Gist 186528

100%

✓ Published0🌍 Public

Last edited Sep 14, 2009

Created on Sep 14, 2009

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.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 0c13c9decf2cc5a3bf2c

This visualization maps bushfire locations across New South Wales as point features from a GeoJSON feed, using the D3.js library to project and draw the data. It renders the fire incidents as small circles positioned by their coordinates through a `d3.geoPath`-compatible projection, with each point colored or styled according to its alert level and status. The underlying dataset comes from the NSW Rural Fire Service’s public feed, and the code likely uses D3’s data join and enter/update/exit pattern to bind the GeoJSON features, though the visualization focuses on static spatial distribution rather than animation or interaction.

DDylanFM

63% match

Loading thumbnail…

Gist c0cfc71e8fe1e4190066

This example visualizes the progression of the Kerripit Fire in New South Wales, Australia, using a GeoJSON feed from the NSW Rural Fire Service. It plots the fire’s point location and a multi-polygon boundary, with multiple features tracking status changes from “out of control” to “being controlled” and a size increase from 0 to 13 hectares. The code relies on D3’s `geoPath` to render geographic shapes and standard GeoJSON structures for point and polygon data, presenting the fire’s perimeter and position over time.

DDylanFM

63% match

Loading thumbnail…

Gist 3b609858c3d3d4fa3e9a

This visualization maps wildfire incidents across New South Wales, Australia, using a GeoJSON file of point coordinates. It shows the spatial distribution of various fire types—such as forest, grass, and scrub fires, plus vehicle accidents—by plotting them on a geographic canvas. The code likely uses D3.js’s `d3.geoPath` for map projection and rendering, with points positioned via `d3.forceSimulation` or direct coordinate mapping. Styling encodes fire type through color or symbols, while the underlying data source is the NSW Rural Fire Service feed, as indicated by the linked incident titles.

DDylanFM

62% match

Loading thumbnail…

Gist 09dc3605fda7e3165781

This visualization maps wildfire incidents across New South Wales using a GeoJSON dataset, plotting each fire as a point on a geographic canvas. It encodes each incident’s location, alert level, and status through colored markers, with hover interactions likely revealing details like size and council area. The code uses D3’s `d3.geoPath` to project boundaries and a custom force-directed layout to prevent overlapping labels. It also leverages Canvas pixel analysis via `getImageData` to detect and avoid collisions, ensuring readable annotations. Data comes from the provided GeoJSON file, rendered dynamically with D3’s event handling.

DDylanFM

62% match

Loading thumbnail…

Gist 9991179

This visualization uses the D3.js library to render a live map of current fire incidents across New South Wales, Australia, plotting each incident as a point on a geographic projection. It shows the spatial distribution of fires, with each point colored or styled to reflect its alert level or status. The code relies on `d3.forceSimulation` for layout and `d3.geoPath` to draw the map boundaries, while the underlying incident data comes from the provided GeoJSON file, which includes properties like fire type, council area, and responsible agency. The map updates dynamically to reflect the current incident data.

DDylanFM

61% match

Loading thumbnail…

Geojson test response from bushfires API

This example displays a single point on a map representing a bushfire incident near Hume Highway in Marulan, based on GeoJSON data retrieved from a bushfires API. The visualization uses a D3.js-based rendering pipeline where geographical coordinates are projected and drawn as a map, with the incident point plotted based on its latitude and longitude. The code accesses the dataset through an API call to `reportfeature.json`, which provides a `FeatureCollection` containing the point's geometry and properties, and the map is displayed using D3's geographic projection methods. The author, DylanFM, sources this GeoJSON test data from a gist.

DDylanFM

61% match

Loading thumbnail…

Gist 2d8b386a09c41fa28e3f

This visualization maps wildfire incidents across New South Wales using a GeoJSON dataset of fire points. It shows the spatial distribution of active fires, with each point colored by alert level (Advice) and sized by reported area. The map is rendered using Leaflet.js with custom circle markers positioned via geographic coordinates, while tooltips display fire details such as location, council area, and responsible agency. The visualization relies on the `d3.geoPath` projection to place markers, and the underlying data comes from the NSW Rural Fire Service feed.

DDylanFM

61% match

Loading thumbnail…

Gist 8ac9d468bdb3b27d1780

This example maps a set of fire-incident points from a New South Wales GeoJSON feed using D3. Each feature is rendered as a circle on a geographic projection, with point locations derived from the GeoJSON geometry and visual encoding driven by the alertLevel and fire status fields. Color and symbol cues distinguish incidents marked as "Advice," "Not Applicable," or out-of-control fires, while tooltips expose the title, council area, status, and reported size. The map emphasizes the spatial distribution of emergency events across the region, letting viewers quickly compare incident types and alert levels at a glance.

DDylanFM

60% match