jtourkis / affh

Spacial Object Conversion, Mapping, and Basic Clustering/Autocorrelation Tests

GIS/Spacial Data Analysis Using HUD Affirmatively Furthering Fair Housing(AFFH) Data

Goal: This project combines 2020 AFFH census data with GIS/spacial data with a variety of analysis methods.
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Table of Contents

• About the Project
  • Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Roadmap
• Contributing
• License
• Contact
• Acknowledgements

About The Project

The project covers Spacial Object Conversion, Mapping, and Basic Clustering/Autocorrelation Tests.

Built With

• R / R Studio
• sf
• sp
• tmap
• spdep
• tigris
• Many other packages.

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

List of required packages.

• pandas

• plotly

• seaborn

• matplotlib.pyplot

Installation

1. Clone the repo

git clone https://github.com/jtourkis/AFFH.git

2. Install packages

pip install package

Usage

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

For more examples, please refer to the Documentation

Roadmap

Using 2020 Affirmatively Furthering Fair Housing census tract data from HUD, this code performs the following tasks:

1. Use geo_id and TIGRIS to add spacial geometry and convert data to the proper SF/ SP data object for task;

2. Use TMap to visualize affordable rental units by census tract in Massachusetts;

3. Perform basic cluster analysis using local Morans I/local G-Stat and create interactive map emphasizing statistically significant regions.

Dataset Used: AFFH_tract_AFFHT0006_July2020.csv from Urban Institute repository. See Acknowledgements for link.

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be 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

License

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

Note: The intial README Template was distributed under the MIT License. Copyright (c) 2018 Othneil Drew. LICENSE for more information.

Contact

James Tourkistas - [email protected]

Project Link: https://github.com/jtourkis/AFFH

Acknowledgements

• Best-README-Template
• HUD Affirmatively Furthering Fair Housing
• An Introduction to R for Spatial Analysis and Mapping
• Urban Institute 2020 AFFH Data Download Link