Next Article in Journal
Mapping and Analyzing Stream Network Changes in Watonwan River Watershed, Minnesota, USA
Next Article in Special Issue
Towards Sustainable Urban Planning Through Transit-Oriented Development (A Case Study: Tehran)
Previous Article in Journal
An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method
Previous Article in Special Issue
Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(11), 370; https://doi.org/10.3390/ijgi6110370

An Integrated Spatial Clustering Analysis Method for Identifying Urban Fire Risk Locations in a Network-Constrained Environment: A Case Study in Nanjing, China

1,2
,
1,2,* and 1
1
School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
2
Institute of Remote Sensing and Spatial information, Hohai University, Nanjing 211100, China
*
Author to whom correspondence should be addressed.
Received: 23 September 2017 / Revised: 10 November 2017 / Accepted: 15 November 2017 / Published: 17 November 2017
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
Full-Text   |   PDF [93107 KB, uploaded 17 November 2017]   |  

Abstract

The spatial distribution of urban geographical events is largely constrained by the road network, and research on spatial clusters of fire accidents at the city level plays a crucial role in emergency rescue and urban planning. For example, by knowing where and when fire accidents usually occur, fire enforcement can conduct more efficient aid measures and planning department can work out more reasonable layout optimization of fire stations. This article proposed an integrated method by combining weighted network-constrained kernel density estimation (NKDE) and network-constrained local Moran’s I (ILINCS) to detect spatial cluster pattern and identify higher-risk locations of fire accidents. The proposed NKDE-ILINCS weighted a set of crucial non-spatial attributes of point events and links, and considered the impact factors of road traffic states, intersection roads and fire severity in NKDE to reflect real urban environment. This method was tested using the fire data in 2015 in Nanjing, China. The results demonstrated that the method was appropriate to detect network-constrained fire cluster patterns and identify high–high road segments. Besides, the first 14 higher-risk road segments in Nanjing are listed. These findings of this case study enhance our knowledge to more accurately observe where fire accidents usually occur and provide a reference for fire departments to improve emergency rescue effectiveness. View Full-Text
Keywords: urban fire accidents; emergency rescue; spatial distribution; network-constrained; kernel density estimation; local Moran’s I urban fire accidents; emergency rescue; spatial distribution; network-constrained; kernel density estimation; local Moran’s I
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Xia, Z.; Li, H.; Chen, Y. An Integrated Spatial Clustering Analysis Method for Identifying Urban Fire Risk Locations in a Network-Constrained Environment: A Case Study in Nanjing, China. ISPRS Int. J. Geo-Inf. 2017, 6, 370.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top