Population and household density are key indicators of concentrated human settlement and are closely linked to socio-economic vulnerability. Villages with a population density exceeding 1,500 individuals per square kilometer, and a household density of more than 400 households per square kilometer, are at a high risk of vulnerability. This indicator reflects the number of households per unit area. High household density can strain infrastructure and services, increasing vulnerability during disasters. Major coastal cities like Kochi, Thiruvananthapuram, Kollam, Alappuzha and villages like Kuzhippuly, Puthuvype, show a very high degree of vulnerability. And Very High to High vulnerability can be seen in the regions extending from Kollam to Thiruvananthapuram coastal stretch (Figure 2).

The proportion of females in a population is significant, as women may face unique challenges during disasters, including social and economic disparities. Often in times of disaster women and children are mostly vulnerable and might be found alone to tackle a risky situation. In the study villages with female population >51% are categorized as very highly vulnerable. Mavelikkara, Ayiroor, Manamboor, and Thazhuthala fall under this category (Figure 3).

A higher proportion of children indicates greater vulnerability, as children are particularly susceptible during disasters due to their dependence and limited capacity to respond. In the study villages with children (between 0 -6 years) were taken and areas with child population >10% were categorized as very highly vulnerable. Kudavoor, Veiloor, Ayiroor, Sarkara are some of the villages ranked under very high vulnerability. A high number of villages in the Kollam and Thiruvananthapuram district fall under high vulnerability due to increased number of child population (Figure 3).

This refers to individuals engaged in primary sectors like agriculture, forestry, and fisheries. These workers are often more vulnerable to natural hazards that directly impact their livelihoods. Primary workers play a crucial role in driving rural development, yet they often face significant vulnerabilities due to various insecurities and challenges. In this study villages with more than 30 percent of the working population that has primary workers are taken and are classified as very highly vulnerable population. Villages in the Alappuzha district were mostly included in this category and villages in Pathanamthitta were also included in this category. Kumarakom, Kainakary North, Veloor are some of the villages (Figure 4).

Understanding socio-economic vulnerability requires examining the proportion of socially disadvantaged groups within the total population. While it can be challenging to precisely identify the socially weaker sections in a village, census data on marginalized communities, such as Scheduled Castes (SC) and Scheduled Tribes (ST), offers a reliable estimate, especially within the Indian context. Populations marginalized due to socio-economic factors often have limited access to resources, making them more susceptible to disaster impacts. In this study a proportion of more than 20 percent of these deprived castes in villages were perceived under very high vulnerability and below 15 percent was classified as high vulnerability. Northern villages of Thiruvananthapuram and southern villages of Alappuzha district mostly come under the category (Figure 3).

Literacy rates play a significant role in disaster preparedness and tend to have an inverse relationship with socio-economic vulnerability—higher literacy often corresponds to lower vulnerability levels. As the average literacy rate of Kerala is 94 percent, Literacy rate below 80 percent is taken as very high vulnerability. Karode, Karumkulam, Kadinamkulam, Azhoor, Kadakkavur are some of the villages (Figure 3).

Indicators related to exposure, such as population density, household density, child population ratio, female population ratio, primary workers, and socially disadvantaged groups, were categorized into five vulnerability levels: very high, high, moderate, low, and very low. This classification assumed that higher values indicate greater vulnerability. On the other hand, literacy rates are grouped into the same levels, but here, higher values reflect lower vulnerability. To assess combined socio-economic vulnerability on a normalized scale, it is essential to assign ranks to each vulnerability class across all indicators. This approach ensures a standardized evaluation, allowing for a more accurate and comprehensive analysis of socio-economic vulnerability. (Sanchez-Martin et al., 2019). The ranges for these classes were determined using histogram distributions, ensuring the analysis was grounded in the data. In this study, vulnerability was categorized into five levels, ranked on a scale of 1 to 5, with 1 representing very low vulnerability and 5 representing very high vulnerability. The rankings assigned to each indicator for every village were added together to calculate composite socio-economic vulnerability. To simplify the analysis and identify similar vulnerability patterns, the composite scores were further classified into five categories, ranging from very high to very low vulnerability. Hotspot analysis is used to identify clusters within a specific area, where these clusters represent either high or low values of a particular variable, referred to as hot spots and cold spots, respectively in the coastal regions. To pinpoint these areas, the Getis-Ord Gi statistic was applied, a method available through the Mapping Clusters tool in the Spatial Statistics Tools suite of ArcGIS. (Sanchez-Martin et al., 2019). The spatial autocorrelation tool analyzes the ranking of each village in relation to the ranks of its neighboring villages. It evaluates whether there is a spatial pattern by testing hypotheses about the clustering of the sum of x-values associated with the j points within a specified distance (d) from the i^th point. The statistics are denoted as

Gid=∑j=1nwijdxj∑j=1nxj

Here, w_ij represents a symmetric spatial weight matrix, where a value of 1 is assigned to all links within a specified distance d of a given point i, and 0 is assigned to all other links, including the self-link of point i. The statistic Gi(d) is used to assess the concentration or dispersion of village ranks based on socio-economic vulnerability variables in the coastal plains, providing insights into spatial patterns of vulnerability. Villages are classified into spatially significant hot or cold spots based on the concentration of ranks, with varying levels of confidence. Villages that have rankings notably different from those of their neighboring villages are considered statistically insignificant and do not contribute to the formation of hot or cold spots.