





n

p

l

p

l

a

ca

1

,

 ...................................(3) 

138(

1)

3

(

1) 



,

1

max

100

pl

r

lpi

a

A



.............................(4) 

1

,

1

,,

1

1

1

1

100

n

pl

p

l

n

r

pl

pl

p

P

coh

C

Pc





























 

 

2.3  

 ...................................(5) 

2.3.1  

0

1

(logistic model)
(odds ratio)

 

,,,

1

n

lm

plm

p

es

ec







 ...................................(6) 

a

p,l 

(m

2

)

l

p

A

r

r (m)

P

p,l

l

p

c

p,l

l

p

C

r

r

(m)

ec

p,l,m

p

l

m

 

2.3.2 

(Land Use Pattern 

Optimization Library, LUPOlib) 

34 

01

1,

2

2,

......

1

i

ii

n

i

hsi

,

ni

Log

X

X

X

hsi























 

 ....................................(1)

 

1

m

i

i

HSI

hs







i ....................................(2) 

hsi

i

iX

n,i

i

n



 0



 k

 

(class 
area, ca

l

)(largest patch index, 

lpi)

(edge sum, 

es

l,m

)

(patch cohesion, 

coh

l

)

(Holzkämper 

et al., 2006) 

Holzkämper

(20062007

a

2007

b

)

LUPOlib

LUPOlib

C/C++

(Genetic AlgorithmsGA)

(Goldberg, 1989)

(

2005)

2

0

1

(production)

(crossover)

(mutation)

(genetic 

operators)

(

)