Walking for Sustainable Cities: Factors Affecting Users’ Willingness to Walk
Abstract
:1. Introduction
2. Materials and Methods
Analytical Methods
- ➢
- Retention of most of the information in the data: PCA aims to retain as much information as possible from the original data while reducing the dimensionality of the problem, thus minimizing the loss of relevant information.
- ➢
- Speed of calculation: PCA is relatively fast compared to other dimension reduction methods, such as linear discriminant analysis (LDA) or factor analysis. This makes PCA suitable for working with large amounts of data.
- ➢
- Ease of interpretation: PCA provides a visual representation of the data in a new coordinate system in which the dimensions are ordered according to their importance. This makes it possible to visualize the relationships between variables and identify hidden patterns in the data.
- ➢
- Reduction of correlation: PCA is able to reduce the correlations between variables, simplifying the problem and making it easier to understand the data.
- ➢
- Ability to test complex causal models: Path analysis allows researchers to test complex models that include multiple variables and pathways. This allows researchers to investigate the direct and indirect effects of variables on each other, which can be useful for understanding the underlying mechanisms of complex phenomena.
- ➢
- Ability to handle multiple dependent variables: Path analysis can be used to simultaneously analyze multiple dependent. This is particularly useful in situations where there are multiple outcomes of interest that are interrelated.
- ➢
- Allows for the inclusion of measurement error: Path analysis can account for measurement error in the variables being studied. This is important because measurement error can lead to biased estimates of the relationships between variables.
- ➢
- Provides a visual representation of the relationships between variables: Path analysis produces a diagram that shows the relationships between variables in the model. This can be useful for communicating the results of the analysis to others and for generating new hypotheses for future research.
- ➢
- Can be used to test alternative hypotheses: Path analysis allows researchers to test alternative hypotheses about the relationships between variables. This can be useful for exploring different theoretical perspectives and comparing the fit of different models to the data.
3. Results
3.1. Principal Component Analysis
- The first was formed by variables that have a high positive weight with respect to PC2 and a low weight with respect to PC3. This cluster contained V14, V16, V17, V18, V19, V20, and V25.
- The second was formed by variables that had a high negative weight with respect to PC2 and a low weight with respect to PC3. This cluster contained V9, V10, V11, V12, and V13.
- The third was formed by variables that had a high positive weight with respect to PC3 and a low weight with respect to PC2. This cluster contained V15, V21, V22, V23, and V24.
- The fourth was formed by variables that had a high negative weight with respect to PC3 and a low weight with respect to PC2. This cluster contained V1, V2, V3, V4, V5, V6, V7, and V8.
3.2. Path Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Factor | |
---|---|
V1 | Continuity of the sidewalk |
V2 | Sidewalk width |
V3 | Good condition of the sidewalk surface |
V4 | Reduced slope of the path |
V5 | Absence of fixed obstacles (trees, poles, etc.) |
V6 | Absence of obstacles and obstructions (parked vehicles, merchandise from shops, etc.) |
V7 | Absence of driveways |
V8 | Cleanliness of the pedestrian path |
V9 | Presence of protection from atmospheric agents (trees, porches, etc.) |
V10 | Presence of benches or seats |
V11 | High perception of security |
V12 | Ease of getting to a public transport stop |
V13 | Good artificial lighting system of the path |
V14 | Not excessive width of the carriageway |
V15 | Low flows of vehicular traffic |
V16 | Presence of speed limits for vehicular flows |
V17 | Presence of traffic-calming measures on the carriageway |
V18 | Presence of a bike lane adjacent to the pedestrian path |
V19 | Presence of a parking lane adjacent to the pedestrian path |
V20 | Absence of large parking areas |
V21 | Ease of crossing at intersections |
V22 | Ease of crossing out of intersections |
V23 | Presence of commercial activities (bars, shops, etc.) |
V24 | High artistic/landscape value of the streetscape |
V25 | Presence of other pedestrians |
KMO and Bartlett’s Tests | ||
---|---|---|
KMO of Sampling Adequacy | 0.872 | |
Bartlett’s Test of Sphericity | Approx. Chi-Square | 2203.336 |
df | 300 | |
Sig. | 0 |
Component | Initial Eigenvalues | Extraction Sums of Squared Loadings | ||||
---|---|---|---|---|---|---|
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
1 | 6.821 | 32.283 | 32.283 | 6.821 | 32.283 | 32.283 |
2 | 1.94 | 14.76 | 47.043 | 1.94 | 14.76 | 47.043 |
3 | 1.737 | 9.948 | 56.991 | 1.737 | 9.948 | 56.991 |
4 | 1.336 | 6.998 | 63.989 | 1.336 | 6.998 | 63.989 |
5 | 1.094 | 5.03 | 69.019 | 1.094 | 5.03 | 69.019 |
6 | 1.007 | 3.684 | 72.703 | 1.007 | 3.684 | 72.703 |
7 | 0.966 | 3.422 | 76.125 | |||
8 | 0.917 | 3.325 | 79.45 | |||
9 | 0.875 | 3.158 | 82.608 | |||
10 | 0.818 | 2.828 | 85.436 | |||
11 | 0.742 | 2.624 | 88.06 | |||
12 | 0.725 | 2.455 | 90.515 | |||
13 | 0.649 | 2.177 | 92.692 | |||
14 | 0.624 | 1.951 | 94.643 | |||
15 | 0.598 | 1.864 | 96.507 | |||
16 | 0.559 | 1.711 | 98.218 | |||
17 | 0.363 | 1.352 | 99.57 | |||
18 | 0.196 | 0.43 | 100 |
Principal Component | ||||||
---|---|---|---|---|---|---|
PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | |
V1 | 0.599 | −0.096 | −0.409 | 0.38 | −0.013 | 0.111 |
V2 | 0.672 | −0.207 | −0.463 | 0.297 | −0.064 | 0.023 |
V3 | 0.62 | −0.282 | −0.338 | 0.39 | −0.132 | −0.051 |
V4 | 0.411 | 0.205 | −0.328 | 0.084 | 0.347 | 0.05 |
V5 | 0.633 | −0.123 | −0.238 | −0.248 | 0.013 | 0.025 |
V6 | 0.573 | 0.005 | −0.326 | −0.212 | −0.172 | 0.061 |
V7 | 0.446 | 0.286 | −0.261 | −0.296 | 0.209 | −0.262 |
V8 | 0.631 | −0.135 | −0.1 | −0.204 | −0.243 | 0.136 |
V9 | 0.56 | −0.257 | 0.089 | −0.414 | −0.072 | −0.113 |
V10 | 0.512 | −0.091 | 0.112 | −0.332 | −0.205 | 0.399 |
V11 | 0.559 | −0.405 | 0.124 | −0.128 | 0.35 | −0.097 |
V12 | 0.581 | −0.173 | 0.129 | −0.21 | 0.035 | 0.158 |
V13 | 0.541 | −0.446 | 0.243 | −0.048 | 0.056 | 0.027 |
V14 | 0.487 | 0.434 | −0.113 | −0.04 | 0.172 | −0.04 |
V15 | 0.536 | −0.002 | 0.206 | 0.188 | 0.192 | −0.12 |
V16 | 0.539 | 0.123 | 0.037 | −0.175 | 0.004 | −0.515 |
V17 | 0.538 | 0.335 | 0.148 | −0.047 | −0.296 | −0.355 |
V18 | 0.354 | 0.536 | 0.09 | −0.065 | −0.188 | 0.321 |
V19 | 0.449 | 0.501 | 0.174 | 0.037 | −0.288 | 0.117 |
V20 | 0.356 | 0.432 | −0.099 | 0.106 | 0.329 | 0.276 |
V21 | 0.476 | −0.06 | 0.305 | 0.353 | −0.196 | −0.06 |
V22 | 0.504 | 0.036 | 0.333 | 0.323 | −0.203 | −0.121 |
V23 | 0.419 | −0.01 | 0.361 | −0.007 | 0.369 | 0.223 |
V24 | 0.443 | −0.18 | 0.497 | 0.183 | 0.088 | 0.107 |
V25 | 0.449 | 0.325 | 0.267 | 0.123 | 0.201 | −0.105 |
S | C | Q | W | |
---|---|---|---|---|
S | 1.000 | |||
C | 0.891 | 1.000 | ||
Q | 0.872 | 0.909 | 1.000 | |
W | 0.879 | 0.888 | 0.890 | 1.000 |
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Distefano, N.; Leonardi, S.; Liotta, N.G. Walking for Sustainable Cities: Factors Affecting Users’ Willingness to Walk. Sustainability 2023, 15, 5684. https://doi.org/10.3390/su15075684
Distefano N, Leonardi S, Liotta NG. Walking for Sustainable Cities: Factors Affecting Users’ Willingness to Walk. Sustainability. 2023; 15(7):5684. https://doi.org/10.3390/su15075684
Chicago/Turabian StyleDistefano, Natalia, Salvatore Leonardi, and Nilda Georgina Liotta. 2023. "Walking for Sustainable Cities: Factors Affecting Users’ Willingness to Walk" Sustainability 15, no. 7: 5684. https://doi.org/10.3390/su15075684