Identifying Employment Subcenters: The Method of Exponentially Declining Cutoffs
The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification...
Ausführliche Beschreibung
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| Autor*in: |
Jifei Ban [verfasserIn] Richard Arnott [verfasserIn] Jacob L. Macdonald [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Land - MDPI AG, 2013, 6(2017), 1, p 17 |
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| Übergeordnetes Werk: |
volume:6 ; year:2017 ; number:1, p 17 |
| Links: |
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| DOI / URN: |
10.3390/land6010017 |
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| Katalog-ID: |
DOAJ011990244 |
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| 520 | |a The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification to this method aimed at remedying this weakness. The modification entails using cutoffs that decline exponentially with distance from the metropolitan center, thereby giving consideration to the employment density of a location relative to that of its locality. In urban studies, there is a long history of estimating employment density “gradients”, the exponential rate at which employment density declines with distance from the metropolitan center. These density gradients differ substantially across metropolitan areas and across time for a particular metropolitan area. Applying our method to Los Angeles, Calgary and Paris, we have found that using cutoffs that decline exponentially at one-half the estimated density gradients achieves an appealing balance between subcenters identified close to the metropolitan center and those identified at the metropolitan periphery. Many other methods of subcenter identification have been proposed that use sophisticated econometric procedures. Our method should appeal to practitioners who are looking for a simple method to apply. | ||
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10.3390/land6010017 doi (DE-627)DOAJ011990244 (DE-599)DOAJ3fa2afebb3a846f5ade595fd08c326a6 DE-627 ger DE-627 rakwb eng Jifei Ban verfasserin aut Identifying Employment Subcenters: The Method of Exponentially Declining Cutoffs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification to this method aimed at remedying this weakness. The modification entails using cutoffs that decline exponentially with distance from the metropolitan center, thereby giving consideration to the employment density of a location relative to that of its locality. In urban studies, there is a long history of estimating employment density “gradients”, the exponential rate at which employment density declines with distance from the metropolitan center. These density gradients differ substantially across metropolitan areas and across time for a particular metropolitan area. Applying our method to Los Angeles, Calgary and Paris, we have found that using cutoffs that decline exponentially at one-half the estimated density gradients achieves an appealing balance between subcenters identified close to the metropolitan center and those identified at the metropolitan periphery. Many other methods of subcenter identification have been proposed that use sophisticated econometric procedures. Our method should appeal to practitioners who are looking for a simple method to apply. subcenter employment subcenter subcenter identification Giuliano–Small Los Angeles Paris Calgary Agriculture S Richard Arnott verfasserin aut Jacob L. Macdonald verfasserin aut In Land MDPI AG, 2013 6(2017), 1, p 17 (DE-627)72649500X (DE-600)2682955-1 2073445X nnns volume:6 year:2017 number:1, p 17 https://doi.org/10.3390/land6010017 kostenfrei https://doaj.org/article/3fa2afebb3a846f5ade595fd08c326a6 kostenfrei http://www.mdpi.com/2073-445X/6/1/17 kostenfrei https://doaj.org/toc/2073-445X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4367 GBV_ILN_4700 AR 6 2017 1, p 17 |
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10.3390/land6010017 doi (DE-627)DOAJ011990244 (DE-599)DOAJ3fa2afebb3a846f5ade595fd08c326a6 DE-627 ger DE-627 rakwb eng Jifei Ban verfasserin aut Identifying Employment Subcenters: The Method of Exponentially Declining Cutoffs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification to this method aimed at remedying this weakness. The modification entails using cutoffs that decline exponentially with distance from the metropolitan center, thereby giving consideration to the employment density of a location relative to that of its locality. In urban studies, there is a long history of estimating employment density “gradients”, the exponential rate at which employment density declines with distance from the metropolitan center. These density gradients differ substantially across metropolitan areas and across time for a particular metropolitan area. Applying our method to Los Angeles, Calgary and Paris, we have found that using cutoffs that decline exponentially at one-half the estimated density gradients achieves an appealing balance between subcenters identified close to the metropolitan center and those identified at the metropolitan periphery. Many other methods of subcenter identification have been proposed that use sophisticated econometric procedures. Our method should appeal to practitioners who are looking for a simple method to apply. subcenter employment subcenter subcenter identification Giuliano–Small Los Angeles Paris Calgary Agriculture S Richard Arnott verfasserin aut Jacob L. Macdonald verfasserin aut In Land MDPI AG, 2013 6(2017), 1, p 17 (DE-627)72649500X (DE-600)2682955-1 2073445X nnns volume:6 year:2017 number:1, p 17 https://doi.org/10.3390/land6010017 kostenfrei https://doaj.org/article/3fa2afebb3a846f5ade595fd08c326a6 kostenfrei http://www.mdpi.com/2073-445X/6/1/17 kostenfrei https://doaj.org/toc/2073-445X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4367 GBV_ILN_4700 AR 6 2017 1, p 17 |
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10.3390/land6010017 doi (DE-627)DOAJ011990244 (DE-599)DOAJ3fa2afebb3a846f5ade595fd08c326a6 DE-627 ger DE-627 rakwb eng Jifei Ban verfasserin aut Identifying Employment Subcenters: The Method of Exponentially Declining Cutoffs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification to this method aimed at remedying this weakness. The modification entails using cutoffs that decline exponentially with distance from the metropolitan center, thereby giving consideration to the employment density of a location relative to that of its locality. In urban studies, there is a long history of estimating employment density “gradients”, the exponential rate at which employment density declines with distance from the metropolitan center. These density gradients differ substantially across metropolitan areas and across time for a particular metropolitan area. Applying our method to Los Angeles, Calgary and Paris, we have found that using cutoffs that decline exponentially at one-half the estimated density gradients achieves an appealing balance between subcenters identified close to the metropolitan center and those identified at the metropolitan periphery. Many other methods of subcenter identification have been proposed that use sophisticated econometric procedures. Our method should appeal to practitioners who are looking for a simple method to apply. subcenter employment subcenter subcenter identification Giuliano–Small Los Angeles Paris Calgary Agriculture S Richard Arnott verfasserin aut Jacob L. Macdonald verfasserin aut In Land MDPI AG, 2013 6(2017), 1, p 17 (DE-627)72649500X (DE-600)2682955-1 2073445X nnns volume:6 year:2017 number:1, p 17 https://doi.org/10.3390/land6010017 kostenfrei https://doaj.org/article/3fa2afebb3a846f5ade595fd08c326a6 kostenfrei http://www.mdpi.com/2073-445X/6/1/17 kostenfrei https://doaj.org/toc/2073-445X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4367 GBV_ILN_4700 AR 6 2017 1, p 17 |
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10.3390/land6010017 doi (DE-627)DOAJ011990244 (DE-599)DOAJ3fa2afebb3a846f5ade595fd08c326a6 DE-627 ger DE-627 rakwb eng Jifei Ban verfasserin aut Identifying Employment Subcenters: The Method of Exponentially Declining Cutoffs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification to this method aimed at remedying this weakness. The modification entails using cutoffs that decline exponentially with distance from the metropolitan center, thereby giving consideration to the employment density of a location relative to that of its locality. In urban studies, there is a long history of estimating employment density “gradients”, the exponential rate at which employment density declines with distance from the metropolitan center. These density gradients differ substantially across metropolitan areas and across time for a particular metropolitan area. Applying our method to Los Angeles, Calgary and Paris, we have found that using cutoffs that decline exponentially at one-half the estimated density gradients achieves an appealing balance between subcenters identified close to the metropolitan center and those identified at the metropolitan periphery. Many other methods of subcenter identification have been proposed that use sophisticated econometric procedures. Our method should appeal to practitioners who are looking for a simple method to apply. subcenter employment subcenter subcenter identification Giuliano–Small Los Angeles Paris Calgary Agriculture S Richard Arnott verfasserin aut Jacob L. Macdonald verfasserin aut In Land MDPI AG, 2013 6(2017), 1, p 17 (DE-627)72649500X (DE-600)2682955-1 2073445X nnns volume:6 year:2017 number:1, p 17 https://doi.org/10.3390/land6010017 kostenfrei https://doaj.org/article/3fa2afebb3a846f5ade595fd08c326a6 kostenfrei http://www.mdpi.com/2073-445X/6/1/17 kostenfrei https://doaj.org/toc/2073-445X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4367 GBV_ILN_4700 AR 6 2017 1, p 17 |
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10.3390/land6010017 doi (DE-627)DOAJ011990244 (DE-599)DOAJ3fa2afebb3a846f5ade595fd08c326a6 DE-627 ger DE-627 rakwb eng Jifei Ban verfasserin aut Identifying Employment Subcenters: The Method of Exponentially Declining Cutoffs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification to this method aimed at remedying this weakness. The modification entails using cutoffs that decline exponentially with distance from the metropolitan center, thereby giving consideration to the employment density of a location relative to that of its locality. In urban studies, there is a long history of estimating employment density “gradients”, the exponential rate at which employment density declines with distance from the metropolitan center. These density gradients differ substantially across metropolitan areas and across time for a particular metropolitan area. Applying our method to Los Angeles, Calgary and Paris, we have found that using cutoffs that decline exponentially at one-half the estimated density gradients achieves an appealing balance between subcenters identified close to the metropolitan center and those identified at the metropolitan periphery. Many other methods of subcenter identification have been proposed that use sophisticated econometric procedures. Our method should appeal to practitioners who are looking for a simple method to apply. subcenter employment subcenter subcenter identification Giuliano–Small Los Angeles Paris Calgary Agriculture S Richard Arnott verfasserin aut Jacob L. Macdonald verfasserin aut In Land MDPI AG, 2013 6(2017), 1, p 17 (DE-627)72649500X (DE-600)2682955-1 2073445X nnns volume:6 year:2017 number:1, p 17 https://doi.org/10.3390/land6010017 kostenfrei https://doaj.org/article/3fa2afebb3a846f5ade595fd08c326a6 kostenfrei http://www.mdpi.com/2073-445X/6/1/17 kostenfrei https://doaj.org/toc/2073-445X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4367 GBV_ILN_4700 AR 6 2017 1, p 17 |
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Identifying Employment Subcenters: The Method of Exponentially Declining Cutoffs |
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The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification to this method aimed at remedying this weakness. The modification entails using cutoffs that decline exponentially with distance from the metropolitan center, thereby giving consideration to the employment density of a location relative to that of its locality. In urban studies, there is a long history of estimating employment density “gradients”, the exponential rate at which employment density declines with distance from the metropolitan center. These density gradients differ substantially across metropolitan areas and across time for a particular metropolitan area. Applying our method to Los Angeles, Calgary and Paris, we have found that using cutoffs that decline exponentially at one-half the estimated density gradients achieves an appealing balance between subcenters identified close to the metropolitan center and those identified at the metropolitan periphery. Many other methods of subcenter identification have been proposed that use sophisticated econometric procedures. Our method should appeal to practitioners who are looking for a simple method to apply. |
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The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification to this method aimed at remedying this weakness. The modification entails using cutoffs that decline exponentially with distance from the metropolitan center, thereby giving consideration to the employment density of a location relative to that of its locality. In urban studies, there is a long history of estimating employment density “gradients”, the exponential rate at which employment density declines with distance from the metropolitan center. These density gradients differ substantially across metropolitan areas and across time for a particular metropolitan area. Applying our method to Los Angeles, Calgary and Paris, we have found that using cutoffs that decline exponentially at one-half the estimated density gradients achieves an appealing balance between subcenters identified close to the metropolitan center and those identified at the metropolitan periphery. Many other methods of subcenter identification have been proposed that use sophisticated econometric procedures. Our method should appeal to practitioners who are looking for a simple method to apply. |
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The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification to this method aimed at remedying this weakness. The modification entails using cutoffs that decline exponentially with distance from the metropolitan center, thereby giving consideration to the employment density of a location relative to that of its locality. In urban studies, there is a long history of estimating employment density “gradients”, the exponential rate at which employment density declines with distance from the metropolitan center. These density gradients differ substantially across metropolitan areas and across time for a particular metropolitan area. Applying our method to Los Angeles, Calgary and Paris, we have found that using cutoffs that decline exponentially at one-half the estimated density gradients achieves an appealing balance between subcenters identified close to the metropolitan center and those identified at the metropolitan periphery. Many other methods of subcenter identification have been proposed that use sophisticated econometric procedures. Our method should appeal to practitioners who are looking for a simple method to apply. |
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