README

Wei Wang 2025-04-22

CCRC Green Job Seek Mapping

A Shiny web application for interactive mapping of green job postings across U.S. Commuting Zones (CZs).

Table of Contents

• Overview
• Features
• Installation
• Data Preparation
• Usage
• File Structure
• Dependencies
• Contributing
• License
• Authors

Overview

This project provides an interactive dashboard to visualize the distribution and trends of green job postings by U.S. Commuting Zone. It leverages: - Mapbox GL JS for dynamic mapping - Shiny for the web interface - GeoJSON for spatial data - Plotly for interactive charts

Features

• Choropleth Map: Displays green job postings intensity per CZ
• Year Selector: Filter data from 2010 through the latest available year
• Hover & Popup: View detailed CZ metrics on hover and click
• Trend Plot: See national green job posting trends over time
• CZ Plot: View CZ-specific job postings trend when selected
• Institution Search: Locate and highlight a specific institution on the map

Installation

1. Clone the repository:

   git clone https://github.com/data-edu/CCRC_GreenSeek-Mapping.git
   cd CCRC_Mapping_JS

2. Install R dependencies:

   install.packages(c("shiny", "jsonlite", "geojsonio", "dplyr", "sf", "ggplot2", "plotly"))
   install.packages(c("rnaturalearth", "rnaturalearthdata")) # For mask polygon script

3. Set up Mapbox token:

   • Create a file named mapboxtoken_setup.R in the project root containing:

     mapbox_token <- "YOUR_MAPBOX_ACCESS_TOKEN"

   • Replace "YOUR_MAPBOX_ACCESS_TOKEN" with your actual Mapbox token.

4. Download Private Data

   • Presently, we are storing the required private data in a [Sharepoint folder]. We plan to make this public in the future. This folder contains:
     • cz_job_post.rds

Data Preparation

The repository includes helper scripts to generate required GeoJSON files: - prepare_mask_polygon.R: Fetches U.S. state boundaries and creates a global mask polygon (world minus U.S.), outputting mask_polygon.geojson. - pre_cz jason.R: Reads CZ_jobpostings_final1 and writes yearly CZ GeoJSON files (CZData_<YEAR>.json).

Place the generated files in the www/ folder:

www/
├── mask_polygon.geojson
├── CZData_2010.json
├── CZData_2011.json
└── ...

Ensure the following data files exist in the app root: - CZ_job_post.rds (with columns: CZ20, YEAR, green_job_postings, geometry, id) - P_CZ_plotly.rds/p_SOC_plotly.rds (with columns: YEAR, total_green)

Usage

Launch the Shiny app by running in R:

shiny::runApp("app.R")

Or open app.R in RStudio and click Run App.

File Structure

├── mapbox.js                           # Mapbox initialization and layer logic
├── app.R                               # Shiny UI & server code
├── prepare_mask_polygon.R              # Script to generate mask_polygon.geojson
├── pre_cz jason.R                      # Script to generate CZData_<YEAR>.json files
├── mapboxtoken_setup.R                 # (Not committed) Defines mapbox_token
├── CZ_job_post.rds                     # CZ job postings data
├── P_CZ_plotly.rds/p_SOC_plotly.rds    # National green job posting trends
├── www/                                # Static assets
│   ├── style.css
│   ├── mask_polygon.geojson
│   ├── CZData_2010.json
│   └── ...
└── README.md                  # Project documentation

Dependencies

• R packages: shiny, jsonlite, geojsonio, dplyr, sf, ggplot2, plotly, rnaturalearth, rnaturalearthdata
• JavaScript libraries: Mapbox GL JS v2.14.1, Turf.js
• Data: U.S. Commuting Zone boundaries, green job postings dataset

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

Authors

• Wei Wang
• Joshua Rosenberg
• Cameron Sublet
• Bret Staudt Willet

Built with the Community College Research Center at Teachers College, Columbia University. Funded by JPMorgan Chase.