Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS
Abstract Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional var...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhu, Jin [verfasserIn] Li, Jiansong [verfasserIn] Jiang, Zilong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Cluster computing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998, 22(2018), Suppl 6 vom: 16. März, Seite 14669-14677 |
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| Übergeordnetes Werk: |
volume:22 ; year:2018 ; number:Suppl 6 ; day:16 ; month:03 ; pages:14669-14677 |
| Links: |
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| DOI / URN: |
10.1007/s10586-018-2378-6 |
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| Katalog-ID: |
SPR011525282 |
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| 245 | 1 | 0 | |a Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS |
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| 520 | |a Abstract Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional varied earth surface. Clustering of hybrid spatiotemporal correlation based on manifold is further proposed to solve the problems of relationship redundancy. Nonlinear Granger causality is introduced to analyze the driving factor for regional varied earth surface. Through the simulation and the actual data analysis, the new method proposed in this paper can improve the residual error estimation of 20.5% and the mean square error of 36.2%. | ||
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| 650 | 4 | |a Dimension reduction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Unsteady Granger |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Li, Jiansong |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Zilong |e verfasserin |4 aut | |
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10.1007/s10586-018-2378-6 doi (DE-627)SPR011525282 (SPR)s10586-018-2378-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Zhu, Jin verfasserin aut Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional varied earth surface. Clustering of hybrid spatiotemporal correlation based on manifold is further proposed to solve the problems of relationship redundancy. Nonlinear Granger causality is introduced to analyze the driving factor for regional varied earth surface. Through the simulation and the actual data analysis, the new method proposed in this paper can improve the residual error estimation of 20.5% and the mean square error of 36.2%. Spatiotemporal big data (dpeaa)DE-He213 Spatiotemporal model (dpeaa)DE-He213 Causal correlation (dpeaa)DE-He213 Dimension reduction (dpeaa)DE-He213 Unsteady Granger (dpeaa)DE-He213 Li, Jiansong verfasserin aut Jiang, Zilong verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 16. März, Seite 14669-14677 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:16 month:03 pages:14669-14677 https://dx.doi.org/10.1007/s10586-018-2378-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2018 Suppl 6 16 03 14669-14677 |
| spelling |
10.1007/s10586-018-2378-6 doi (DE-627)SPR011525282 (SPR)s10586-018-2378-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Zhu, Jin verfasserin aut Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional varied earth surface. Clustering of hybrid spatiotemporal correlation based on manifold is further proposed to solve the problems of relationship redundancy. Nonlinear Granger causality is introduced to analyze the driving factor for regional varied earth surface. Through the simulation and the actual data analysis, the new method proposed in this paper can improve the residual error estimation of 20.5% and the mean square error of 36.2%. Spatiotemporal big data (dpeaa)DE-He213 Spatiotemporal model (dpeaa)DE-He213 Causal correlation (dpeaa)DE-He213 Dimension reduction (dpeaa)DE-He213 Unsteady Granger (dpeaa)DE-He213 Li, Jiansong verfasserin aut Jiang, Zilong verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 16. März, Seite 14669-14677 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:16 month:03 pages:14669-14677 https://dx.doi.org/10.1007/s10586-018-2378-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2018 Suppl 6 16 03 14669-14677 |
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10.1007/s10586-018-2378-6 doi (DE-627)SPR011525282 (SPR)s10586-018-2378-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Zhu, Jin verfasserin aut Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional varied earth surface. Clustering of hybrid spatiotemporal correlation based on manifold is further proposed to solve the problems of relationship redundancy. Nonlinear Granger causality is introduced to analyze the driving factor for regional varied earth surface. Through the simulation and the actual data analysis, the new method proposed in this paper can improve the residual error estimation of 20.5% and the mean square error of 36.2%. Spatiotemporal big data (dpeaa)DE-He213 Spatiotemporal model (dpeaa)DE-He213 Causal correlation (dpeaa)DE-He213 Dimension reduction (dpeaa)DE-He213 Unsteady Granger (dpeaa)DE-He213 Li, Jiansong verfasserin aut Jiang, Zilong verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 16. März, Seite 14669-14677 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:16 month:03 pages:14669-14677 https://dx.doi.org/10.1007/s10586-018-2378-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2018 Suppl 6 16 03 14669-14677 |
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10.1007/s10586-018-2378-6 doi (DE-627)SPR011525282 (SPR)s10586-018-2378-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Zhu, Jin verfasserin aut Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional varied earth surface. Clustering of hybrid spatiotemporal correlation based on manifold is further proposed to solve the problems of relationship redundancy. Nonlinear Granger causality is introduced to analyze the driving factor for regional varied earth surface. Through the simulation and the actual data analysis, the new method proposed in this paper can improve the residual error estimation of 20.5% and the mean square error of 36.2%. Spatiotemporal big data (dpeaa)DE-He213 Spatiotemporal model (dpeaa)DE-He213 Causal correlation (dpeaa)DE-He213 Dimension reduction (dpeaa)DE-He213 Unsteady Granger (dpeaa)DE-He213 Li, Jiansong verfasserin aut Jiang, Zilong verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 16. März, Seite 14669-14677 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:16 month:03 pages:14669-14677 https://dx.doi.org/10.1007/s10586-018-2378-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2018 Suppl 6 16 03 14669-14677 |
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10.1007/s10586-018-2378-6 doi (DE-627)SPR011525282 (SPR)s10586-018-2378-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Zhu, Jin verfasserin aut Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional varied earth surface. Clustering of hybrid spatiotemporal correlation based on manifold is further proposed to solve the problems of relationship redundancy. Nonlinear Granger causality is introduced to analyze the driving factor for regional varied earth surface. Through the simulation and the actual data analysis, the new method proposed in this paper can improve the residual error estimation of 20.5% and the mean square error of 36.2%. Spatiotemporal big data (dpeaa)DE-He213 Spatiotemporal model (dpeaa)DE-He213 Causal correlation (dpeaa)DE-He213 Dimension reduction (dpeaa)DE-He213 Unsteady Granger (dpeaa)DE-He213 Li, Jiansong verfasserin aut Jiang, Zilong verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 16. März, Seite 14669-14677 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:16 month:03 pages:14669-14677 https://dx.doi.org/10.1007/s10586-018-2378-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2018 Suppl 6 16 03 14669-14677 |
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004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS Spatiotemporal big data (dpeaa)DE-He213 Spatiotemporal model (dpeaa)DE-He213 Causal correlation (dpeaa)DE-He213 Dimension reduction (dpeaa)DE-He213 Unsteady Granger (dpeaa)DE-He213 |
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spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in gis |
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Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS |
| abstract |
Abstract Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional varied earth surface. Clustering of hybrid spatiotemporal correlation based on manifold is further proposed to solve the problems of relationship redundancy. Nonlinear Granger causality is introduced to analyze the driving factor for regional varied earth surface. Through the simulation and the actual data analysis, the new method proposed in this paper can improve the residual error estimation of 20.5% and the mean square error of 36.2%. |
| abstractGer |
Abstract Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional varied earth surface. Clustering of hybrid spatiotemporal correlation based on manifold is further proposed to solve the problems of relationship redundancy. Nonlinear Granger causality is introduced to analyze the driving factor for regional varied earth surface. Through the simulation and the actual data analysis, the new method proposed in this paper can improve the residual error estimation of 20.5% and the mean square error of 36.2%. |
| abstract_unstemmed |
Abstract Spatial–temporal relationships are fundamental. This paper focuses on the improvement for relationship redundancy and causality fuzzy by regional varied earth surface. We propose a method of spatial–temporal correlation dimension reduction and unsteady Granger driving factor in regional varied earth surface. Clustering of hybrid spatiotemporal correlation based on manifold is further proposed to solve the problems of relationship redundancy. Nonlinear Granger causality is introduced to analyze the driving factor for regional varied earth surface. Through the simulation and the actual data analysis, the new method proposed in this paper can improve the residual error estimation of 20.5% and the mean square error of 36.2%. |
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Suppl 6 |
| title_short |
Spatial–temporal correlation dimension reduction and unsteady granger driving factor in regional varied earth surface in GIS |
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https://dx.doi.org/10.1007/s10586-018-2378-6 |
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Li, Jiansong Jiang, Zilong |
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Li, Jiansong Jiang, Zilong |
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| doi_str |
10.1007/s10586-018-2378-6 |
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2024-07-03T23:10:29.351Z |
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| score |
7.3996754 |