Skip to content

Latest commit

 

History

History
108 lines (85 loc) · 6.17 KB

README.md

File metadata and controls

108 lines (85 loc) · 6.17 KB
Raw

sql4housing

Background

Sql4housing is based on a broader effort to encourage collaboration between civic hackers and housing advocates. Read more about this work on our blog here:

Hacking for Housing: How open data and civic hacking creates wins for housing advocates

Ownership, evictions, and violations: an overview of housing data use cases

Introduction

Sql4housing is based on a cloned copy of Dallas Morning News' socrata2sql. Socrata2sql is a tool which allows you to import any dataset on the Socrata API and copy it into a SQL database of your choice using a command line interface. Here, I aim to adapt socrata2sql to be able to import datasets from the following sources:

-HUD's Open Data Portal
-Any locally saved Excel file or Excel download hyperlink
-Any locally saved .csv file or .csv download hyperlink
-Any locally saved .shp file or .zip download hyperlink containing a .shp file
-Any locally saved .geojson file or .geojson download hyperlink
-Any dataset on a Socrata open data portal
-Census variables within the 5-year American Community Survey or Decennial Census

Requirements

  • Python 3.x
  • Any database supported by SQLAlchemy
  • Download package via: pip install sql4housing

Usage

Changes in usage will be periodically updated and documented within the docstring of cli.py

See /chicago_examples for a detailed use case.

"""Housing data to SQL database loader

Load a dataset directly from an API (Socrata, HUD) or file (csv or shp)
into a SQL database. The loader supports any database supported by SQLalchemy.
This file is adapted from a forked copy of DallasMorningNews/socrata2sql

Usage:
  sql4housing bulk_load
  sql4housing hud <site> [--d=<database_url>] [--t=<table_name>]
  sql4housing socrata <site> <dataset_id> [--a=<app_token>] [--d=<database_url>] [--t=<table_name>]
  sql4housing csv <location> [--d=<database_url>] [--t=<table_name>]
  sql4housing excel <location> [--d=<database_url>] [--t=<table_name>]
  sql4housing shp <location> [--d=<database_url>] [--t=<table_name>]
  sql4housing geojson <location> [--d=<database_url>] [--t=<table_name>]
  sql4housing census (decennial2010 | (acs [--y=<year>])) <variables> (--m=<msa> | --c=<csa> | --n=<county> | --s=<state> | --p=<place>) [--l=<level>] [--d=<database_url>] [--t=<table_name>]
  sql4housing (-h | --help)
  sql4housing (-v | --version)

Options:
  <bulk_load>        Loads all datasets documented within a file entitled bulk_load.yaml.
                     Must be run in the same folder where bulk_load.yaml is saved.
  <site>             The domain for the open data site. For Socrata, this is the
                     URL to the open data portal (Ex: www.dallasopendata.com).
                     For HUD, this is the Query URL as created in the API
                     Explorer portion of each dataset's page on the site
                     https://hudgis-hud.opendata.arcgis.com. See example use cases
                     for detailed instructions.
  <dataset_id>       The ID of the dataset on Socrata's open data site. This is
                     usually a few characters, separated by a hyphen, at the end
                     of the URL. Ex: 64pp-jeba
  <location>         Either the path or download URL where the file can be accessed.
  --d=<database_url> Database connection string for destination database as
                     diacdlect+driver://username:password@host:port/database.
                     Default: "postgresql:///mydb"
  --t=<table_name>   Destination table in the database. Defaults to a sanitized
                     version of the dataset or file's name.
  --a=<app_token>    App token for the Socrata site. Only necessary for
                     high-volume requests. Default: None
  --y=<year>         Optional year specification for the 5-year American Community
                     survey. Defaults to 2017.
  --m=<msa>          The metropolitan statistical area to include. 
                     Ex: --m="new york-newark-jersey city"
  --c=<csa>          The combined statistical area to include.
                     Ex: --c="New York-Newark, NY-NJ-CT-PA"
  --n=<county>       The county to include.
                     Ex: --n="cook county, IL"
  --s=<state>        The state to include.
                     Ex: --s="illinois"
  --p=<place>        The census place to include.
                     Ex: --p="chicago, IL"
  --l=<level>        The geographic level at which to extract data. i.e. tract,
                     block, county, region, division. Reference cenpy documentation
                     to learn more: https://github.com/cenpy-devs/cenpy
  -h --help          Show this screen.
  -v --version       Show version.

Examples:

  Load the Dallas check register into a local SQLite file (file name chosen
  from the dataset name):
  $ sql4housing socrata www.dallasopendata.com 64pp-jeba

  Load it into a PostgreSQL database called mydb:
  $ sql4housing socrata www.dallasopendata.com 64pp-jeba -d"postgresql:///mydb"

  Load Public Housing Buildings from HUD into a PostgreSQL database called mydb:
  $ sql4housing hud "https://services.arcgis.com/VTyQ9soqVukalItT/arcgis/rest/services/Public_Housing_Buildings/FeatureServer/0/query?outFields=*&where=1%3D1" -d=postgresql:///mydb

  Load Public Housing Physical Inspection scores into a PostgreSQL database called housingdb:
  $ sql4housing excel "http://www.huduser.org/portal/datasets/pis/public_housing_physical_inspection_scores.xlsx" -d=postgresql:///housingdb
"""