Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

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Frontiers of Earth Science

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Front. Earth Sci.

2014, Vol. 8

Issue (4) : 512-523 https://doi.org/10.1007/s11707-014-0426-y

RESEARCH ARTICLE

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

Yingzhi LIN^1,^*(

),Xiangzheng DENG^2,³,Xing LI¹,Enjun MA¹

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

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Abstract

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.

Keywords multinomial logistic regression land use change logistic regression land use suitability land use allocation

Corresponding Author(s): Yingzhi LIN

Online First Date: 30 April 2014 Issue Date: 13 January 2015

Cite this article:

Yingzhi LIN,Xiangzheng DENG,Xing LI, et al. Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?[J]. Front. Earth Sci., 2014, 8(4): 512-523.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-014-0426-y
https://academic.hep.com.cn/fesci/EN/Y2014/V8/I4/512

Fig.1 Pixels selected for parameter estimation in multinomial logistic regression (Panel A), and for land use suitability evaluation and comparative analysis (Panel B).

Tab.1 Descriptive statistics of the variables at the pixel level involved in logistic regression and multinomial logistic regression

Tab.2 Descriptive statistics of the variables at the pixel level prepared for the evaluation of land use suitability

Tab.3 Results of the multicollinearity test

Tab.4 Logistic regression results of land use suitability

Tab.5 Multinomial logistic regression results of land use suitability

Fig.2 Land use allocation results of multinomial logistic regression (MNL) and logistic regression (LR) in the Jiangxi Province in 2005.

Tab.6 Number and proportion of selected pixels being allocated correctly in 2005

Tab.7 Paired t-test results of land use suitability evaluated by multinomial logistic regression (MNL) and logistic regression (LR)

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