Assessment of Social Vulnerability in Malaga Province, Spain: A Comparison of Indicator Standardization Techniques

Revista de Estudios Andaluces (REA) | Núm. 41, febrero (2021) | e-ISSN: 2340-2776. https://dx.doi.org/10.12795/rea.2021.i41 | © Editorial Universidad de Sevilla 2021 https://editorial.us.es/es/revistas/revista-de-estudios-andaluces https://revistascientificas.us.es/index.php/REA CC BY-NC-ND 4.0 87 DOI: https://dx.doi.org/10.12795/rea.2021.i41.05 Formato de cita / Citation: Gayen, S. etal. (2021). Assessment of social vulnerability in Malaga province, Spain: A comparison of indicator standardization techniques. Revista de Estudios Andaluces, 41, 87-108. https://dx.doi.org/10.12795/rea.2021.i41.05 Correspondencia autor: [email protected] (Sumita Gayen) 87 https://dx.doi.org/10.12795/rea.2021.i41.05 Assessment of Social Vulnerability in Malaga Province, Spain: A Comparison of Indicator Standardization Techniques Evaluación de la Vulnerabilidad Social en la Provincial de Málaga, España: Comparación de Técnicas de Estandarización de Indicadores Sumita Gayen [email protected] 0000-0002-5587-9206 Ismael Vallejo-Villalta [email protected] 0000-0001-7077-259X Departamento de Geografía Física y Análisis Geográfico Regional, Universidad de Sevilla. C/ Doña María De Padilla, S/N. 41004 Sevilla. Spain. Sk. Mafizul Haque [email protected] 0000-0002-2604-5704 Department of Geography, University of Calcutta 35, B. C. Road. Kolkata. India, Pin-700 019. Sumita Gayen et al. INFO ARTÍCULO ABSTRACT Received: 30-06-2020 Revised: 27-12-2020 Accepted: 08-01-2021 KEYWORDS Social vulnerability Indicators Standardization Natural disasters Hazards are any sort of extreme events that occurred by natural causes, human activities or sometimes both reasons are responsible. Social vulnerability exposes people’s condition in hazards. To prevent the loss and damages by hazards, it is necessary to identify vulnerable population of a region by constructing social vulnerability maps of that region. Here in this work, study region is Malaga province, located in southern part of Spain, under the autonomous community of Andalusia. 23 key indicators have been adopted to understand the vulnerability of people in Malaga province at a municipality scale. Three different standardization methodologies (maximum value transformation, z score transformation and min–max rescaling transformation) have been used to build five vulnerable zones. There exists strong to very strong relationship between the results obtained by using three methodologies. The final result shows that most socio-economically vulnerable municipalities are located mainly in western part of the Malaga province. PALABRAS CLAVE RESUMEN Vulnerabilidad social Indicadores Estandarización Desastres naturales La peligrosidad viene motivada por una serie de fenómenos extremos que pueden ser debidos a causas naturales, a actividades humanas o a una combinación de ambos aspectos. La vulnerabilidad social expresa las condiciones de las personas que pueden ser afectadas por determinados peligros. De cara a la prevención de los daños y pérdidas vinculadas a estos peligros, se hace necesario identificar a la población vulnerable en un territorio determinado a través de la generación de cartografía sobre vulnerabilidad social. En este trabajo, la zona estudiada se corresponde con la provincia de Málaga, que se localiza en la zona sur de España dentro de la Comunidad Autónoma de Andalucía. Un conjunto de 23 indicadores han sido aplicados para analizar la vulnerabilidad de la población de Málaga a nivel municipal. Para ello se han empleado tres técnicas de estandarización (transformación del valor máximo, transformación de las puntuaciones Z y transformación de valores mínimo y máximo) que han permitido la clasificación de los municipios en 5 categorías de Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 88 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 vulnerabilidad. Los resultados muestran que existe una importante similitud entre las diferentes técnicas de estandarización elegidas. En todos ellas se detecta que los municipios más vulnerables desde un punto de vista socio-económico se sitúan en la zona occidental de la provincia de Málaga. 1. INTRODUCTION Natural process or phenomena occurring in earth that may constitute a damaging event is known as natural hazard. Almost all countries of the world are affected by natural hazards in each and every year and most of them bring about some sort of natural disaster. Data on natural disasters show an increase both in the number of disasters and in their economic repercussions (Gu, 2019), that it is argued to be mainly linked to an increase in frequency and severity of climate-related hazards associated to climate change (Thomas & López, 2015; Djalante, 2019). However, natural disasters are dependant not only on natural hazards, as they result from the interaction between these natural hazards and socio-economic conditions of a society; thus, the risk from natural disaster is defined as the probability of this interaction to occur in a particular place (Birkman etal., 2013). In a broad sense, these socio-economic conditions are known as vulnerability. According to the exposed scheme, natural risk management comprises two basic set of actions. On the one hand, societies have tried to manage and control natural environment, in such a way that extreme events or hazards could be suppressed or reduced both in term of frequency or intensity. Some examples of these measures to manage and control nature include flood control dams and barriers, landslides retaining walls, or breakwater against coastal storms and tsunamis. On the other hand, once societies have assumed that natural hazards cannot be completely prevented, vulnerability reduction measurements are the alternative way to diminish damages and losses from occurring extreme events. Regardless of the adopted measures (hazard or vulnerability reduction), a common necessary precondition is required that it is related to a depth knowledge of the situation that is to be treated. In the case of hazard reduction measurements this knowledge is about the natural extreme phenomena that usually take place in a particular zone, including some basic parameters related to their magnitude (intensity), temporal patterns (frequency, duration, temporal spacing, speed of onset) and spatial patterns (areal extent and spatial dispersion) (Burton etal., 1978). In the case of vulnerability reduction measurements, the focus of a previous research is the affected society, in order to understand the existence of differences in damages and losses among social groups and along the territory they occupy. Thus, vulnerability assessment and mapping is an essential stage of natural risk management and it constitutes the research context of this particular study. This paper takes part of wider study for social vulnerability assessment and mapping at different scales, with the aim of gaining knowledge on social vulnerability assessment by the use of several set of data sources, and the application of different statistical and mapping methods. As a first approach, the present study tries to assess social vulnerability for the province of Malaga, from a generic point of view, so it is not focused on a particular hazard. The study makes use of three simple standardization techniques for statistical data simplification in order to compare obtained results, and employs the 103 municipalities of this province as mapping units. 2. THEORETICAL BACKGROUND In the context of risk analysis, United Nations defines vulnerability as “the conditions determined by physical, social, economic and environmental factors or processes that increase the susceptibility of an individual, a community, assets or systems to the impact of hazards” (UN, 2016). Despite this general and clear definition, vulnerability has undergone a long and depth theoretical debate (Birkman et al., 2013). The outcome of this debate is a much more complex concept in which different factors (exposure, fragility, coping capacity, or resilience) and multiple dimensions (physical, social, economic or environmental) could be Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 89 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 distinguished (Cardona, 2003; Cutter etal., 2003; Wisner etal., 2004; Birkman etal., 2013). As a consequence of this long debate and of the theoretical complexity of the resulted concept, a lack of vulnerability assessment studies was reported, especially in the case of vulnerability mapping (Perles-Roselló, 2010), although in the last decade a great number of this sort of studies has taken place. Apart from the physical vulnerability assessment in which real physical elements (buildings, infrastructures, land-cover patches) are used to evaluate and mapping susceptibility or fragility to hazards (Duzgun etal., 2011; Papathoma-Köhle etal., 2019), most of these aforementioned studies are especially focused on social vulnerability (Beccari, 2016; Ran etal., 2020). Social vulnerability has been defined as the susceptibility of social groups to potential losses from hazard or resistance and resilience power of a society from hazard (Blaikie etal., 1994), or as a measure of the capability of some elements to withstand events of a certain physical character (Weichselgartner, 2001). Cutter etal. (2003) indicate that it is a pre-existing condition of the population that influences its ability to prepare for, respond to, and recover from hazard events, and Cannon etal. (2003) defined it as the complex set of some characteristics that include a person’s initial well-being, livelihood and resilience, self-protection, social-protection, social and political networks and institutions. Social vulnerability assessment is a process of identifying, quantifying, and analyzing the vulnerability factors in a particular area. Most of these assessment studies are based on statistical data and indicators gathered for different spatial units (mainly, administrative units) at different scales (from national level to census tracts) and these same units are used for mapping purposes (Ran etal., 2020; Contreras etal., 2020). In general terms, the assessment process consists of some basic phases (Beccari, 2016). As a first stage, (i) statistical data are gathered for these spatial units. Indicator selection could be driven according to the type of approach, namely hazard-specific, for those studies focused on a particular hazard (flood, earthquake etc.), or generic, for those others in which social variables are used to measure vulnerability to any hazard (Ran etal., 2020). Other authors (Barrionuevo & Perles Roselló, 2017; Perles Roselló etal., 2017) indicate the relevance to define the type of vulnerability, namely susceptibility/fragility (vulnerability to the hazard), or resilience (vulnerability to damage). In a second stage, (ii), some data treatments are carried out, including some sort of normalization, reduction, weighting or aggregation techniques. Finally, (iii) vulnerability mapping is accomplished through the classification of the same spatial units. As stated above, a wide range of scales have been used, ranging from global to local/urban studies (Bukvic etal., 2020) and, according to these scales a great variety of spatial units have been employed ranging from countries, usually employed in global studies, to different kind of census units for local and urban analysis. Following are some examples of these varied studies. As stated above, in the case of global analysis, countries are usually employed as spatial units, and it is quite common that development data are used as vulnerability indicators (André, 2012). At regional scales, different spatial units can be found in vulnerability assessment; some examples are countries, as in the case of Latin America or Asia (CAF, 2014; Hoffmann & Blecha, 2020), NUTs, used in Europe (EEA, 2018), or river-ba- sins, used in different world regions (Balica etal., 2009). Studies at national level show a great variety of spatial units according to the different administrative units that can be found in each country. In the classical study by Cutter et. al., (2003), Social Vulnerability Index (SoVI) was calculated for all the counties in the United States. In China, the province is frequently used in most of the national scope studies (Yang etal., 2014; Yan & Li, 2016). National scale assessments in European countries usually employ municipalities or equivalent administrative sub national divisions, as in the case of Norway (Holand etal., 2011), Italy (Frigerio etal., 2018), or Germany (Fekete, 2018). Below the national level, vulnerability assessments are usually carried out within these last mentioned spatial units (municipalities) or in urban environments (Diaz-Sarachaga & Jato-Espino, 2020). In the case of Spain, census district is a widely employed spatial unit for this type of studies (Morales etal., 2016; Navarro etal., 2020); however, some other more detailed studies have been accomplished, in which cadastral parcels have been used for social vulnerability assessment and mapping (Tascón-González etal., 2020). Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 90 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 3. MATERIAL AND METHODS 3.1. Study area Malaga province is located (Figure 1) in the southern part of the Spain and on the northern coast of the Mediterranean Sea, and is under autonomous community of Andalusia which is the most populous and the second largest in area of the autonomous communities in the country. The area of the province is 7,308 square kilometers. The population of Malaga is 1,641,121 (2018) and population density is 224.53/ sq. km. Malaga has highest population density in Andalusia, and this population density is high mainly in the coastal zone. The province contains 103 municipalities and the capital of the province is Malaga. From a geological point of view, Malaga province belongs to the Betic System, that forms the most western segment of the Mediterranean alpine orogeny. The relief gains in elevation from the coast to the inner lands, although one of its main characteristics is some sort of segmentation by which mountain ridges and depressions take place (Insua-Arévalo etal., 2012; Gallegos Reina & Perles Roselló, 2019). Guadalhorce River is the main river of the province and it divided the province into east and west. Guadalmedina, Guadiaro, Genal, Rio Grande, Chillar, Rio Velez, Hozgarganta are other important rivers. Malaga has a Mediterranean climate, with some climate subtypes, as subtropical in the east coastal sector, oceanic mediterranean in the west part of the coast, and continental mediterranean in the inner areas of the province. The maximum temperature in summer often surpass 40°C, and in winter it is quite soft in the coastal area, although it drops to the freezing point in some places of the higher and inner areas. Annual rainfall is about 500 mm in average, but heavy rain episodes are frequent and cause severe soil erosion and flood events (Gallegos Reina, 2013). Figure 1. Location map of Malaga Province. Source: Own elaboration. Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 91 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 River floods are the main hazard in the province of Malaga, especially as flash floods. Apart from the aforementioned heavy rain episodes, some others natural contributing factors are the short and stepped profiles of the typical mediterranean rivers, the proximity to the coast of high mountain ridges and the reduction of the vegetation cover in these mountain areas where main rainfalls take place (Madeca, 2017). As the typical mediterranean paradox, another of the main hazards in Malaga province are droughts, that are naturally caused by long period of rainfall scarcity (Ayuso etal., 2015), although human induced factors are of great relevance (Sánchez & Del Moral, 2014). Another types of climate related hazards are forest fires, especially during the summer period (Gutiérrez-Hernández etal., 2015), and coastal storms and erosion that frequently take place during autumn and winter months (Prieto et. al., 2017). In the case of geophysical hazards, the province of Malaga is located at the boundary region between Eurasian plate and African plate. So, earthquake is more frequent than the other parts of Spain. The province has suffered earthquakes greater than or equal to maximum intensity VIII (epicenter within Malaga province) in 1494 and 1680, or has been affected by another relevant earthquake with epicenter in the province of Granada (Gayen etal., 2019). Except the mega earthquakes happened in past, every year many minor earthquakes are also happened. 3.2. Selection of indicators An indicator is a measurable variable which represents an associated factor. Pencheon (2007) explained, indicator helps to understand, compare and improve a system. The indicators are not fixed and different authors used different indicators. Selection of indicator depends on the purpose of the study (Weichselgartner, 2001; Tapsell etal., 2005). The assessment of social vulnerability is a key issue of indicator based measurement. Indicators are not static. It changes with time and space. In same place and time, and working with identical hazard, selection of indicators may be different based on views, analysis criteria etc. of researchers. For identical hazard, indicators can vary from one region to another region though researcher and time are unchanged. It also depends upon country and event-specific characteristics (Kuhlicke etal., 2011). Data under the different aspects have been collected from secondary source for the detail understanding of social vulnerability. All the data have been collected from Instituto de Estadística y Cartografía de Andalucía (IECA,) Junta de Andalucía. There are various physical, demographic, economic, social, and health indicators which determine people’s level of exposure to vulnerable. Regarding to this, two types of relationship found between indicator and social vulnerability (Table 1). For positive relationship, vulnerability of local people increases with the presence of indicator whereas for negative relationship, vulnerability decreases with increasing value of indicator. Negative value reduces the vulnerability and positive value increases the vulnerability of local people of a region. 3.3. Description of indicators 3.3.1. Distance from capital Province’s capital is the main functional nuclei of the area which provides all types of essential services, medical supports and emergency recovery treatments. On the other hand, capital city plays an important role for different administrative services which are very crucial during the hazard related policies. Citizen having good economic condition as well as better job opportunities in and around this node and economic growth rate is higher in capital compared to other region. Capital region is well connected by other regions and it provides better relief facilities during the hazards. These all are responsible for declining the unsafe condition of local people and decrease the social vulnerability. Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 92 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 Table 1. Classifications of indicators. Aspect Indicators Name Relationship with vulnerability Year of data source Reference Locational Distance from capital DCA Positive 2014 Proposed in this research work Demographic Gender GEN Positive 2018 Müller etal., 2011 Child population (below 5 years) CPO Positive 2018 Török, 2018 Aged population (65 years and above) APO Positive 2018 Reid etal., 2009 Dependency ratio DRA Positive 2018 Kalaycıoğlu etal., 2006 Single parent nuclei SPN Positive 2011 Flanagan etal., 2011 Median age MAG Negative 2018 Burton etal., 2010 Foreigners FOR Positive 2011 Aksha etal., 2019 Birth rate BRA Positive 2017 Mavhura etal., 2017 Social Population density PDE Positive 2017 Caliskan etal., 2006 Large household (5 or more) LHO Positive 2011 Ge et al., 2017 Single and isolated living SLI Positive 2011 Girasole etal., 2017 Literacy rate LRA Negative 2001 Ahmad etal., 2016 Sport equipments/ 1000 population SEQ Negative 2005 de Oliveria Mendes, 2009 Economic Renters REN Positive 2001 Armenakis etal., 2017 Employment rate ERA Negative 2018 Dwyer etal., 2004 Population related to agriculture PRA Positive 2009 Chen etal., 2013 Average net income (annual in euro) ANI Negative 2016 Kirby, 2015 Per capita income (annual in euro) PCI Negative 2016 Emrich & Cutter, 2011 Health Mortality rate MRA Positive 2017 Lowe etal., 2013 Infant mortality rate IMR Positive 2017 Gayen etal., 2020 Primary health care/1000 population PHC Negative 2016 Fekete, 2009 Number of pharmacy/1000 population NPH Negative 2016 Guillard-Gonçalves etal., 2014 Source: Own elaboration. 3.3.2. Gender Gender is one of the most common determinants of hazard’s susceptibility and it has been considered by several researchers for people exposure examination like Dwyer etal. (2004), Tapsell etal. (2005) and Kuhliche etal. (2011). Biologically women section of society is more vulnerable than men specially to overcome the negativity of hazards condition (Dwyer etal., 2004; Morrow, 1999). When any natural hazards strikes then they are busy to protect their children and other family members and take care the injured and sick persons of the family. So, the morbidity and co-morbidity rates of women have been found higher in many hazard events. Women are less educated, so they have few job opportunities and it becomes more during Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 93 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 post-disaster period. Sometimes they obtained a low status job (Morrow, 1999). Disaster forced women to do work outside the home in an unsafe condition. Economically, women are weaker than men, so they are more vulnerable. Women's vulnerability is also increased due to the loss of family members. It increases more when the head of the family has died and all the responsibilities come on their shoulder. After the hazard women's responsibilities are also expanded at a large extend. Thus, more numbers of women depended family creates high vulnerability in the society. 3.3.3. Child population Children below five years age are the most vulnerable groups in the society during disaster events (Cutter etal., 2003). Children have less physical strength to survive from disasters and they are frequently dependent on others for survival. Children under the five years old are more impacted age group in society due to the presence of abnormal surroundings. They are innocent and they are not able to take their decisions i.e. they are dependent on others for any types of activities support. Children are vulnerable due to lack of their education, previous experience and knowledge. They don't understand what to do during hazardous moments. As children are more closed to women group, their openness towards negativity due to hazards is also wider. 3.3.4. Aged population The older persons have knowledge, but sometimes they are physically disabled and challenged to work for activity. They could also have some hearing and visibility problems. They don't move quickly from one place to another place. They have lack of advance knowledge like communicating others people by internet. Generally elder population has less resistance power of disease. So they are easily affected by waterborne infectious diseases, which are common consequences of many disasters. Which age is more vulnerable? Nobody mentioned any fixed age for it. People above 65, are more vulnerable (Dwyer etal., 2004). Generally, the vulnerability of elders depends on their physical strength. Some old persons are physically stronger than their below aged persons but due to the co-morbidity condition and long term hazardous exposure they are more vulnerable during in society. 3.3.5. Dependency ratio Here dependency ratio is calculated by % of population under the age of 16 and over 65 years of age along with the people of physically challenged in respect to the population between the ages of 16 and 65 years. Actually it is a ratio between non-working and working population. It shows the pressure of economically productive and working population in a society (Chen etal., 2013). Depended peoples need extra services for their safety and security. Area of high depended population claims extra improvement in the infrastructure sector to minimize the dynamic pressure of hazards event. Higher dependency ratio indicates lower collection of government tax, because working population has a greater responsibility to pay tax. Higher dependency ratio could create labor shortages that can reduce agriculture and industrial production. Lower dependency ratio might be better for pension providers and health care providers but it promotes economic growth of a society. Families with large number of dependents have limited financial opportunities. So during the recovery phase of hazard, they have to face many problems. 3.3.6. Single parent nuclei Child nurturing is very difficult for single parent under any circumstances and parent has sole liability for all activities to support their livelihood. In this case the parent’s responsibility is greater as the child is dependent on only one person. All the activities are financially difficult and this family nucleus is socially isolated. During the extreme event condition the single parent try to provide all supports and company to their child. Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 94 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 Beside the all physical hindrances and community resources it is harder for parent to leave the child at home (Copeland & Synder, 2011). These all result extra pressure, stress and fatigue on the parent which increase high risk of depression. So, this type of family structure is considered as a root cause of social stress and backwardness for hazards management. 3.3.7. Median age Median age is the midpoint or middle age that divides a population into two equal groups - that is, half the people are younger than this age and half are older. It indicates the average age of a population. Low median age means major part of the population is fertile. Whereas, higher median age signifies declining fertility rates and rising life expectancy with poor equipped to hazards management. Four main aspects are more closely linked with heightened vulnerability of higher median age society and these are i) physical decline including poor health, mobility, sight and hearing, ii) paucity of service provision, support and information, iii) age discrimination which serve to exclude and isolate them, iv) high poverty level which increase low social protection and better opportunities. Thus the high median age reduces the capability towards the preparedness to disasters i.e it is negatively linked with social vulnerability of a region. 3.3.8. Foreigners Generally, foreigners are not well integrated into the society of destination country normally they have limited social and family networks here. New foreigners have lack of connections to the larger community and they feel hesitate to communicate outside their ethnic group for a variety of reasons (Morrow, 1999). They are not well familiar about the local area and also have language barrier, lack of awareness of health services and lack of proper knowledge about their rights and rules. Similarly, they have very little scope to cope up the extreme event results. 3.3.9. Birth rate The birth rate is the expression of total number of live births per 1,000 populations in a year. Along with the increasing numbers of dependent population, high birth rate puts heavy burdens on social infrastructure like health care, essential services, education, land, environment, per capita income, mental supports and public utility services (water, electricity, transport). Infant population requires larger demand on nutritional food without any interruption. High birth rate also indicates large number of infant and child population. Due to the larger exposure to hazards they are most likely to be seriously affected by natural hazards, but they do not able to recover them self without any support. During the recovery phase of hazard, they could also be affected by disease. 3.3.10. Population density Population density is calculated by dividing the population by the geographical land area. Thus the spatial concentration of population in any region is subjected to significant risk of death or damage caused by prominent hazards. Day and night time concentration of population is also controlled the impact of hazards. Like central business district of a city has high probability of life loss during the day time and night time it has very low impacts on society. Positive relationship is found between population density and vulnerability that means high population density incumbents high vulnerability of the society and vice versa. For example, if a large earthquake happens in a large open field, it has little danger. On the other hand, a relatively weak earthquake can pose great risks to human life and economy in densely populated areas. Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 95 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 3.3.11. Large household Large household increases high chances of vulnerability to hazards. During the critical time this types of household put extra pressure to accumulate huge numbers of recovery resources for members. Similarly, it takes more recovery options to come out from the unusual condition. Large household has numerous residents with low availability of community resources. On the other hand, large household has lower per capita income. If they have few working persons, they cannot able to increase the income to cover the losses by hazards. 3.3.12. Single and isolated living Sometime single and isolated households are economically poor. After hazard they could be suffered with economic problems. The single living persons have also problems during the time of hazard. They may have lack of sufficient human resources to rescue. After the hazard event they could also have the migration problem. If they are injured, no other person is there to take care of them. During or after the hazard they are mentally disturbed also. On that moment they need family support and want some family members to release anxiety and pressure. They can feel loneliness which increases more chances towards long vulnerability. 3.3.13. Literacy rate Education can play an important role to reduce the vulnerability level. A strong negative relationship has been found between education and vulnerability. With proper education one person can learn how to save himself during hazard. Education is related with access to weather forecasts and warnings as well as the types of technologies used to access weather information (Rodríguez etal., 2007). Higher standard of education presents greater chance of employment. Education level can enhance ability to cope with environmental disasters and stress (Wamsler etal., 2012). Educated people can better use the social network which is important tool for emergency planning and preparedness. 3.3.14. Sport equipments/ 1000 population Sport along with the physical exercise helps to develop mental and physical toughness and makes people strong and active. Sport is important for everybody as it reduces stress and enhances mood, it helps to grow the immunity power of human body. It is an excellent way of preventing several chronic diseases such as cardiovascular diseases. Beside these all this facility can encourage youth to involve in the disaster rescue and recovery process. Thus, it has a negative relationship with vulnerability. 3.3.15. Renters Rented peoples don’t have their own house. They live in a rent house because they are either transient or they do not have sufficient money to buy their own home (Cutter etal., 2003). Sometimes they have to pay higher rents. They are not able to repair their homes without permission of landlords. Even the rented house became dilapidated condition due to the lack of proper maintenance. Both the indoor and outdoor conditions are found unhygienic. Similarly, after the hazard, rented house becomes too costly and so renters are lack of sufficient shelter options. So, rented people are more vulnerable, compared to people who have their own house. 3.3.16. Employment rate Employed peoples have good standard of living and opportunities of health care. The employee has liable to pay tax which is further invested for the different type infrastructural development. As they have secure job Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 102 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 Table 5. Pearson’s Correlation Coefficient between different indicators. DCA GEN CPO APO DRA SPN MAG FOR BRA PDE LHO SLI LRA SEQ REN ERA PRA ANI PCI MRA IMR PHC NPH DCA 1 GEN -0.12 1 CPO -0.36 0.39 1 APO 0.36 -0.35 -0.74 1 DRA 0.15 -0.09 -0.37 0.82 1 SPN 0.25 0.22 0.18 -0.11 -0.11 1 MAG 0.41 -0.40 -0.80 0.97 0.69 -0.08 1 FOR -0.21 -0.03 0.06 0.11 0.35 -0.18 0.04 1 BRA -0.29 0.14 0.48 -0.47 -0.36 -0.04 -0.45 -0.01 1 PDE -0.27 0.30 0.18 -0.21 -0.15 0.09 -0.22 0.22 0.09 1 LHO -0.36 0.34 0.45 -0.57 -0.39 0.07 -0.60 -0.07 0.30 0.18 1 SLI 0.31 -0.26 -0.39 0.61 0.43 0.05 0.65 0.09 -0.35 -0.05 -0.52 1 LRA -0.27 0.18 0.14 -0.33 -0.29 -0.03 -0.34 0.26 0.13 0.29 0.21 -0.23 1 SEQ 0.44 -0.24 -0.30 0.41 0.21 0.18 0.47 0.11 -0.33 -0.05 -0.43 0.55 -0.06 1 REN 0.09 0.21 -0.02 -0.19 -0.19 0.15 -0.15 -0.03 0.14 0.11 0.19 -0.15 0.21 -0.14 1 ERA -0.06 0.14 0.34 -0.26 -0.09 0.16 -0.30 0.15 0.01 0.12 0.08 -0.19 0.10 -0.04 -0.04 1 PRA 0.13 -0.48 -0.48 0.59 0.33 -0.05 0.63 -0.16 -0.40 -0.32 -0.43 0.51 -0.27 0.35 -0.45 -0.14 1 ANI -0.27 0.49 0.55 -0.67 -0.41 0.22 -0.67 0.27 0.28 0.46 0.42 -0.42 0.43 -0.20 0.38 0.41 -0.68 1 PCI -0.24 0.53 0.35 -0.56 -0.37 0.24 -0.57 0.00 0.25 0.38 0.45 -0.45 0.44 -0.35 0.48 0.28 -0.61 0.81 1 MRA 0.27 -0.13 -0.29 0.41 0.27 0.10 0.44 -0.12 -0.18 -0.17 -0.31 0.44 -0.18 0.37 -0.17 0.28 0.34 -0.39 -0.35 1 IMR 0.00 0.04 0.04 -0.08 -0.08 0.05 -0.08 -0.01 -0.40 -0.01 0.28 -0.07 -0.02 0.01 0.00 0.15 -0.07 0.10 0.08 -0.02 1 PHC 0.46 -0.40 -0.36 0.54 0.32 0.15 0.58 -0.15 -0.40 -0.21 -0.48 0.61 -0.18 0.66 -0.20 -0.07 0.56 -0.50 -0.49 0.34 -0.08 1 NPH 0.45 -0.27 -0.28 0.51 0.38 0.27 0.53 -0.11 -0.34 -0.15 -0.43 0.62 -0.28 0.61 -0.17 0.02 0.48 -0.40 -0.44 0.37 -0.07 0.85 1 Source: Own elaboration. Table 6. Pearson’s correlation coefficient between three methodologies. Maximum value transformation Z score transformation Min–max rescaling transformation Maximum value transformation 1 Z score transformation 0.89 1 Min–max rescaling transformation 0.93 0.98 1 Source: Own elaboration. Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 103 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 5. DISCUSSION As a first approach, this analysis of social vulnerability in Malaga province has highlighted the present locational, demographic, social, economic, and health condition of the province. This municipality-wise analysis has explained all the indicators adopted and identified different groups of vulnerable municipalities. These groups are very similar regardless of the applied standardization method, as has been shown by correlation coefficients. Equivalent results have been obtained in other studies where the outcomes of different deductive methodologies are very highly correlated with each other (Yoon, 2012). Main causes of very high vulnerability are mainly low net income and low income/population, high female population, moderately high unemployment rate and high dependency ratio. In a broad sense, similar results are usually obtained by other studies in different contexts (Cutter etal., 2003; Morales etal., 2016; Navarro etal., 2020). In spite of these acceptable results, among the main aims of this initial research are to detect and outline weaknesses and limitations to be overcome in future works. The following are the most relevant ones. As general weaknesses of social vulnerability assessments are to examine the validation of obtained vulnerability indicators (Beccari, 2016). In few cases this validation is accomplished by comparison among vulnerability indicators and disaster damages and losses information (Yoon, 2012). In order to develop a similar approach in our context, different data sources should be explored in future research, namely direct information from municipalities, data compiled by civil protection authorities, or by private or public insurance companies (Consorcio de Compensación de Seguros). Also related to data and indicators, future vulnerability studies could be focused on a specific hazard or group of hazards, in such a way as enable some improvement on indicators selection criteria. Similarly, data reduction and aggregation techniques need to be largely improved. Although in the present study a comparison of normalization methods was accomplished, a very simple aggregation technique was employed and no data weighting or reduction was applied. In the next research steps, principal component analysis (PCA) would be adopted, as one of the most extended and successful methodologies (Contreras etal., 2020). Municipalities show some advantages mainly related to data gathering, both on socio-economic and disaster damages data. Nevertheless, municipality extent should be very unsuitable if social vulnerability assessments and the resulted maps are to be used for subsequent risk studies by mean of integration of vulnerability and hazard maps. While an increasing spatial detail has been reached in hazard mapping (Muhadi etal., 2020), only some aspects of vulnerability related to characteristics of buildings, infrastructures or land-use patches (Papathoma-Köhle etal., 2019), or related to spatial indicators like accessibility (Barrionuevo & Perles Roselló, 2017), show comparable levels of spatial resolution. To overcome this gap among social vulnerability and hazard maps, indicators based social vulnerability assessment should look for other types of spatial units as census district or cadastral parcels and buildings (Navarro etal., 2020; Tascón-González etal., 2020). 6. CONCLUSION Natural hazards cause great damages all over the world. Hazard maps can identify hazard prone areas but cannot able to measure the vulnerability of population in any region. Social vulnerability measures the susceptibility of social groups to the impacts of hazards and it is an important tool of hazard risk reduction system. The analysis of social vulnerability in Malaga province has highlighted the present locational, demographic, social, economic and health condition of the province. Key causes of very high vulnerability are mainly economic factors like, high dependency ratio, unemployment rate and low income. To overcome from this vulnerability, vulnerable municipalities need to have more economic activities. This study also investigated spatial patterns of social vulnerability in Malaga province using three different standardization methods. The results obtained from these methodologies are almost same. So any of the methodologies used in this paper, could potentially be employed to identify vulnerable municipalities for another province. Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 104 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 Municipality-wise survey is a small scale survey and there is scope for detail studies which could be very useful for planning to development. This study will help to build the distribution of vulnerable municipalities in Malaga province which is important for hazard management and thus to identify the socio-economically vulnerable community. Acknowledgements This work take part of the project PID2019-106834RB-I00 “Prototipo Cloud de Evaluación de Riesgos a Través de la Integración Espacial de Geodatos (Grid Multiescalares) y Clientes Web (Dashboard) para su Gestión y Medidas de Adapta”. Conflicts of interest and authorship statement The authors declare no conflict of interest. Author Contributions: ‒Introduction, data gathering, methodology, calculation, result analysis, conclusion: Sumita Gayen. ‒Conceptualization, introduction, mapping, discussion and supervision: Ismael Vallejo Villalta. ‒Conceptualization, introduction, methodology, result analysis: Sk. Mafizul Haque. REFERENCES Ahmad, H. F., Bhat, M. S., Alam, A. & Ahmad, S. (2016). Flood hazard zonation and vulnerability assessment of Greater Srinagar, J&K, India. Int. J. Adv. Res., 4(12), 1679-1690. http://dx.doi.org/10.21474/IJAR01/2570 Aksha, S. K., Juran, L., Resler, L. M. & Zhang, Y. (2019). An analysis of social vulnerability to natural hazards in Nepal using a modified social vulnerability index. Int J Disaster Risk Sci., 10, 103–116. https://doi.org/10.1007/s13753-018-0192-7 André, G. (2012). Natural hazard mapping across the world. A comparative study between a social approach and an economic approach to vulnerability. Cybergeo: European Journal of Geography. https://doi.org/10.4000/cybergeo.25297 Armenakis, C., Du, E. X., Natesan, S., Persad, R. A. & Zhang Y. (2017). Flood risk assessment in urban areas based on spatial analytics and social factors. Geosciences, 7(4), 123. https://doi.org/10.3390/geosciences7040123 Ayuso, J. L., Ayuso-Ruiz, P, García-Marín, A. P., Estévez, J. & Taguas, E. V. (2015). Local analysis of the characteristics and frequency of extreme droughts in Málaga using the SPI (Standardized Precipitation Index). 17th International Congress on Project Management and Engineering Logroño, 17-19th July 2013 (pp. 881-894). http://dx.doi.org/10.1007/978-3- 319-12754-5_13 Balica, S.F., Douben, N. & Wright, N.G. (2009). Flood vulnerability indices at varying spatial scales. Water Science & Technology, 60(10). https://doi.org/10.2166/wst.2009.183 Barrionuevo, J. F. S. & Perles Roselló, M. J. (2017). Methodology for vulnerability mapping of the territory against the risk of flood related to assistance systems. Application to bajo Guadalhorse flood area. Geography Papers, 63. http:// dx.doi.org/10.6018/geografia/2017/307051 Beccari, B. (2016). A comparative analysis of disaster risk, vulnerability and resilience composite indicators. PLOS Currents Disasters, 14(1). https://doi.org/10.1371/currents.dis.453df025e34b682e9737f95070f9b970 Birkmann, J., Cardona, O.D., Carreño, M.L., Barbat, A.H., Pelling, M., Schneiderbauer, S., Kienberger, S., Keiler, M., Alexander, D., Zeil, P. & Welle, T. (2013). Framing vulnerability, risk and societal responses: the MOVE framework. Nat Hazards, 67, 193–211. https://doi.org/10.1007/s11069-013-0558-5 Blaikie, P., Cannon, T., Davis, I. & Wisner, B. (1994). At risk: natural hazards, people vulnerability and disasters. 1st edition. Routledge. Bukvic, A., Rohat, G., Apotsos, A. & de Sherbinin, A. (2020). A systematic review of coastal vulnerability mapping. Sustainability, 12, 2822. https://doi.org/10.3390/su12072822 Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 105 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 Burton, C. G. (2010). Social vulnerability and hurricane impact modeling. Nat. Hazards Rev.,1182, 58–68. https://doi. org/10.1061/(ASCE)1527-6988(2010)11:2(58) Burton, I., Kates, R. W. & White, G. F. (1978). The environment as hazard. Oxford University Press. CAF (2014). Vulnerability index to climate change in the Latin American and Caribbean Region. Corporación Andina de Fomento. Development Bank of Latin America. 206 p. Caliskan, S., Taubenböck, H., Hinz, S. & Roth A. (2006). Earthquake vulnerability indicators and vulnerability assessment using remote sensing, Istanbul. 1st EARSeL Workshop of the SIG Urban Remote Sensing. Humboldt-Universität zu Berlin, Germany. https://www.researchgate.net/publication/224798942 Cannon, T., Twigg, J. & Rowell, J. (2003). Social vulnerability, sustainable livelihoods and disasters. Report to DFID Conflict and Humanitarian Assistance Department (CHAD) and Sustainable Livelihoods Support Office. https://www.eldis.org/ document/A21628 Cardona, 0. D. (2003). The need for rethinking the concepts of vulnerability and risk from a holistic perspective: A necessary review and criticism for effective risk management. In Bankoff, G. Frerks, D. Hilhorst (ed.) Mapping Vulnerability: Disasters, Development and People, Chapter 3. Earthscan Publishers. Chakraborty, J., Tobin, G. A. & Montz, B. E. (2005). Population evacuation: assessing spatial variability in geophysical risk and social vulnerability to natural hazards. Natural Hazards Review, 6(1), 23–33. https://doi.org/10.1061/(ASCE)1527- 6988(2005)6:1(23) Chen, W., Cutter, S. L., Emrich, C. T. & Shi, P. (2013). Measuring social vulnerability to natural hazards in the Yangtze River delta region, China. Int. J. Disaster Risk Sci., 4(4), 169–181. https://link.springer.com/content/pdf/10.1007/s13753- 013-0018-6.pdf Contreras, D., Chamorro, A. & Wilkinson, S. (2020). Review article: The spatial dimension in the assessment of urban socio-economic vulnerability related to geohazards. Nat. Hazards Earth Syst. Sci., 20, 1663–1687. https://doi. org/10.5194/nhess-20-1663-2020 Copeland, V. C. & Snyder, K. (2011). Barriers to mental health treatment services for low-income African American women whose children receive behavioral health services: An ethnographic investigation. Social Work in Public Health, 26(1), 78-95. https://doi.org/10.1080/10911350903341036 Cutter, S. L., Boruff, B. J. & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242-261. https://doi.org/10.1111/1540-6237.8402002 Cutter, S. L., Burton, C. G. & Emrich, C. T. (2010). Disaster resilience indicators for benchmarking baseline conditions. Journal of Homeland Security and Emergency Management, 7(1), 51. https://doi.org/10.2202/1547-7355.1732 de Mello Rezende, G. B. (2016). Social vulnerability index: A methodological proposal for application in the cities of Barra do Garcas—MT, Pontal Do Araguaia—MT and Aragarcas—GO, Brazil. Open Journal of Social Sciences, 4, 32-45. https://file.scirp.org/Html/4-1761151_72561.htm de Oliveira Mendes, J. M. (2009). Social vulnerability indexes as planning tools: beyond the preparedness paradigm. Journal of Risk Research, 12(1), 43–58. https://doi.org/10.1080/13669870802447962 Diaz-Sarachaga, J. M. & Jato-Espino, D. (2020). Analysis of vulnerability assessment frameworks and methodologies in urban areas. Natural Hazards, 100, 437-457. https://doi.org/10.1007/s11069-019-03805-y Djalante, R. (2019). Key assessments from the IPCC special report on global warming of 1.5 °C and the implications for the Sendai framework for disaster risk reduction. Progress in Disaster Science, 1. https://doi.org/10.1016/j.pdisas.2019.100001 Duzgun, H. S. B., Yucemen, M. S., Kalaycioglu, H. S., Celik, K., Kemec, S., Ertugay, K. & Deniz, A. (2011). An integrated earthquake vulnerability assessment framework for an urban area. Nat Hazards, 59, 917. https://doi.org/10.1007/ s11069-011-9808-6 Dwyer, A., Zoppou, C., Nielsen, O., Day, S. & Roberts, S. (2004). Quantifying social vulnerability: A methodology for identifying those at risk to natural hazards. Geoscience Australia Record, 14. http://www.geosci.usyd.edu.au/users/prey/ Teaching/Geos-2111GIS/Ref/GA4267-IdentifyingRisks.pdf EEA (2018). Unequal exposure and unequal impacts: social vulnerability to air pollution, noise and extreme temperatures in Europe. European Environment Agency. EEA Report No 22. https://doi.org/10.2800/324183 Emrich, C. T. & Cutter, S. L. (2011). Social vulnerability to climate-sensitive hazards in the Southern United States. Weather, Climate, and Society, 3 (3), 193–208. https://doi.org/10.1175/2011WCAS1092.1 Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 106 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 Evans, J. M., Hauer, M., Hardy, D. & Pippin, J. S. (2014). Assessing social vulnerability using ‘‘SoVI-Lite’’: A demonstration study at Glynn County, GA. Carl Vinson Institute of Government, University of Georgia. https://doi.org/10.13140/2.1.2329.3765 Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L. & Lewis, B. (2011). A social vulnerability index for disaster management. Journal of Homeland Security and Emergency Management, 8(1), 3. https://svi.cdc.gov/A%20Social%20 Vulnerability%20Index%20for%20Disaster%20Management.pdf Fekete, A. (2009). Validation of a social vulnerability index in context to river-floods in Germany. Nat. Hazards Earth Syst. Sci., 9, 393–403. https://doi.org/10.5194/nhess-9-393-2009 Fekete, A. (2018). Social vulnerability change assessment: monitoring longitudinal demographic indicators of disaster risk in Germany from 2005 to 2015. Natural Hazards, 95, 585–614. https://doi.org/10.1007/s11069-018-3506-6 Frigerio, I., Carnelli, F., Cabinio, M. & Amicis, M. D. (2018). Spatiotemporal pattern of social vulnerability in Italy. Int J Disaster Risk Sci., 9(2), 249–262. https://doi.org/10.1007/s13753-018-0168-7 Gallegos Reina, A., (2013). Cuantificación y distribución cartográfica de la generación de escorrentía y sedimentos en la provincia de Málaga. Baetica. Estudios de Arte, Geografía e Historia, 35, 57-74. http://hdl.handle.net/10630/7823 Gallegos Reina, A. & Perles Roselló, M. J. (2019). Relaciones entre los cambios en los usos del suelo y el incremento de los riesgos de inundabilidad y erosión: análisis diacrónico en la provincia de Málaga (1957–2007). Boletín de La Asociación de Geógrafos Españoles, 81, 2740, 1–38. http://dx.doi.org/10.21138/bage.2740 Gayen, S., Villalta, I. V. & Haque, S. K. (2019). Earthquake hazard scenario and seismic micro-zoning assessment in Málaga province of Spain. European Journal of Geography, 10(1), 117-133. http://www.eurogeographyjournal.eu/articles/0810012019.pdf Gayen, S., Villalta, I. V. & Haque, S. M. (2020). Comparative Social Vulnerability Assessment in Purba Medinipur District, West Bengal, India. European Journal of Geography, 11(1), 093-107. https://doi.org/10.48088/ejg.s.gay.11.1.93.107 Ge, Y., Dou, W. and Dai, J. (2017). A new approach to identify social vulnerability to climate change in the Yangtze River delta. Sustainability, 9(12), 2236. https://doi.org/10.3390/su9122236 Girasole, E. G., and Cannatella, D. (2017). Social vulnerability to natural hazards in urban systems. An application in Santo Domingo (Dominican Republic). Sustainability, 9(11), 2043. https://doi.org/10.3390/su9112043 Gu, D. (2019). Exposure and vulnerability to natural disasters for world’s cities. United Nations, Department of Economics and Social Affairs, Population Division, Technical Paper No. 4. https://www.un.org/en/development/desa/population/ publications/pdf/technical/TP2019-4.pdf Guillard-Gonçalves, C., Cutter, S. L., Emrich, C. T. & Zêzere, J. L. (2015). Application of social vulnerability index (SoVI) and delineation of natural risk zones in Greater Lisbon, Portugal. Journal of Risk Research, 18(5), 651-674. https://doi.or g/10.1080/13669877.2014.910689 Gutiérrez-Hernández, O., Senciales-González, J. M. & García, L. V. (2015). Los incendios forestales en Andalucía: investigación exploratoria y modelos explicativos. FLAMMA, 6(3), 144-148. https://www.academia.edu/26652060/Los_incen- dios_forestales_en_Andaluc%C3%ADa_investigaci%C3%B3n_exploratoria_y_modelos_explicativos_Wildfires_in_An- dalusia_Spain_from_exploratory_research_to_explanatory_models Hoffmann, R. & Blecha, D. (2020). Education and disaster vulnerability in Southeast Asia: Evidence and policy implications. Sustainability, 12, 1401. https://doi.org/10.3390/su12041401 Holand, I. S., Lujala, P. & Rød, J. K. (2011). Social vulnerability assessment for Norway: A quantitative approach. Norsk Geografisk Tidsskrift - Norwegian Journal of Geography, 65(1), 1-17. https://doi.org/10.1080/00291951.2010.550167 Insua-Arévalo, J. M., Martínez-Díaz, J. J., García-Mayordomo, J. & Martín-González, F. (2012). Active tectonics in the Malaga Basin: evidences from morphotectonic markers (Western Betic Cordillera, Spain). Journal of Iberian Geology, 38 (1), 175–190. http://dx.doi.org/10.5209/rev_JIGE.2012.v38.n1.39212 Kablan, M.K.A., Dongo, K. & Coulibaly, M. (2017). Assessment of social vulnerability to flood in Urban Côte d’Ivoire Using the MOVE Framework. Water, 9(4), 292. https://doi.org/10.3390/w9040292 Kalaycıoğlu, S., Rittersberger-Tılıç, H., Çelik, K. & Güneş, F. (2006). Integrated natural disaster risk assessment: The socio-economic dimension of earthquake risk in the urban area. Geohazards- Engineering Conferences International. https://dc.engconfintl.org/cgi/viewcontent.cgi?article=1013&context=geohazards Khan, F. A. & Salman, A. (2012). A simple human vulnerability index to climate change hazards for Pakistan. Int. J. Disaster Risk Sci., 3(3), 163–176. https://link.springer.com/content/pdf/10.1007/s13753-012-0017-z.pdf Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 107 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 Kirby, R. H. (2015). Measuring social vulnerability to environmental hazards in the Dutch Province of Zeeland. LSU Master’s Theses. 3800. https://digitalcommons.lsu.edu/gradschool_theses/3800 Koks, E. E., Jongman, B., Husby, T. G. & Botzen, W. J. W. (2015). Combining hazard, exposure and social vulnerability to provide lessons for flood risk management. Environmental Science & Policy, 47, 42–52. https://doi.org/10.1016/j. envsci.2014.10.013 Kuhlicke, C., Scolobig, A., Tapsell, S., Steinfu¨hrer, A. & Marchi, B. D. (2011). Contextualizing social vulnerability: findings from case studies across Europe. Nat Hazards, 58, 789–810. https://link.springer.com/article/10.1007/s11069-011- 9751-6 Lee, Y. J. (2014). Social vulnerability indicators as a sustainable planning tool. Environmental Impact Assessment Review, 44, 31–42. https://doi.org/10.1016/j.eiar.2013.08.002 Lowe, D., Ebi, K. L. & Forsberg, B. (2013). Factors increasing vulnerability to health effects before, during and after floods. Int. J. Environ. Res. public Health, 10(12), 7015–7067. https://doi.org/10.3390/ijerph10127015 Mavhura, E., Manyena, B. & Collins, A. E. (2017). An approach for measuring social vulnerability in context: The case of flood hazards in Muzarabani district, Zimbabwe. Geoforum, 86, 103–117. https://doi.org/10.1016/j.geoforum.2017.09.008 Madeca (2017). Estudio sobre la problemática por el riesgo de inundaciones en la provincia de Málaga. Madeca. Page, 112. Morales, A. P., Hervás, F. N. & Rogel, Y. A. (2016). Propuesta metodológica para la evaluación de la vulnerabilidad social en poblaciones afectadas por el peligro de inundación: el caso de Águilas (Murcia, sureste). Documents d’Anàlisi Geogràfica, 62(1). https://doi.org/10.5565/rev/dag.242 Morrow, B. H. (1999). Identifying and mapping community vulnerability. Disasters, 23(1), 1-18. https://doi.org/10.1111/1467- 7717.00102 Muhadi, N. A., Abdullah, A. F., Bejo, S. K., Mahadi, M. R. & Mijic, A. (2020). The use of LiDAR-Derived DEM in flood applications: A review. Remote Sens., 12 (14), 2308. https://doi.org/10.3390/rs12142308 Müller, A., Reiter, J. & Weiland, U. (2011). Assessment of urban vulnerability towards floods using an indicator-based approach – a case study for Santiago de Chile. Nat. Hazards Earth Syst. Sci., 11(8), 2107-2123. https://doi.org/10.5194/ nhess-11-2107-2011 Navarro, D., Vallejo, I. & Navarro, M. (2020). Análisis de la vulnerabilidad social a los riesgos naturales mediante técnicas estadísticas multivariantes. Investigaciones Geográficas, 74, 29-49. https://doi.org/10.14198/INGEO2020.NVN Papathoma-Köhle, M., Schlögl, M. & Fuchs, S. (2019). Vulnerability indicators for natural hazards: an innovative selection and weighting approach. Sci Rep, 9, 15026. https://doi.org/10.1038/s41598-019-50257-2 Pencheon, D. (2007). The good indicators guide: Understanding how to use and choose indicators. NHS Institute for Innovation and Improvement, Coventry, UK. https://www.england.nhs.uk/improvement-hub/wp-content/uploads/ sites/44/2017/11/The-Good-Indicators-Guide.pdf Perles-Roselló, M. J. (2010). Apuntes para la evaluación de la vulnerabilidad social frente al riesgo de inundación. Baetica. Estudios de Arte, Geografía e Historia, 32, 67-87. https://doi.org/10.24310/BAETICA.2010.v0i32.133 Perles Roselló, M.J., Sortino Barrionuevo, J.F. & Cantarero Prados, F. (2017). Cartografía de la vulnerabilidad del territorio frente al riesgo de inundación. Propuesta adaptada a la directiva europea de inundaciones ynormativas derivadas. Boletín de la Asociación de Geógrafos Españoles, 75. 341-372. https://doi.org/10.21138/bage.2504 Pharr, J. R., Moonie, S. & Bungum, T. J. (2012). The impact of unemployment on mental and physical health, access to health care and health risk behaviors. ISRN Public Health, 483432. https://doi.org/10.5402/2012/483432 Prieto, A., Díaz, M. P., Ojeda J. & Álvarez, J. I. (2017). Tasas de erosión en las playas de Andalucía: el efecto de infraestructuras costeras longitudinales y urbanización. Geotemas, 243-246. https://www.researchgate.net/publication/324720318_Tasas_de_erosion_en_las_playas_de_Andalucia_el_efecto_de_infraestructuras_costeras_longitudi- nales_y_urbanizacion Ran, J., MacGillivray, B. H., Gong, Y. & Hales, T. C. (2020). The application of frameworks for measuring social vulnerability and resilience to geophysical hazards within developing countries: A systematic review and narrative synthesis. Science of the Total Environment, 711, 134486. https://doi.org/10.1016/j.scitotenv.2019.134486 Reid, C. E., O’Neill, M. S., Gronlund, C. J., Brines, S. J., Brown, D. G., Diez-Roux, A. V. & Schwartz, J. (2009). Mapping community determinants of heat vulnerability. Environmental Health Perspectives, 117(11), 1730-1736. https://doi. org/10.1289/ehp.0900683 Rodríguez, H., Quarantelli, E. L. & Dynes, R. R. (2007). Handbook of Disaster Research. Springer. Sumita Gayen etal. / REA N. 41 (2021) 87 - 108 108 © Editorial Universidad de Sevilla 2021 | Sevilla, España| CC BY-NC-ND 4.0 | e-ISSN: 2340-2776 | doi: https://dx.doi.org/10.12795/rea.2021.i41.05 Sajjad, H. & Jain, P. (2014). Assessment of socio-economic vulnerabilities among urban migrants in South-East Delhi, India. Journal of Studies in Social Sciences, 7(1), 65-81. https://infinitypress.info/index.php/jsss/article/view/652 Sánchez, D. S. & Del Moral, L. (2014). Tres décadas de política de aguas en Andalucía. Análisis de procesos y perspectiva territorial. Cuadernos Geográficos, 53(1), 36-67. http://hdl.handle.net/11441/43624 Sen, A. (1998). Mortality as an indicator of economic success and failure. The Economic Journal, 108 (446), 1-25. https:// doi.org/10.1111/1468-0297.00270 Tali, M.G., Naeimi, A. & Esfandiary, M. (2016). Physical development of Arak City applying natural indicators. European Journal of Geography, 7(3), 99 – 110. http://www.eurogeographyjournal.eu/articles/7.PHYSICAL%20DEVELOPMENT%20 OF%20ARAK%20CITY%20APPLYING%20NATURAL%20INDICATORS.pdf Tapsell, S., Tunstall, S., Green, C. & Fernández-Bilbao, A. (2005). Task 11 social indicator set. FLOODsite Project Report, T11–5-01. http://www.floodsite.net/html/partner_area/project_docs/M11.1_Indicator-set_7-05.pdf Tascón-González, L., Ferrer-Julià, M., Ruiz, M. & García-Meléndez, E. (2020). Social vulnerability assessment for flood risk analysis. Water, 12(2), 558. https://doi.org/10.3390/w12020558 Thomas, V. & López, R., (2015). Global increase in climate-related disasters. Asian Development Bank Economics Working Paper Series No. 466. http://dx.doi.org/10.2139/ssrn.2709331 Török, I. (2018). Qualitative assessment of social vulnerability to flood hazards in Romania. Sustainability, 10, 3780. https://doi.org/10.3390/su10103780 UN (2016). Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction. Prevention web, UNDRR, Geneva, Switzerland. Wamsler, C., Brink, E. & Rantala, O. (2012). Climate change, adaptation, and formal education: The role of schooling for increasing societies’ adaptive capacities in El Salvador and Brazil. Ecology & Society, 17(2), 2. https://doi.org/10.5751/ ES-04645-170202 Weichselgartner, J. (2001). Disaster mitigation: The concept of vulnerability revisited. Disaster Prevention and Management, 10(2), 85-95. https://doi.org/10.1108/09653560110388609 Wisner, B., Blaikie, P., Cannon, T. & Davis, I. (2004). At risk: natural hazards, people’s vulnerability and disasters. Second edition, Routledge. Wu, S. Y., Yarnal, B. & Fisher, A. (2002). Vulnerability of coastal communities to sea-level rise: A case study of Cape May County, New Jersey, USA. Climate Research, 22, 255–270. https://www.int-res.com/abstracts/cr/v22/n3/p255-270/ Yan, X. & Li, X. (2016). Evaluation on social vulnerability to natural disasters. The Anthropologist, 24(2), 570–580. https://doi.org/10.1080/09720073.2016.11892051 Yang, S., He, S., Du, J. & Sun, X. (2014). Screening of social vulnerability to natural hazards in China. Nat Hazards, 76, 1–18. https://doi.org/10.1007/s11069-014-1225-1 Yoon, D. K. (2012). Assessment of social vulnerability to natural disasters: a comparative study. Nat. Hazards, 63, 823–843. https://doi.org/10.1007/s11069-012-0189-2 Zhang, N. & Huang, H. (2013). Social vulnerability for public safety: A case study of Beijing, China. Chinese Science Bulletin, 58 (19), 2387-2394. https://doi.org/10.1007/s11434-013-5835-x Žurovec, O., Cˇadro, S. & Sitaula, B. K. (2017). Quantitative assessment of vulnerability to climate change in rural municipalities of Bosnia and Herzegovina. Sustainability, 9(7), 1208. https://doi.org/10.3390/su9071208