Analyzing of the 1980 journey-to-work Census data in the Los Angeles region, this paper has shown that the polycentric density functions fit the actual spatial structure statistically better than the monocentric model. For both resident worker and employment distributions, the monocentric model is rejected at a significance level of 0.0001 in favor of the polycentric model.
This result suggests that polycentricity exists in large urban areas and indicates that the overall access to major employment centers is more important in location choices than the access to the central business district.
19
This paper has determined a positive effect of polycentricity on the estimate of minimum required commute, by comparing the monocentric and polycentric required commutes. The results show that the monocentric model requires a small amount of commute: 1.02 miles by Hamilton’s calculation and
1.99 miles by White’s calculation; whereas the polycentric model requires 4.35 miles. Comparing with the actual average commute of 10.81 miles, they account for 9.4 percent, 18.4 percent, and 40 percent of the actual average commuting distance. These results indicate that the polycentric structure increases the estimate of minimum required urban commuting, and that the polycentric model explains better the observed commuting pattern than the monocentric model.
Hamilton’s original finding, that the monocentric model does a very bad job in predicting the actual commuting behavior, is more an indictment of the monocentricity assumption than a rejection of the assumption on commuting behavior in location choices. The monocentric model greatly underpredicts the actual commute because it inadequately represents the spatial structure in large urban areas; Hamilton’s calculation, which assumes there exists a radial transportation network and commuting trips proceed along radial routes, also contributes to this bias. Once polycentricity is substituted for
monocentricity and the actual road network is used, actual commuting behavior is much better explained.
The findings that the polycentric model explains much better both the observed commuting distance and the actual distribution patterns help to preserve the assumption that urban households make strong attempts to economize on commuting. In turn, they suggest the policy implication that efforts to promote more efficient urban form, such as the jobs-housing policy may have the potential to succeed.
2O
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23
Table I. Employment Centers Identified in 1980
Center Total
Location Emp.
Emp. Den. Dist. from (Emp/Acre) CBD(miles)
Downtown LA (CBD) 429869 42.26 0.1 UCLA/Santa Monica 208166 25.31 15.8
LA Airport 48510 18.77 18.8
Orange Co. Airport 47459 16.01 40.7 West Hollywood 43761 23.01 7.3
Santa Ana 37305 17.18 32.9
Pasadena 35675 25.14 12.6
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Table 2. Estimates on Monocentric Density Functions
Intercept Gradient ~ R (logD
o) (g)
Integration (1000s)
Employment
8.5321" 0.0498" 0.343 (0.0688) (0.0021)
3,577
Resident Worker
8.6574* O. 0457* O o 386 (0.0586) (0.0017)
4,487
Standard errors are in parentheses.
There are 1124 observations.
The data set has 4,528 thousand jobs (and resident workers).
* Estimate is statistically significant at 0.05 level, l-sided test.
25
Table 3. Estimates on Polycentric Density Function: Resident Workers
Center Impact
Location Intercept Gradient (1000s)
Downtown LA (CBD) 2125" 0.3361" 58.1
(2.76) (1.87) (I.09)
UCLA/Santa Monica 2673" 0.0665" 937.4
(3.53) (1.69) (1.46)
LA Airport 2517" 0.0270" 2789.6"
(2.80) (2o15) (2.69)
West Hollywood 6936" 0.2484* 311.1"
(7.63) (4.86) (2.63)
Santa Ana 2493" 0o 0614" 1269.0*
(6.49) (2.83) (2.24)
Pasadena 701 0.0344 707.9
(1.08) (0.55) (0.71)
R2 = 0.481
t-values are in parentheses.
There are 1124 observations.
* Estimate is statistically significantat 0.05 level, l-sided test.
26
Table 4. Estimates on Polycentric Density Function: Employment
Center Impact
Location Intercept Gradient (I000s)
Downtown LA (CBD) 254990" 1.2199" 422.5"
(35.00) (38.98) (21.19) UCLA/Santa Monica 76020" 1. 5472" 39.5"
(I.69) (2.22) (2.07)
LA Airport 3925* 0.0234* 4957.6"
(5.09) (3.30) (5.92)
West Hollywood 12539" 0.2910" 400.8"
(3.16) (3.15) (2.08)
Santa Aria 6467 0.3114 211.0
(1.19) (1.31) (1.02)
Pasadena 33516 1.4203" 33.2
(1.51) (1.67) (1.28)
R2 : 0.705
t-values are in parentheses.
There are 1124 observations.
* Estimate is statistically significantat 0.05 level,l-sided test.
27
Table 5. Estimates of Average Required and Excess Commute
Required Excess
(miles) (%)
Monocentric Modela Monocentric Model b
Polycentric Model b
1.02 90.56
1.99 81.59
4.35 59.76
The average actual commuting distance is 10.81 mi]eSo a Estimates with Hamilton’s calculation.
b Estimates with White’s calculation.
28
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