2.2. Spatial Mapping and Modelling

One is the spatial, which is talking about the mapping and modelling, this is very useful in risk and vulnerability assessment, it can be used to identify hazards and dangerous locations, what community already know about this. You know, the identified areas affected by erosion, loss of vegetation or pest infestation and identify vulnerable groups and capacities and assets within the community.

Spatial mapping involves visualizing geographic data related to community risks and assets. It helps in assessing how certain areas are exposed to hazards like erosion or pest infestations. By mapping these factors, communities can understand where they are most vulnerable and what strengths or assets they possess to mitigate risks.

Think of spatial mapping like creating a treasure map. Instead of marking where treasure is buried, you're marking areas that are at risk (like eroding land) and noting community strengths (like access to clean water). This map helps the community know which areas need attention and which resources can be leveraged.

The second aspect is a nominal which is the collecting, naming or listing, it can collect information about communities and their environment like for instance, we are talking about who are the beneficiaries, who are the what kind of crops they have lost, how much and it can also look at the sequence in the coping strategies used in times of food crisis, health problems in order of frequency or importance and also the associated reasons for it so such consequences of deforestation.

Nominal data involves collecting specific information about a community's circumstances, such as identifying beneficiaries of aid and the types of crops that have been damaged. This data can also include understanding coping strategies during crises, helping to prioritize which issues need immediate attention and what causes potential problems.

Imagine conducting a survey in your neighborhood after a storm to find out who lost their garden and what types of vegetables were affected. This information not only tells you who needs help but also helps in making a plan for recovery, just like how a doctor needs to know what symptoms a patient has before deciding on treatment.

The temporal; so putting all these events in a sequence, whether it is through personal and ecological histories, disaster timelines, disaster visualization, seasonal calendars, community timelines or re-enacting events because these methods will actually reveal the changing nature of vulnerability, if you look at a historical record of any disaster and its impact, one can see where we are progressing, where how it is actually heading to.

Temporal mapping helps communities understand how vulnerabilities change over time. By documenting past events such as disasters and tracing their impacts, communities can gain insights into patterns and trends in vulnerability, which can guide future preparedness strategies.

This is like keeping a diary that tracks your life events. If you look back and see a pattern of bad weather affecting your harvest every spring, you can prepare better by planting at a different time next year. Similarly, communities can change strategies based on how vulnerabilities have evolved over time.

Ordinal; when we say ordinal, sorting and comparing and ranking because this is where we can identify the most vulnerable individuals and households.

Ordinal methods involve ranking or comparing different data points, which helps in identifying which individuals or households are most vulnerable. This approach allows decision-makers to prioritize those who need support the most in times of crisis.

Consider a teacher who is assessing student performance. By ranking students based on their test scores, the teacher can identify which students need extra help. In the same way, ordinal methods help communities figure out who to help first when disaster strikes.

Numerical, which is talking on most of the economic aspect of it; counting, estimating, comparing and scoring and methods of this kind could be used in assessing disaster losses and quantifying which normally any of the district agency is going to do on this, how much of the livelihood stock has been damaged, how much of the animals have been killed you know, this is how mostly assessed with the numbers.

Numerical analysis focuses on collecting and interpreting quantitative data. It is crucial for assessing economic impacts after disasters, such as counting livestock losses or estimating damage to property. This data is essential for guiding recovery efforts and allocating resources effectively.

After a hurricane, an accountant might tally the number of homes destroyed and the total value of damage incurred. This number helps in deciding how much aid to request. Similarly, communities need these figures to make informed decisions about recovery and resilience-building.

Relational; this is often related to how we link and how we relate, how different facilitators understand that how different parts of the community relate to one another and how they identify the power structures and because it can associate of one cause with another impact and one impact with another reason of it, so in that way, the effects of drought might be linked to a land tenure arrangements or to gender-based divisions of labour using the problem tree.

Relational mapping examines the connections within a community, highlighting how different groups interact and influence each other. Understanding these relationships can reveal underlying causes of vulnerability, such as how social structures can affect resource allocation and disaster response.

Think about how a family functions. If one sibling often takes care of chores, the others might rely on them, creating an imbalance. Similarly, in a community, if one group consistently handles resources, it can leave others vulnerable. Mapping these relationships helps identify potential inequities that need to be addressed.

In fact, the one of the important challenges how to integrate the CBRDRM with official development planning; this is a good case which they have given about Bhandari and Malakar work on Nepal, wherein the districts of Chitwal and Nawalparasi in Nepal, there is a practical action group was working in 59 village development committees and which are the lowest administrative units of government.

Integrating Community-Based Disaster Risk Management (CBDRM) with official development plans ensures that local needs are considered in broader governmental strategies. In Nepal, for instance, local institutions (like village development committees) systematically assess vulnerabilities and incorporate them into larger district development plans.

Imagine a community gardening project that partners with a city council. When developing the garden, they consider city plans for water access and waste management, ensuring a coordinated approach. Similarly, integrating CBDRM with development planning ensures that local communities' disaster preparedness is part of broader government strategies.

So, what are the futures of the successful CBDRM, how do we measure it? Of course, the IFRC; the Red Cross and Red Crescent Societies have actually looked at the 9 key determinants of a successful community-based DRR program; the motivation and capacity of the community and community leaders so, how it has motivated the community.

Measuring the success of community-based disaster risk management (CBDRM) involves examining several factors, such as community motivation, the capacity of leaders, partnerships with organizations, and levels of participation. These elements are critical in determining how effectively a community can prepare for and respond to disasters.

Think of a sports team. A winning team has motivated players, skilled coaches, strong teamwork, and support from fans. Similarly, a successful CBDRM effort depends on community buy-in, skilled leadership, and collaboration with external partners to withstand disasters effectively.

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