Satisfaction with the Pedestrian Environment and Its Relationship to Neighborhood Satisfaction in Seoul, South Korea
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Data
3.2. Variables
3.2.1. Dependent Variable
3.2.2. Independent Variables of Interest
3.2.3. Control Variables
3.3. Analysis Methods
3.3.1. Proportional Odds Logistic Regression
3.3.2. Gradient Boosting Decision Tree Regressor
4. Results
4.1. Proportional Odds Logistic Regression
4.2. Gradient Boosting Decision Tree Regressor
4.2.1. Feature Importance
4.2.2. Partial Dependence
5. Discussion
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Features | Study Sample | Population in Seoul | ||||
---|---|---|---|---|---|---|
Unweighted | Weighted | |||||
Count | % | Count | % | Count | % | |
Gender | ||||||
Male | 14,380 | 71.90% | 13,294 | 66.47% | 2,132,468 | 57.45% |
Female | 5620 | 28.10% | 6706 | 33.53% | 1,579,268 | 42.55% |
Age | ||||||
Under 29 | 1089 | 5.45% | 1372 | 6.86% | 210,249 | 5.66% |
30~39 | 4316 | 21.58% | 4230 | 21.15% | 417,970 | 11.26% |
40~49 | 5089 | 25.45% | 4453 | 22.27% | 428,783 | 11.55% |
50~59 | 4288 | 21.44% | 3858 | 19.29% | 618,204 | 16.66% |
Over 60 | 5218 | 26.09% | 6087 | 30.44% | 2,036,530 | 54.87% |
Homeownership | ||||||
Own | 10,695 | 53.48% | 8418 | 42.09% | 1,730,671 | 43.46% |
Rent | 9305 | 46.53% | 11,582 | 57.91% | 2,251,619 | 56.54% |
Education Attainment | ||||||
Under middle school | 1194 | 5.97% | 2641 | 13.21% | Unknown | |
High school | 4846 | 24.23% | 5602 | 28.01% | Unknown | |
College or Bachelor’s | 12,561 | 62.81% | 9627 | 48.14% | Unknown | |
Graduate School | 1399 | 7.00% | 2130 | 10.65% | Unknown | |
Household Income | ||||||
Less than KRW 2,000,000 | 2228 | 11.14% | 3550 | 17.75% | Unknown | |
KRW 2,000,000~KRW 4,000,000 | 6243 | 31.22% | 6701 | 33.51% | Unknown | |
KRW 4,000,000~KRW 6,000,000 | 5945 | 29.73% | 5014 | 25.07% | Unknown | |
KRW 6,000,000~KRW 8,000,000 | 3178 | 15.89% | 2555 | 12.78% | Unknown | |
Over KRW 8,000,000 | 2406 | 12.03% | 2180 | 10.90% | Unknown |
Name | Description | Mean | S.D. |
---|---|---|---|
Dependent variable | |||
st_neigh | The degree to which study participants were satisfied with the neighborhood in which they lived (10-point satisfaction Likert scale: 0. Very dissatisfied; 10. Very satisfied) | 6.55 | 1.75 |
Independent variables | |||
Satisfaction-related Factors | |||
st_ped | Satisfaction with the pedestrian environment in the neighborhood in which they lived (5-point satisfaction Likert scale: 1. Very dissatisfied; 5. Very satisfied) | 3.59 | 0.80 |
st_ped_n | Satisfaction with the pedestrian environment during the night in the neighborhood in which they lived (5-point satisfaction Likert scale: 1. Very dissatisfied; 5. Very satisfied) | 3.27 | 0.87 |
st_econ | Satisfaction with the economic environment in the neighborhood in which they lived (5-point satisfaction Likert scale: 1. Very dissatisfied; 5. Very satisfied) | 3.28 | 0.90 |
st_soci | Satisfaction with the social environment in the neighborhood in which they lived (5-point satisfaction Likert scale: 1. Very dissatisfied; 5. Very satisfied)) | 3.44 | 0.82 |
st_educ | Satisfaction with the educational environment in the neighborhood in which they lived (5-point satisfaction Likert scale: 1. Very dissatisfied; 5. Very satisfied) | 3.31 | 0.81 |
st_home | Satisfaction with a home where the respondent lives (5-point satisfaction Likert scale: 1. Very dissatisfied; 5. Very satisfied) | 3.46 | 0.92 |
st_infra | Satisfaction with infrastructures in the neighborhood in which they lived (5-point satisfaction Likert scale: 1. Very dissatisfied; 5. Very satisfied) | 3.63 | 0.82 |
Socio-demographic characteristics | |||
male | 1 if the respondent is male, 0 otherwise | 0.47 | 0.49 |
married | 1 if the respondent is married, 0 otherwise | 0.61 | 0.48 |
job_pro | 1 if the respondent has a professional job, 0 otherwise | 0.13 | 0.34 |
job_white | 1 if the respondent has a white-color job, 0 otherwise | 0.36 | 0.48 |
job_blue | 1 if the respondent has a blue-color job, 0 otherwise | 0.17 | 0.38 |
disab | 1 if the respondent has a disability, 0 otherwise | 0.02 | 0.13 |
age | The age of the respondent (1. 10~19; 2. 20~29; 3. 30~39; 4. 40~49; 5. 50~59; 6. more than 60) | 4.10 | 1.44 |
edu | The education attainment of the respondent (1. less than high school; 2. High school; 3. College or bachelors’ degree; 4. Graduate school) | 2.66 | 0.68 |
hh_inc | The household income of the respondent (1. less than KRW 1,000,000; 2. KRW 1,000,000~KRW 2,000,000; 3. KRW 2,000,000~KRW 3,000,000; 4. KRW 3,000,000~KRW 4,000,000; 5. KRW 4,000,000~KRW 5,000,000; 6. KRW 5,000,000~KRW 6,000,000; 7. KRW 6,000,000~KRW 7,000,000; 8. KRW 7,000,000~KRW 8,000,000; 9. KRW 8,000,000~KRW 9,000,000; 10. more than KRW 9,000,000) | 6.61 | 2.22 |
own | 1 if the respondent owns a home, 0 otherwise | 0.60 | 0.49 |
apt | 1 if the respondent lives in an apartment, 0 otherwise | 0.46 | 0.49 |
sfr | 1 if the respondent lives in a single-family home, 0 otherwise | 0.23 | 0.42 |
resi_dt | 1 if the respondent lives in a downtown area, 0 otherwise | 0.08 | 0.27 |
resi_en | 1 if the respondent lives in the east-northern area of Seoul, 0 otherwise | 0.32 | 0.46 |
resi_wn | 1 if the respondent lives in the west-northern area of Seoul, 0 otherwise | 0.12 | 0.32 |
resi_ws | 1 if the respondent lives in the west-southern area of Seoul, 0 otherwise | 0.29 | 0.45 |
hl_seoul | The number of years for residing in Seoul | 31.1 | 15.8 |
hl_gu | The number of years for residing in a Gu (an administrative level in South Korea) inside Seoul | 14.9 | 12.0 |
Algorithms | Brief Description |
---|---|
LR | LR deals with linear functions of the input features on the outcome target. LR usually serves as a baseline regressor in machine learning. |
SVM | SVM finds a separating linear decision boundary called hyperplane (optimal decision surface) that maximizes the distance between data points [23]. |
DT | DT is used to predict a classification outcome by splitting training data based on the splitter for input features [24]. DT runs a sequential and hierarchical decision based on features. |
RF | RF fits the same underlying algorithm to each bootstrapped copy of the original training data and then creates a final prediction by averaging the predictions from the different copies [25]. |
ADA | Boosting trains multiple models with subsets of data in a sequential fashion. ADA begins by assigning equal initial weights to all training data for weak learning training and then adjusts the weight distribution based on the results of the prediction [26]. |
GBDT | GBDT is a decision tree approach that is iterative. Its weak learners have strong dependencies between one another and are trained through progressive iterations based on the residuals. The ultimate result is calculated by adding up the results of all weak learners [27]. |
SGBDT | SGBDT is a hybrid algorithm that takes advantage of bagging and boosting techniques to improve prediction accuracy [28]. Using the term "stochastic" means that a random percentage of training data points will be used for each iteration rather than using all of the data for training [29]. |
CGBDT | CGBDT introduces modified target-based statistics that allow for the utilization of the entire dataset for training while avoiding the possibility of overfitting by performing random permutations [30]. |
XGBDT | XGBDT is an upgraded version of the GBDT. It obtains the residual by using second-order Taylor expansion on the cost function and incorporates a regularization term to regulate the complexity of the model simultaneously [30]. |
Model | Negative Mean Absolute Error | R Squared | Explained Variance | |||
---|---|---|---|---|---|---|
Mean | Std | Mean | Std | Mean | Std | |
LR | −0.919 | 0.026 | 0.247 | 0.012 | 0.248 | 0.012 |
SVM | −0.898 | 0.028 | 0.104 | 0.047 | 0.111 | 0.048 |
DT | −0.909 | 0.031 | 0.246 | 0.019 | 0.247 | 0.020 |
RF | −0.857 | 0.026 | 0.319 | 0.013 | 0.321 | 0.013 |
ADA | −0.902 | 0.030 | 0.266 | 0.016 | 0.268 | 0.017 |
GBDT | −0.847 | 0.022 | 0.342 | 0.009 | 0.343 | 0.009 |
SGBDT | −0.868 | 0.023 | 0.309 | 0.011 | 0.310 | 0.011 |
CGBDT | −0.853 | 0.017 | 0.325 | 0.016 | 0.326 | 0.016 |
XGBDT | −0.853 | 0.022 | 0.327 | 0.017 | 0.328 | 0.017 |
Variables | Value | Std. Err | T-Value | p-Value | Odds Ratio |
---|---|---|---|---|---|
st_ped (reference: 1. very dissatisfied) | |||||
2. dissatisfied | 0.068 | 0.312 | 0.219 | 0.827 | 1.071 |
3. neutral | 0.068 | 0.307 | 0.222 | 0.825 | 1.070 |
4. satisfied | 0.565 | 0.307 | 1.839 | 0.066 | 1.759 |
5. very satisfied | 0.614 | 0.309 | 1.984 | 0.047 | 1.847 |
st_ped_n (reference: 1. very dissatisfied) | |||||
2. dissatisfied | 0.516 | 0.097 | 5.304 | 0.000 | 1.675 |
3. neutral | 0.594 | 0.095 | 6.250 | 0.000 | 1.812 |
4. satisfied | 0.685 | 0.096 | 7.123 | 0.000 | 1.983 |
5. very satisfied | 0.693 | 0.113 | 6.126 | 0.000 | 1.999 |
st_econ (reference: 1. very dissatisfied) | |||||
2. dissatisfied | 0.020 | 0.081 | 0.251 | 0.802 | 1.021 |
3. neutral | 0.086 | 0.079 | 1.084 | 0.278 | 1.089 |
4. satisfied | 0.006 | 0.081 | 0.075 | 0.940 | 1.006 |
5. very satisfied | −0.040 | 0.094 | −0.427 | 0.669 | 0.960 |
st_soci (reference: 1. very dissatisfied) | |||||
2. dissatisfied | 0.016 | 0.124 | 0.132 | 0.895 | 1.016 |
3. neutral | 0.044 | 0.123 | 0.360 | 0.719 | 1.045 |
4. satisfied | 0.059 | 0.124 | 0.475 | 0.635 | 1.061 |
5. very satisfied | −0.014 | 0.133 | −0.108 | 0.914 | 0.986 |
st_educ (reference: 1. very dissatisfied) | |||||
2. dissatisfied | 0.085 | 0.110 | 0.770 | 0.441 | 1.089 |
3. neutral | 0.106 | 0.108 | 0.978 | 0.328 | 1.112 |
4. satisfied | 0.065 | 0.111 | 0.587 | 0.557 | 1.067 |
5. very satisfied | 0.024 | 0.123 | 0.193 | 0.847 | 1.024 |
st_home (reference: 1. very dissatisfied) | |||||
2. dissatisfied | 0.668 | 0.389 | 1.717 | 0.086 | 1.951 |
3. neutral | 1.423 | 0.384 | 3.703 | 0.000 | 4.150 |
4. satisfied | 2.078 | 0.385 | 5.403 | 0.000 | 7.991 |
5. very satisfied | 2.958 | 0.387 | 7.642 | 0.000 | 19.265 |
st_infra (reference: 1. very dissatisfied) | |||||
2. dissatisfied | 2.709 | 0.765 | 3.541 | 0.000 | 15.016 |
3. neutral | 2.126 | 0.762 | 2.790 | 0.005 | 8.383 |
4. satisfied | 2.848 | 0.762 | 3.736 | 0.000 | 17.260 |
5. very satisfied | 3.206 | 0.763 | 4.200 | 0.000 | 24.692 |
male (ref: 0. no) | −0.077 | 0.028 | −2.798 | 0.005 | 0.926 |
married (ref: 0. no) | 0.137 | 0.035 | 3.956 | 0.000 | 1.147 |
age (reference: 1. 10~19) | |||||
2. 20~29 | −0.050 | 0.085 | −0.593 | 0.553 | 0.951 |
3. 30~39 | −0.182 | 0.088 | −2.069 | 0.039 | 0.834 |
4. 40~49 | −0.262 | 0.090 | −2.921 | 0.003 | 0.770 |
5. 50~59 | −0.417 | 0.088 | −4.726 | 0.000 | 0.659 |
6. over 60 | −0.390 | 0.088 | −4.417 | 0.000 | 0.677 |
edu (reference: 1. less than high school) | |||||
2. high school | 0.356 | 0.055 | 6.488 | 0.000 | 1.428 |
3. college or bachelor | 0.376 | 0.061 | 6.121 | 0.000 | 1.456 |
4. graduate school | 0.591 | 0.156 | 3.789 | 0.000 | 1.806 |
hh_inc (reference: 1. less than KRW 1,000,000) | |||||
2. KRW 1,000,000~KRW 2,000,000 | 0.600 | 0.359 | 1.670 | 0.095 | 1.823 |
3. KRW 2,000,000~KRW 3,000,000 | 0.711 | 0.350 | 2.029 | 0.043 | 2.036 |
4. KRW 3,000,000~KRW 4,000,000 | 1.077 | 0.349 | 3.083 | 0.002 | 2.936 |
5. KRW 4,000,000~KRW 5,000,000 | 1.106 | 0.349 | 3.165 | 0.002 | 3.022 |
6. KRW 5,000,000~KRW 6,000,000 | 0.903 | 0.350 | 2.580 | 0.010 | 2.467 |
7. KRW 6,000,000~KRW 7,000,000 | 1.015 | 0.350 | 2.901 | 0.004 | 2.760 |
8. KRW 7,000,000~KRW 8,000,000 | 1.289 | 0.351 | 3.677 | 0.000 | 3.629 |
9. KRW 8,000,000~KRW 9,000,000 | 1.304 | 0.352 | 3.703 | 0.000 | 3.685 |
10. over KRW 9,000,000 | 1.436 | 0.352 | 4.080 | 0.000 | 4.203 |
OWN_HOME (ref: 0. no) | 0.144 | 0.029 | 4.928 | 0.000 | 1.155 |
APT (ref: 0. no) | −0.076 | 0.030 | −2.529 | 0.011 | 0.926 |
SFR (ref: 0. no) | 0.067 | 0.035 | 1.898 | 0.058 | 1.069 |
resi_dt (ref: 0. no) | −0.345 | 0.054 | −6.396 | 0.000 | 0.709 |
resi_en (ref: 0. no) | −0.118 | 0.039 | −2.988 | 0.003 | 0.889 |
resi_wn (ref: 0. no) | −0.035 | 0.048 | −0.741 | 0.459 | 0.965 |
resi_ws (ref: 0. no) | −0.453 | 0.040 | −11.437 | 0.000 | 0.636 |
job_pro (ref: 0. no) | 0.016 | 0.058 | 0.284 | 0.777 | 1.016 |
job_white (ref: 0. no) | 0.077 | 0.038 | 2.046 | 0.041 | 1.080 |
job_blue (ref: 0. no) | −0.049 | 0.039 | −1.253 | 0.210 | 0.952 |
disab (ref: 0. no) | −0.432 | 0.118 | −3.651 | 0.000 | 0.649 |
hl_seoul | 0.006 | 0.001 | 5.351 | 0.000 | 1.006 |
hl_gu | −0.003 | 0.001 | −1.885 | 0.059 | 0.997 |
Intercept | |||||
0|1 | −1.908 | 0.927 | −2.057 | 0.040 | |
1|2 | −0.982 | 0.876 | −1.121 | 0.262 | |
2|3 | 0.241 | 0.864 | 0.279 | 0.781 | |
3|4 | 1.830 | 0.863 | 2.119 | 0.034 | |
4|5 | 3.405 | 0.865 | 3.937 | 0.000 | |
5|6 | 4.826 | 0.865 | 5.576 | 0.000 | |
6|7 | 6.091 | 0.866 | 7.035 | 0.000 | |
7|8 | 7.448 | 0.866 | 8.600 | 0.000 | |
8|9 | 9.219 | 0.866 | 10.642 | 0.000 | |
9|10 | 12.353 | 0.870 | 14.196 | 0.000 | |
Model Statistics | |||||
Observations | 20,000 | ||||
McFadden | 0.079 | ||||
CoxSnell Score | 0.235 | ||||
Nagelkerke Score | 0.243 | ||||
AIC | 62,889.630 |
Variables | Impurity-Based Feature Importance | Permutation-Based Feature Importance | ||
---|---|---|---|---|
Magnitude | Rank | Magnitude | Rank | |
st_ped | 0.068 | 5 | 0.103 | 3 |
st_ped_n | 0.048 | 7 | 0.057 | 6 |
st_econ | 0.035 | 9 | 0.003 | 20 |
st_soci | 0.033 | 10 | 0.002 | 22 |
st_educ | 0.032 | 11 | 0.000 | 26 |
st_home | 0.156 | 1 | 0.299 | 1 |
st_infra | 0.110 | 2 | 0.178 | 2 |
male | 0.016 | 17 | 0.001 | 24 |
married | 0.015 | 18 | 0.007 | 17 |
job_pro | 0.010 | 24 | 0.001 | 24 |
job_white | 0.013 | 21 | 0.008 | 15 |
job_blue | 0.013 | 21 | 0.006 | 18 |
disab | 0.004 | 26 | 0.002 | 22 |
age | 0.044 | 8 | 0.029 | 10 |
edu | 0.023 | 12 | 0.009 | 14 |
hh_inc | 0.064 | 6 | 0.064 | 4 |
OWN_HOME | 0.018 | 14 | 0.012 | 13 |
APT | 0.018 | 14 | 0.005 | 19 |
SFR | 0.015 | 18 | 0.008 | 15 |
resi_dt | 0.007 | 25 | 0.003 | 20 |
resi_en | 0.017 | 16 | 0.030 | 9 |
resi_wn | 0.012 | 23 | 0.024 | 11 |
resi_ws | 0.020 | 13 | 0.044 | 7 |
resi_es | 0.014 | 20 | 0.016 | 12 |
hl_seoul | 0.103 | 3 | 0.031 | 8 |
hl_gu | 0.093 | 4 | 0.061 | 5 |
1996 | 2002 | 2006 | 2010 | |||||
---|---|---|---|---|---|---|---|---|
Active transportation | 4,389,859 | 13.64% | 5,230,690 | 14.98% | 6,110,389 | 16.38% | 6,499,084 | 17.26% |
Vehicle | 6,829,224 | 21.22% | 7,982,832 | 22.87% | 8,188,781 | 21.95% | 7,501,988 | 19.92% |
Taxi | 2,901,178 | 9.01% | 2,194,799 | 6.29% | 1,959,612 | 5.25% | 2,236,058 | 5.94% |
Bus | 8,357,730 | 25.96% | 7,705,001 | 22.07% | 8,616,326 | 23.10% | 8,745,685 | 23.23% |
Transit | 8,182,634 | 25.42% | 10,284,673 | 29.46% | 10,839,341 | 29.05% | 11,289,362 | 29.98% |
Others | 1,528,794 | 4.75% | 1,512,971 | 4.33% | 1,592,022 | 4.27% | 1,382,479 | 3.67% |
Total | 32,189,419 | 100.00% | 34,910,966 | 100.00% | 37,306,471 | 100.00% | 37,654,656 | 100.00% |
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Lee, S. Satisfaction with the Pedestrian Environment and Its Relationship to Neighborhood Satisfaction in Seoul, South Korea. Sustainability 2022, 14, 9343. https://doi.org/10.3390/su14159343
Lee S. Satisfaction with the Pedestrian Environment and Its Relationship to Neighborhood Satisfaction in Seoul, South Korea. Sustainability. 2022; 14(15):9343. https://doi.org/10.3390/su14159343
Chicago/Turabian StyleLee, Sangwan. 2022. "Satisfaction with the Pedestrian Environment and Its Relationship to Neighborhood Satisfaction in Seoul, South Korea" Sustainability 14, no. 15: 9343. https://doi.org/10.3390/su14159343