NOLA Neighborhood Annotation

I wrote this script after reading Jeff Asher's blog post. He mentioned that it was hard to segment the call for service data by neighborhood given just the lat, lng. This script adds a new column to the data with the neighborhood name of the origin call using the neighborhood boundaries from this dataset.

Setup

You'll need python and pip. This will depend on your system.

Run pip on the requirements file to get the dependencies:

pip install -r requirements.txt

Usage

The annotate.py script takes a csv file for input and outputs a new csv file with the new column of your choosing. It streams the new rows into the output file one at a time.

usage: annotate.py [-h] [--lat-column LAT_COLUMN] [--lng-column LNG_COLUMN]
                   [--loc-column LOC_COLUMN]
                   input_file output_file output_column shape_dataset

Annotate csv file with shape tags

positional arguments:
  input_file            the input csv file path
  output_file           the output csv file path
  output_column         the name of the column you wish to add
  shape_dataset         the dataset of shapes to use

optional arguments:
  -h, --help            show this help message and exit
  --lat-column LAT_COLUMN
                        the 0-indexed column position for latitude (if in it's
                        own column)
  --lng-column LNG_COLUMN
                        the 0-indexed column position for longitude (if in
                        it's own column)
  --loc-column LOC_COLUMN
                        the 0-indexed column position for location (if lat and
                        lng are in one column)

Example

$ python annotate.py ~/Desktop/calls_for_service/2016.csv output.csv Neighborhood neighborhoods --loc-column=20

#2 lat: 29.98645605 lng: -90.06910049 -> FAIRGROUNDS
#3 lat: 29.94662744 lng: -90.06570836 -> CENTRAL BUSINESS DISTRICT
#4 lat: 30.03599373 lng: -89.98642993 -> LITTLE WOODS
#5 lat: 29.94441639 lng: -90.11338583 -> AUDUBON
#6 lat: 29.94556892 lng: -90.09426884 -> CENTRAL CITY
#7 lat: 29.99285922 lng: -90.10284506 -> NAVARRE
#8 lat: 29.95096705 lng: -90.07023215 -> CENTRAL BUSINESS DISTRICT
#9 lat: 30.02696323 lng: -89.95745953 -> READ BLVD EAST
#10 lat: 29.95783188 lng: -90.06637035 -> FRENCH QUARTER
#11 lat: 29.96243871 lng: -90.11365436 -> GERT TOWN
#12 lat: 30.03205044 lng: -89.99409817 -> LITTLE WOODS
#13 lat: 29.95552589 lng: -90.06830144 -> FRENCH QUARTER
#14 lat: 29.97898906 lng: -90.0963655 -> CITY PARK
#15 lat: 30.03610874 lng: -89.9747228 -> READ BLVD EAST
#16 lat: None lng: None -> N/A
#17 lat: 29.97669572 lng: -90.07510558 -> SEVENTH WARD
#18 lat: 29.9617467 lng: -90.11389764 -> GERT TOWN
#.......... Will end when every row is processed