Temperature-Based Analysis

Temperature-Based Analysis is an exploratory project for studying how local climate conditions vary across the United States and, eventually, whether those conditions show any relationship with mood-based metrics.

The project currently provides the US County Climate Explorer, an interactive county-level map for viewing and filtering climate data. The mood dataset and climate-to-mood correlation analysis are not implemented yet. At this stage, the project is focused on building, validating, and presenting the climate side of the analysis.

Current Features

Interactive Leaflet map with county boundaries and county selection
Metric groups for climate classification, temperature, precipitation, moisture, and solar exposure
Numeric range filters and categorical filters
County detail panel showing all available climate metrics
Legends and in-app source descriptions
Local CSV-based data loading with no application backend or build step
Offline Python scripts for assembling and updating county climate data

The browser currently loads 3,221 county-level records from data/climate-data.csv.

Available Climate Metrics

Group	Metrics
Koppen-Geiger Classification	Majority county climate class
Temperature & Extremes	Annual average temperature, diurnal temperature range, absolute extreme days per year, and heat-index days
Precipitation & Moisture	Annual precipitation, precipitation seasonality, wettest month, driest month, and summer specific humidity
Solar Exposure	Average daily global horizontal irradiance (GHI) and cloudiness index

Most long-term climate metrics use a 1991-2020 reference period. The absolute extreme-days metric currently uses 1991-2025 data. Definitions and time periods are shown in the application's Sources dialog and documented in more detail in scripts/county_data_sources.md.

Run the Explorer

Requirements

A modern web browser
Python 3
PowerShell for the included convenience script
An internet connection for Leaflet, map tiles, and hosted fonts

No Node.js installation or frontend build is required.

From the project root, run:

.\serve.ps1

Then open:

http://localhost:8000/

To use another port:

.\serve.ps1 -Port 8080

The site must be served over HTTP because the browser loads the climate CSV and county GeoJSON with fetch(). Opening index.html directly as a local file will not work.

Using the Map

Choose a metric group and metric from the control panel.
Adjust the value range or category filter to highlight matching counties.
Click a county to zoom to it and inspect all available metrics.
Use Reset Country View or Clear Selection to return to the broader map.
Open Sources for metric definitions and data provenance.

Project Structure

Path	Purpose
`index.html`	Application structure and controls
`styles.css`	Layout and visual styling
`app.js`	Map rendering, filtering, county details, and source metadata
`serve.ps1`	Local HTTP server launcher
`data/climate-data.csv`	Browser-ready county climate records
`data/geojson-counties-fips.json`	County geometry keyed by FIPS code
`scripts/`	Climate-data download, aggregation, and update tools
`tests/`	Tests for the NSRDB polygon request and download workflow

Climate Data Pipeline

The checked-in browser assets are the final outputs needed to run the explorer. The scripts directory contains the larger offline workflow used to derive those outputs from sources including:

Beck et al. Koppen-Geiger climate classification data
NOAA NCEI Climate Normals and nClimGrid data
gridMET humidity data
NREL National Solar Radiation Database data
U.S. Census Bureau county geometry

To work on the Python data pipeline, create a virtual environment and install the ETL dependencies:

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -r scripts\requirements_county_etl.txt

Some data-generation workflows download large files or require NREL/NSRDB API credentials. Generated source datasets are intentionally excluded from Git; only the browser-ready CSV and GeoJSON are tracked.

Run the current automated tests with:

python -m unittest discover -s tests

Planned Mood Analysis

The longer-term goal is to add mood-based metrics and investigate whether patterns in those metrics are associated with climate characteristics such as temperature, sunlight, cloudiness, humidity, precipitation, or extreme-weather frequency.

That phase still requires decisions about:

How mood data will be collected or sourced
Geographic and temporal granularity
Privacy, consent, and aggregation requirements
Confounding variables and missing-data handling
Appropriate statistical and visualization methods

No mood records, mood visualizations, or correlation results are currently part of the application. Any future relationship found by the project should be treated as an association to investigate, not evidence that climate alone causes changes in mood.

Current Limitations

The application presents long-term county summaries rather than live weather.
Climate values aggregate conditions within county boundaries and do not represent every location inside a county.
Source datasets use different methods and, in some cases, different time periods.
Some solar metrics use representative-point values where county polygon summaries are unavailable.
The current interface is an exploratory visualization, not a completed climate-and-mood research analysis.

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