**6. Conclusions**

inhabitants throughout the year, causing small but cumulative impact resulting in deteriora‐

It is at the local level that risk is built and consequences of adverse events experienced. Hence our analysis recognizes the significance of conducting risk assessment at the local level, taking into account its main components (hazard, vulnerability, and exposure), to generate a signif‐

The assessment at the block level helps to improve decision‐making regarding resource allocation for disaster risk reduction, as it identifies the most critical blocks for prioritization of intervention. At the same time, it is also possible to work on prospective risk management, addressing and seeking to avoid the construction of new or increased disaster risks, and

**Table 8** shows a comparative analysis between the two cities evaluated with the same model (Puerto Varas and Puerto Montt). An "average" block or representative of each model H, E, PSV, and SR was selected as the arithmetic average of the values of the cells of each city to

**PuertoVaras** 0.580 0.2820 0.4440 0.420 0.2570 0.3230 **0.2648** Medium‐low **Puerto Montt** 0.580 0.4362 0.4857 0.420 0.2566 0.5120 **0.2430** Medium‐low

In analyzing the above table, it can be seen that the perceived DRI Puerto Varas is 9.0% higher than that of Puerto Montt. Even though it seems counterintuitive at first (it is the general perception that Puerto Varas has a lower risk than Puerto Montt), the result is considered reasonable. The hazard in Puerto Montt (0.4362) is rated 55% higher than in Puerto Varas (0.282) but at the same time, the SR in Puerto Montt (0.5120), 59% higher than Puerto Varas

Note that both E as well as PSV are almost the same in both cities (9.5% and 0.2% difference),

In an analysis by rating of cell, it can be said that there is only 9% difference in the overall perceived disaster risk between the two cities. Puerto Montt has more cells qualified in medium‐high risk than in Puerto Varas, but this is offset by the fact that Puerto Montt has

The perception that Puerto Varas' hazards have less potential impact compared with Puerto Montt holds true. However, a comprehensive risk assessment considering all the variables

α1 Hazards Exposure α2 PSV SR DRI Qualification

54.6% 9.5% 0.2% 58.7% **9.0%** Differences (%)

**5.4. Comparative analysis of the two cities: Puerto Varas and Puerto Montt**

188 Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

tion of their health and quality of life.

icant differentiation between and within cities.

defining the best options for the city expansion.

**Comparative analysis Of The Average Behavior Puerto Montt V/S Puerto Varas**

**Table 8.** Comparative analysis of the average behavior: Puerto Montt v/s Puerto Varas.

several cells qualified as low risk cells, while Puerto Varas has none.

calculate the representative DRI.

(0.3230), compensates for the hazard.

making the overall difference indecisive.

The result of each model as well as the perceived comprehensive DRI was represented cartographically (**Figures 2**–**4**), finding spatial patterns in the disaster risk level of the city and its explanatory variables (risk drivers). The DRI showed a clear spatial pattern in the cities three zones of different risk levels are seen, predominantly medium‐high level, in most of the consolidated urban area.

The result is the combination of the four models (hazards, exposure, PSV and SR) that were used for the evaluation of risk at the city block level, according to the weights set, representing the current or actual perceived risk of the cities. This complete model can also be used to build future disaster risk scenarios, using the possible values of the four models as parameters to analyze potential interventions and their ability to reduce risks.

The sensitivity analysis shows a high susceptibility of SR, demonstrating the need to focus efforts on improving the capabilities of self‐protection and self‐management of the population. Any change in these capabilities is first reflected in the population's perception, and then immediately in the overall disaster risk of the city.

The three cities analyzed have different levels of risk associated with their geographical location and hazards determined by the geological and morpho‐climatic context. The risk also responds to social fragility situations such as poverty, lack of education, precarious housing, among others, as well as to the population's lack of capacity for self‐development and self‐ management. These variables, aggravated by exposure of the population and their livelihoods to socio‐natural hazards, result in a significant heterogeneity in disaster risk levels among and within the three cities analyzed.

The block‐level modeling allows us to acquire detailed information about the factors that contribute to building disaster risk within the cities, informing the decision making process geared to reduce it. The variables considered are dynamic, vary in time and space, and most of them can be mitigated. The modeling of natural hazards can be generalized to different settlements with similar geographic conditions. The social vulnerability and SR variables must be locally analyzed as they present great variations that resulting in distinct disaster risk levels. The relevance of social risk construction and its future trends is acknowledged.

This comprehensive analysis allowed us to objectively measure the comprehensive DRI level of each city. When metrics allowed, we compared the results for the studied cities (Iquique, Puerto Montt and Perto Varas) using four individual models (H, E, PSV, and SR) as well as the comprehensive DRI average. This *holistic* assessment approach can be transferred to other cities, countries, and regions, allowing generic and standardized processes, while respecting unique local features.
