A spatio-temporal model to understand forest fires causality in Europe

Oscar Rodriguez de Rivera1,*, Antonio López-Quílez2, Marta Blangiardo3,
Martyna Wasilewska1

1 Statistical Ecology @ Kent, National Centre for Statistical Ecology. School
of Mathematics, Statistics and Actuarial Science, University of Kent.
Canterbury, UK.
2 Dept. Estadística i Investigació Operativa; Universitat de València.
Valencia, Spain.
2 Faculty of Medicine, School of Public Health; Imperial College of London.
London, UK.

*<EMAIL_ADDRESS>
###### Abstract

Forest fires are the outcome of a complex interaction between environmental
factors, topography and socioeconomic factors (Bedia _et al_ , 2014).
Therefore, understand causality and early prediction are crucial elements for
controlling such phenomenon and saving lives.

The aim of this study is to build spatio-temporal model to understand
causality of forest fires in Europe, at NUTS2 level between 2012 and 2016,
using environmental and socioeconomic variables. We have considered a disease
mapping approach, commonly used in small area studies to assess the spatial
pattern and to identify areas characterised by unusually high or low relative
risk.

_K_ eywords Hierarchical Bayesian models $\cdot$ disease mapping $\cdot$
integrated nested laplace approximation $\cdot$ forest fires $\cdot$ causality
$\cdot$ spatio-temporal model

##  1 Introduction

Nowadays, wildfires have become one of the most significant disturbances
worldwide (Flannigan _et al_ , 2009; Pechony and Shindell, 2010; Pausas _et
al_ , 2012; Cardil _et al_ , 2014; Boer _et al_ , 2017; Molina _et al_ ,
2018). The combination of a longer drought period and a higher woody biomass
and flammability of dominant species creates an environment conducive to fire
spread (Piñol _et al_ , 1998; Millán _et al_ , 2005). Furthermore, vegetation
pattern changes with the abandonment of traditional rural activities plays a
direct role in the increase of fire severity and ecological and economic fire
impacts (Flannigan _et al_ , 2009; Chuvieco _et al_ , 2014). Fire behavior
exceeds most frequently firefighting capabilities and fire agencies have
trouble in suppressing flames while providing safety for both firefighters and
citizens (Werth _et al_ , 2016).

Many areas across the world have seen a rise in extreme fires in recent years.
Those include South America and southern and western Europe. They also include
unexpected places above the Arctic Circle, like the fires in Sweden during the
summer of 2018 (de Groot _et al_ , 2013; European Commission, 2019).

Extreme fire events, which are also referred to as “megafires”, are becoming
frequent on a global scale; recent fires in Portugal, Greece, Amazone and
other areas confirm this fact. There is not complete agreement on the term
“megafires”, which often refers to catastrophic fire events in terms of human
casualties, economic losses or both (San-Miguel-Ayanz _et al_ , 2013b).

Climate change will reduce fuel moisture levels from present values around the
Mediterranean region and the region will become drier, increasing the weather-
driven danger of forest fires. The countries in highest danger are Spain,
Portugal, Turkey, Greece, parts of central and southern Italy, Mediterranean
France and the coastal region of the Balkans, according to recent research of
the Joint Research Centre (JRC) (De Rigo _et al_ , 2017).

Most international reports on biomass burning recognize the importance of the
human factors in fire occurrence (Food, 2007). Although fire is a natural
factor in many ecosystems, human activities play a critical role in altering
natural fire conditions, either by increasing ignitions (Leone _et al_ ,
2003), or by suppressing natural fires (Johnson _et al_ , 2001; Keeley _et al_
, 1999). Both factors are contradictory, and act mainly through the mixture of
fire policy practices, on one hand, and land uses and demographic changes on
the other. Most developed countries have maintained for several decades a fire
suppression policy, which has lead to almost total fire exclusion. The long
term impact of that policy has implied an alteration of traditional fire
regimes, commonly by increasing average burn severity and size, as a result of
higher fuel accumulation (Pyne, 2001), although other authors are more
critical about the real implication of fire suppression policy (Johnson _et
al_ , 2001), or they tend to put more emphasis on the impact of climate
changes (Westerling _et al_ , 2006). For developing countries, fire is still
the most common tool for land clearing, and therefore it is strongly
associated to deforestation, especially in Tropical areas (Cochrane _et al_ ,
1999; DeFries _et al_ , 2002). The traditional use of fire in shifting
cultivation has turned in the last decades to permanent land use change, in
favour of cropland and grasslands. In addition, fire is a traditional tool to
manage permanent grasslands, which are burned annually to favour new shoots
and improve palatability (Hobbs _et al_ , 1991; Chuvieco _et al_ , 2010).

Global and local implications of changing natural fire circumstances have been
widely recognized, with major effects on air quality, greenhouse gas
emissions, soil degradation and vegetation succession (Goetz _et al_ , 2006;
Parisien _et al_ , 2006; Randerson _et al_ , 2005). The role of human
activities in changing those conditions has not been assessed at global scale.
Several local studies have identified factors that are commonly associated to
human fire ignition, such as distance to roads, forest-agricultural or forest-
urban interfaces, land use management, and social conflicts (unemployment,
rural poverty, hunting disputes,) (Leone _et al_ , 2003; Martínez _et al_ ,
2009; Vega-García _et al_ , 1995). On the other hand, humans not only cause
fires, but they suffer their consequences as well. Fire is recognized as a
major natural hazard (Food, 2007), which imply severe losses of human lives,
properties and other socio-economic values (Radeloff _et al_ , 2005; Reisen
and Brown, 2006).

Fire is no longer a significant part of the traditional systems of life;
however, it remains strongly tied to human activity (Leone _et al_ , 2009).
Knowledge of the causes of forest fires and the main driving factors of
ignition is an indispensable step toward effective fire prevention (Ganteaume
_et al_ , 2013). It is widely recognized that current fire regimes are
changing as a result of environmental and climatic changes (Pausas and Keeley
2009) with increased fire frequency in several areas in the Mediterranean
Region of Europe (Rodrigues _et al_ , 2013). In Mediterranean-type ecosystems,
several studies have indicated that these changes are mainly driven by fire
suppression policies (Minnich, 1983), climate (Pausas _et al_ , 2012), and
human activities (Bal _et al_ , 2011). Human drivers mostly have a temporal
dimension, which is why an historical/temporal perspective is often required
(Zumbrunnen _et al_ , 2011; Carmona _et al_ , 2012). In Mediterranean Europe,
increases in the number of fires have been detected in some countries,
including Portugal and Spain (San-Miguel-Ayanz _et al_ , 2013a; Rodrigues _et
al_ , 2013). In addition, a recent work by Turco _et al_ (2016) suggests huge
spatial and temporal variability in fire frequency trends especially in the
case of Spain, where increasing and decreasing trends were detected depending
on the analysis period and scale. This increase in wildfire frequency and
variability, with its associated risks to the environment and society (Moreno
_et al_ , 2011, 2014), calls for a better understanding of the processes that
control wildfire activity (Massada _et al_ , 2013). In recent decades, major
efforts have been made to determine the influence of climate change on natural
hazards, and to develop models and tools to properly characterize and quantify
changes in climatic patterns. While physical processes involved in ignition
and combustion are theoretically simple, understanding the relative influence
of human factors in determining wildfire is an ongoing task (Mann _et al_ ,
2016). It is clear that human-caused fires that occur repeatedly in a given
geographical area are not simply reducible to individual personal factors, and
thus subject to pure chance. They are usually the result of a spatial pattern,
whose origin is in the interaction of environmental and socioeconomic
conditions (Koutsias _et al_ , 2015). This is particularly true in human-
dominated landscapes such as Spain, where anthropogenic ignitions surpass
natural ignitions, and humans interact to a large degree with the landscape,
changing its flammability, and act as fire initiators or suppressors. In such
cases, human influence may cause sudden changes in fire frequency, intensity,
and burned area size (Pezzatti _et al_ , 2013).

Fire is an integral component of Mediterranean ecosystems since at least the
Miocene (Dubar _et al_ , 1995). Although humans have used fires in the region
for tens of thousands of years (Goren-Inbar _et al_ , 2004), it is only in the
last 10,000 or so that man has significantly influenced fire regime (Daniau
_et al_ , 2010). The use of fire as a management tool has persisted until
these days, although the second half of the past century saw a major change
and a regime shift due to abandonment of many unproductive lands (Moreno _et
al_ , 1988; Pausas _et al_ , 2012). Although fire still is a traditional
management tool in some rural areas for control of vegetation and enhancement
of pastures for cattle feed, most fires these days are no longer related to
the management of the land (San-Miguel-Ayanz _et al_ , 2012, 2013a, 2013b).

The European Mediterranean region is a highly populated area where nearly 200
Million people live in just 5 European Union countries, Portugal, Spain,
France, Italy and Greece. Population density varies but remains very high with
about 2500 inhabitants/km2 in the French Riviera (with peaks of up to 750,000
tourists per day during the summer) (Corteau, 2007) versus an average of 111
inhabitants/km2 in the region. The region is characterized by an extensive
wildland urban interface (WUI). Large urban areas have expanded into the
neighboring wildland areas, where expensive households are built. The WUI has
been further increased by the construction of second holiday homes in the
natural environment. Fire prone areas along the Mediterranean coast have been
extensively built up, reducing in some cases the availability of fuels, but
increasing largely the probability of fire ignition by human causes (Ganteaume
_et al_ , 2013). In other areas of the same region, abandonment of the rural
environment has lead to low utilization of forests, which are generally of
limited productivity, and the subsequent accumulation of fuel loads (San-
Miguel-Ayanz _et al_ , 2012, 2013a, 2013b; Moreira _et al_ , 2011). The
combination of the above factors converts the European Mediterranean region in
a high fire risk area (Sebastián-López _et al_ , 2008), especially during the
summer months when low precipitations and very high temperatures favor fire
ignition and spread.

About 65,000 fires take place every year in the European region, burning, on
average, around half a million ha of forest areas (European Commission, 2011).
Approximately 85% of the total burnt area occurs in the EU Mediterranean
region (San-Miguel-Ayanz _et al_ , 2010). Although fires ignite and spread
under favorable conditions of fuel availability and low moisture conditions,
ignition is generally caused by human activities. Over 95% of the fires in
Europe are due to human causes. An analysis of fire causes show that the most
common cause of fires is “agricultural practices”, followed by “negligence”
and “arson” (Vilar Del Hoyo _et al_ , 2009; Reus Dolz _et al_ , 2003).

Most fires in the region are small, as a fire exclusion (extinction) policy
prevails in Europe. Fires are thus extinguished as soon as possible, and only
a small percentage escapes the initial fire attack and the subsequent
firefighting operations. An enhanced international collaboration for
firefighting exists among countries in the European Mediterranean region. This
facilitates the provision of additional firefighting means to those in a given
country from the neighbouring countries in case of large fire events. The
trend of large fires, those larger than 500 ha, is shown quite stable in the
last decades (San-Miguel-Ayanz _et al_ , 2010). However, among these large
fires, several fire episodes caused catastrophic damages and the loss of human
lives (San-Miguel-Ayanz _et al_ , 2013a).

A first step is to identify all the factors linked to human activity,
establishing their relative importance in space and time (Martínez _et al_ ,
2009, 2013). According to Moreno _et al_ (2014), the number of fires over the
past 50 years in Spain has increased, driven by climate and land-use changes.
However, this tendency has been recently reversed due to fire prevention and
suppression policies. This highlights the influence of changes in the role of
human activities as some of the major driving forces. For instance, changes in
population density patterns—both rural and urban—and traditional activities
have been linked to an increase in intentional fires. In this sense, several
works have previously investigated the influence of human driving factors of
wildfires in Spain. These works have explored in detail a wide range of human
variables (Martínez _et al_ , 2009; Chuvieco _et al_ , 2010) and methods.
Specifically, Generalized Linear Models (Vilar Del Hoyo _et al_ , 2009;
Martínez _et al_ , 2009; Moreno _et al_ , 2014), machine learning methods (Lee
_et al_ , 1996; Rodrigues and de la Riva, 2014), and more spatial-explicit
models like Geographically Weighted Regression (Martínez _et al_ , 2013;
Rodrigues _et al_ , 2014) have previously been employed. However, all these
approaches could be considered as stationary from a temporal point of view,
since they are based on ‘static’ fire data information summarized or
aggregated for a given time span. However, the influence of human drivers
cannot be expected to be stationary (Rodrigues _et al_ , 2016). Zumbrunnen _et
al_ (2011) stress the importance of dealing with the temporal dimension of
human drivers of wildfires. Therefore, exploring temporal changes in
socioeconomic or anthropogenic drivers of wildfire will enhance our
understanding of both current and future patterns of fire ignition, and thus
help improve suppression and prevention policies (Rodrigues _et al_ , 2016).

Disease risk mapping analyses can help to better understand the spatial
variation of the disease, and allow the identification of important public
health determinants (Moraga, 2018). Spatio-temporal disease mapping models are
a popular tool to describe the pattern of disease counts and to identify
regions with an unusual incidence levels, time trend or both (Schrödle and
Held, 2011). This class of models is usually formulated within a hierarchical
Bayesian framework with latent Gaussian model (Besag _et al_ , 1991). Several
proposals have been made including a parametric (Bernardinelli _et al_ , 2014)
and nonparametric (Knorr-Held, 2000; Lagazio _et al_ , 2003; Schmid and Held,
2004) formulation of the time trend and the respective space-time
interactions.

Areal disease data often arise when disease outcomes observed at point level
locations are aggregated over subareas of study region due to constraints such
as population confidentiality. Producing disease risk estimates at area level
is complicated by the fact that raw rates can be very unstable in areas with
small population and for rare circumstances, an also by the presence of
spatial autocorrelation that may exist due to spatially correlated risk
factors (Leroux _et al_ , 2000). Thus, generalised linear mixed models are
often used to obtain disease risk estimates since they enable to improve local
estimates by accommodating spatial correlation and the effects of explanatory
variables. Bayesian inference in these models can be performed using
Integrated Nested Laplace Approximation (INLA) approach (Rue _et al_ , 2009)
which is a computational alternative to the commonly used Markov chain Monte
Carlo methods (MCMC); INLA allows to run fast approximate Bayesian inference
in latent Gaussian models.

INLA is implemented in the INLA package for the R programming language, that
provides an easy way to fit models via inla() function, which works in a
similar way as other functions to fit models, such as glm() or gam() (Palmi-
Perales _et al_ , 2019).

Statistical reporting in the European Union is done according to the
Nomenclature of Units for Territorial Statistics (NUTS) system. The NUTS is a
five-level hierarchical classification based on three regional levels and two
local levels. Each member state is divided into a number of NUTS-1 regions,
which in turn are divided into a number of NUTS-2 regions and so on. There are
78 NUTS-1 regions, 210 NUTS-2 and 1093 NUTS-3 units within the current 15 EU
countries (Eurostat, 2002).

In this paper, we explore the application of these models to understand forest
fires causality using environmental and socio-economic variables. We will work
with areal data using the number of forest fires at NUTS-2 regional level in
Europe and consider forest fires between 2012 and 2016.

##  2 Material and Methods

We extend the analysis of globalization to the NUTS-2 regions of the 27
countries of the European Union (EU-27), as not all the regions have been
included due to absence of information (forest fires or socio-economic data)
(Figure 1).

Figure 1: Study area, in grey administrative areas included in the analysis.

Our main data set comprises the number of fires in Europe at NUTS-2 level,
requested to the European Forest Fire Information System (EFFIS) (San-Miguel-
Ayanz _et al_ , 2012). We have chosen this level due to the variables that we
are interested to analyse (socioeconomic and environmental).

In order to summarise the forest fires in Europe we can see that the number of
forest fires and the area affected have decreased between 2012 and 2014.
However, the minimum was achieved in 2014, with a subsequent increase during
2015 and 2016 (Figure 2).

Figure 2: Summary of number of forest fires (left) and area affected between
2012 and 2016 (right)

The following environmental variables were obtained from the AGRI4CAST
Resources Portal: Maximum air temperature (∘C); Minimum air temperature (∘C);
Mean air temperature (∘C); Mean daily wind speed at 10 m. (m/s); Vapour
pressure (hPa); Daily precipitation (mm/day); Potential evaporation from the
water surface (mm/day); Potential evaporation from moist bare soil surface
(mm/day); Potential evapotranspiration from crop canopy (mm/day); Total global
radiation (kJ/m2/day). For each region we have the average by year. In Figure
3 we can see the average of the different variables by year for all the NUTS 2
regions.

Figure 3: Trend of average of NUTS 2 regions by year of environmental
variables between 2012 and 2015. (a) Maximum air temperature by year; (b)
Miminum air temperature;(c) Mean air temperature; (d) Mean daily wind speed;
(e) Vapour pressure; (f) Daily precipitation; (g) Potential evaporation from
the water surface; (g) Potential evaporation from moist bare soil surface; (h)
Potential evapotranspiration from crop canopy; (j)Total global radiation.

The following socio-economic variables were obtained from Eurostat: Active
population (*1000 employed persons), Woodland (*1000 hectares of Woodland in
the area), Manufactured (*1000 employed persons working in manufactured
products from woodland); Forestry (*1000 employed persons working in Forest
sector); Economic aggregates of forestry (million euro) and Unemployment (%).
In this case we have included in our model totals values by year and region.
In order to summarise the different variables we have included in Figure 4 the
total values by year for all the variables except for Unemployment where we
have done the average for all regions by year.

Figure 4: Trend the socioeconomic variables between 2012 and 2016. Totalisers
by year in the following graphs: (a) Active population; (c) Economic
aggregates of forestry; (d) Employed persons working in Forest sector; (e)
Employed persons working in manufactured products from woodland); and (b)
Average of Unemployment.

###  2.1 Spatio-Temporal model

Here we consider a disease mapping approach, commonly used in small area
studies to assess the spatial pattern of a particular outcome and to identify
areas characterised by unusually high or low relative risk (Lawson, 2013;
Pascutto _et al_ , 2000).

For the _i-th_ area, the number of forest fires $y_{i}$ is modelled as

$y_{it}\sim Poisson(\lambda_{it});\lambda_{it}=E_{it}\rho_{it}$ (1)

where the $E_{it}$ are the expected number of forest fires and $\rho_{it}$ is
the rate.

We specify a log-linear model on $\rho_{i}$ and include spatial, temporal and
a space-time interaction, which would explain differences in the time trend
for different areas. We use the following specification to explain these
differences:

$\rho_{it}=\alpha+\upsilon_{i}+\nu_{i}+\gamma_{t}+\phi_{t}+\delta_{it},$ (2)

There are several ways to define the interaction term: here, we assume that
the two unstructured effects $\nu_{i}$ and $\phi_{t}$ interact. We re-write
the precision matrix as the product of the scalar $\tau_{\nu}$ (or
$\tau_{\phi}$) and the so called structure matrix $\textbf{\emph{F}}_{\nu}$
(or $\textbf{\emph{F}}_{\phi}$), which identifies the neighboring structure;
here the structure matrix $\textbf{\emph{F}}_{\delta}$ can be factorised as
the Kronecker product of the structure matrix for $\nu$ and $\phi$ (Clayton,
1996):
$\textbf{\emph{F}}_{\phi}=\textbf{\emph{F}}_{\nu}\otimes\textbf{\emph{F}}_{\phi}=\textbf{\emph{I}}\otimes\textbf{\emph{I}}=\textbf{\emph{I}}$
(because both $\nu$ and $\phi$ are unstructured). Consequently, we assume no
spatial and/or temporal structure on the interaction and therefore
$\delta_{it}\sim Normal(0,\tau_{\phi})$ — see Knorr-Held (2000) for a detailed
description of other specifications.

In the model presented we assume the default specification of R-INLA for the
distribution of the hyper-parameters; therefore, log$\tau_{\upsilon}$ $\sim$
logGamma(1,0.0005) and log$\tau_{\nu}$ $\sim$ logGamma(1,0.0005). In addition
we specify a logGamma(1,0.0005) prior on the log-precision of the random walk
and of the two unstructured effects (Blangiardo and Cameletti, 2015).

To evaluate the fit of this model, we have applied the Watanabe-Akaike
information criterion (WAIC) (Watanabe, 2010). WAIC was suggested as an
appropriate alternative for estimating the out-of-sample expectation in a
fully Bayesian approach. This method starts with the computed log pointwise
posterior predictive density and then adds a correction for the effective
number of parameters to adjust for overfitting (Gelman and Shalizi, 2013).
Watanabe-Akaike information criterion works on predictive probability density
of detected variables rather than on model parameter; hence, it can be applied
in singular statistical models (i.e. models with non-identifiable
parameterization) (Li _et al_ , 2016).

We have used Integrated Nested Laplace Approximation (INLA) implemented in
R-INLA within the R statistical software (version 3.6.0).

##  3 Results

In this section, we show how the forest fires have evolved between 2012 and
2016.

Analysing the temporal trend, we can see graphically (Figure 5), the posterior
temporal trend for forest fires in Europe. In this graph we show how the
number of forest fires tend to be reduced over time.

Figure 5: Global linear temporal trend for number of forest fires in Europe at
NUTS2 region level. The solid line identifies the posterior mean for
$\beta_{t}$ , while the dashed lines are the 95% credibility intervals.

Analysing the posterior distribution of forest fires (Figure 6) in Europe we
can see that there is a “hot point” in western of the continent (North of
Portugal and North West of Spanish peninsula). Also, as we can see, in
general, the predicted number of forest fires is low in central Europe.

Figure 6: Map of the number of forest fires posterior distribution by region.

Comparing the different years, as we pointed previously, during 2014 the
number of forest fires decreased in all areas except in some regions of Spain
and Sicily. In addition, analysing the number of forest fires by region we can
see that the region with stronger variations is the North region from
Portugal.

In Figure 7 we can see a more detailed map focused in Mediterranean countries
(France, Greece, Italy and Spain). In this case it is clear that variability
in France is almost inexistent only with some increase in the number of forest
fires in Southern regions in 2016. The results from the data available for
Greece, show that there are not big changes during the time analysed. However,
Italy and Spain show more fluctuations during this period. The Southern part
of Italy shows great changes along the time, starting with almost 150 forest
fires in Sicily in 2012 to reduce until about 30 forest fires in 2015 and
increase again in 2016 (67 forest fires). Similarly, in Spain the Northwest
region shows several fluctuations. However, in Spain higher number of forest
fires affects more regions.

Figure 7: Detail of posterior distribution of forest fires in the
Mediterranean region.

As we can see in Table1, several variables are affecting the quantity of
forest fires. But two of them have more impact (have higher values) than the
others. Evaporation in water surface (EvaporationW) is affecting positively
the volume of forest fires at region level. On the other hand, and with
similar magnitude but negative sign, Evapotranspiration from crop canopy
(Evapotrans.) is affecting negatively the presences of forest fires.

Table 1: Posterior estimates summary (Mean, Standard deviation and 95% Credible Interval). Fixed effects and hyperparameters for spatio-temporal model. Fixed effects |  |  |  |
---|---|---|---|---
| mean | sd | 0.025quant | 0.975quant
Active | 0.3045 | 0.1739 | -0.0375 | 0.6468
Aggregates | -0.2919 | 0.1568 | -0.6008 | 0.0152
Forestry | 0.6073 | 0.3321 | -0.058 | 1.2472
Manufactured | -0.9303 | 0.3944 | -1.717 | -0.1669
MaxTemperature | 0.1761 | 0.14 | -0.0999 | 0.4503
MinTemperature | 0.584 | 0.2153 | 0.1631 | 1.0083
AvgTemperature | -0.1396 | 0.4931 | -1.1094 | 0.8277
Wind | 0.5609 | 0.2512 | 0.0655 | 1.0522
Presion | -0.28 | 0.2978 | -0.8653 | 0.3046
Precipitation | -0.0224 | 0.0984 | -0.2158 | 0.1707
Evapotrans | -23.6247 | 9.8466 | -43.0572 | -4.3758
EvaporationW | 24.5911 | 10.907 | 3.2597 | 46.0829
EvaporationS | 0.9008 | 0.6832 | -0.4398 | 2.2435
Radiation | -0.2677 | 0.9264 | -2.0907 | 1.5486
Woodland | 0.7649 | 0.2249 | 0.3331 | 1.2172
Model hyperparameters |  |  |  |
| mean | sd | 0.025quant | 0.975quant
Precision for AREA_ID | 2.02E-01 | 3.85E-02 | 0.1347 | 2.85E-01
Precision for Year | 1.14E-01 | 6.61E-02 | 0.0299 | 2.80E-01
Precision for AREA_ID.YEAR | 1.59E+00 | 2.66E-01 | 1.1262 | 2.17E+00

However, evapotranspiration from crop canopy is having a negative effect in
forest fires quantity. In this group we need to highlight variables having
more impact (higher values) than the others. Evaporation in water surface
(EvaporationW) is affecting positively the volume of forest fires at region
level. On the other hand, and with similar magnitude but negative sign,
Evapotranspiration from crop canopy (Evapotrans) is affecting negatively the
presences of forest fires. The rest of the variables that are affecting
positively the amount of forest fires are Minimum temperature at 10 m.
(MinTemperature) and Mean daily wind speed at 10 m (WIND). Finally,
Manufactured is affecting negatively the quantity of forest fires.

Graphical representation of estimation for the fixed effects is presented in
Figure 8. This chart presents the variables and their relationships with
forest fires. Variables distributed in a positive side contribute to higher
number of forest fires; the opposite, with variables with negative
distribution. Variables present in both areas (positive and negative) do not
have a clear relationship with answer.

Figure 8: Graphical representation of fixed effecs. Evaporation in water
surface (EvaporationW) and Evapotranspiration from crop canopy (Evapotrans)
were excluded in order to obtain more detail of the rest of the variables.

##  4 Conclusions

We have built spatio-temporal models to predict the quantity of forest fires
in Europe at NUTS-2 regional level. We have shown the relationship between the
different variables and the number of forest fires by region. We have shown
that this relationship not only is between some of the variables (fixed
effects), but also the evolution of forest fires along the time is affected
not only by time and spatial effects but also by the combination of both
(Precision for AREA_ID.YEAR).

Initially our main objective in this project was to apply these models to
Europe with more granularity, assuming that more local information will help
to understand better the causality of forest fires. However due to data
availability it was not possible to develop the project in that way. Currently
not all the socioeconomic data is available for all the NUTS-3 regions in a
continuous timestamp, being this characteristic necessary to carry a spatio-
temporal analysis.

Also, several factors can affect in different ways depending of the area. In
our case, variables have been assumed in a scale that in some of the cases
local information can help to understand cause-effect of forest fires.

Analysing the models, we believe that the use of spatio-temporal models is an
advantage for the understanding of the different dynamics, given that the
temporal and spatio-temporal perspective is not very frequent analysing forest
hazards.

Summarising, we can generalise that not only environmental factors but also
socioeconomic variables are affecting the causality of forest fires. However,
more data and more granularity in the analysis in needed in order to
understand this causality.

Landscapes became more hazardous with the time, since land abandonment led to
an increase in forest area. Treeless areas burned proportionally more than
treed ones (Urbieta _et al_ , 2019). Fires in southern Europe have more
preference shrublands than for forest types (Moreira _et al_ , 2011; Oliveira
_et al_ , 2014), but may vary along locations (Moreno _et al_ , 2011). This
could be due to a change in the ignition patterns owing to shifts in the
wildland-agricultural and wildland-urban interfaces (Rodrigues _et al_ , 2014;
Modugno _et al_ , 2016). The most vulnerable landscapes were those with
diversity of land uses, with forest-agriculture mixtures (Ortega _et al_ ,
2012). For these reasons, inclusion of vegetation to analyse causality needs
to be studied.

Fire trends can be affected by changes in ignition cause. In European
Mediterranean countries, a minor percentage of fires are caused by lightning,
and most are caused by people. Fires of these two sources tend to occur at
different locations (Vázquez and Moreno, 1998), which could affect the
vegetation they burn and the difficulty of extinction. However, no changes
between these two sources have been observed (Ganteaume _et al_ , 2013).
Regarding people-caused fires, the majority of them are voluntary, followed by
negligence (Urbieta _et al_ , 2019). In recent times, negligence fires are
increasing and voluntary ones decreasing (Ganteaume _et al_ , 2013). Whether
this is differentially affecting the number of fires trends, is something that
needs research (Urbieta _et al_ , 2019).

Spatio-temporal models and the R-INLA package appear to offer additional
benefits beyond the traditional analysis used to understand the causes of this
hazards. The combination of using a complex spatial latent field to capture
spatial processes and an underlying simple additive regression model for the
response variables relationship to the different factors, means that the fixed
effects are potentially more straightforward to interpret (Golding and Purse,
2016). R-INLA models are extremely flexible in their specifications, with
spatial autocorrelation and observer bias being straightforwardly incorporated
as random effects, while standard error distributions, such as Gaussian,
Poisson, Binomial, and a variety of zero-inflated models, can be used
interchangeably (Rue _et al_ , 2009).

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