CARTOmazing

This last lab discusses about scale effect and spatial data aggregation in both vector and raster data. For the scale effect in vectors,  the relationship between different scales (1:1200, 1:24000, and 1:100000) is that length, perimeters and area decreases as you decrease the scale and vice versa. This might mean the level of detail decreases as the length and area decreases. In the readings (Goodchild 2011, p.6), as the resolution or scale becomes finer (increase) then calculated areas, volumes or lengths would increase as their boundaries are given more detail.  Lines and polygons when you increase scale (1:1200) are finer than a lesser scale where they look generalized. Which makes sense because whenever you delineate polygons and lines using an imagery you need to increase scale or zoom in in order to have finer delineation compared to a generalized line. The generalization is good for faster render of data while the finer details are good for accurate analysis. While ras...

Thiessen assumes the values of unsampled locations are equal to the value of the nearest sampled point. It creates a polygon around each data point and assigns that value – the advantage is that it is easy to understand, and values of the sample points remain. However, its disadvantage is it also easily generalize data that can create miscalculated values at unsampled locations. This can be use if you are okay with generalizing your model per area (district, zone, region) - sort of an overview. Inverse Distance Weighting (IDW) uses an interpolation method that estimates values by weights inversely to its distance. The weight of a value decreases as it moves away from an unsampled location and vice versa (more influence or weight value).     While spline creates a smooth curve across sample points and minimizes curvature as it pass through each data point. Regularized spline creates a smooth with gradually changing surface. Tension spline has control to the stiffness or su...