ESNEFT Analysis - Diabetes Inequalities

Table of contents

• Installation
• Setup
• Retrieve Data
  • Download
  • Process
    • Aggregate By Practice Level
    • Aggregate By LSOA Level
• Visualise
  • Practice Map
  • LSOA Map
• Further Documentation
• Contributing
• License
• Contact

Installation

Installation is possible via pip as shown below.

Unix/macOS

python3 -m pip install esneft_tools

Windows

py -m pip install esneft_tools

Install within a Virtual Environment (optional)

python -m venv esneft_tools
source esneft_tools/bin/activate
python3 -m pip install esneft_tools

py -m venv esneft_tools
esneft_tools/Scripts/Activate.ps1
py -m pip install esneft_tools

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

Geospatial Dependencies (optional)

Additional geospatial utilities may be optionally installed as below. Note these packages have non-trivial dependencies and automatic installation may not work on all systems.

An additional optional dependency, OSMnx, must be installed by the user. Please refer to the OSMnx documentation for further installation instructions.

python3 -m pip install esneft_tools[geo]

py -m pip install esneft_tools[geo]

Setup

The logging level of esneft_tools can be set via the setVerbosity() function.

import logging
from esneft_tools.utils import setVerbosity
from esneft_tools import download, process, visualise

setVerbosity(logging.INFO)

Retrieve Public Data

Download

From Host

Each of the esneft_tools.download.getData().fromHost() functions retrieve a static copy of a particular data set from GitHub. A local copy of these tables is saved to ./.data-cache/ by default. Each can be obtained individually but it is recommended to retrieve all data, as below.

# Instantiate data download class.
getData = download.getData(cache='./.data-cache')

# Retrieve all data as dictionary (recommended)
data = getData.fromHost('all')

• all (default)
  • Retrieve all of the below data in dictionary format (recommended).
• postcodeLSOA
• Postcode -> LSOA (2011) lookup Table from ArcGIS
• imdLSOA
• Indices of Deprivation by LSOA in England from National Statistics (.gov.uk)
• populationLSOA
• LSOA population estimates, by age and sex, from ONS
• ethnicityLSOA
  • Proportion of ethnicity minorities (non-white) by LSOA from NOMIS
• areaLSOA
• Land hectare measures by LSOA from ArcGIS
• gpRegistration
  • GP registration by LSOA NHS Digital
• gpPractice
  • GP Practice information from NHS Digital
• gpStaff
  • GPs by GP Practices from NHS Digital
• qof
  • Quality and Outcomes Framework, 2021-22 from NHS Digital
• geoLSOA
  • LSOA GeoJSON from UK Data Service
• esneftLSOA
  • List of LSOAs within ESNEFT trust.
• esneftOSM
  • OpenStreetMap (OSM) data for ESNEFT area from Geofabrik

From Source

Alternatively, the .fromSource() method can be used to retrieve each dataset directly from its public source. By default, esneft_tools will retrieve the source data that matches the fromHost() methods. Each source URL can be updated to newer versions by providing a YAML file to download.getData() as shown below. An example of the YAML file is shown here.

# Instantiate data download class.
getData = download.getData(sourceURL='sourceURL.yaml', cache='./.data-cache')

# Retrieve a specific dataset from host.
# Note: "all" does not work with .fromSource().
imdLSOA = getData.fromSource('imdLSOA')

Processing

Aggregate by Practice Level

The getGPsummary function aggregates the downloaded data to practice level statistics.

GPsummary = process.getGPsummary(**data, iod_cols='IMD')

Aggregate by LSOA Level

The getLSOAsummary function aggregates the downloaded data LSOA level statistics.

LSOAsummary = process.getLSOAsummary(**data, iod_cols='IMD')

Visualise

Practice Map

fig = visualise.scatterGP(GPsummary[GPsummary['Status'] == 'Active'], minCount=250)

Map of Practice Distributions within ESNEFT (Plotly Interactive)

LSOA Map

fig = visualise.choroplethLSOA(LSOAsummary, data['geoLSOA'], colour='IMD')

Choropleth Map of LSOA Domains within ESNEFT coloured by IMD (Plotly Interactive)

Further Documentation

Refer to the additional documentation for further examples of functionality.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

See CONTRIBUTING.md for detailed guidance.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

If you have any other questions please contact the author, Stephen Richer.