GIS Related Enums

class CensusShapeCollection(value)

Collections that are part of the Topologically Integrated Geographic Encoding and Referencing (TIGER) system, a digital database of geographic features, such as roads, rivers, and legal and statistical geographic areas. Each shape file in these collections contains a series of polygons, each corresponding to a specific geographical area.

See also:

genz = 'genz'

TIGER/GENZ collection, most providing simplified representations of selected geographic areas, are specifically designed for small scale thematic mapping and improved visual representations.

tiger = 'tiger'

TIGER/Line collection, recommended for calculations.

class Geography(value)

Type of geography

zip = 'zip'

zcta = 'zcta'

county = 'county'

custom = 'custom'

all = 'all'

class RasterizationStrategy(value)

Rasterization Strategy to rasterize a vector.

While downscale strategy provides the best accuracy it requires significantly more resources, especially RAM.

default = 'default'

The default strategy is to include all pixels along the line render path (for lines), or cells where the center point is within the polygon (for polygons).

all_touched = 'all_touched'

Alternate, all_touched strategy, rasterizes the geometry by including all pixels that it touches.

combined = 'combined'

Calculate statistics using both default and all_touched strategy and combine results, e.g. using arithmetic means

downscale = 'downscale'

A combination of “default” rasterization strategy with affine transformation with downscaling. Downscaling factor is computed based on the grid size and runtime memory available to the process.

Effectively, the grid is interpolated with intermediate nodes, increasing the number of nodes by the factor of 25 (5 x 5). Hence, the accuracy is better, especially for complex and small shapes, however, the aggregation will require 25 times more memory (RAM) and will run slower.

See get_affine_transform

auto = 'auto'

A combination of “all_touched” rasterization strategy with affine transformation with downscaling. Downscaling factor is computed based on the grid size and runtime memory available to the process.

Effectively, the grid is interpolated with intermediate nodes, increasing the number of nodes by the factor of 25 (5 x 5). Hence, the accuracy is better, especially for complex and small shapes, however, the aggregation will require 25 times more memory (RAM) and will run slower.

See get_affine_transform