Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Research Design
3.2. Study Area
3.3. Data Sources
3.4. Built Environment Types: Classification and Factors
3.5. Non-Spatial and Spatial Panel Data Models
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Built Environment Related Variable | Min | Mean | Standard Deviation | Max |
---|---|---|---|---|
Land-use mix (normalized entropy index) | 0 | 0.001 | 0.001 | 0.009 |
Road density (ft/mi2) | 13,913 | 66,997 | 22,877.45 | 166,697 |
Intersection density (units/mi2) | 9.83 | 75.00 | 37.68 | 255.31 |
Housing density (units/mi2) | 32.7 | 1409.5 | 1211.7 | 12,959.3 |
Multifamily housing (%) | 0 | 23.815 | 41.380 | 100 |
Average size of single-family lots (ft2) | 0 | 32,725 | 90,942 | 1,244,304 |
Jobs-housing ratio | 0.005 | 2.032 | 9.583 | 191.076 |
Jobs accessibility (jobs) | 0 | 772 | 1531 | 12,768 |
No vehicle Available (%) | CBD Proximity (mi) | Ph.D. Degree Holders (%) | Bachelor’s Degree Holders (%) | Associate’s Degree Holders (%) | Median Population Age (Years) | Median Housing Value (Dollars) | Median Household Income (Dollars) | Housing Density (Units/mi2) | Asian Population (%) | African American Population (%) | White Population (%) | Year | Independent Variables |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.13 | 0 | 0 | 0 | 10.8 | 937.97 | 6791.47 | 1.66 | 0 | 0 | 0 | Pooled dataset (2000 and 2015) | Min |
7.58 | 7.39 | 0.78 | 22.74 | 5.77 | 35.42 | 189,209.30 | 60,983.93 | 1249.28 | 3.8 | 29.29 | 61.33 | Mean | |
9.29 | 4.03 | 1.19 | 13.08 | 3.11 | 6.39 | 136,096.30 | 32,691.11 | 1102.41 | 5.14 | 27.55 | 29.3 | Standard deviation | |
64.8 | 19.41 | 13.14 | 57.98 | 21.08 | 80.5 | 1,169,800.00 | 250,001 | 12,959.33 | 54.87 | 99.33 | 100 | Max | |
0 | 0.13 | 0 | 0 | 0 | 19.5 | 937.96 | 6791.46 | 1.65 | 0 | 0 | 0 | 2000 | Min |
7.16 | 7.39 | 0.81 | 26.56 | 6.61 | 34.23 | 161,953.90 | 56,414.17 | 1089.08 | 2.94 | 27.21 | 65.53 | Mean | |
9.73 | 4.03 | 1.2 | 13.89 | 2.87 | 4.58 | 103,069.70 | 26,785.43 | 955.63 | 2.71 | 28.08 | 29.5 | Standard deviation | |
64.8 | 19.41 | 13.13 | 57.98 | 16.81 | 51.95 | 784,989.10 | 199,375.20 | 6402.19 | 18.92 | 99.32 | 100 | Max | |
0 | 0.13 | 0 | 0 | 0 | 10.8 | 16,100.00 | 12,212.00 | 32.7 | 0 | 0 | 0 | 2015 | Min |
7.98 | 7.39 | 0.75 | 18.92 | 4.93 | 36.59 | 216,467.00 | 65,553.68 | 1409.47 | 4.66 | 31.37 | 57.12 | Mean | |
8.8 | 4.03 | 1.16 | 10.95 | 3.11 | 7.61 | 158,008.00 | 37,149.00 | 1211.68 | 6.64 | 28.5 | 28.5 | Standard deviation | |
45.5 | 19.41 | 7.86 | 48.11 | 21.08 | 80.5 | 1,169,600.00 | 250,001.00 | 12,959.33 | 54.86 | 100 | 100 | Max |
Spatial Autocorrelation in Dependent Variable | Spatial Autocorrelation in Model Residuals | |||
---|---|---|---|---|
Year | Index | p Value | Index | p Value |
2000 | 0.751 | 0 | 0.33 | 0 |
2015 | 0.508 | 0 | 0.14 | 0 |
Test | LM-Error | LM-Lag | RLM-Error | RLM-Lag |
---|---|---|---|---|
LM | 79.3781 | 78.3621 | 4.9183 | 3.9023 |
p-value | 0 | 0 | 0.027 | 0.048 |
Built Environment Related Variable | CTAD | Urban | Suburban | Exurban |
---|---|---|---|---|
Land-use mix (normalized entropy index) | 0.0023 | 0.0016 | 0.0007 | 0.0002 |
Road density (ft/mi2) | 97,172 | 80,597 | 52,600 | 25,239 |
Intersection density (units/mi2) | 122.95 | 95.55 | 52.19 | 24.43 |
Housing density (units/mi2) | 4001.7 | 1875.5 | 743.2 | 138.6 |
Multifamily housing (%) | 91.25 | 28.52 | 11.95 | 0 |
Average size of single-family lots (ft2) | 9590 | 33,738 | 30,516 | 88,570 |
Jobs-housing ratio | 14.691 | 1.123 | 1.706 | 1.010 |
Jobs accessibility (jobs) | 2986 | 492 | 851 | 411 |
Independent Variable | Coefficient | t-Stat | z-Probability |
---|---|---|---|
Intercept | 28.283 | 5.072 | 0.000 |
Year 2015 | −1.519 | −3.626 | 0.000 |
CTAD | −4.554 | −5.159 | 0.000 |
CTAD × Year 2015 | 2.576 | 2.202 | 0.028 |
Urban | −0.726 | −1.830 | 0.068 |
Urban × Year 2015 | 1.084 | 2.191 | 0.029 |
White population (%) | −0.096 | −4.716 | 0.000 |
African American population (%) | −0.065 | −3.302 | 0.001 |
Log (Asian population (%)) | −0.351 | −1.915 | 0.056 |
Log (Housing density (units/mi2)) | 0.045 | 0.276 | 0.782 |
Log (Median household income (dollars)) | 0.618 | 1.232 | 0.218 |
Log (Median housing value (dollars)) | −1.160 | −3.327 | 0.001 |
Log (Median population age (years)) | 2.356 | 2.905 | 0.004 |
Associate’s degree holder (%) | 0.068 | 1.585 | 0.113 |
Bachelor’s degree holders (%) | 0.000 | 0.001 | 0.999 |
Log (Ph.D. degree holders (%)) | −0.596 | −2.091 | 0.037 |
CBD proximity (mi) | 0.709 | 16.776 | 0.000 |
No vehicle available (%) | 0.094 | 4.688 | 0.000 |
R2 | 0.342 | ||
Adjusted R2 | 0.331 | ||
N | 1092 |
Variable | Coefficient | t-Stat | z-Probability |
---|---|---|---|
Intercept | −7.735 | −0.842 | 0.400 |
Year 2015 | −1.329 | −3.307 | 0.001 |
CTAD | −3.605 | −4.281 | 0.000 |
CTAD × Year 2015 | 2.614 | 2.333 | 0.020 |
Urban | −0.521 | −1.386 | 0.166 |
Urban × Year 2015 | 0.968 | 2.095 | 0.036 |
White population (%) | −0.073 | −3.749 | 0.000 |
Black population (%) | −0.050 | −2.653 | 0.008 |
Log (Asian population (%)) | −0.307 | −1.751 | 0.080 |
Log (Housing density (units/mi2)) | −0.057 | −0.351 | 0.726 |
Log (Median household income (dollars)) | 0.277 | 0.595 | 0.552 |
Log (Median housing value (dollars)) | −0.762 | −2.237 | 0.025 |
Log (Median population age (years)) | 1.899 | 2.442 | 0.015 |
Associate’s degree holders (%) | 0.023 | 0.568 | 0.570 |
Bachelor’s degree holders (%) | 0.009 | 0.515 | 0.606 |
Log (PhD degree holders (%)) | −0.477 | −1.769 | 0.077 |
CBD proximity (mi) | 0.922 | 11.987 | 0.000 |
No vehicle available (%) | 0.110 | 5.366 | 0.000 |
Spatial lag | 0.327 | 9.118 | 0.000 |
Spatial error terms: | |||
W×White population (%) | −0.034 | −0.839 | 0.401 |
W×Black population (%) | −0.022 | −0.573 | 0.566 |
W×log (Asian population (%)) | −0.104 | −0.272 | 0.786 |
W×log (Housing density (units/mi2)) | 0.528 | 1.986 | 0.047 |
W×log (Median household income (dollars)) | 3.552 | 14.752 | 0.000 |
W×log (Median housing value (dollars)) | −1.406 | −2.095 | 0.036 |
W×log (Median population age (years)) | 1.428 | 0.964 | 0.335 |
W×Associate’s degree holders (%) | 0.290 | 3.318 | 0.001 |
W×Bachelor’s degree holders (%) | −0.041 | −1.348 | 0.178 |
W×log (PhD degree holders (%)) | 0.080 | 0.137 | 0.891 |
W×CBD proximity (mi) | −0.486 | −5.306 | 0.000 |
W×No vehicle (%) | −0.009 | −0.249 | 0.803 |
R-squared | 0.422 | ||
Squared correlation coefficient | 0.388 | ||
Log Likelihood | −2973.560 | ||
N | 1092 |
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Hatami, F.; Thill, J.-C. Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration. Sustainability 2022, 14, 7179. https://doi.org/10.3390/su14127179
Hatami F, Thill J-C. Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration. Sustainability. 2022; 14(12):7179. https://doi.org/10.3390/su14127179
Chicago/Turabian StyleHatami, Faizeh, and Jean-Claude Thill. 2022. "Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration" Sustainability 14, no. 12: 7179. https://doi.org/10.3390/su14127179