Assessing the Accessibility of Swimming Pools in Nanjing by Walking and Cycling Using Baidu Maps
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Processing and Analysis
- (1)
- Distribution of pools: First, we scraped the POI dataset returned when we searched in the Baidu API with ‘swimming pool’ and ‘fitness room’ using Python. Next, we eliminated infant swim stores from that dataset, and manually checked that each of the remaining POIs is open for business and actually had a pool (because some of the fitness rooms have a pool, but they do not put the word ‘pool’ in their names) by looking up the comment pictures and dialing the telephone number left on the review sites. Then, we calculated the average nearest neighbor ratio (ANN) to identify the spatial point patterns these pools followed, and did the kernel density analysis (Table 2) to map the overall distribution of the pools.
- (2)
- Pool catchment areas: Two types of catchment areas (Table 1) were delineated in this study. For catchment areas based on route (‘route catchments’), taking one pool as an example, we first picked out the residential building POIs accessible within 5/10/15 min using route data. Then intersected those with the Residential Building Grid to get a new grid called the route catchment of the pool. For the circular buffers, following the same method used in most planning texts and some studies, we obtained a circle with the pool as the center and 5/10/15 minutes’ travel as the radius using the Buffer Tool in ArcGIS.
- (3)
- Accessibility index (Table 1): First, we created a new field for each route catchment obtained in step 2 and assigned it the value of 1. Then, using the Union Tool, we integrated all 162 catchments into one shapefile to map out the overall route catchment of the pools. Finally, we added the 162 fields together and got the accessibility index.
- (4)
- The pool-underserved area (Table 1): The relative complement set of the overall route catchment in the Residential Building Grid is the pool-underserved area.
- (5)
- Analysis of the internal disparities of the accessibility index: First, we divided the accessibility index into quartiles by value. Then, we counted the number of grid squares in each quartile and calculated them as percentages. This would help us gain an overview of the distribution of the accessibility index. Then, we explored the spatial disparity of the accessibility index statistically, using the Cluster and Outlier Analysis Tool (Table 2), to reveal inequalities in pool distribution at a further level.
3. Results
3.1. Where Are the Pools?
3.2. Where Are the Pool Catchment Boundaries?
3.2.1. Route Catchment and the Underserved Area
3.2.2. Differences between Route Catchment Area and Circular Buffers
3.3. What Are the Spatial Differences in Accessibility?
3.3.1. The Accessibility Index
3.3.2. Spatial Disparities of the Accessibility Index
4. Discussion
4.1. Reasons for the Spatial Disparity in the Accessibility Index
4.2. Advantages and Limitations of the Method
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name | Description |
---|---|
POI | An abbreviation for Point of Interests. In this study, two kinds of POIS were scraped from the Baidu Maps API; they are swimming pool POIs and residential building POIS. Each POI includes the information about the name, address, latitude, and longitude (both with the coordinate system of WGS84). |
Navigation route | A graphic file we scraped from the Baidu Maps API; it shows the shape of the route between the pool and the residential buildings. It includes the information about starting point, ending point, distance, and time costs. |
Pool catchment area | The area which is accessible within 5/10/15 min walking or cycling from the swimming pool. |
Pool-underserved area | The area which is inaccessible within 5/10/15 min walking or cycling from the swimming pool. |
Accessibility index | A grid-based index which is constructed by the author. The value of each grid square represents the number of pools accessible within 5/10/15 min of that square. |
Name | Description |
---|---|
Kernel Density Analysis | Calculating the density of point or line elements in their surrounding neighborhoods. |
Average Nearest Neighbor Analysis | This measures the distance between each feature centroid and its nearest neighbor’s centroid and then averages all these nearest-neighbor distances. If the average nearest neighbor ratio (ANN) is less than 1, the pattern exhibits clustering. If the index is greater than 1, the trend is toward dispersion. The ANN is given as where is the observed mean distance between each feature and its nearest neighbor, and is the expected mean distance for the features [37]. |
Cluster and Outlier Analysis (Anselin Local Moran’s I) | Given a set of weighted features, one can identify statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran’s I statistic. As the final result, HH indicates that high-value elements are clustered together, meaning the accessibility of the area is relatively high; LL indicates that low-value elements are clustered together and the accessibility of the area is lower. There are also two types of outliers, HL and LH. HL indicates high-value elements that are surrounded by low-value elements, and LH indicates low-value elements that are surrounded by high-value elements. The rest of the area is not statistically significant (NS), meaning there is no significant trend of clustering of either high-value or low-value elements in this area. |
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Share and Cite
Dong, Y.; Zhang, B.; Zhou, Z.; Xu, Z. Assessing the Accessibility of Swimming Pools in Nanjing by Walking and Cycling Using Baidu Maps. ISPRS Int. J. Geo-Inf. 2022, 11, 515. https://doi.org/10.3390/ijgi11100515
Dong Y, Zhang B, Zhou Z, Xu Z. Assessing the Accessibility of Swimming Pools in Nanjing by Walking and Cycling Using Baidu Maps. ISPRS International Journal of Geo-Information. 2022; 11(10):515. https://doi.org/10.3390/ijgi11100515
Chicago/Turabian StyleDong, Yifan, Bing Zhang, Zhenqi Zhou, and Zhen Xu. 2022. "Assessing the Accessibility of Swimming Pools in Nanjing by Walking and Cycling Using Baidu Maps" ISPRS International Journal of Geo-Information 11, no. 10: 515. https://doi.org/10.3390/ijgi11100515
APA StyleDong, Y., Zhang, B., Zhou, Z., & Xu, Z. (2022). Assessing the Accessibility of Swimming Pools in Nanjing by Walking and Cycling Using Baidu Maps. ISPRS International Journal of Geo-Information, 11(10), 515. https://doi.org/10.3390/ijgi11100515