Gist cf3923e7f58c91a2953a2d9b91f9d0db

100%

Gist cf3923e7f58c91a2953a2d9b91f9d0db

✓ Published0🌍 Public

Last edited Mar 27, 2022

Created on Mar 27, 2022

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.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 457a39b7788e49db95f32294a6b921d8

This example demonstrates a geocoding API built with AWS Lambda that converts Irish postal addresses into geographic coordinates. It loads county and townland boundary data from GeoJSON files using GeoPandas, then parses the incoming address string to match the county, townland, and road components. The code uses the Shapely geometry objects within the GeoDataFrames to extract point coordinates, falling back from townland-level to county-level precision when a townland match fails. The implementation handles JSON request bodies, returns appropriate HTTP status codes, and formats coordinate responses as JSON, with boto3 included for potential AWS service integration.

CConorAspell

82% match

Loading thumbnail…

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.

CConorAspell

63% match

Loading thumbnail…

Gist 1a7ae0708d7a8ee9e113f13654d39a4e

This example visualizes a single geographic polygon representing an outline near Denver, Colorado, derived from a GeoJSON FeatureCollection. It shows the shape’s boundary as a closed loop, with coordinates defining a series of vertices that trace an irregular, elongated region. The rendering relies on a mapping library that processes GeoJSON data, likely using `d3.geoPath` to project the polygon onto a canvas or SVG, while the precise coordinate pairs (longitude and latitude) suggest the use of a geographic projection. The source gist provides the raw GeoJSON, and the visualization emphasizes the polygon’s spatial extent without additional layers or interaction.

WWill Carter

62% match

Loading thumbnail…

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

62% match

Loading thumbnail…

Gist bdb4994137a7b0f75652f01bb4c170d2

This visualization converts a tab-separated dataset of Stolpersteine memorial stones into an interactive GeoJSON FeatureCollection. It shows each victim’s name, address, deportation, murder, and death details as a point location on a map. The XQuery script parses the TSV file using `csv:parse` with a tab separator, then constructs a GeoJSON structure with point geometries. It uses `fetch:text` to load the remote data and `json:serialize` to output the final GeoJSON, which can then be rendered by a mapping library.

CCliffordAnderson

61% match

Loading thumbnail…

Gist 496f20b47774ec679ab5dd48beff9d7e

This example maps French regional data from a CSV file onto a geographic outline of France. It uses a conic conformal projection and a quantized color scale to shade each region according to a numeric value. The visualization loads GeoJSON topology for the region boundaries and joins it with the CSV data, filling regions with a five-step green color ramp, while regions without data appear gray. The code relies on D3 v4’s `d3.geoConformal`, `d3.geoPath`, `d3.scaleQuantize`, and CSV/JSON loading functions.

EElisaDnr

61% match

Loading thumbnail…

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

61% match

Loading thumbnail…

Gist 10676346

This map displays labeled points from a GeoJSON file, plotting locations like "Internet Archive," "China," and "New York" at their geographic coordinates. The visualization likely renders a large-scale point cloud, with overlapping labels suggesting a messy or aggregated dataset. Using a mapping library, likely D3.js with `d3.geoPath` for drawing map features and standard SVG or Canvas techniques for placing text labels, the code creates a simple geographical scatter plot. The rendering emphasizes the spatial distribution of these named places across the globe.

CCliffordAnderson

60% match