# Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis

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## Abstract

**:**

## 1. Introduction

## 2. Study Area and Data Sources

#### 2.1. Study Area

#### 2.2. Data Sources

## 3. Methods

#### 3.1. Network Kernel Density Estimation

#### 3.2. Network K-Function

#### 3.3. Multiple Centrality Assessment Model and Correlation Coefficient

^{B}measure indicates the extent to which a node is passed by the shortest path between pairs of other nodes in the network [30], such as:

## 4. Results

#### 4.1. Weighted NetKDE Analysis for Detecting Hot Spots

#### 4.2. Spatial Cluster Pattern Analysis

#### 4.3. Street Centrality Indexes and Correlation Analysis

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- China Health Statistics Yearbook, 2013. Available online: http://www.nhfpc.gov.cn/htmlfiles/zwgkzt/ptjnj/year2013/index2013.html (accessed on 1 January 2016).
- Lee, L. The current state of public health in China. Annu. Rev. Public Health
**2004**, 25, 327–339. [Google Scholar] [CrossRef] - Health and Family Planning Statistics. Available online: http://www.nhfpc.gov.cn/zwgkzt/pwstj/list.shtml (accessed on 1 January 2016).
- Wang, E. Understanding the “retail revolution” in urban China: A survey of retail formats in Beijing. Serv. Ind. J.
**2011**, 31, 169–194. [Google Scholar] [CrossRef] - Bailey, T.C.; Gatrell, A.C. Interactive Spatial Data Analysis; Longman Scientific & Technical: London, UK, 1995. [Google Scholar]
- O’Sullivan, D.; Unwin, D. Geographic Information Analysis; John Wiley & Sons: Hoboken, NJ, USA, 2014. [Google Scholar]
- Silverman, B.W. Density Estimation for Statistics and Data Analysis; CRC Press: Boca Raton, FL, USA, 1986. [Google Scholar]
- Delmelle, E.; Thill, J.-C. Urban bicyclists: Spatial analysis of adult and youth traffic hazard intensity. Transp. Res. Rec.
**2008**, 2074, 31–39. [Google Scholar] [CrossRef] - Erdogan, S.; Yilmaz, I.; Baybura, T.; Gullu, M. Geographical information systems aided traffic accident analysis system case study: City of Afyonkarahisar. Accid. Anal. Prev.
**2008**, 40, 174–181. [Google Scholar] [CrossRef] [PubMed] - Anderson, T.K. Kernel density estimation and K-means clustering to profile road accident hotspots. Accid. Anal. Prev.
**2009**, 41, 359–364. [Google Scholar] [CrossRef] [PubMed] - Yamada, I.; Thill, J.-C. Local indicators of network-constrained clusters in spatial point patterns. Geogr. Anal.
**2007**, 39, 268–292. [Google Scholar] [CrossRef] - Borruso, G. Network density estimation: analysis of point patterns over a network. In Proceedings of the International Conference on Computational Science and Its Applications—ICCSA 2005, Suntec City, Singapore, 9–12 May 2005; Gervasi, O., Gavrilova, M.L., Kumar, V., Laganà, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K., Eds.; Springer: Heidelberg, Germany, 2005. [Google Scholar]
- Borruso, G. Network density estimation: A GIS approach for analysing point patterns in a network space. Trans. GIS
**2008**, 12, 377–402. [Google Scholar] [CrossRef] - Xie, Z.; Yan, J. Kernel density estimation of traffic accidents in a network space. Comput. Environ. Urban Syst.
**2008**, 32, 396–406. [Google Scholar] [CrossRef] - Xie, Z.; Yan, J. Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: An integrated approach. J. Transp. Geogr.
**2013**, 31, 64–71. [Google Scholar] [CrossRef] - Okabe, A.; Satoh, T.; Sugihara, K. A kernel density estimation method for networks, its computational method and a GIS-based tool. Int. J. Geogr. Inf. Sci.
**2009**, 23, 7–32. [Google Scholar] [CrossRef] - Okabe, A.; Yamada, I. The K-function method on a network and its computational implementation. Geogr. Anal.
**2001**, 33, 271–290. [Google Scholar] [CrossRef] - Yamada, I.; Thill, J.-C. Comparison of planar and network K-functions in traffic accident analysis. J. Transp. Geogr.
**2004**, 12, 149–158. [Google Scholar] - Porta, S.; Strano, E.; Iacoviello, V.; Messora, R.; Latora, V.; Cardillo, A.; Wang, F.; Scellato, S. Street Centrality and Densities of Retail and Services in Bologna, Italy. Environ. Plan. B
**2009**, 36, 450–465. [Google Scholar] [CrossRef] - Porta, S.; Latora, V.; Wang, F.; Rueda, S.; Strano, E.; Scellato, S.; Cardillo, A.; Belli, E.; Càrdenas, F.; Cormenzana, B.; et al. Street centrality and the location of economic activities in Barcelona. Urban Stud.
**2012**, 49, 1471–1488. [Google Scholar] [CrossRef] - Wang, F.; Antipova, A.; Porta, S. Street centrality and land use intensity in Baton Rouge, Louisiana. J. Transp. Geogr.
**2011**, 19, 285–293. [Google Scholar] [CrossRef] [Green Version] - Rui, Y.; Ban, Y. Exploring the relationship between street centrality and land use in Stockholm. Int. J. Geogr. Inf. Sci.
**2014**, 28, 1425–1438. [Google Scholar] [CrossRef] - Rui, Y.; Yang, Z.; Qian, T.; Khalid, S.; Xia, N.; Wang, J. Network-constrained and category-based point pattern analysis for Suguo retail stores in Nanjing, China. Int. J. Geogr. Inf. Sci.
**2016**, 30, 186–199. [Google Scholar] [CrossRef] - The 2014 Annual Nanjing Statistical Yearbook. Available online: http://221.226.86.104/file/nj2004/2014/renkou/3–1.htm (accessed on 1 January 2016).
- Query of the Province’s Medical Institutions. Available online: http://www.jswst.gov.cn:8083/wstcx/ylwsedit.action (accessed on 1 January 2016).
- Chinese Hospital Level Inquiry System. Available online: http://www.hqms.org.cn/usp/roster/index.jsp (accessed on 1 January 2016).
- Levine, N. CrimeStat III: A Spatial Statistics Program for the Analysis of Crime Incident Locations; Version 3.0; Ned Levine & Associates: Houston, TX, USA, 2004. [Google Scholar]
- Gibin, M.; Longley, P.; Atkinson, P. Kernel Density Estimation and Percent Volume Contours in General Practice Catchment Area Analysis in Urban Areas. In Proceedings of the GIScience Research UK Conference GISRUK, Maynooth, Ireland, 11–13 April 2007; Citeseer: Princeton, NJ, USA, 2007. [Google Scholar]
- Porta, S.; Crucitti, P.; Latora, V. The network analysis of urban streets: A primal approach. Environ. Plan. B
**2006**, 33, 705–725. [Google Scholar] [CrossRef] - Freeman, L.C. Centrality in social networks conceptual clarification. Soc. Netw.
**1978**, 1, 215–239. [Google Scholar] [CrossRef] - Hauke, J.; Kossowski, T. Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaest. Geogr.
**2011**, 30, 87–93. [Google Scholar] [CrossRef] - Mao, L.; Nekorchuk, D. Measuring spatial accessibility to healthcare for populations with multiple transportation modes. Health Place
**2013**, 24, 115–122. [Google Scholar] [CrossRef] [PubMed] - Delamater, P.L. Spatial accessibility in suboptimally configured health care systems: A modified two-step floating catchment area (M2SFCA) metric. Health Place
**2013**, 24, 30–43. [Google Scholar] [CrossRef] [PubMed] - Cheng, Y.; Wang, J.; Rosenberg, M.W. Spatial access to residential care resources in Beijing, China. Int. J. Health Geogr.
**2012**, 11, 1–11. [Google Scholar] [CrossRef] [PubMed] - Epanechnikov, V.A. Non-parametric estimation of a multivariate probability density. Theory Probab. Appl.
**1969**, 14, 153–158. [Google Scholar] [CrossRef] - Porter, M.D.; Reich, B.J. Evaluating temporally weighted kernel density methods for predicting the next event location in a series. Ann. GIS
**2012**, 18, 225–240. [Google Scholar] [CrossRef] - Okabe, A.; Sugihara, K. Spatial Analysis along Networks: Statistical and Computational Methods; John Wiley & Sons: Hoboken, TX, USA, 2012. [Google Scholar]
- Urban Network Analysis Toolbox for ArcGIS. Available online: http://cityform.mit.edu/projects/urban-network-analysis.html (accessed on 1 January 2016).
- Ni, J.; Wang, J.; Rui, Y.; Qian, T.; Wang, J. An enhanced variable two-step floating catchment area method for measuring spatial accessibility to residential care facilities in Nanjing. Int. J. Environ. Res. Public Health
**2015**, 12, 14490–14504. [Google Scholar] [CrossRef] [PubMed]

**Figure 3.**(

**a**) Unweighted NetKDE results; (

**b**) weighted NetKDE results; and (

**c**) weighted NetKDE results around the city center for hospitals.

**Figure 4.**(

**a**) Network auto K-function analysis of main urban districts and (

**b**) network auto K-function analysis of downtown area.

**Figure 5.**(

**a**) Network auto K-function analysis of first- and second-class hospitals and (

**b**) network auto K-function analysis of other hospitals in main urban districts.

**Figure 6.**Network cross K-function analysis between hospitals and pharmacy stores in main urban districts.

**Figure 7.**(

**a**) Street betweenness; (

**b**) straightness; and (

**c**) closeness centrality in main urban areas.

**Figure 8.**(

**a**) KDE of street betweenness; (

**b**) straightness; and (

**c**) closeness centrality in main urban areas.

**Table 1.**Numbers of hospitals divided by comprehensive strength and ownership in main urban districts.

Comprehensive Strength | Numbers | Ownership | Numbers |
---|---|---|---|

First class | 33 | Private | 376 |

Second class | 38 | Public | 356 |

Third class | 152 | ||

Fourth class | 135 | ||

Fifth class | 374 | ||

Total number | 732 | 732 |

Categories | C^{S} | C^{C} | C^{B} |
---|---|---|---|

First class | 0.493 | 0.523 | 0.529 |

Second class | 0.509 | 0.526 | 0.527 |

Third class | 0.729 | 0.748 | 0.748 |

Fourth class | 0.657 | 0.679 | 0.698 |

Fifth class | 0.786 | 0.813 | 0.824 |

Public | 0.777 | 0.799 | 0.812 |

Private | 0.811 | 0.832 | 0.840 |

All hospitals | 0.846 | 0.866 | 0.870 |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ni, J.; Qian, T.; Xi, C.; Rui, Y.; Wang, J.
Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis. *Int. J. Environ. Res. Public Health* **2016**, *13*, 833.
https://doi.org/10.3390/ijerph13080833

**AMA Style**

Ni J, Qian T, Xi C, Rui Y, Wang J.
Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis. *International Journal of Environmental Research and Public Health*. 2016; 13(8):833.
https://doi.org/10.3390/ijerph13080833

**Chicago/Turabian Style**

Ni, Jianhua, Tianlu Qian, Changbai Xi, Yikang Rui, and Jiechen Wang.
2016. "Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis" *International Journal of Environmental Research and Public Health* 13, no. 8: 833.
https://doi.org/10.3390/ijerph13080833