greg mcinerny @ gregmci 2020science/people/greg-mcinerny gmcinerny@hotmail
DESCRIPTION
Interpreting and visualising outputs. Greg McInerny @ GregMcI www.2020science.net/people/greg-mcinerny [email protected]. 2020 Scienc e. 1. Visualisation. do we spend too much time exhibiting our work?. Goals in data visualisation. X1, Y1, x2, y2 …. data. Encoding. Decoding. - PowerPoint PPT PresentationTRANSCRIPT
G r e g M c I n e r n y@ G r e g M c I w w w . 2 0 2 0 s c i e n c e . n e t / p e o p l e / g r e g - m c i n e r n y g m c i n e r n y @ h o t m a i l . c o m
I n t e r p r e ti n g a n d v i s u a l i s i n g o u t p u t s
2 0 2 0 S C I E N C E
1 . V i s u a l i s a ti o nDO WE SPEND TOO MUCH TIME EXHIBITING OUR WORK?
E x h i b i t “ W o w, X & Y l o o k s a m a z i n g ,I n e e d t o fi n d o u t m o r e ! ”
D A T A E N C O D I N G D E C O D I N G
E x p l o r e “ I w o n d e r h o w x r e l a t e s t o y ”
E x p l a i n “ X d o e s y ”
X1, Y1, x2, y2 …
G o a l s i n d a t a v i s u a l i s a ti o n
E x h i b i t “ W o w, X & Y l o o k s a m a z i n g ,I n e e d t o fi n d o u t m o r e ! ”
D A T A E N C O D I N G D E C O D I N G
E x p l o r e “ I w o n d e r h o w x r e l a t e s t o y ”
E x p l a i n “ X d o e s y ”
X1, Y1, x2, y2 …
G o a l s i n d a t a v i s u a l i s a ti o n
E l i t h , J . & L e a t h w i c k , J . R . ( 2 0 0 9 ) A n n u a l R e v i e w o f E c o l o g y , E v o l u ti o n a n d S y s t e m a ti c s , 4 0 , 6 7 7 – 6 9 7 .
( 1 ) R e c o d e
( 2 ) H o p e
T h u i l l e r , W . e t a l . ( 2 0 0 5 ) G E B . 1 4 , 3 4 7 – 3 5 7 .
“ w e o b s e r v e d t h a t 8 3 % o f a r ti c l e s s t u d i e s f o c u s e d e x c l u s i v e l y o n m o d e lo u t p u t ( i . e . m a p s ) w i t h o u t p r o v i d i n g r e a d e r s w i t h a n y m e a n s t o c r i ti c a l l y e x a m i n e m o d e l l e d r e l a ti o n s h i p s ”
Y a c k u l i c , C . B . e t a l . ( 2 0 1 2 ) M E E . 3 , 5 4 5 - 5 5 4
( 3 ) S u m m a r i s e
5 , 0 4 1 p i x e l s o fi n f o r m a ti o n
“ t h e r e s u l t s r e v e a l a n i n t r i g u i n g p a tt e r n ”
A r a u j o , M . B . & N e w , M . 2 0 0 7 . T R E E . 2 2 , 4 2 – 4 7 .
I n d i v i d u a l m o d e l s
A v e r a g e m o d e l
H o f , C . e t a l . 2 0 1 1 . N a t u r e 4 8 0 , 5 1 6 – 5 1 9
h tt p : / / w w w . f s . f e d . u s / n e / n e w t o w n _ s q u a r e /p u b l i c a ti o n s / o t h e r _ p u b l i s h e r s / O C R /n e _ 2 0 0 1 _ i v e r s o n 0 0 1 . p d f
? !
( 4 ) C r a m i t i n
E x h i b i t “ W o w, X & Y l o o k s a m a z i n g ,I n e e d t o fi n d o u t m o r e ! ”
D A T A E N C O D I N G D E C O D I N G
E x p l o r e “ I w o n d e r h o w x r e l a t e s t o y ”
E x p l a i n “ X d o e s y ”
X1, Y1, x2, y2 …
E x p l a i n ( 2 ) “ … b e c a u s e o f A & B ,X d o e s y ”?
L e t s t r y ‘ m o d e l v i s u a l i s a ti o n ’ …
h tt p : / / x k c d . c o m / 1 1 3 8 /
2 . I n t e r p r e t a ti o nDO WE RECOGNISEWHY WE DISAGREE?
W h a t a r e t h e s e ?
G e o g r a p h i c d i s t r i b u ti o n
P o t e n ti a l d i s t r i b u ti o n
A b i o ti c e n v . r e s p o n s e
H a b i t a t s u i t a b i l i t y
E n v . / E c o . n i c h e
F u n d . / R e a l n i c h e
C l i m a t e a ffi n i t y
B i o - c l i m a t e e n v e l o p e
M u l ti v a r i a t e e n v . s p a c e
F u n c ti o n a l r e s p o n s e
S p e c i e s ’ E n v . r e s p o n s e
E n v . C o r r e l a t e s
I n t e r p o l a t e dP a tt e r n
S o b e r o n
H u n t l e y
A u s ti nE l i t h
K e a r n e y
F r a n k l i n
T h u i l l e rA r a u j oT h o m a sO ’ H a r a
N o g u e s - B r a v o
P e t e r s o n T h e o r y S t a ti s ti c a l m e t h o d
V a r i a b l e
R e s p o n s e f u n c ti o n
M o d e l t u n i n g
M o d e l s e l e c ti o n
A p p l i c a ti o n
D a t aT e r m i n o l o g y
A u d i e n c e
W h o i s r i g h t ?
Reason(abstract)
idea/concept
Describe(concrete)
model
output
assumption
definition
code/formula
graph
numbers
words
words/algorithm/
formula
data
Encode(concrete)
Understand(abstract)
idea/concept
goals
Deductive Reasoning(agreements are c lear)
Reason(abstract)
Describe(concrete)
Encode(concrete)
Understand(abstract)
model
output
code/formula
graph
numbers
data
goals
MAXENT, R, BIOMOD, OPENMODELLER, MODECO, GARP, BIOMAPPER, CANOCO, WINBUGS, OPENBUGS, DOMAIN, SPECIES, HYPERNICHE, HYKL, DISMO… ANN, AQUAMAPS, BIOCLIM, BRT, CSM, CTA, ENFA, ENVELOPE SCORE, ENV DISTANCE, BUGS, GA, GAM, GBM, GLM, GLS, MAHALANOBIS DISTANCE, MARS, MAXENT, MODECO, RANDOM FORESTS, SRE, SVM ...
goals
Reason(abstract)
idea/concept
Describe(concrete)
model
output
assumption
definition
code/formula
graph
numbers
words
words/algorithm/
formula
data
Encode(concrete)
Understand(abstract)
idea/concept
Inductive Model l ing(understand the pitfal ls)
1 . V i s u a l i s a ti o nDO WE SPEND TOO MUCH TIME EXHIBITING OUR WORK?
2 . I n t e r p r e t a ti o nDO WE RECOGNISE WHY WE DISAGREE?