Skip to main content
SimilarExport ZIP
100%

Malaria cases

✓ Published0🌍 Public
LLuisSevillano
Last edited May 4, 2016
Created on May 4, 2016

A world map displays malaria case locations as translucent red and yellow points over grey countries, visualized using the `spam.js` library with a static canvas. The map uses an azimuthal equal-area projection, with a graticule drawn via `d3.geo.graticule()`. Each case is projected from longitude and latitude coordinates and rendered as an SVG circle with semi-transparent strokes and fills. Data comes from the Malaria Atlas Project.

AI-generated description

Testing spam.js a nice, easy and lightweight (9.8kB) javascript library to make maps from Lukas Appelhans and Martín González. Data from Malaria Atlas Project

mit Licensed

Similar vizzes

Loading thumbnail…

Gist d6e714e97e5e937623a7

This visualization plots each country as a point positioned by latitude and longitude, with circle size representing total population and color indicating HIV/AIDS prevalence. The chart uses D3’s `d3.geoPath` and a geographic projection to place points over a world map background. Data is loaded from a CSV containing country statistics, and hover interactions reveal specific country details. The rendering relies on standard SVG elements with scales for sizing and color encoding, following a scatterplot-over-map approach.

LLTymn
70% match
Loading thumbnail…

ebola cases

This interactive map visualizes the geographic distribution of Ebola cases using a Leaflet-powered SVG overlay. Points mark affected locations across Guinea, the Democratic Republic of the Congo, the United States, and Spain, loaded from an external GeoJSON feature collection. The map uses Mapbox tiles with D3-rendered SVG circles to represent case locations, while checkboxes above the map allow users to toggle visibility for cases in the DRC, Guinea, or Liberia. The visualization combines geographic context with an interactive filtering interface to explore outbreak data across countries.

BBrieLewis
70% match
Loading thumbnail…

Zika Virus 2015

This choropleth map visualizes Zika virus cases across the United States in 2015, with states colored on a logarithmic scale from white to black based on total reported cases. Built with D3 v3 and an Albers USA projection, the map renders each state as an SVG path, with the color encoding derived from the "Total cases" column of the dataset. States with zero cases remain white, while those with higher case counts appear progressively darker—California leads with 13 cases, followed by Florida with 9 and Texas with 8, creating a visual hierarchy of geographic impact. The map includes interactive tooltips on hover, implemented using Malcolm Maclean's tooltip pattern, and a legend adapted from Mike Bostock's example, allowing viewers to quickly identify states with higher Zika virus activity. This simple choropleth, adapted from Scott Murray's approach and earlier West Nile Virus examples, uses a log color scale to differentiate case counts across the United States.

77man10
70% match
Loading thumbnail…

ebola cases

An interactive map plots reported Ebola cases as animated dots across Western Africa and beyond, using real-world geographic coordinates from a GeoJSON dataset. The visualization combines Leaflet with Mapbox for the base map and D3.js for the dynamic overlay, placing each case as a circle that expands on load. Users can toggle visibility by country using checkboxes, while the map supports zooming and panning with the dots repositioning in sync via D3's projection logic.

KKunnyKou
70% match
Loading thumbnail…

ebola map

This interactive map visualizes the spread of Ebola cases, plotting individual locations as dots on a tiled satellite basemap. The geographic coordinates (longitude, latitude) of each case are stored in a GeoJSON FeatureCollection, with properties indicating the country and sometimes a specific address or town (e.g., "Dallas, TX", "Gueckedou", "Boende moke"). Rendered as an animated SVG overlay on a Leaflet map, the visualization supports zooming and panning. The map is initialized to a world view centered at [20,-37] with a zoom level of 3, and the D3-selected SVG layer is synced with Leaflet's overlay pane to ensure that the points remain correctly positioned as users interact with the map. The points are plotted based on their latitude and longitude coordinates from the dataset, with labels providing the location name and country. This example demonstrates a straightforward pattern for combining Leaflet's interactive map controls with D3's data-driven SVG rendering, useful for geographically distributed point data like disease outbreak locations.

BBrieLewis
70% match
Loading thumbnail…

ebola cases

The visualization maps reported Ebola cases as animated dots on a world map, using a Leaflet map via Mapbox with D3.js for rendering. Each dot represents a case location from a GeoJSON file, initially expanding from a large radius to a small point over three seconds. The code projects geographic coordinates to screen positions using Leaflet's `latLngToLayerPoint` and re-renders the dots on map pan or zoom events. It combines Mapbox's base map with D3's data binding and transitions.

LLisaPicado
70% match
Loading thumbnail…

Simple Mapping- Ebola

This interactive map visualizes the locations of Ebola cases, displayed as animated dots on a Mapbox base layer. The visualization plots individual data points from a GeoJSON file on a world map, with each circle representing a case location. The code uses D3.js to handle data loading and SVG circle creation, while Mapbox.js manages the map rendering and projection. As users pan or zoom, the map triggers a render function that recalculates the pixel coordinates of each dot, ensuring they stay aligned with their geographic positions.

LLisaPicado
70% match
Loading thumbnail…

Zika Virus 2016

This choropleth map visualizes the spread of Zika virus cases across the United States in 2016, alongside a comparison with West Nile virus data. Using D3.js with an Albers USA projection, the map colors each state on a logarithmic scale based on total cases, with hover interactions that reveal state-level details via tooltips. The visualization is built with SVG rendering and includes a legend to help interpret the color scale, effectively highlighting geographic disparities in reported infections.

77man10
69% match