- Introduction
- Creating density information from points
- Creating density information from lines
- Creating density information from areas
- Comments
- Step 1 Rasterise data at an initial resolution and reclass cell values
- Step 2 Aggregate by factor of 2 from all corners of the raster summing values
- Step 3 Disaggregate the aggregate grids to the resolution prior to aggregating in Step 2 and add together
- Step 4 Aggregate by factor of 2
- Step 5 Repeat steps 2 to 4 until data at desired resolution.
- The grid values can be reclassed so there are fewer resulting shapes/polygons;
- the original raster can be split into smaller bits in the first place and the resulting density grid at a 1DM resolution can be meshed or mosaiced together; or,
- At the iteration of the algorithm when the shapefile/polygon coverage begins to get too big, the grid can be split into bits to create the shapefiles/polygon coverages and make the diaggregate grids one at a time.

Rasterisation of point, line and area data to produce location, distance, density or direction grids should be done in projections which respectively minimise the spatial bias in the resulting grids. For example, when producing distance grids it would be best to transform the geographical input data into a distance preserving projection calculate the distances and reproject the results into the analytical projection. I would have liked to have done this for our analysis but this must be left until later. For now I believe that the error propogation caused by projection distortions will result in only a small effect on model output uncertainty as their are larger uncertainties lurking elsewhere.

Direction grids of some variables (eg. the aspect of the DEM) maybe useful in the landuse classification but it is hard to see how they could be useful for socio-economic data interpolation. Distance and location grids are useful and there creation is easy, the hard bit is the density surfaces.

The lines shown below are part of the rdline coverage from the DCW:

The maps after the second iteration are as follows:

Iteraction 2 step 2;

All the above maps show values of each grid each classed seperately into 7 categories using ArcViews natural break algorithm.

There can be a problem of lack of disc space when processing large relatively dense line datasets. Despite as the resolution of the density grid gets less detailed it takes less space to store, there are more values in the grid and the space needed to store the shapefile or polygon coverage at step 3 maybe huge. There are several ways of overcoming this problem:

1 and 2 have problems associated with loss of information and edge effects and for 3 you may eventually after hours of effort discover that you were over optimistic in that the original raster was too high a resolution for you to cope with.

There are other spatially unbiased methods for creating the density grids. One alternative strategy is to create the initial raster at a 0.1DM resolution in the same way then aggregate by a factor of 10 from all intersecting points on the raster, disaggregate the results to a 1DM resolution and add them all together. Using tidy as you go data management this may take less space to do as the grid sumation doesn't need doing all at the same time. The most appropriate method depends on the data and what it will be used for.

With an appropriate initial rasterisation resolution it is easy to create kernel density surfaces of lines going beyond the analysis resolution.

As yet the benefits of using such spatially unbiased density surfaces in the population modelling are unclear. In Model_4 I plan to find out so both Bartholomew and DCW data density grids are being calculated using the method outlined above. The proof of the pudding is in the eating.