# Full text: Mapping without the sun

certain mathematical model for such a quantitative correlation
between simulation to the estimated on the basis of inference
and prediction, the offender usually this process is known as
regression analysis. It can take advantage of certain
mathematical model for such a quantitative correlation between
simulation to the estimated on the basis of inference and
prediction, the offender usually is known as regression analysis.
In this paper, we use regression analysis of multivariate linear
regression model to determine the area of land that the major
driving factor; promote regional land-use sustainable
development.
income X3) Disaster category (collapsed area X4), and
construction sites X5.
Use each type of factor and cultivated areas for stepwise
regression analysis, to determine the coefficient of such factors
contribution rate arable land area by regression equations of the
variable in order to determine changes in the main driving
factors. Process to take into account various indicators
dimensional data volume is relatively varied more, so first of
all make raw data standardization by formula.
4.2.2 Establish driving model
We make experiment based on the statistical of the study area
during 1976-2005, take a land type for example, select an area
of arable land due to variable Y, and choose five categories,
eight indicators as independent variables to analyze its driving
mechanism: population category (the local population XI1,
agricultural population XI2, the number of total households
X13). Economic output (total agricultural output X21,
industrial output X22), the standard of living (per capita
Arable
land area
>;i; i¡
Total
population
end of year
Agricultura
1 population
Total
home end
of year
Agriculture
output
Industrial
output
Per capita
income
Collapse
area
Constructio
n area
-.12
.21
.26
.15
.37
.45
.47
.54
.56
-.23
.18
.21
.18
.27
.43
.53
.60
.49
-.17
.22
.24
.11
.18
.37
.39
.46
.41
v=-
y.-y
(a-l H)
(4-3)
Ya is the dimensionless data for non-dimensional data, the
average variable Syy is the deviation of the square and square
root. The following table by the above formula standardized
data:
Fig.4-4.Standardized statistical data table
ani rn
By the use of standardized statistical data table stepwise
regression analysis, obtained the regression equation:
Y=0.350+0.453X11+0.369X21-0.789X4-0.537X5
We can see that total population end of year; construction land
and collapse area and agriculture have very important relation
with the change of arable land, population end of year, the total
output value of agriculture and farmland area is a positive
correlation, and the collapse area, Construction sites, with the
area of cultivated land is a negative correlation, it note that the
increase in population and the increase in agricultural output
led to the increase in the amount of arable land, and the
collapse area and the construction area will inevitably lead to
an increase in the decrease in the amount of arable land, And
these two factors on changes in land area is greater than that of
the local population and total output value of agriculture, Total
causing the result of the decrease in arable land area. However,
we still see a positive side, that the population and agricultural
output value of the two drivers factor also plays an important
role, This shows that the state of agricultural and rural
investment is increasing, and especially for mine land
reclamation and reconstruction increased the intensity of the
treatment, it is gratifying. In addition, we also note that mining
subsidence factor is cultivated acreage change the dominant
factor, it is the need to reduce the coal mining subsidence and
subsidence areas have land structure, this is the significance of
the study lies.
asumvB adtio aoridmonsriq vrit ol boom iso items 1 ism
land use drivers analysis found: Mine as a special geographical
area, due to resource development and the cumulative affects
of ongoing, facing a serious ecological damage to the
environment. Which mine land resources and the evolution
triggered by a series of negative effects is the most serious
problem impact of the mining area of sustainable development.
Shenyang mine district need to reduce the coal mining
subsidence and make land rectification for subsidence areas.
Reference
[1] Yin Zhang-cai ,Li Lin,AI Zi-xing, 2003. A study of spatio-
temporal data model based on graph theory [Jj.ACTA
GEODAETICA et CARTOGRAPHICAL SINICA,pp.32(2):
168
rf/v j .. ,, • 7 r, r ,;r r - t-j, ri i rvrlt l'i trr •f-rf'' iri* '7
[2] Shu Hong,Chen jun,Du Daosheng., 1997.definition of spatio-
temporal topological relationships and description of temporal
topological relationships^]. .ACTA GEODAETICA et
CARTOGRAPHICAL SINICA., pp.26 (4)299
[3] LI Xiao-juan,YIN Lian-wang, 2002.CUI Wei-hong.spatio-
temporal data model for landuse monitoring[J], Journal of
remote sensing,pp.6(5): 370.
[4] ZHANG Shan-shan, BIAN Fu-ling, 2003. Object-oriented
Three-leveled spatiotemporal Data model[J].Computer
Applications,pp.23(11):29-35.
5 conclusions
Using remote sensing and geographic information system as
the core technology, After the Shenyang mine the land and
[5]Lu Yangsheng, Qin chuan, Tang Bo, 2003. A new object-
oriented spatio-temporal data model based on attribute-

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