Vector data models are a fundamental component in Geographic Information Systems (GIS) and play a significant role in climate change assessment tools. They represent geographic features through points, lines, and polygons, allowing precise modeling of real-world objects such as rivers, roads, cities, vegetation patches, and administrative boundaries. This representation is particularly useful in climate change studies because it enables detailed spatial analysis, which is essential for assessing vulnerability, risk, and impacts of climate-related events on both natural and human systems. The vector data model’s strength lies in its ability to handle discrete features, provide high spatial accuracy, and facilitate complex spatial queries, all of which are critical for climate change assessments. In climate change assessment tools, vector data models are often used to map the distribution of critical infrastructure and populations that are at risk from extreme weather events such as floods, hurricanes, heatwaves, and droughts. Points can represent specific locations, such as weather stations, hospitals, or monitoring sites, which serve as key sources of environmental data. Lines can represent linear features like rivers, roads, and utility networks, helping researchers analyze potential disruptions caused by climate events. Polygons are especially useful for defining areas such as floodplains, urban zones, agricultural lands, forested regions, or protected ecosystems, which are assessed for their susceptibility to climate impacts. The ability to associate attributes with each vector feature allows for richer analysis, for instance, linking flood-prone areas with population density or agricultural productivity to estimate potential economic losses. One of the primary advantages of the vector data model in climate change assessment is its precision in representing