Package: demographic-modeling
demographic-modeling-moduleDemographic Modeling isopinionatedtooling for performing demographic analysis using both geography and machine
learning.OpinionatedNo, this set of tooling written in Python is not going to have a political debate with you. Rather, while flexible
enough to be used in a variety of ways, this tooling provides a clear way to perform analysis. This enables you to
get started and be productive as quickly as possible.Getting StartedFrom the project directory, create an environment with all dependencies installed and linked.> make envThis creates a conda environment cloned from the ArcGIS Pro default environmentarcgispro-py3, and names this new
environmentdemographic-modeling, and also activates this environment for ArcGIS Pro at the same time. If opening
a new command prompt, you can easily activate this environment using the command..> make env_activate...which simply calls> activate demographic-modelingfor you.From there, the example workflow can be found in the notebooks in the./notebooksdirectory of the project, and
explored by simply calling.> make jupyterThis command takes care of activating the environment, and also starting jupyter lab, so you can get started quickly.Project Organization├── LICENSE
    ├── Makefile           <- Makefile with commands like `make data`
    ├── make.bat           <- Windows batch file with commands like `make data`
    ├── setup.py           <- Setup script for the library (dm)
    ├── .env               <- Any environment variables here - created as part of project creation, 
    │                         but NOT syncronized with git repo for project.                
    ├── README.md          <- The top-level README for developers using this project.
    ├── arcgis             <- Root location for ArcGIS Pro project created as part of
    │   │                     data science project creation.
    │   ├── demographic-modeling-module.aprx <- ArcGIS Pro project.    
    │   └── demographic-modeling-module.tbx  <- ArcGIS Pro toolbox associated with the project.
    ├── scripts            <- Put scripts to run things here.
    ├── data
    │   ├── external       <- Data from third party sources.
    │   ├── interim        <- Intermediate data that has been transformed.
    │   │   └── interim.gdb
    │   ├── processed      <- The final, canonical data sets for modeling.
    │   │   └── processed.gdb
    │   └── raw            <- The original, immutable data dump.
    │       └── raw.gdb
    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
    ├── models             <- Trained and serialized models, model predictions, or model summaries
    ├── notebooks          <- Jupyter notebooks. Naming convention is a 2 digits (for ordering),
    │   │                     descriptive name. e.g.: 01_exploratory_analysis.ipynb
    │   └── notebook_template.ipynb
    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures        <- Generated graphics and figures to be used in reporting
    ├── environment.yml    <- The requirements file for reproducing the analysis environment. This 
    │                         is generated by running `conda env export > environment.yml` or
    │                         `make env_export`.                         
    └── src                <- Source code for use in this project.
        └── dm <- Library containing the bulk of code used in this 
                                                  project.Project based on thecookiecutter GeoAI project template. This template, in turn, is simply an extension and light modification of thecookiecutter data science project template. #cookiecutterdatascience