedf864/retrieve"},"pageCount":5,"title":"","keywords":[],"chapters":[{"head":"M E T H O D S & D A T A","index":1,"paragraphs":[{"index":1,"size":80,"text":"To model the underlying structure of the climate-socio-economic-conflict relationships, we use a regularized partial correlation networks (Epskamp & Fried, 2018), as part of the more general class of pairwise Markov random fields models (Koller & Friedman, 2009). It is a statistical modelling technique used to analyze the relationships between variables in a complex system that can be represented as an undirected network with nodes and edges. A network is a graphical representation of the relationships (edges) between different entities (nodes)."}]},{"head":"Network representation of the climate-socio-economic-conflict relationships","index":2,"paragraphs":[{"index":1,"size":121,"text":"The variables, represented by the nodes, are categorized as (a) climate variables, (b) conflict variables, and (c) socio-economic risk variables which are further sub-categorized as follows: inequality, low productivity, migration, resources scarcity and undernutrition. The edges between nodes, representing the partial correlation coefficients encode the remaining linear dependency between two variables after controlling for the effect of the rest of the variables in the network (conditional independence associations). The width of an edge reflects the strength of the corresponding partial correlation. The size of a node reflects the sum of the widths of all edges that are linked to that node, and reflects the importance of that node through its level of degree of connectivity, also termed as its weighted degree."}]},{"head":"Building the network model","index":3,"paragraphs":[{"index":1,"size":75,"text":"To estimate the partial correlations, we first compute the direct correlations between all pairs of variables as a basis. After controlling for multicollinearity among the variables, the correlation coefficients were calculated from Spearman's rank correlations for continuous variables. Polychoric correlations were used for categorical variables, polyserial and biserial correlations used between variables of different types. The resulting correlation matrix is then used to estimate the inverse covariance matrix of which elements represent the partial correlations.  "}]},{"head":"e d t o e a c h o t h e r a l t h o u g h t h e y s h o w a n o n -z e r o p a r t i a l c o r r e l a t i o n . T h i s e n s u r e s t h a t w h e n e s t i m a t i n g t h e i n v e r s e c o v a r i a n c e m a t r i x ( i . e . t h e p a r t i a l c o r r e l a t i o n m a t r i x ) , w e r e t a i n t h e n e t w o r k s t r u c t u r e t h a t e l i m i n a t e s n o n -s i g n i f i c a n t ( s p u r i o u s ) r e l a t i o n s h i p s . F o r t h e","index":4,"paragraphs":[]}],"figures":[{"text":" T h e n , i n a r e g u l a r i z e d p a r t i a l c o r r e l a t i o n n e t w o r k , t h e g o a l i s t o e s t i m a t e t h e p a r t i a l c o r r e l a t i o n s b e t w e e n a l l p a i r s o f v a r i a b l e s w h i l e t a k i n g i n t o a c c o u n t t h e s p a r s i t y o f t h e n e t w o r k . T h e s p a r s i t y a s s u m p t i o n o f a r e g u l a r i z e d n e t w o r k a s s u m e s t h a t m o s t v a r i a b l e s i n t h e n e t w o r k a r e n o t d i r e c t l y r e l a t "},{"text":" r e g u l a r i z a t i o n p r o c e s s , w e u s e d t h e g r a p h i c a l l a s s o a l g o r i t h m ( F r i e d m a n e t a l . , 2 0 0 8 ) . I t c o n s i s t s o f i m p o s i n g a p e n a l t y o n t h e n u m b e r o f n o n -z e r o p a r t i a l c o r r e l a t i o n s , e n c o u r a g i n g s p a r s i t y i n t h e n e t w o r k s t r u c t u r e w h i l e e l e m e n t s o f t h e i n v e r s e c o v a r i a n c e m a t r i x a r e u p d a t e d i t e r a t i v e l y i n a n a lg o r i t h m t h a t o p t i m i z e s t h e l i k e l i h o o d o f o b s e r v i n g t h e d a t a f o r e a c h e s t i m a t e p a r t i a l c o r r e l a t i o n . T h e s e l e c t i o n o f t h e b e s t c o n f i g u r a t i o n o f t h e p a r t i al c o r r e l a t i o n n e t w o r k f o l l o w s f r o m a n E B I C m o d e l s e l e c t i o n ( C h e n & C h e n , 2 0 0 8 ) . W e b u i l t t h e n e t w o r k m o d e l u s i n g t h e R e n v i r o n m e n t f o r s t a t i s t i c a l c o m p u t i n g ( R C o r e T e a m ) w i t h t h e p a c k a g e q g r a p h ( E p s k a m p e t a l . , 2 0 1 2 ) . D a t a s o u r c e s C o n f l i c t d a t a a r e f r o m A C L E D ; c l i m a t e d a t a a r e f r o m C H I R P S , T e r r a C l i m a t e , a n d A g E R A 5 ; a n d s o c i o -e c o n o m i c v a r i a b l e s a r e f r o m t h e I n s t i t u t e f o r H e a l t h M e t r i c s a n d E v a l u a t i o n ( I H M E ) , F a c e b o o k ' s w e a l t h m a p s , M a l a r i a A t l a s P r o j e c t , M O D I S , N A S A S E D A C a t t h e C e n t e r f o r I n t e r n a t i o n a l E a r t h S c i e n c e I n f o r m a t i o n N e t w o r k , E a r t h O b s e r v a t i o n G r o u p , P a y n e I n s t i t u t e f o r P u b l i c P o l i c y , C o l o r a d o S c h o o l o f M i n e s , E n v i r o m e t r i X L t d , a m o n g s t o t h e r s . M o s t o f t h e s e d a t a a r e d i r e c t l y a v a i l a b l e t h r o u g h G o o g l e E a r t h E n g i n e p l a t f o r m . "},{"text":"  "}],"sieverID":"b6f78d8c-d4e7-4eab-8e7c-458649aa3e04","abstract":""}