Which type of data would be best for modeling land use changes over time?

Raster data is the best choice for modeling land use changes over time as it is specifically designed to represent continuous surfaces and can easily capture variations across space and throughout different time intervals. In raster format, land use is represented in grid cells, which can accommodate large datasets, allowing for the analysis of changes in land cover and patterns over time. This makes it ideal for applications such as environmental monitoring, urban expansion studies, and agricultural assessments, where the temporal aspect of land use change is crucial.

Additionally, raster data can integrate well with remote sensing technologies, which capture repetitive measurements of land cover. This allows for the comparison of land use over time through methods such as land cover classification, where changes can be visualized effectively.

Other data types like vector data, while useful for representing discrete features and boundaries, may not effectively illustrate the nuanced changes in land use across wider areas over time. Interval and ordinal data pertain more to statistical measurements and categorizations that are less suited for spatial analysis of land use changes. Hence, raster data clearly stands out as the most suitable format for this purpose.