dorchester 0.1

A toolkit for making dot-density maps in Python

dorchester

A toolkit for making dot-density maps in Python

Installation

Install this tool using pip:

$ pip install dorchester

Usage

The main command is dorchester plot. That takes an input file, an output file and one or more property keys to extract population counts.

dorchester plot --help
Usage: dorchester plot [OPTIONS] SOURCE DEST

  Generate data for a dot-density map. Input may be any GIS format readable
  by Fiona (Shapefile, GeoJSON, etc).

Options:
  -k, --key TEXT              Property name for a population. Use multiple to
                              map different population classes.

  -f, --format [csv|geojson]  Output format. If not given, will guess based on
                              output file extension.

  -m, --mode [w|a|x]          File mode for destination.
  --help                      Show this message and exit

Input can be in any format readable by Fiona, such as Shapefiles and GeoJSON. The input file needs to contain both population data and boundaries. You may need to join different files together before plotting with dorchester.

Output format (--format) can be CSV or GeoJSON (more formats coming soon). For GeoJSON, the output will be a stream of newline-delimited Point features, like this:

{"type": "Feature", "geometry": {"type": "Point", "coordinates": [76, 38]}, "properties": {"group": "population", "fid": 1}}
{"type": "Feature", "geometry": {"type": "Point", "coordinates": [77, 39]}, "properties": {"group": "population", "fid": 1}}
{"type": "Feature", "geometry": {"type": "Point", "coordinates": [78, 37]}, "properties": {"group": "population", "fid": 1}}

This will be big files, because we are creating a point for every individual. Massachusetts, for example, had a population of 6.631 million in 2010, which means a dot density CSV file will be 6,336,107 lines long and 305 mb.

Each key (--key) should correspond to a property on each feature whose value is a whole number. In a block like this, use --key POP10 to extract population:

{
  "geometry": {
    "coordinates": [...],
    "type": "Polygon"
  },
  "id": "0",
  "properties": {
    "BLOCKCE": "4023",
    "BLOCKID10": "250010112004023",
    "COUNTYFP10": "001",
    "HOUSING10": 16,
    "PARTFLG": "N",
    "POP10": 12,
    "STATEFP10": "25",
    "TRACTCE10": "011200"
  },
  "type": "Feature"
}

You can pass multiple --key options to create different groups that will be layered together. This is how you would create a map showing different racial groups, for example.

Finally, the --mode option controls how the output file is opened:

• w will create or overwrite the output file
• a will append to an existing file
• x will try to create a new file and fail if that file already exists

About the name

Dorchester is the largest and most diverse neighborhood in Boston, Mass, and is often referred to as Dot.

The name is also a nod to Englewood, built by the Chicago Tribune News Apps team. This is, hopefully, a worthy successor.

Development

To contribute to this tool, first checkout the code. Then create a new virtual environment:

cd dorchester
python -m venv .venv
source .venv/bin/activate

Or if you are using pipenv:

pipenv shell

Now install the dependencies and tests:

pip install -e '.[test]'

To run the tests:

pytest