Journal of Spatial Analysis

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In the present era, most cities have been faced with numerous problems. But the environmental dimension has been more challenging. Many urban professionals constantly seek to present effective solutions in order to prevent damage to the environment. Thus, theories, models and many views in this subject have taken, including livability approach derived by the school of sustainable development. So today, as one of the views livability approach is rooted in the theory of sustainable development has been focused on, and the above approach can cause problems in multiple cities. In this research, Region 17 of Tehran as the most problem area, was selected; and the overall goal of this study is assessing the livability of the Region 17’s neighborhoods, and the objective aims are including the assessing of livability dimensions, i.e. the environmental, the historical pattern, the urban management policies, the social, services, activities and facilities, the urban economy at the local level and identifying the livability homogeneous clusters and assessment the impact of livability’s variables, dimensions and indicators in that area. According to the study of the history and theoretical foundations of livability, the most important dimensions, indicators and items related to livability were extracted and the all selected dimensions and indicators, rooted in history and theoretical basis of their livability. In the present study include the following six dimensions of environmental, historical pattern, urban management policies, social, services, activities and facilities, urban economy with 20 indicators and 94 items were considered and the pattern of research in terms of goal, is cognitive; in terms of nature and method, is comparative – assessment; and about location of territory is Tehran’s Region 17, in respect of timing, is temporal and related to the 2015. Combined data collection method is combined0 (documents, survey) and it is the type of quantitative-qualitative data (questionnaire). The data used in the research is preliminary data that were obtained by questionnaire. The statistical society are the residents and citizens of Tehran’s Region 17 who are questioning. The necessary actions to operationalize the research was conducted in several stages: 1) Adjustment of questionnaire (using five Likert scale ranging from very low to very high range, verifying the validity by experts, verifying the performance reliability of the questionnaire by using the Cronbach's alpha in software of SPSS as a result 0.8), 2) Determining the sample size and sampling (400 samples determined by Cochran formula, using multi-stage sampling), 3) Entering data into SPSS software and doing the statistical tests (parametric statistical tests such as one sample T-test, ANOVA, Friedman) analyzing the data by SPSS and statistical tests of one sample T-test, ANOVA, Friedman, represents the undesirable od livability and its dimensions, the difference between neighborhood in terms of livability and more economic effectiveness on the livability of Region’s 17 and its neighborhoods, 4) The showing of spatial diagrams of research findings and preparing the livability’s maps by using ArcGis software and interpolation method. Ultimately, according to the findings and viewpoints of researchers and field observations, it can be concluded that the causes of problems in this area should be within the region and neighborhoods, it's time to overcome the situation that has been searched. In other words, the root of the problems in the above range is due to its geographical bed’s situation and other substrate characteristics. The meaning of geographical bed’s situation, climatic and tectonic characteristics of the area and the order of the micro-feature is the problems with the nature of the social, economic, administrative, infrastructure etc, so that were formed following the influx of population. Until two important problems raised in this region is not considered to be flows: 1) Geographic bed features, 2) the capacity of Region 17 for accommodation of population and services to them.

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Determining Effective Factors on Forest Fire Using the Compound of Multivariate Adaptive Regression Spline and Genetic Algorithm, a Case Study: Golestan, Iran   
Pahlavani, P., Assistant professor at School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran
Raei, A., PhD Candidate of GIS at School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran
Bigdeli, B., Assistant professor at School of Civil Engineering, Shahrood University of Technology
 
Keywords: Forest Fire, Multivariate adaptive regression spline, Multiple linear regression, Logistic regression, Genetic Algorithm.
 

1. Introduction

Nowadays, Determining the effective factors on fire is so important, because the plenty areas of forests around the world are destroyed annually by fire and recurrence of that in the long term can irreparably damage to the earth and its inhabitants. It helps us to identify most dangerous locations and times in forest fire. Hence, we can prevent many of driving factors of forest fire by law enforcement, efficient forest management policies and more supervision. In the current study, we identified the effective factors on the fire in Golestan forest through integration of three different methods including multiple linear regression, logistic regression and multivariate adaptive regression spline with Genetic Algorithm.

2. Study Area

Golestan Province is in the North of Iran and 18% of it is covered by forests. Golestan Province is a touristic province and several roads pass through its forests and according to statistical records, most of the occurred fires were in proximity of these roads. Our study area is located in 36°53′-37°25′N and 55°5′- 55°50′E and its area is about 3719.5 km2. We selected this area, because includes the most of fires have been occurred in Golestan Province in recent years.

3. Materials and Methods

A big fire was occurred on 12 December, 2010 in our study area and we used it as the dependent variable. The actual burnt area and some other data, such as Digital Elevation Model (DEM), the roads network, the rivers, the land uses, and soil types in the area were provided from Golestan Province Department of Natural Resources. Also, geographic coordination of the synoptic weather stations near the area and their data, including maximum, minimum, and mean temperature; total rainfall, as well as maximum wind speed and azimuth in December 2010 were obtained from National Meteorological Organization of Iran.
The land use and soil layers were in scale of 1:100000 and the roads and the rivers layers were in 1:5000 and all of them were provided in 2006. The region DEM is generated from topographic maps of Iran National Cartographic Center in scale of 1:25000 with positional resolution of 30m and we produced the slope and the aspect layers from it in ArcGIS software with the same resolution. The roads and the rivers were in vector format, hence, we used the Euclidean Distance analysis to generate rasters that each cell of them shows the distance from the nearest road or river.
At first we had 5 weather stations, which is very few for GWR. In this regard, we generated 1000 random points in the area and interpolated data to these points using Ordinary Kriging method with exponential semivariogram model in 30m resolution in ArcGIS software.
The multiple linear regression (MLR) model is the generalization of simple linear regression that is modeling the linear relation between one dependent variable and some independent variables. The general formula of MLR is seen below:
                                                                                                                                    (1)
The unknown coefficients are obtained using least squares adjustment as follows:
                                                                                                                                                      (2)
The logistic regression (LR) model is a nonlinear model for determination of the relation between a binary dependent variable and some independent variables. If we use the values of 0 and 1 for non-fire and fire points respectively, then the probability that a point be a fire point is obtained by Eq. (3):
                                                                                            (3)
If the number of parameters is insignificant compared to the observations, then we use the unconditional maximum likelihood estimation shown by Eq. (4) to compute the unknown coefficients of this model.
                                                                                                                                (4)
The multivariate adaptive regression spline (MARS) model is a flexible non-parametric model that requires no assumption about the relation between the dependent and independent variables. Hence it has a high ability in determination of complex nonlinear relations among the variables. The general formula of MARS is seen below:
                                                                                                             (5)
 is the m’th basic function that is obtained by Eq. (6):
                                                                                                  (6)
These basic functions are chosen in such a way that leads to minimum RMSE of model.
We use the genetic algorithem (GA) with the fitness function of the normalized RMSE to select the optimum combination of effective factors on forest fire.
 

4. Results and Discussion

In this paper we study the dependence of the forest fire to 14 factors shown in table 1, in the study area. Our results are shown in figures 1 to 3.
 
Table 1. The studied factors in the present research

Factor	Num.	Factor	Num.	Factor	Num.
Aspect	11	Maximum Wind Speed (m/s)	6	Maximum Temperature (℃)	1
Slope	12	Soil Type	7	Minimum Temperature (℃)	2
Elevation (m)	13	Land Use	8	Mean Temperature (℃)	3
Distance from The Residential Zones (m)	14	Distance from The Roads (m)	9	Total Rainfall (mm)	4
		Distance from The Rivers (m)	10	Maximum Wind Azimuth	5

 
 
  
Figure 1. (a) The best and the mean values of fitness, (b) The last best individuals, (c) The average distance between individuals, (d) The fitness of each individual in the last generation using MLR
 
Figure 2. (a) The best and the mean values of fitness, (b) The last best individuals, (c) The average distance between individuals, (d) The fitness of each individual in the last generation using LR
 

Figure 3. (a) The best and the mean values of fitness, (b) The last best individuals, (c) The average distance between individuals, (d) The fitness of each individual in the last generation using MARS

5. Conclusion

This research shows that both of the biophysical and anthropogenic factors have significant effects on forest fire in our study area. Just two factors were identified as impressive factors in all three cases including the minimum temperature and the maximum speed of wind. This study concluded to the NRMSE=0.4291 and R2=0.9862 for the multiple linear regression, NRMSE=0.9416 and R2=0.9912 for the logistic regression and NRMSE=0.1757 and R2=0.9886 for the multivariate adaptive regression spline and totally the multivariate adaptive regression spline method showed a better performance in comparison to the other two methods.