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

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 mo...
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Autor*in:	Lin, Yingzhi [verfasserIn] Deng, Xiangzheng [verfasserIn] Li, Xing [verfasserIn] Ma, Enjun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	multinomial logistic regression land use change logistic regression land use suitability land use allocation

Übergeordnetes Werk:	Enthalten in: Frontiers of earth science in China - Beijing : Higher Education Press, 2007, 8(2014), 4 vom: 05. Mai, Seite 512-523
Übergeordnetes Werk:	volume:8 ; year:2014 ; number:4 ; day:05 ; month:05 ; pages:512-523

Links:	Volltext

DOI / URN:	10.1007/s11707-014-0426-y

Katalog-ID:	SPR021963827

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allfields	10.1007/s11707-014-0426-y doi (DE-627)SPR021963827 (SPR)s11707-014-0426-y-e DE-627 ger DE-627 rakwb eng 550 ASE Lin, Yingzhi verfasserin aut Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. multinomial logistic regression (dpeaa)DE-He213 land use change (dpeaa)DE-He213 logistic regression (dpeaa)DE-He213 land use suitability (dpeaa)DE-He213 land use allocation (dpeaa)DE-He213 Deng, Xiangzheng verfasserin aut Li, Xing verfasserin aut Ma, Enjun verfasserin aut Enthalten in Frontiers of earth science in China Beijing : Higher Education Press, 2007 8(2014), 4 vom: 05. Mai, Seite 512-523 (DE-627)546007406 (DE-600)2389435-0 1673-7490 nnns volume:8 year:2014 number:4 day:05 month:05 pages:512-523 https://dx.doi.org/10.1007/s11707-014-0426-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 8 2014 4 05 05 512-523
spelling	10.1007/s11707-014-0426-y doi (DE-627)SPR021963827 (SPR)s11707-014-0426-y-e DE-627 ger DE-627 rakwb eng 550 ASE Lin, Yingzhi verfasserin aut Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. multinomial logistic regression (dpeaa)DE-He213 land use change (dpeaa)DE-He213 logistic regression (dpeaa)DE-He213 land use suitability (dpeaa)DE-He213 land use allocation (dpeaa)DE-He213 Deng, Xiangzheng verfasserin aut Li, Xing verfasserin aut Ma, Enjun verfasserin aut Enthalten in Frontiers of earth science in China Beijing : Higher Education Press, 2007 8(2014), 4 vom: 05. Mai, Seite 512-523 (DE-627)546007406 (DE-600)2389435-0 1673-7490 nnns volume:8 year:2014 number:4 day:05 month:05 pages:512-523 https://dx.doi.org/10.1007/s11707-014-0426-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 8 2014 4 05 05 512-523
allfields_unstemmed	10.1007/s11707-014-0426-y doi (DE-627)SPR021963827 (SPR)s11707-014-0426-y-e DE-627 ger DE-627 rakwb eng 550 ASE Lin, Yingzhi verfasserin aut Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. multinomial logistic regression (dpeaa)DE-He213 land use change (dpeaa)DE-He213 logistic regression (dpeaa)DE-He213 land use suitability (dpeaa)DE-He213 land use allocation (dpeaa)DE-He213 Deng, Xiangzheng verfasserin aut Li, Xing verfasserin aut Ma, Enjun verfasserin aut Enthalten in Frontiers of earth science in China Beijing : Higher Education Press, 2007 8(2014), 4 vom: 05. Mai, Seite 512-523 (DE-627)546007406 (DE-600)2389435-0 1673-7490 nnns volume:8 year:2014 number:4 day:05 month:05 pages:512-523 https://dx.doi.org/10.1007/s11707-014-0426-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 8 2014 4 05 05 512-523
allfieldsGer	10.1007/s11707-014-0426-y doi (DE-627)SPR021963827 (SPR)s11707-014-0426-y-e DE-627 ger DE-627 rakwb eng 550 ASE Lin, Yingzhi verfasserin aut Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. multinomial logistic regression (dpeaa)DE-He213 land use change (dpeaa)DE-He213 logistic regression (dpeaa)DE-He213 land use suitability (dpeaa)DE-He213 land use allocation (dpeaa)DE-He213 Deng, Xiangzheng verfasserin aut Li, Xing verfasserin aut Ma, Enjun verfasserin aut Enthalten in Frontiers of earth science in China Beijing : Higher Education Press, 2007 8(2014), 4 vom: 05. Mai, Seite 512-523 (DE-627)546007406 (DE-600)2389435-0 1673-7490 nnns volume:8 year:2014 number:4 day:05 month:05 pages:512-523 https://dx.doi.org/10.1007/s11707-014-0426-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 8 2014 4 05 05 512-523
allfieldsSound	10.1007/s11707-014-0426-y doi (DE-627)SPR021963827 (SPR)s11707-014-0426-y-e DE-627 ger DE-627 rakwb eng 550 ASE Lin, Yingzhi verfasserin aut Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. multinomial logistic regression (dpeaa)DE-He213 land use change (dpeaa)DE-He213 logistic regression (dpeaa)DE-He213 land use suitability (dpeaa)DE-He213 land use allocation (dpeaa)DE-He213 Deng, Xiangzheng verfasserin aut Li, Xing verfasserin aut Ma, Enjun verfasserin aut Enthalten in Frontiers of earth science in China Beijing : Higher Education Press, 2007 8(2014), 4 vom: 05. Mai, Seite 512-523 (DE-627)546007406 (DE-600)2389435-0 1673-7490 nnns volume:8 year:2014 number:4 day:05 month:05 pages:512-523 https://dx.doi.org/10.1007/s11707-014-0426-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 8 2014 4 05 05 512-523
language	English
source	Enthalten in Frontiers of earth science in China 8(2014), 4 vom: 05. Mai, Seite 512-523 volume:8 year:2014 number:4 day:05 month:05 pages:512-523
sourceStr	Enthalten in Frontiers of earth science in China 8(2014), 4 vom: 05. Mai, Seite 512-523 volume:8 year:2014 number:4 day:05 month:05 pages:512-523
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	multinomial logistic regression land use change logistic regression land use suitability land use allocation
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container_title	Frontiers of earth science in China
authorswithroles_txt_mv	Lin, Yingzhi @@aut@@ Deng, Xiangzheng @@aut@@ Li, Xing @@aut@@ Ma, Enjun @@aut@@
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author	Lin, Yingzhi
spellingShingle	Lin, Yingzhi ddc 550 misc multinomial logistic regression misc land use change misc logistic regression misc land use suitability misc land use allocation Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?
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topic_title	550 ASE Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use? multinomial logistic regression (dpeaa)DE-He213 land use change (dpeaa)DE-He213 logistic regression (dpeaa)DE-He213 land use suitability (dpeaa)DE-He213 land use allocation (dpeaa)DE-He213
topic	ddc 550 misc multinomial logistic regression misc land use change misc logistic regression misc land use suitability misc land use allocation
topic_unstemmed	ddc 550 misc multinomial logistic regression misc land use change misc logistic regression misc land use suitability misc land use allocation
topic_browse	ddc 550 misc multinomial logistic regression misc land use change misc logistic regression misc land use suitability misc land use allocation
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title	Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?
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title_full	Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?
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journal	Frontiers of earth science in China
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author_browse	Lin, Yingzhi Deng, Xiangzheng Li, Xing Ma, Enjun
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title_sort	comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?
title_auth	Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?
url	https://dx.doi.org/10.1007/s11707-014-0426-y
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author2	Deng, Xiangzheng Li, Xing Ma, Enjun
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up_date	2024-07-04T01:12:22.122Z
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