Gist 457a39b7788e49db95f32294a6b921d8

Gist cf3923e7f58c91a2953a2d9b91f9d0db

This example shows a serverless function that converts a free-text address into geographic coordinates by matching the address’s county against a GeoJSON file of Irish county boundaries. The visualization highlights the geometry of the matched county, returning its centroid’s x and y coordinates alongside the parsed town and road names. The code uses `geopandas.read_file` to load the spatial data and `row['geometry'].iat[0].x/y` to extract the coordinate pair from the selected polygon. The response is delivered as JSON, with a 400 status code if parsing fails.

82% match

Gist 117a8471a45ca6ae36119e5930c915ca

This example shows a map-based visualization of user data, rendered by a Python script that generates geographic positions. It displays spatial points derived from a data source, with the rendering process handled through a pipeline that uses the `geopandas` and `matplotlib` libraries for map creation. The code updates team information and positions based on the environment variables for email, password, and user ID, pulling from a remote dataset. The visualization emphasizes the spatial distribution of the user’s data, using a standard geographic projection to plot the points.

62% match

Gist d8085662349426513b73

This map visualizes a geospatial polygon from a GeoJSON file, outlining a specific land parcel or boundary area with high coordinate precision. The shape is rendered as a MultiPolygon, with vertices tracing a detailed, irregular perimeter typical of cadastral or survey data. The minimal, line-only representation emphasizes the geometry’s raw form, highlighting the boundary’s complexity and the spatial distribution of the region without additional contextual layers. The visualization focuses on the raw geographic footprint, making the coordinate data the primary visual element.

DDylanFM

Gist 652595e55960a8e04a29

This example visualizes the spatial relationships between metropolitan statistical areas and their constituent counties, townships, and other administrative geometries. The code first builds a combined geometry table using SQL, merging overlapping regions across multiple spatial layers while excluding special cases like Boston. It then renders these shapes using D3.js, likely leveraging `d3.geoPath` for projection and canvas or SVG drawing. The query explicitly handles hierarchical and overlapping geographies, prioritizing township-level detail where available and falling back to county unions for other areas. The final visualization shows how different government boundaries interlock within U.S. metropolitan regions, with the SQL preprocessing ensuring no duplicated or missing polygons.

CCaged

Gist 186528

This example shows a geospatial search for golf courses near a specific postcode in New South Wales, Australia. The visualization maps the query’s results by displaying course locations as points, with the postcode’s coordinates as a central reference. It uses the GeoKit Rails plugin, invoked via `Postcode.find_by_postcode` and `Course.find(:all, :origin => [postcode.lat, postcode.lng], :within => 20)`, to filter courses within a 20-mile radius. The data comes from a database of course records, each with latitude, longitude, and review metrics, rendered as a simple point-based map. The code does not use d3 or Three.js; it relies on ActiveRecord and GeoKit for spatial queries.

DDylanFM

Gist 82c5f73258108bef4df748b8e77ae770

This example shows an AWS Lambda function that automatically downloads trending Twitch clips for a specified game and stores them in an S3 bucket, organized by date. It first queries the Twitch Kraken API for top clips, extracts the MP4 URLs from the thumbnail paths, then downloads each video file to the Lambda’s temporary storage before uploading it to S3. The code uses the `requests` library for HTTP calls, `boto3` for S3 operations, and reads configuration from environment variables. The visualization highlights how serverless functions can automate media archiving pipelines.

polymaps.appspot.com

A web app serves pre-scraped GeoJSON boundary data for US states and counties from a Google App Engine backend. The app's URL handlers route requests for state or county data, while a Python scraper fetches OpenStreetMap data via ModestMaps, clips geometries using Shapely, and outputs compressed GeoJSON at multiple zoom levels. The frontend, not shown, would likely render these features as interactive polygons, with the server enabling cross-origin requests for client-side visualization.

Mmbostock

56% match

Gist 634708b061502ecb288faed61b1b15d4

This example builds a data pipeline that fetches Fantasy Premier League player and fixture data, merges it with betting odds from an S3 bucket, and uploads the enriched results back to S3 as CSV files. The code uses the `requests` library to query the FPL API, `pandas` for data manipulation, and `boto3` to interact with AWS S3. It calculates a difficulty metric (`diff`) by comparing home and away win probabilities from the odds. The visualization would show how player value or form changes across gameweeks based on this aggregated data, though the gist itself focuses on data preparation rather than rendering.