1. School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China 2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 3. Center for Chinese Agricultural Policy, Chinese Academy of Sciences, Beijing 100101, China
Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.
. [J]. Frontiers of Earth Science, 2014, 8(4): 512-523.
Yingzhi LIN,Xiangzheng DENG,Xing LI,Enjun MA. Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?. Front. Earth Sci., 2014, 8(4): 512-523.
Dependent variable: DCLand1995-2000 (=1 is the comparison group)
(=2)
(=3)
(=4)
(=5)
(=6)
Elevation
-6.42×10-5(0.40)
1.85×10-3(3.83)***
-6.06×10-3(5.67)***
2.54×10-4(0.42)
4.55×10-3(0.61)
Slope
-0.03(3.69)***
-0.03(0.94)
-0.07(1.48)***
4.47(0.14)
-0.71(1.09)
Aspect
-2.17×10-3(7.24)***
-2.88×10-3(3.36)**
2.22×10-3(3.20)**
1.24×10-3(1.86)*
2.33×10-3(0.57)
Precipitation
-1.15×10-4(0.74)
9.59×10-5(0.18)
-7.95×10-4(1.66)*
-9.95×10-4(2.47)**
1.83×10-3(0.47)
Temperature
-0.08(5.03)***
0.21(3.94)***
0.12(3.32)***
0.02(0.73)
-0.56(3.37)***
Dtoroad
-0.01(0.58)
-0.10(1.34)
0.36(8.25)***
-0.03(0.51)
0.16(0.63)
Dtocity
4.87×10-3(1.49)
2.61×10-4(0.25)
4.53×10-3(4.95)***
-1.77×10-3(2.27)**
-8.24×10-3(1.15)
pH
-0.04(0.40)
0.45(2.15)**
-0.03(0.15)
0.09(0.39)
-0.97(1.57)
Loam
-6.50×10-3(1.45)
-0.05(4.24)***
0.01(1.34)
4.04×10-4(0.05)
0.01(0.28)
Nitrogen
-1.20(0.64)
10.69(3.08)***
1.07(0.30)
1.88(0.45)
-16.67(1.13)
Phosphorous
1.52(0.18)
6.15(0.28)
13.96(0.61)
0.90(0.04)
-22.95(0.29)
Potassium
0.16(0.81)
-1.29(2.64)***
-0.44(0.77)
-0.31(0.61)
4.60(1.51)
Bufferfarmland
-6.21×10-3(2.96)***
-0.09(35.06)***
-0.06(28.80)***
-2.26×10-3(0.64)
-0.09(8.55)***
Bufferbuiltup
-0.03(4.88)***
-0.11(8.96)***
-0.05(7.32)***
0.10(22.40)***
-0.17(2.20)**
Bufferforest
0.04(22.26)***
-0.08(33.81)***
-0.05(19.99)***
-0.01(2.52)**
-0.14(3.68)***
Intercept
-1.14(2.18)**
-0.75(0.48)
1.29(1.12)
-2.70(2.36)**
6.43(0.85)***
Pseudo R2
0.23
Tab.5
Fig.2
Land use type
Remote sensing
Pixels being allocated correctly
Logistic regression
Multinomial logistic regression
Number
Proportion/%
Number
Proportion/%
Cultivated land
28,227
24,169
85.62
27,531
97.53
Forest
12,151
11,835
97.40
11,704
96.32
Grassland
1,117
501
44.85
421
37.69
Water area
1,171
602
51.41
651
55.59
Built-up area
1,862
1,092
58.65
1,126
60.47
Unused land
33
0
0.00
0
0.00
Total
38,199
85.72
41,433
92.98
Tab.6
(1)
(2)
(3)
(4)
(5)
(6)
MNL
(1)
—
413.89
865.64
898.11
924.46
993.63
(1)
(2)
129.09
—
151.89
135.92
132.59
170.00
(2)
(3)
404.25
161.02
—
-40.27
-35.04
56.92
(3)
(4)
418.51
160.14
0.10
—
-3.62
115.49
(4)
(5)
429.33
150.24
-14.93
-17.68
—
111.64
(5)
(6)
444.60
186.11
84.43
120.29
97.17
—
(6)
LR
(1)
(2)
(3)
(4)
(5)
(6)
Tab.7
1
Alabi M O (2011). Analytical approach to examining drivers of residential land use development in Lokoja, Nigeria. British Journal of Educational Research, 1(2): 144–152
2
Bahadur K C K (2011). Linking physical, economic and institutional constraints of land use change and forest conservation in the hills of Nepal. For Policy Econ, 13(8): 603–613
https://doi.org/10.1016/j.forpol.2011.07.010
Briz T, Ward R W (2009). Consumer awareness of organic products in Spain: an application of multinomial logit models. Food Policy, 34(3): 295–304
https://doi.org/10.1016/j.foodpol.2008.11.004
5
Cao K, Ye X Y (2013). Coarse-grained parallel genetic algorithm applied to a vector based land use allocation optimization problem: the case study of Tongzhou Newtown, Beijing, China. Stochastic Environ Res Risk Assess, 27(5): 1133–1142
https://doi.org/10.1007/s00477-012-0649-y
6
Chatterjee S, Price B (1991). Regression Analysis by Example. New York: John Wiley & Sons, 85–120
Chen Y Q, Verburg P H (2000). Modeling land use change and its effects by GIS. Ecologic Sci, 19(3): 1–7
9
Choi S W, Sohngen B, Alig R (2011). An assessment of the influence of bioenergy and marketed land amenity values on land uses in the Midwestern US. Ecol Econ, 70(4): 713–720
https://doi.org/10.1016/j.ecolecon.2010.11.005
10
Claessens L, Schoorl J M, Verburg P H, Geraedts L, Veldkamp A (2009). Modelling interactions and feedback mechanisms between land use change and landscape processes. Agric Ecosyst Environ, 129(1–3): 157–170
https://doi.org/10.1016/j.agee.2008.08.008
Deng X Z, Huang J K, Rozells S, Uchida E (2008). Growth, population and industrialization, and urban land expansion of China. J Urban Econ, 63(1): 96–115
https://doi.org/10.1016/j.jue.2006.12.006
14
Duan Z Q, Verburg P H, Zhang F R, Yu Z R (2004). Construction of a land-use change simulation model and its application in Haidian District, Beijing. Acta Geogr Sin, 59(6): 1037–1047
15
Feng Z M, Yang Y Z, Zhang Y Q, Zhang P T, Li Y Q (2005). Grain-for-green policy and its impacts on grain supply in West China. Land Use Policy, 22(4): 301–312
https://doi.org/10.1016/j.landusepol.2004.05.004
16
Gellrich M, Baur P, Koch B, Zimmermann N E (2007). Agricultural land abandonment and natural forest re-growth in the Swiss mountains: a spatially explicit economic analysis. Agric Ecosyst Environ, 118(1–4): 93–108
https://doi.org/10.1016/j.agee.2006.05.001
17
Geoghegan J, Villar S C, Klepeis P, Mendoza P M, Ogneva-Himmelberger Y, Chowdhury R R, Turner B L II, Vance C (2001). Modeling tropical deforestation in the southern Yucatán peninsular region: comparing survey and satellite data. Agric Ecosyst Environ, 85(1–3): 25–46
https://doi.org/10.1016/S0167-8809(01)00201-8
18
Han J G, Zhang Y J, Wang C J, Bai W M, Wang Y R, Han G D, Li L H (2008). Rangeland degradation and restoration management in China. Rangeland J, 30(2): 233–239
https://doi.org/10.1071/RJ08009
19
Hosmer D, Lemeshow S (2000). Applied Logistic Regression. New York: John Wiley & Sons, 31–46
20
Hsu H, Lachenbruch P A (2008). Paired t Test. Wiley Encyclopedia of Clinical Trials, 1–3
21
Jiang Y, Liu J, Cui Q, An X H, Wu C X (2011). Land use/land cover change and driving force analysis in Xishuangbanna Region in 1986–2008. Frontiers of Earth Science, 5(3): 288–293
Lin Y P, Chu H J, Wu C F, Verburg P H (2011). Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling: a case study. Int J Geogr Inf Sci, 25(1): 65–87
https://doi.org/10.1080/13658811003752332
25
Liu J Y, Zhang Z X, Zhuang D F, Wang Y M, Zhou W C, Zhang S W, Li R D, Jiang N, Wu S X (2003). A study on the spatial-temporal dynamic changes of land-use and driving forces analyses of China in the 1990s. Geographical Research, 22(1): 1–12 (in Chinese)
26
Luce R D (1959). Individual Choice Behavior: A Theoretical Analysis. New York: John Wiley & Sons, 139–141
27
McFadden D (1974). Conditional logit analysis of qualitative choice behavior. In: Zarembka P, ed. Frontiers in Econometrics. New York: Academic Press, 105–142
28
Meiyappan P, Jain A K (2012). Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Frontiers of Earth Science, 6(2): 122–139
https://doi.org/10.1007/s11707-012-0314-2
29
Millington J D A, Perry G L W, Romero-Calcerrada R (2007). Regression techniques for examining land use/cover change: a case study of a Mediterranean landscape. Ecosystems (N. Y.), 10(4): 562–578
https://doi.org/10.1007/s10021-007-9020-4
30
Nahuelhual L, Carmona A, Lara A, Echeverría C, González M E (2012). Land-cover change to forest plantations: proximate causes and implications for the landscape in south-central Chile. Landsc Urban Plan, 107(1): 12–20
https://doi.org/10.1016/j.landurbplan.2012.04.006
31
Ostwald M, Chen D L (2006). Land-use change: impacts of climate variations and policies among small-scale farmers in the Loess Plateau, China. Land Use Policy, 23(4): 361–371
https://doi.org/10.1016/j.landusepol.2005.04.004
Pueyo Y, Beguería S (2007). Modelling the rate of secondary succession after farmland abandonment in a Mediterranean mountain area. Landsc Urban Plan, 83(4): 245–254
https://doi.org/10.1016/j.landurbplan.2007.04.008
Schaldach R, Alcamo J (2006). Coupled simulation of regional land use change and soil carbon sequestration: a case study for the state of Hesse in Germany. Environ Model Softw, 21(10): 1430–1446
https://doi.org/10.1016/j.envsoft.2005.07.005
36
Serneels S, Lambin E F (2001). Proximate causes of land use change in Narok district Kenya: a spatial statistical model. Agric Ecosyst Environ, 85(1–3): 65–81
https://doi.org/10.1016/S0167-8809(01)00188-8
37
Sohl T L, Sleeter B M, Zhu Z L, Sayler K L, Bennett S, Bouchard M, Reker R, Hawbaker T, Wein A, Liu S G, Kanengieter R, Acevedo W (2012). A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes. Appl Geogr, 34: 111–124
https://doi.org/10.1016/j.apgeog.2011.10.019
38
van Doorn A M, Bakker M M (2007). The destination of arable land in a marginal agricultural landscape in South Portugal: an exploration of land use change determinants. Landscape Ecol, 22(7): 1073–1087
https://doi.org/10.1007/s10980-007-9093-7
39
Verburg P H, Overmars K P (2009). Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecol, 24(9): 1167–1181
https://doi.org/10.1007/s10980-009-9355-7
40
Verburg P H, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura S S (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ Manage, 30(3): 391–405
https://doi.org/10.1007/s00267-002-2630-x
pmid: 12148073
41
Verburg P H, Veldkamp A (2004). Projecting land use transitions at forest fringes in the Philippines at two spatial scales. Landscape Ecol, 19(1): 77–98
https://doi.org/10.1023/B:LAND.0000018370.57457.58
42
Walsh S J, Messina J P, Mena C F, Malanson G P, Page P H (2008). Complexity theory, spatial simulation models, and land use dynamics in the Northern Ecuadorian Amazon. Geoforum, 39(2): 867–878
https://doi.org/10.1016/j.geoforum.2007.02.011
43
Wang J, Chen Y Q, Shao X M, Zhang Y Y, Cao Y G (2012). Land-use changes and policy dimension driving forces in China: present, trend and future. Land Use Policy, 29(4): 737–749
https://doi.org/10.1016/j.landusepol.2011.11.010
44
Williams N S G (2007). Environmental, landscape and social predictors of native grassland loss in western Victoria, Australia. Biol Conserv, 137(2): 308–318
https://doi.org/10.1016/j.biocon.2007.02.017
45
Wu G P, Zeng Y N, Feng X Z, Xiao P F, Wang K (2010). Dynamic simulation of land use change based on the improved CLUE-S model: a case study of Yongding County, Zhangjiajie. Geographical Research, 29(3): 460–470 (in Chinese)
46
Zhan J Y, Shi N N, He S J, Lin Y Z (2010). Factors and mechanism driving the land-use conversion in Jiangxi Province. J Geogr Sci, 20(4): 525–539
https://doi.org/10.1007/s11442-010-0525-y
47
Zhong T Y, Huang X J, Zhang X Y, Wang K (2011). Temporal and spatial variability of agricultural land loss in relation to policy and accessibility in a low hilly region of southeast China. Land Use Policy, 28(4): 762–769
https://doi.org/10.1016/j.landusepol.2011.01.004