Does the Visibility of Greenery Increase Perceived Safety in Urban Areas? Evidence from the Place Pulse 1.0 Dataset
Abstract
:1. Introduction
2. Data
2.1. Place Pulse 1.0 Dataset
2.2. Land Use Map
Land Use Type | Descriptions |
---|---|
Multifamily residential land | Duplexes (usually with two front doors, two entrance pathways and sometimes two driveways), apartment buildings, condominium complexes, including buildings and maintained lawns. In this study, it also includes medium-density residential land. |
High-density residential land | Housing on smaller than 1/4 acre lots. |
Transportation land | Airports (including landing strips, hangars, parking areas and related facilities), railroads and rail stations and divided highways (related facilities would include rest areas, highway maintenance areas, storage areas and on/off ramps). |
Urban public/institutional land | Lands comprising schools, churches, colleges, hospitals, museums, prisons, town halls or court houses, police and fire stations, including parking lots, dormitories and university housing; also may include public open green spaces, like town commons. |
Commercial land | Malls, shopping centers and larger strip commercial areas, plus neighborhood stores and medical offices (not hospitals). |
Industrial land | Light and heavy industry, including buildings, equipment and parking areas. |
Open land | Vacant land, idle agriculture, rock outcrops and barren areas. Vacant land is not maintained for any evident purpose, and it does not support large plant growth. In this study, it also includes participation recreation, marina, cemetery, transitional land. |
3. Methodology
3.1. Extraction of Vegetation Information from GSV Images
3.1.1. Vegetation Classification from GSV Images
Comment: G, R and B are three bands in segmented images |
Comment: Vegetation is the vegetation extraction result |
ExG = 2 × G – R – B |
Threshold = OTSU(ExG) |
for each pixel [i, j] in ExG: |
if ExG [i, j] > Threshold: |
Classify Vegetation [i, j] as green vegetation |
Mask out pixels with values in green, red, blue band higher than 0.6 in the Vegetation image |
3.1.2. Vertical Distribution of Greenery
3.2. Image Quality of Geo-Tagged Imagery
3.2.1. Brightness
3.2.2. Contrast
3.3. Variables’ Preparation and Regression Analysis
- High density residential land: x1 = 1, x2 = x3 = x4 = x5 = x6 = 0
- Urban public/institutional land: x1 = 0, x2 = 1, x3 = x4 = x5 = x6 = 0
- Transportation: x1 = x2 = 0, x3 =1, x4 = x5 = x6 = 0
- Commercial land: x1 = x2 = x3 = 0, x4 = 1, x5 = x6 = 0
- Industrial land: x1 = x2 = x3 = x4, x5 = 1, x6 = 0
- Open land: x1 = x2 = x3 = x4 = x5 = 0, x6 = 1
4. Results
Variables | Pearson’s Correlation | Sig (2-tailed) | N |
---|---|---|---|
Percentage of vegetation | 0.388** | 0.000 | 1217 |
Percentage of vegetation above horizon | 0.358** | 0.000 | |
Percentage of vegetation below horizon | 0.296** | 0.000 | |
Brightness | −0.188** | 0.000 | |
Contrast | 0.304** | 0.000 |
Multiple Linear Regression | |||||
---|---|---|---|---|---|
Standardized Coefficients | t-Value | p-Value | VIF | ||
Vegetation | Percentage of vegetation above the horizon | 0.194** | 6.603 | 0.000 | 1.56 |
Percentage of vegetation below the horizon | 0.102** | 3.773 | 0.000 | 1.32 | |
Image quality | Contrast | 0.208** | 7.950 | 0.000 | 1.41 |
Brightness | −0.111** | −3.986 | 0.000 | 1.25 | |
Land use types | High-density residential land | 0.029 | 1.173 | 0.241 | 1.11 |
Urban public/institutional land | −0.051* | −2.056 | 0.040 | 1.11 | |
Transportation land | −0.132** | −5.396 | 0.000 | 1.10 | |
Commercial land | −0.181** | −7.130 | 0.000 | 1.18 | |
Industrial land | −0.284** | −11.512 | 0.000 | 1.10 | |
Open land | −0.099** | −4.118 | 0.000 | 1.05 | |
R2 | 0.337 | ||||
F-statistic | 61.178 | ||||
Adjusted R2 | 0.331 | ||||
Multicollinearity condition number | 21.863 |
Dependent Variable: Perceived Safety | ||||||||
---|---|---|---|---|---|---|---|---|
Independent Variables | Multifamily Residential | High-Density Residential | Urban Public/ Institutional | Transportation | Commercial | Industrial | Open Land | |
Vegetation above horizon | 0.180 ** | 0.247 * | 0.320 ** | 0.115 | 0.258 ** | −0.013 | 0.451 * | |
Vegetation below horizon | 0.164 ** | 0.123 | 0.186 * | −0.054 | 0.015 | 0.159 | −0.144 | |
Contrast | 0.165 ** | 0.328 ** | 0.047 | 0.476 ** | 0.259 ** | 0.408 ** | 0.197 | |
Brightness | −0.075 | −0.052 | −0.037 | −0.376 ** | −0.237 ** | −0.312 ** | −0.082 | |
F-statistic | 21.239 | 9.310 | 8.680 | 6.984 | 12.39 | 6.362 | 4.646 | |
R2 | 0.128 | 0.223 | 0.194 | 0.383 | 0.219 | 0.285 | 0.297 | |
Adjust R2 | 0.122 | 0.199 | 0.172 | 0.328 | 0.201 | 0.240 | 0.233 | |
Multicollinearity condition number | 20.296 | 23.724 | 20.773 | 24.494 | 22.160 | 19.372 | 28.852 |
5. Discussions
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Li, X.; Zhang, C.; Li, W. Does the Visibility of Greenery Increase Perceived Safety in Urban Areas? Evidence from the Place Pulse 1.0 Dataset. ISPRS Int. J. Geo-Inf. 2015, 4, 1166-1183. https://doi.org/10.3390/ijgi4031166
Li X, Zhang C, Li W. Does the Visibility of Greenery Increase Perceived Safety in Urban Areas? Evidence from the Place Pulse 1.0 Dataset. ISPRS International Journal of Geo-Information. 2015; 4(3):1166-1183. https://doi.org/10.3390/ijgi4031166
Chicago/Turabian StyleLi, Xiaojiang, Chuanrong Zhang, and Weidong Li. 2015. "Does the Visibility of Greenery Increase Perceived Safety in Urban Areas? Evidence from the Place Pulse 1.0 Dataset" ISPRS International Journal of Geo-Information 4, no. 3: 1166-1183. https://doi.org/10.3390/ijgi4031166
APA StyleLi, X., Zhang, C., & Li, W. (2015). Does the Visibility of Greenery Increase Perceived Safety in Urban Areas? Evidence from the Place Pulse 1.0 Dataset. ISPRS International Journal of Geo-Information, 4(3), 1166-1183. https://doi.org/10.3390/ijgi4031166