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...
Ausführliche Beschreibung
Autor*in: |
Lin, Yingzhi [verfasserIn] Deng, Xiangzheng [verfasserIn] Li, Xing [verfasserIn] Ma, Enjun [verfasserIn] |
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Sprache: |
Englisch |
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2014 |
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Enthalten in: Frontiers of earth science in China - Beijing : Higher Education Press, 2007, 8(2014), 4 vom: 05. Mai, Seite 512-523 |
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Übergeordnetes Werk: |
volume:8 ; year:2014 ; number:4 ; day:05 ; month:05 ; pages:512-523 |
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DOI / URN: |
10.1007/s11707-014-0426-y |
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SPR021963827 |
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520 | |a 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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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 |
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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 |
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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 |
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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 |
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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. 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Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use? |
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comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use? |
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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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