Spatial Difference of Transit-Based Accessibility to Hospitals by Regions Using Spatially Adjusted ANOVA
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Resources
2.2.1. Transit Data
2.2.2. Hospital Data and Population Data
2.3. Methods
2.3.1. Group Design
2.3.2. Accessibility Measurement
2.3.3. Spatially Adjusted ANOVA
2.3.4. Multiple Comparison for The Detection of Spatial Difference
Algorithm 1 |
Simple model (the less the average travel time, the better the accessibility) for i = 0, 1, 2, …, 85 high=0, low=0 for j = 0, 1, 2, …, 85 if < 0.05 & < high=high+1 else ( < 0.05) low=low+1 Gravity model (the higher the accessibility score, the better the accessibility) for i = 0, 1, 2, …, 85 high=0, low=0 for j = 0, 1, 2, …, 85 if < 0.05 & > high=high+1 else ( < 0.05) low=low+1 |
3. Results
3.1. Administrative District Scale
3.1.1. Accessibility Measurements and Preprocessing
3.1.2. Spatial Autocorrelation Analysis and Elimination
3.1.3. Spatial Difference Analysis and Multiple Comparison
3.2. Subdistrict Scale
3.2.1. Accessibility Measurements and Preprocessing
3.2.2. Spatial Autocorrelation Analysis and Elimination
3.2.3. Spatial Difference Analysis and Multiple Comparison
4. Discussion
4.1. Spatial Difference Analysis Based on ANOVA
4.2. Comparison Between Simple Model and Gravity Model
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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p-Value | Simple Model | Gravity Model |
---|---|---|
before transformation | <0.0001 | 0.05 |
after transformation | 0.076 | 0.12 |
Accessibility | Liner Regression Model | Spatial Lag Model | ||
---|---|---|---|---|
Moran’s I | p-Value | Moran’s I | p-Value | |
Simple model | 0.3796 | <0.001 | −0.032 | 0.6302 |
Gravity model | 0.5158 | <0.001 | −0.0154 | 0.523 |
(a) Simple Model | Degree of Freedom | Sum of Square | Mean Square | F-Value | p-Value |
Traditional ANOVA | |||||
Y | 6 | 0.00000007 | 0.00000001 | 22.651 | 0.00000000 |
Residuals | 79 | 0.00000004 | 0.0000000005 | ||
Spatially adjusted ANOVA | |||||
Y | 6 | 0.00000006 | 0.0000000009 | 4.9269 | 0.0002504 |
Residuals | 79 | 0.00000001 | 0.0000000002 | ||
(b) Gravity Model | Degree of Freedom | Sum of Square | Mean Square | F-Value | p-Value |
Traditional ANOVA | |||||
Y | 6 | 0.110570 | 0.0184283 | 21.557 | 0.00000000 |
Residuals | 79 | 0.067533 | 0.0008549 | ||
Spatially adjusted ANOVA | |||||
Y | 6 | 0.0078432 | 0.0013072 | 4.5522 | 0.000489 |
Residuals | 79 | 0.0226855 | 0.0002872 |
Simple Model | Gravity Model | ||||||||
---|---|---|---|---|---|---|---|---|---|
Comparison Pairs | Traditional | Spatially Adjusted | Traditional | Spatially Adjusted | |||||
Difference | p-Value | Difference | p-Value | Difference | p-Value | Difference | p-Value | ||
Hongshan | Jiang’an | −0.00007265 | 0.0000 *** | −0.00002344 | 0.0113 ** | −0.079888 | 0.0000 *** | −0.025619 | 0.0434 * |
Jianghan | −0.00008579 | 0.0000 *** | −0.00002507 | 0.0084 *** | −0.109038 | 0.0000 *** | −0.030125 | 0.0130 ** | |
Hanyang | −0.00005974 | 0.0000 *** | −0.00001970 | 0.1075 | −0.068268 | 0.0001 *** | −0.020041 | 0.2943 | |
Wuchang | −0.00005962 | 0.0000 *** | 0.00001865 | 0.1246 | −0.074259 | 0.0000 *** | −0.023797 | 0.1069 | |
Qingshan | −0.00001597 | 0.8490 | 0.00000807 | 0.9347 | −0.020734 | 0.8506 | −0.010835 | 0.9088 | |
Qiaokou | −0.00007137 | 0.0000 *** | −0.00002056 | 0.0829 * | −0.102001 | 0.0000 *** | −0.030520 | 0.0172 ** | |
Jiang’an | Jianghan | −0.00001453 | 0.8063 | −0.00000163 | 1.0000 | −0.029151 | 0.3212 | −0.004506 | 0.9978 |
Hanyang | 0.00001152 | 0.9347 | 0.00000374 | 0.9996 | 0.011620 | 0.9814 | 0.005577 | 0.9936 | |
Wuchang | 0.00001165 | 0.9238 | 0.00000478 | 0.9895 | 0.005628 | 0.9996 | 0.001822 | 1.0000 | |
Qingshan | 0.0000553 | 0.0000 *** | 0.00001537 | 0.2426 | 0.059154 | 0.0010 *** | 0.014783 | 0.5672 | |
Qiaokou | −0.00001058 | 1.0000 | 0.00000288 | 0.9995 | −0.022114 | 0.7125 | 0.004901 | 0.9973 | |
Jianghan | Hanyang | 0.00002605 | 0.2254 | 0.00000537 | 0.9956 | 0.040771 | 0.0723 * | 0.010083 | 0.9023 |
Wuchang | 0.00002680 | 0.1986 | 0.00000642 | 0.9634 | 0.034779 | 0.1783 | 0.006328 | 0.9891 | |
Qingshan | 0.00006983 | 0.0000 *** | 0.00001700 | 0.1836 | 0.088304 | 0.0000 *** | 0.019289 | 0.2872 | |
Qiaokou | 0.00001442 | 0.8706 | 0.00000451 | 0.9954 | 0.007037 | 0.9992 | −0.00040 | 1.0000 | |
Hanyang | Wuchang | 0.00000013 | 1.0000 | 0.00000105 | 0.9999 | −0.005992 | 0.9996 | −0.003756 | 0.9995 |
Qingshan | 0.00004378 | 0.0044 *** | 0.00001263 | 0.5682 | 0.047534 | 0.0348 ** | 0.009206 | 0.9468 | |
Qiaokou | −0.00001163 | 0.9553 | −0.00000014 | 1.0000 | −0.033734 | 0.2792 | −0.010478 | 0.9037 | |
Wuchang | Qingshan | 0.00004365 | 0.0036 *** | 0.00001057 | 0.7399 | 0.053525 | 0.0079 *** | 0.012962 | 0.7551 |
Qiaokou | −0.00001176 | 0.9484 | −0.00000191 | 1.0000 | −0.027742 | 0.5035 | −0.006723 | 0.9880 | |
Qingshan | Qiaokou | −0.00005540 | 0.0001 *** | −0.00001248 | 0.6072 | −0.081268 | 0.0000 *** | −0.019685 | 0.3111 |
Accessibility | Liner Regression Model | Spatial Lag Model | ||
---|---|---|---|---|
Moran’s I | p-Value | Moran’s I | p-Value | |
Simple model | 0.49573 | <0.0001 | −0.07436 | 1 |
Gravity model | 0.39730 | <0.0001 | −0.073657 | 1 |
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Share and Cite
Chen, M.; Chen, Y.; Wang, X.; Tan, H.; Luo, F. Spatial Difference of Transit-Based Accessibility to Hospitals by Regions Using Spatially Adjusted ANOVA. Int. J. Environ. Res. Public Health 2019, 16, 1923. https://doi.org/10.3390/ijerph16111923
Chen M, Chen Y, Wang X, Tan H, Luo F. Spatial Difference of Transit-Based Accessibility to Hospitals by Regions Using Spatially Adjusted ANOVA. International Journal of Environmental Research and Public Health. 2019; 16(11):1923. https://doi.org/10.3390/ijerph16111923
Chicago/Turabian StyleChen, Meijie, Yumin Chen, Xiaoguang Wang, Huangyuan Tan, and Fenglan Luo. 2019. "Spatial Difference of Transit-Based Accessibility to Hospitals by Regions Using Spatially Adjusted ANOVA" International Journal of Environmental Research and Public Health 16, no. 11: 1923. https://doi.org/10.3390/ijerph16111923