📦 Final cleanup: updated README, thumbnail, final notebook, and proce…

README.md

@@ -1,22 +1,22 @@
-# (Replace_with_your_title) Cookbook
+# Bias Correction of Crowdsourced Bicycle Count Data Cookbook
 
-<img src="thumbnails/thumbnail.png" alt="thumbnail" width="300"/>
+<img src="thumbnails/biascorr_thumbnail.png" alt="thumbnail" width="300"/>
 
 [![nightly-build](https://github.com/ProjectPythia/cookbook-template/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ProjectPythia/cookbook-template/actions/workflows/nightly-build.yaml)
-[![Binder](https://binder.projectpythia.org/badge_logo.svg)](https://binder.projectpythia.org/v2/gh/ProjectPythia/cookbook-template/main?labpath=notebooks)
+[![Binder](https://binder.projectpythia.org/badge_logo.svg)](https://binder.projectpythia.org/v2/gh/ProjectPythia/BiasCorrectionCrowdsourcedData-cookbook/main?labpath=notebooks)
 [![DOI](https://zenodo.org/badge/475509405.svg)](https://zenodo.org/badge/latestdoi/475509405)
 
 _See the [Cookbook Contributor's Guide](https://projectpythia.org/cookbook-guide) for step-by-step instructions on how to create your new Cookbook and get it hosted on the [Pythia Cookbook Gallery](https://cookbooks.projectpythia.org)!_
 
-This Project Pythia Cookbook covers ... (replace `...` with the main subject of your cookbook ... e.g., _working with radar data in Python_)
+This Project Pythia Cookbook covers **bias correction of crowdsourced bicycle ridership data (Strava) using traditional count sources (EcoCounter)** in Northwest Arkansas, incorporating spatial clustering, multivariate regression, and residual analysis.
 
 ## Motivation
 
-(Add a few sentences stating why this cookbook will be useful. What skills will you, "the chef", gain once you have reached the end of the cookbook?)
+This cookbook provides a reproducible workflow to identify, quantify, and correct bias in Strava bicycle count data using EcoCounter ground-truth data. You'll learn how to clean and merge weekly count data, engineer features, model spatial-temporal patterns, and evaluate model performance using regression and clustering methods.
 
 ## Authors
 
-[First Author](https://github.com/first-author1), [Second Author](https://github.com/second-author2), etc. _Acknowledge primary content authors here_
+[Nelofar Qulizada](https://github.com/nqulizada835)
 
 ### Contributors
 
@@ -26,15 +26,23 @@ This Project Pythia Cookbook covers ... (replace `...` with the main subject of
 
 ## Structure
 
-(State one or more sections that will comprise the notebook. E.g., _This cookbook is broken up into two main sections - "Foundations" and "Example Workflows."_ Then, describe each section below.)
+This cookbook is broken up into two main sections: **"Data Preprocessing and Exploratory Analysis"** and **"Modeling and Bias Correction"**.
 
-### Section 1 ( Replace with the title of this section, e.g. "Foundations" )
+### Section 1: Data Preprocessing and Exploratory Analysis
 
-(Add content for this section, e.g., "The foundational content includes ... ")
+Includes:
+- Loading and merging weekly EcoCounter and Strava data
+- Creating spatial hexbin aggregations
+- Visualizing counts over time and space
+- Exploring potential covariates
 
-### Section 2 ( Replace with the title of this section, e.g. "Example workflows" )
+### Section 2: Modeling and Bias Correction
 
-(Add content for this section, e.g., "Example workflows include ... ")
+Includes:
+- Building linear regression models to predict EcoCounter counts
+- Performing seasonal regression breakdowns
+- Visualizing spatial clustering of residuals
+- Generating interactive maps and outputs
 
 ## Running the Notebooks
 
@@ -48,43 +56,18 @@ The simplest way to interact with a Jupyter Notebook is through
 important for now. All you need to know is how to launch a Pythia
 Cookbooks chapter via Binder. Simply navigate your mouse to
 the top right corner of the book chapter you are viewing and click
-on the rocket ship icon, (see figure below), and be sure to select
+on the rocket ship icon, and be sure to select
 “launch Binder”. After a moment you should be presented with a
-notebook that you can interact with. I.e. you’ll be able to execute
-and even change the example programs. You’ll see that the code cells
-have no output at first, until you execute them by pressing
-{kbd}`Shift`\+{kbd}`Enter`. Complete details on how to interact with
-a live Jupyter notebook are described in [Getting Started with
-Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter).
+notebook that you can interact with.
 
 Note, not all Cookbook chapters are executable. If you do not see
 the rocket ship icon, such as on this page, you are not viewing an
 executable book chapter.
 
-
 ### Running on Your Own Machine
 
-If you are interested in running this material locally on your computer, you will need to follow this workflow:
-
-(Replace "cookbook-example" with the title of your cookbooks)
+If you are interested in running this material locally on your computer, follow this workflow:
 
-1. Clone the `https://github.com/ProjectPythia/cookbook-example` repository:
-
-   ```bash
-    git clone https://github.com/ProjectPythia/cookbook-example.git
-   ```
-
-1. Move into the `cookbook-example` directory
-   ```bash
-   cd cookbook-example
-   ```
-1. Create and activate your conda environment from the `environment.yml` file
-   ```bash
-   conda env create -f environment.yml
-   conda activate cookbook-example
-   ```
-1. Move into the `notebooks` directory and start up Jupyterlab
+1. Clone the repository:
    ```bash
-   cd notebooks/
-   jupyter lab
-   ```
+   git clone https://github.com/ProjectPythia/BiasCorrectionCrowdsourcedData-cookbook.git