ACRER: Accessibility Model for Urban Recreation

ACRER (Accessibility for Recreation in R) is an R package implementing an optimization and simulation model for urban recreation accessibility. It models how different household types and group types distribute visits across surrounding destination recreation areas (such as forests, nature reserves, and parks) based on transport modes, travel costs, available time budgets, and spatial distance.

📌 Features

• Spatial Accessibility Simulation: Simulates recreational migration and demand from origin zip codes to destination areas.
• Multi-Modal Transport Support: Models travel behaviors across different transport modes (on foot, by bicycle, or by car).
• Optimization Procedures: Employs Linear and Quadratic Programming optimization (quadprog, interior-point method) for decision modeling across time segments and household constraints.
• Embedded Database Engine: Utilizes RSQLite and sqldf for fast querying of origin-destination matrices, activity patterns, and duration budgets.
• Pivot Tables & Reporting: Provides built-in cross-tabulation and pivot reporting utilities (generate_pivot, percent_GT_pivot).

🚀 Installation

You can install the latest version of ACRER directly from GitHub using remotes or devtools:

# Install remotes if not already installed
if (!requireNamespace("remotes", quietly = TRUE)) {
  install.packages("remotes")
}

# Install ACRER from GitHub
remotes::install_github("martakis/ACRER")

💡 Quick Start

library(ACRER)

# Run main ACRER model simulation
run_Acrer_model()

# Execute model SQL queries against SQLite dataset
Run_Queries()

# Generate summary pivot tables
generate_pivot()

📁 Package Structure & Datasets

• R/: Core R functions (run_acrer_model, Run_Queries, generate_pivot, crosstab, etc.).
• inst/extdata/: Bundled example datasets, SQLite databases (Basic_Tables.db), and RDS pivot files.
• vignettes/: Detailed documentation and supplementary materials (ACRER_vignette.Rmd).
• tests/testthat/: Unit tests verifying model calculations and cross-tabulations.

📖 Vignettes & Documentation

For a comprehensive guide on the model mathematical framework, database tables, and workflow, consult the package vignette:

vignette("ACRER_vignette", package = "ACRER")

🌿 GitHub Branching Strategy

To keep the main branch stable and ensure all code passes R CMD check before being published, follow this Feature Branch workflow:

Workflow Overview

flowchart TD
    A["main branch (stable code)"] -->|1. Create feature branch| B["feature/my-new-feature"]
    B -->|2. Code & test locally| C["git commit -m '...'"]
    C -->|3. Push branch| D["git push -u origin feature/my-new-feature"]
    D -->|4. GitHub Actions runs R CMD check| E{"CI Check Passed?"}
    E -->|No| B
    E -->|Yes| F["5. Merge Pull Request into main"]
    F -->|6. Sync main locally & clean up| G["git checkout main && git pull"]

Step-by-Step Command Guide

Step 1: Start from an updated main branch

Always create new branches from an up-to-date main branch:

git checkout main
git pull origin main
git checkout -b feature/my-new-feature

Step 2: Develop, test, and commit your changes

Make your code changes, run tests locally, then stage and commit:

git status
git add .
git commit -m "Add new recreation analysis function and tests"

Step 3: Push your feature branch to GitHub

Push your branch to GitHub to trigger automated CI checks:

git push -u origin feature/my-new-feature

Step 4: Create a Pull Request (PR) on GitHub

1. Go to your repository on GitHub.
2. Click Compare & pull request.
3. Wait for the green R-CMD-check checkmark from GitHub Actions.
4. Click Merge pull request to integrate your code into main.

Step 5: Clean up your local environment

Once merged, return to main, pull the updated code, and delete the temporary branch:

git checkout main
git pull origin main
git branch -d feature/my-new-feature

📄 License

This package is licensed under the GPL-2 license.

👤 Author

Georgios Martakis
Email: georgios.martakis@gmail.com
GitHub: @martakis