Next Article in Journal
Rural Tourism: Development, Management and Sustainability in Rural Establishments
Previous Article in Journal
The Energy Rebound Effect for the Construction Industry: Empirical Evidence from China
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sustainability 2017, 9(5), 819; doi:10.3390/su9050819

A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence

1
State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China
2
Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
3
Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, P.O. Box 80125, 3508 TC Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Fausto Cavallaro
Received: 18 February 2017 / Revised: 19 April 2017 / Accepted: 11 May 2017 / Published: 14 May 2017
View Full-Text   |   Download PDF [8852 KB, uploaded 15 May 2017]   |  

Abstract

Fire occurrence, which is examined in terms of fire density (number of fire/km2) in this paper, has a close correlation with multiple spatiotemporal factors that include environmental, physical, and other socioeconomic predictors. Spatial autocorrelation exists widely and should be considered seriously for modeling the occurrence of fire in urban areas. Therefore, spatial econometric models (SE) were employed for modeling fire occurrence accordingly. Moreover, Random Forest (RF), which can manage the nonlinear correlation between predictors and shows steady predictive ability, was adopted. The performance of RF and SE models is discussed. Based on historical fire records of Hefei City as a case study in China, the results indicate that SE models have better predictive ability and among which the spatial autocorrelation model (SAC) is the best. Road density influences fire occurrence the most for SAC, while network distance to fire stations is the most important predictor for RF; they are selected in both models. Semivariograms are employed to explore their abilities to explain the spatial structure of fire occurrence, and the result shows that SAC works much better than RF. We give a further explanation for the generation of residuals between fire density and the common predictors in both models. Therefore, decision makers can make use of our conclusions to manage fire safety at the city scale. View Full-Text
Keywords: fire risk; Random Forest; spatial econometric models; autocorrelation; residuals fire risk; Random Forest; spatial econometric models; autocorrelation; residuals
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Song, C.; Kwan, M.-P.; Song, W.; Zhu, J. A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence. Sustainability 2017, 9, 819.

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]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top