Time Evolution of City Distributions in Germany

Abstract This paper aims to capture characteristic agglomeration patterns in population data in Germany from 1987 to 2011, encompassing pre- and post-unification periods. We utilize a group-theoretic double Fourier spectrum analysis procedure (Ikeda et al. 2018) as a systematic means to capture char...
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Gespeichert in:

Autor*in:	Ikeda, Kiyohiro [verfasserIn] Osawa, Minoru Takayama, Yuki

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Central place theory City distribution Core–satellite pattern German reunification Hexagons Megalopolis Spectrum analysis

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Networks and spatial economics - Dordrecht [u.a.] : Springer Science Business Media B.V., 2001, 22(2022), 1 vom: März, Seite 125-151
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:1 ; month:03 ; pages:125-151

Links:	Volltext

DOI / URN:	10.1007/s11067-021-09557-2

Katalog-ID:	SPR046855769

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allfields	10.1007/s11067-021-09557-2 doi (DE-627)SPR046855769 (SPR)s11067-021-09557-2-e DE-627 ger DE-627 rakwb eng Ikeda, Kiyohiro verfasserin (orcid)0000-0002-0291-4346 aut Time Evolution of City Distributions in Germany 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper aims to capture characteristic agglomeration patterns in population data in Germany from 1987 to 2011, encompassing pre- and post-unification periods. We utilize a group-theoretic double Fourier spectrum analysis procedure (Ikeda et al. 2018) as a systematic means to capture characteristic agglomeration patterns in population data. Among a plethora of patterns to be self-organized from a uniform state, we focus on a megalopolis pattern, a rhombic pattern, and a core–satellite pattern (a downtown surrounded by hexagonal satellite cities). As the technical contribution of this paper, we newly introduce a principal vector as a superposition of these patterns in order to grasp the multi-scale nature of agglomerations. Benchmark spectra for these patterns are advanced and are found in the population data of Germany in 2011. An incremental population is investigated using this principal vector to successfully detect a shift of predominant population increase/decrease patterns in the pre- and post-unification periods. Central place theory (dpeaa)DE-He213 City distribution (dpeaa)DE-He213 Core–satellite pattern (dpeaa)DE-He213 German reunification (dpeaa)DE-He213 Hexagons (dpeaa)DE-He213 Megalopolis (dpeaa)DE-He213 Spectrum analysis (dpeaa)DE-He213 Osawa, Minoru aut Takayama, Yuki aut Enthalten in Networks and spatial economics Dordrecht [u.a.] : Springer Science Business Media B.V., 2001 22(2022), 1 vom: März, Seite 125-151 (DE-627)325570701 (DE-600)2037373-9 1572-9427 nnns volume:22 year:2022 number:1 month:03 pages:125-151 https://dx.doi.org/10.1007/s11067-021-09557-2 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2022 1 03 125-151
spelling	10.1007/s11067-021-09557-2 doi (DE-627)SPR046855769 (SPR)s11067-021-09557-2-e DE-627 ger DE-627 rakwb eng Ikeda, Kiyohiro verfasserin (orcid)0000-0002-0291-4346 aut Time Evolution of City Distributions in Germany 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper aims to capture characteristic agglomeration patterns in population data in Germany from 1987 to 2011, encompassing pre- and post-unification periods. We utilize a group-theoretic double Fourier spectrum analysis procedure (Ikeda et al. 2018) as a systematic means to capture characteristic agglomeration patterns in population data. Among a plethora of patterns to be self-organized from a uniform state, we focus on a megalopolis pattern, a rhombic pattern, and a core–satellite pattern (a downtown surrounded by hexagonal satellite cities). As the technical contribution of this paper, we newly introduce a principal vector as a superposition of these patterns in order to grasp the multi-scale nature of agglomerations. Benchmark spectra for these patterns are advanced and are found in the population data of Germany in 2011. An incremental population is investigated using this principal vector to successfully detect a shift of predominant population increase/decrease patterns in the pre- and post-unification periods. Central place theory (dpeaa)DE-He213 City distribution (dpeaa)DE-He213 Core–satellite pattern (dpeaa)DE-He213 German reunification (dpeaa)DE-He213 Hexagons (dpeaa)DE-He213 Megalopolis (dpeaa)DE-He213 Spectrum analysis (dpeaa)DE-He213 Osawa, Minoru aut Takayama, Yuki aut Enthalten in Networks and spatial economics Dordrecht [u.a.] : Springer Science Business Media B.V., 2001 22(2022), 1 vom: März, Seite 125-151 (DE-627)325570701 (DE-600)2037373-9 1572-9427 nnns volume:22 year:2022 number:1 month:03 pages:125-151 https://dx.doi.org/10.1007/s11067-021-09557-2 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2022 1 03 125-151
allfields_unstemmed	10.1007/s11067-021-09557-2 doi (DE-627)SPR046855769 (SPR)s11067-021-09557-2-e DE-627 ger DE-627 rakwb eng Ikeda, Kiyohiro verfasserin (orcid)0000-0002-0291-4346 aut Time Evolution of City Distributions in Germany 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper aims to capture characteristic agglomeration patterns in population data in Germany from 1987 to 2011, encompassing pre- and post-unification periods. We utilize a group-theoretic double Fourier spectrum analysis procedure (Ikeda et al. 2018) as a systematic means to capture characteristic agglomeration patterns in population data. Among a plethora of patterns to be self-organized from a uniform state, we focus on a megalopolis pattern, a rhombic pattern, and a core–satellite pattern (a downtown surrounded by hexagonal satellite cities). As the technical contribution of this paper, we newly introduce a principal vector as a superposition of these patterns in order to grasp the multi-scale nature of agglomerations. Benchmark spectra for these patterns are advanced and are found in the population data of Germany in 2011. An incremental population is investigated using this principal vector to successfully detect a shift of predominant population increase/decrease patterns in the pre- and post-unification periods. Central place theory (dpeaa)DE-He213 City distribution (dpeaa)DE-He213 Core–satellite pattern (dpeaa)DE-He213 German reunification (dpeaa)DE-He213 Hexagons (dpeaa)DE-He213 Megalopolis (dpeaa)DE-He213 Spectrum analysis (dpeaa)DE-He213 Osawa, Minoru aut Takayama, Yuki aut Enthalten in Networks and spatial economics Dordrecht [u.a.] : Springer Science Business Media B.V., 2001 22(2022), 1 vom: März, Seite 125-151 (DE-627)325570701 (DE-600)2037373-9 1572-9427 nnns volume:22 year:2022 number:1 month:03 pages:125-151 https://dx.doi.org/10.1007/s11067-021-09557-2 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2022 1 03 125-151
allfieldsGer	10.1007/s11067-021-09557-2 doi (DE-627)SPR046855769 (SPR)s11067-021-09557-2-e DE-627 ger DE-627 rakwb eng Ikeda, Kiyohiro verfasserin (orcid)0000-0002-0291-4346 aut Time Evolution of City Distributions in Germany 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper aims to capture characteristic agglomeration patterns in population data in Germany from 1987 to 2011, encompassing pre- and post-unification periods. We utilize a group-theoretic double Fourier spectrum analysis procedure (Ikeda et al. 2018) as a systematic means to capture characteristic agglomeration patterns in population data. Among a plethora of patterns to be self-organized from a uniform state, we focus on a megalopolis pattern, a rhombic pattern, and a core–satellite pattern (a downtown surrounded by hexagonal satellite cities). As the technical contribution of this paper, we newly introduce a principal vector as a superposition of these patterns in order to grasp the multi-scale nature of agglomerations. Benchmark spectra for these patterns are advanced and are found in the population data of Germany in 2011. An incremental population is investigated using this principal vector to successfully detect a shift of predominant population increase/decrease patterns in the pre- and post-unification periods. Central place theory (dpeaa)DE-He213 City distribution (dpeaa)DE-He213 Core–satellite pattern (dpeaa)DE-He213 German reunification (dpeaa)DE-He213 Hexagons (dpeaa)DE-He213 Megalopolis (dpeaa)DE-He213 Spectrum analysis (dpeaa)DE-He213 Osawa, Minoru aut Takayama, Yuki aut Enthalten in Networks and spatial economics Dordrecht [u.a.] : Springer Science Business Media B.V., 2001 22(2022), 1 vom: März, Seite 125-151 (DE-627)325570701 (DE-600)2037373-9 1572-9427 nnns volume:22 year:2022 number:1 month:03 pages:125-151 https://dx.doi.org/10.1007/s11067-021-09557-2 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2022 1 03 125-151
allfieldsSound	10.1007/s11067-021-09557-2 doi (DE-627)SPR046855769 (SPR)s11067-021-09557-2-e DE-627 ger DE-627 rakwb eng Ikeda, Kiyohiro verfasserin (orcid)0000-0002-0291-4346 aut Time Evolution of City Distributions in Germany 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper aims to capture characteristic agglomeration patterns in population data in Germany from 1987 to 2011, encompassing pre- and post-unification periods. We utilize a group-theoretic double Fourier spectrum analysis procedure (Ikeda et al. 2018) as a systematic means to capture characteristic agglomeration patterns in population data. Among a plethora of patterns to be self-organized from a uniform state, we focus on a megalopolis pattern, a rhombic pattern, and a core–satellite pattern (a downtown surrounded by hexagonal satellite cities). As the technical contribution of this paper, we newly introduce a principal vector as a superposition of these patterns in order to grasp the multi-scale nature of agglomerations. Benchmark spectra for these patterns are advanced and are found in the population data of Germany in 2011. An incremental population is investigated using this principal vector to successfully detect a shift of predominant population increase/decrease patterns in the pre- and post-unification periods. Central place theory (dpeaa)DE-He213 City distribution (dpeaa)DE-He213 Core–satellite pattern (dpeaa)DE-He213 German reunification (dpeaa)DE-He213 Hexagons (dpeaa)DE-He213 Megalopolis (dpeaa)DE-He213 Spectrum analysis (dpeaa)DE-He213 Osawa, Minoru aut Takayama, Yuki aut Enthalten in Networks and spatial economics Dordrecht [u.a.] : Springer Science Business Media B.V., 2001 22(2022), 1 vom: März, Seite 125-151 (DE-627)325570701 (DE-600)2037373-9 1572-9427 nnns volume:22 year:2022 number:1 month:03 pages:125-151 https://dx.doi.org/10.1007/s11067-021-09557-2 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2022 1 03 125-151
language	English
source	Enthalten in Networks and spatial economics 22(2022), 1 vom: März, Seite 125-151 volume:22 year:2022 number:1 month:03 pages:125-151
sourceStr	Enthalten in Networks and spatial economics 22(2022), 1 vom: März, Seite 125-151 volume:22 year:2022 number:1 month:03 pages:125-151
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Central place theory City distribution Core–satellite pattern German reunification Hexagons Megalopolis Spectrum analysis
isfreeaccess_bool	false
container_title	Networks and spatial economics
authorswithroles_txt_mv	Ikeda, Kiyohiro @@aut@@ Osawa, Minoru @@aut@@ Takayama, Yuki @@aut@@
publishDateDaySort_date	2022-03-01T00:00:00Z
hierarchy_top_id	325570701
id	SPR046855769
language_de	englisch
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author	Ikeda, Kiyohiro
spellingShingle	Ikeda, Kiyohiro misc Central place theory misc City distribution misc Core–satellite pattern misc German reunification misc Hexagons misc Megalopolis misc Spectrum analysis Time Evolution of City Distributions in Germany
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topic_title	Time Evolution of City Distributions in Germany Central place theory (dpeaa)DE-He213 City distribution (dpeaa)DE-He213 Core–satellite pattern (dpeaa)DE-He213 German reunification (dpeaa)DE-He213 Hexagons (dpeaa)DE-He213 Megalopolis (dpeaa)DE-He213 Spectrum analysis (dpeaa)DE-He213
topic	misc Central place theory misc City distribution misc Core–satellite pattern misc German reunification misc Hexagons misc Megalopolis misc Spectrum analysis
topic_unstemmed	misc Central place theory misc City distribution misc Core–satellite pattern misc German reunification misc Hexagons misc Megalopolis misc Spectrum analysis
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title	Time Evolution of City Distributions in Germany
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title_full	Time Evolution of City Distributions in Germany
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author_browse	Ikeda, Kiyohiro Osawa, Minoru Takayama, Yuki
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title_sort	time evolution of city distributions in germany
title_auth	Time Evolution of City Distributions in Germany
abstract	Abstract This paper aims to capture characteristic agglomeration patterns in population data in Germany from 1987 to 2011, encompassing pre- and post-unification periods. We utilize a group-theoretic double Fourier spectrum analysis procedure (Ikeda et al. 2018) as a systematic means to capture characteristic agglomeration patterns in population data. Among a plethora of patterns to be self-organized from a uniform state, we focus on a megalopolis pattern, a rhombic pattern, and a core–satellite pattern (a downtown surrounded by hexagonal satellite cities). As the technical contribution of this paper, we newly introduce a principal vector as a superposition of these patterns in order to grasp the multi-scale nature of agglomerations. Benchmark spectra for these patterns are advanced and are found in the population data of Germany in 2011. An incremental population is investigated using this principal vector to successfully detect a shift of predominant population increase/decrease patterns in the pre- and post-unification periods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstractGer	Abstract This paper aims to capture characteristic agglomeration patterns in population data in Germany from 1987 to 2011, encompassing pre- and post-unification periods. We utilize a group-theoretic double Fourier spectrum analysis procedure (Ikeda et al. 2018) as a systematic means to capture characteristic agglomeration patterns in population data. Among a plethora of patterns to be self-organized from a uniform state, we focus on a megalopolis pattern, a rhombic pattern, and a core–satellite pattern (a downtown surrounded by hexagonal satellite cities). As the technical contribution of this paper, we newly introduce a principal vector as a superposition of these patterns in order to grasp the multi-scale nature of agglomerations. Benchmark spectra for these patterns are advanced and are found in the population data of Germany in 2011. An incremental population is investigated using this principal vector to successfully detect a shift of predominant population increase/decrease patterns in the pre- and post-unification periods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstract_unstemmed	Abstract This paper aims to capture characteristic agglomeration patterns in population data in Germany from 1987 to 2011, encompassing pre- and post-unification periods. We utilize a group-theoretic double Fourier spectrum analysis procedure (Ikeda et al. 2018) as a systematic means to capture characteristic agglomeration patterns in population data. Among a plethora of patterns to be self-organized from a uniform state, we focus on a megalopolis pattern, a rhombic pattern, and a core–satellite pattern (a downtown surrounded by hexagonal satellite cities). As the technical contribution of this paper, we newly introduce a principal vector as a superposition of these patterns in order to grasp the multi-scale nature of agglomerations. Benchmark spectra for these patterns are advanced and are found in the population data of Germany in 2011. An incremental population is investigated using this principal vector to successfully detect a shift of predominant population increase/decrease patterns in the pre- and post-unification periods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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title_short	Time Evolution of City Distributions in Germany
url	https://dx.doi.org/10.1007/s11067-021-09557-2
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author2	Osawa, Minoru Takayama, Yuki
author2Str	Osawa, Minoru Takayama, Yuki
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doi_str	10.1007/s11067-021-09557-2
up_date	2024-07-04T00:44:00.373Z
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score	7.4012156

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Time Evolution of City Distributions in Germany

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Zugang & Verfügbarkeit

Vorhandene Bände

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