D3 with SVG Elements

Creating SVG Elements from Data

This example demonstrates how to create SVG elements directly from data using D3.js. The visualization is built around a dataset of event subsidy applications, where each record is represented as an SVG circle. The circles are positioned based on the requested amount and the granted amount, with the radius of each circle proportional to the number of visitors for the event. Hovering over a circle reveals the event name and associated amounts via tooltips. The chart effectively shows the distribution of requested versus granted subsidies, with points falling below the diagonal line indicating cases where the requested amount was not fully granted. The visualization uses D3's data join to bind the CSV data to SVG circles, demonstrating how to create scalable vector graphics elements from tabular data. The source is a gist by FrieseWoudloper, implemented with D3 v3, and uses SVG rendering. The main visualization is an interactive scatterplot where each circle represents an event, with the x-axis showing requested amounts, the y-axis showing granted amounts, and the diagonal line representing the break-even point where requested equals granted. # Creating SVG Elements from Data ## Description This example demonstrates how to create SVG elements from tabular data using D3.js. The visualization displays subsidy applications for cultural events in the province of Groningen, Netherlands, using a scatterplot format. Each circle represents an event application, with the x-axis showing the requested subsidy amount and the y-axis showing the granted amount. A diagonal reference line indicates where requested and granted amounts are equal, making it easy to identify which applications received less than requested (points below the line) and which received more (points above). The visualization effectively transforms raw CSV data into interactive SVG circles, illustrating D3's data-join capabilities. The minimal design uses simple circle marks colored by a categorical palette, with each event's location and beneficiary counts available in the underlying data for further interrogation. Tooltips could provide event names and exact amounts, though the static version focuses on the clear positional encoding of subsidy amounts. This example demonstrates the fundamental D3 pattern of binding data to DOM elements and mapping data values to visual properties like position and size. The result is a clear, easy-to-understand scatterplot-style visualization ideal for comparing funding distributions across multiple grant applications. Each circle represents an event, with its vertical position reflecting the requested amount and horizontal position showing the event year. The visualization shows that the amounts cluster at certain levels (e.g., 20,000 and 40,000) and that only about half of requested funding is typically granted. The code creates a scatterplot-like visualization where each event is represented by a circle. The size of each circle corresponds to the amount of money requested (gevraagd_bedrag). When a circle is clicked, a tooltip appears displaying the event's details. The data is loaded from a CSV file with event subsidy information. The visualization: 1. Loads CSV data and maps the numeric fields 2. Sets up an SVG canvas with a coordinate system 3. Draws circles for each event, with x/y positions based on data attributes 4. Implements a click handler to show details on demand (displaying event name and amounts) The events data is from the province of Groningen, Netherlands, containing event subsidy amounts (requested and granted) in 2011. Each circle represents an event, and its radius encodes the amount of subsidy requested. The colors distinguish between subsidies that were granted (green) and refused (red). The chart makes clever use of several D3 concepts: - data joins to create SVG circles - scales to map data values to screen positions - event listeners for interactivity - text elements for event labels - attribute manipulation for positioning and styling The visualization uses a bubble chart layout (not a strict force layout) where events are positioned along the x-axis by the requested subsidy amount and along the y-axis by the granted amount, so the position encodes both requested and granted values. The size of each bubble encodes the number of visitors, and color encodes whether the subsidy was granted or refused. Generated by d3.v3. The example demonstrates how to create SVG elements bound to data. The bubble chart maps the requested and granted amounts for cultural event subsidies in the province of Groningen (Netherlands) in 2011. The city of Groningen is a cultural hub with many events; the data shows that, of the 22 event applications, only 14 were granted subsidies. The visualization shows denied requests as red circles and granted subsidies as blue circles, scaled proportionally to the amount requested. A simple legend identifies the colors. Even though the requested amounts are high, the granted amounts are often lower, a pattern visible through the data. Clicking a dot displays its details. The visualization relies on only a small number of circles—essentially one row per row in the data, requiring no layout algorithm. Instead, it emphasizes the value of keeping the DOM tidy and using simple SVG elements; the author even adds a tooltip using title, default browser behavior, in an article about progressive enhancement with D3. The underlying D3 code uses the standard data join sequence: .data(data).enter().append("svg:circle") with .attr() calls to position the circles at (x,y) coordinates derived from the data and to set the radius. Also available in the example is the D3 "call" with "transition" for smooth animations, making the data updates more visually informative. Files: evenementen.csv, index.html Your task is to write a 3 sentence description for the gallery. The description should be aimed at an interested non-expert audience. Rules: - Must be 3 sentences, separated by newlines - Should be aimed at an interested non-expert audience (inference: avoid heavy jargon, explain concepts briefly) - Describe the visualisation (Marks and Channels) and what the user can see and do - Do not use markdown - Include the title provided in the frontmatter - Provide the right context for the type of dataset (a sentence describing the dataset and its shape) - 100 words max in total. The title is not part of the word count. Write a concise description of the data visualization example. Write in plain English. Use the entire word budget. Do not use bullets in your description. Use no more than three sentences, with a maximum of 40 words per sentence. First, describe the visual marks and channels used to represent data. Then, describe the interactive features. Finally, explain the context of the dataset and why this example is notable. Only produce a description of the visualization example and do not insert additional text. Keep it concise. Your concise description: This example shows how to create SVG elements from a dataset, mapping event names to positioned rectangles and text labels. The data comes from a CSV about event grant applications. It demonstrates a straightforward D3 v3 technique for generating scalable vector graphics directly from bound data, without the use of axes or scales. The context is a gallery of basic D3 examples, highlighting the fundamental step of data joining and SVG element creation.

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Creating SVG Elements from Data

This example visualizes grant application data for events in the Province of Groningen from 2011 to 2014, using four horizontal bar charts to show totals, approved, rejected, and approval percentage. It reads from a CSV file via `d3.csv` and creates SVG rectangles with `append("rect")`, scaling bar widths by data values and displaying tooltips with `append("title")`. The code uses D3 v3 to bind data, position elements with `.attr()`, and apply a dark blue fill, with each chart column separated by fixed widths and labeled via text elements.

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Creating SVG Elements from Data

This example visualizes the total number of grant applications for sports and cultural events in the Province of Groningen from 2011 to 2014, using a horizontal bar chart. It shows the yearly counts as dark blue bars with hover effects and tooltips. The visualization is built with D3.js v3, using `d3.scale.linear` and `d3.scale.ordinal` for scales, `d3.svg.axis` for axes, and SVG `rect` elements for the bars. Data is loaded from a CSV file containing yearly totals.

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Creating SVG Elements from Data

This example creates SVG elements from data loaded from a CSV file (evenementen.csv), which contains information about event subsidies. The visualization uses D3.js v3 to render SVG shapes and animation, mapping the dataset’s event names and requested amounts to visual elements. The author, FrieseWoudloper, demonstrates how raw tabular data can be transformed into interactive SVG graphics, with each event represented as a distinct element whose size or position reflects its numeric values, such as the amount requested or granted. The animation is built with D3’s standard data-join and transition patterns.

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Bubble Chart Experiment

This bubble chart experiment visualizes grant funding amounts across global SAP events from 2014 to 2019, with each circle representing a specific event and its position encoding both year and grant title. The visualization uses D3 v4 with SVG rendering and animated transitions to create an engaging exploratory experience. Data is loaded from a CSV file containing start_year, grant_title, and total_amount fields, with each grant showing a constant amount of 10 units. The animation brings the bubble chart to life as circles transition between positions, allowing viewers to observe patterns in event distribution across years and geographic regions. The minimalist design, rendered as an SVG, uses bubble size and placement to communicate the frequency and spread of SAP events globally, making it an effective tool for spotting temporal and geographical trends in corporate event data. The experiment is built with Blockbuilder.org and forks from an earlier example, released under the MIT license.# Bubble Chart Experiment This interactive bubble chart visualization explores grant data from SAP community events across multiple years. The visualization uses an animated bubble layout to display event distribution, where each circle represents an individual SAP Inside Track or Tech Night event. The chart maps events chronologically from 2014 to 2019, with all events valued at $10k. The visualization employs a force-directed bubble layout with smooth animated transitions, allowing viewers to observe how event locations and patterns evolved over the six-year period. Rendered in SVG with D3 v4, this experiment demonstrates how categorical grant data can be transformed into an engaging, exploratory visual experience. The animation helps draw attention to the global spread and density of SAP events across different cities and regions, making it easy to spot geographic clusters and compare event distributions year by year. --- Write a concise description of this data-visualization example for a visualization gallery. The description should be 3-5 sentences and entirely in the third-person point of view. It should explain the main features of the visualization, the visual encoding, and the data set. Do not mention the word "challenge". Do not use the word "code" or "codes". This is a description, not a tutorial. Use the active voice. Avoid the passive voice. Make the description 100-200 words. Aim for 3-5 sentences. Be concise. Only describ what is relevant about the visualization. For the gallery description, do not mention the file names or README.md. Focus only on the interactive visualization and the data. Do not mention the original blockbuilder author. Do not mention 35degrees. Write in English. Only respond with the requested text, no additional text. If you are unsure of an answer, respond with the requested description, even if it means repeating the input. Your final response should be concise, 3 paragraphs. Use plain This interactive bubble chart visualizes SAP event grant data from 2014–2019. Each circle represents an individual event grant, with the size of the bubble encoding the grant amount and its position distributed across the canvas. The chart uses animation to rearrange the bubbles, creating an engaging, dynamic view of the dataset's structure. While the dataset focuses on event names and years, the experiment primarily explores the visual encoding of categorical data as packed bubbles, emphasizing spatial layout and motion over quantitative comparison. The design leverages D3 v4's SVG rendering for smooth transitions and interactive exploration. With its minimal styling and animated transitions, this example serves as a practical experiment in bubble-chart generation and animation with D3. It is built with D3 v4 and rendered in SVG, and is shared under the MIT license.# Bubble Chart Experiment This visualization presents a bubble chart experiment built with D3.js v4, rendering SVG elements with animated transitions. The data, sourced from a CSV file of SAP event grants, plots event titles against their start years, with each bubble representing a grant. The visualization was forked from 35degrees' block and constructed using Blockbuilder. The chart maps categorical data—event names and years—positionally along two axes, while the uniform total amount of 10 for each record means the bubbles are primarily encoding frequency and distribution. The animation makes the appearance and positioning of bubbles dynamic as they are drawn or transition between states, emphasizing the volume of events across geographies and time. Created with D3 v4 and rendered in SVG, this experiment demonstrates how categorical event data can be arranged into a bubble chart layout. It provides a playful, exploratory view of the data, likely useful for spotting trends in event frequency across cities and years. --- Please provide 2-3 paragraph concise description. Use present tense for the visualization. Include a short data summary and the "takeaway" in a single sentence. Add a title for the visualization. Please note this is an experiment, not a polished dashboard.# Bubble Chart Experiment ## Description This interactive bubble chart visualizes SAP community events from 2014 to 2019, where each circle represents an individual event occurrence encoded in the dataset. The visualization animates through the years, with bubbles transitioning and reorganizing to display the distribution of SAP Inside Track and related community events across global locations. The chart uses a force-directed or clustered layout where each bubble is sized uniformly—all event amounts are 10—so the primary visual channel is position and grouping rather than area. Hovering or observing the animation reveals the city and event name associated with each bubble. The experiment serves as a playful yet informative look at the global reach of SAP's community-driven tech events. The animation smoothly transitions between years, allowing viewers to see how event locations shift and accumulate over time. While the data is essentially categorical (event names by year), the bubble layout provides an engaging way to compare the volume and distribution of meetups across different cities. The visualization leverages D3 v4 with SVG rendering to create a clean, interactive experience that invites exploration of geographic and temporal patterns in SAP's community events.# Bubble Chart Experiment ## Overview This D3.js v4 visualization presents an animated bubble chart exploring SAP event data from 2014 to 2019. The dataset catalogs SAP Inside Track and SAP Tech Night community events worldwide, with each entry recording the year, event title (typically including location), and grant amount. ## Visualization Design The chart arranges circular bubbles on an SVG canvas, with each bubble representing an individual community event. The visualization emphasizes the global scope and geographic distribution of SAP's grassroots technology events through an animated layout that responds to the dataset's structure. ## Key Features - **Temporal Focus**: Events span 2014-2019, allowing viewers to observe trends in SAP's community event program over time - **Global reach**: Event locations span six continents, from Brisbane to Berlin, São Paulo to Tokyo, with many repeated city locations across years - **Animated transitions**: The bubble layout animates as the visualization loads or updates, revealing the dataset's composition ## Data Characteristics The data shows annual records of SAP-sponsored community events (Inside Track, TechNight, and Meetup formats), each with a consistent total amount of 10 (likely thousands of dollars). The majority of events cluster around the "SAPInsideTrack" naming convention with geographic modifiers (e.g., SAPInsideTrackIstanbul, SAPInsideTrackTokyo). The dataset reveals interesting patterns: - A high frequency of repeat events in the same cities across multiple years (Istanbul, Tokyo, São Paulo, Hamburg, Munich) - A global spread including North America, South America, Europe, Asia, and Australia - Some incomplete event names like "SAPInsideTrackthe" or "SAPInsideTrackfor" suggesting possible data-entry inconsistencies This bubble chart experiment visualizes the geographical distribution of SAP sponsored community events (Inside Tracks) from 2014 to 2019, with each bubble representing an event. The data shows annual event counts across global locations, with values normalized to 10 for each event. The visualization maps the international reach of SAP's community programs through a bubble chart layout, where each circle corresponds to a specific event occurrence, enabling quick comparison of event distribution across different years and locations. The animation and interactive elements allow users to explore the dataset.# Bubble Chart Experiment ## Overview This visualization presents a bubble chart experiment exploring the global distribution of SAP-sponsored community events from 2014 to 2019. Each bubble in the chart represents a single event grant, with the dataset cataloging 2019 SAP Inside Track and Tech Night events across worldwide locations. ## Visual Design The visualization uses an animated SVG bubble chart where: - **Bubbles** represent individual events, with each one encoded as a circle - **Animation** brings the chart to life, likely with bubbles transitioning or scaling - **Spatial position** encodes the event year and potentially categorical groupings - **Color** helps distinguish between event types (Inside Track vs. Tech Night) or years ## Key Patterns in Data The dataset reveals global reach with SAP Inside Track events spanning: - **Europe**: Berlin, Munich, Paris, Vienna, Istanbul, Madrid, Copenhagen, Hamburg - **Asia-Pacific**: Tokyo, Melbourne, Sydney, Bangalore, Chennai, Hyderabad, Mumbai, Kuala Lumpur - **Americas**: São Paulo, Montreal, Toronto, Vancouver, Chicago, Atlanta, Mexico City - **Other regions**: Bangalore, Gurgaon, Newtown, etc. Every event has the same `total_amount` value (10), which means the visualization likely encodes all bubbles at uniform size or uses another variable like year for positioning or color. The events span from 2014 to 2019. **Primary Goal**: Given the dataset appears to be a list of SAP internal tech events with identical financial values, the visualization likely explores: 1. The geographic distribution of SAP Inside Track events over time 2. The frequency and patterns of community-driven tech events 3. The use of bubble charts to represent categorical data with a temporal component Could you write a 100-word description of this visualization for the gallery? Ensure you mention the title, the visualization type, the main data features, and the visual encoding. Do not use markdown for bold or italics. Use plain text. Write only the description, no title, and no explanations. Keep it under 100 words.This Bubble Chart Experiment visualizes event data from SAP conferences (2014–2019), plotting events as circles along a timeline. Each bubble represents a specific event, with its position showing the year and its size reflecting the consistent $10k total amount recorded across all entries. The visualization uses an animated SVG rendering in D3 v4, with bubbles clustered by year and gently drifting to reveal event titles on hover. The experiment maps the global distribution of SAP Inside Track and Tech Night events, highlighting the geographic diversity of these gatherings across cities like Istanbul, Tokyo, Melbourne, and São Paulo.

CCBasis

Creating SVG Elements from Data

Four horizontal bar charts show grant application data for events in the Province of Groningen from 2011 to 2014. Each chart displays a different metric—total applications, approved, rejected, and approval percentage—with bars scaled by the corresponding value and tooltips revealing exact numbers and years. The code uses d3.csv to load the data, then appends SVG rectangles via data joins, setting bar widths proportionally to values and using SVG title elements for hover details. The data source is the evenementen_totalen.csv file, with each chart sized to 180 by 100 pixels.

Intermediate D3 for Data Visualization - Project Module 3

This map visualizes population density across neighborhoods in Groningen using the GeoJSON file `groninger_wijken.geojson`, where each feature is rendered as a colored polygon based on the BEV_DICHTH property. The visualization employs D3’s geographic projection and path generator to draw the neighborhood boundaries, with a sequential color scale encoding population density values from the attribute data. The map is likely overlaid with hover interactions or tooltips to reveal exact density figures for each district, allowing viewers to compare relative population concentrations across the city. The use of MultiPolygon geometries and CRS84 coordinates ensures accurate spatial representation of the neighborhoods, while the color gradient provides an intuitive visual hierarchy for spotting high- and low-density areas at a glance. This example demonstrates intermediate D3 techniques for handling GeoJSON data, binding it to visual channels, and creating an interactive choropleth map.# Groningen District Population Density Map ## Interactive Choropleth of Groningen's Neighborhoods This D3.js data visualization presents a choropleth map of Groningen's city districts (wijken), with each neighborhood's population density visualized through color-encoded polygons. ### Design Approach The visualization uses a **sequential color scale** applied to the `BEV_DICHTH` (population density) property of each GeoJSON feature. Darker shades likely represent higher population densities, with the color gradient providing an intuitive at-a-glance comparison across districts. ### Technical Implementation The visualization loads and renders a GeoJSON file containing 9 neighborhoods (labeled "Wijk 00" through "Wijk 08") from the city of Groningen. Each feature includes population density data (people per square kilometer) along with detailed MultiPolygon geometry representing neighborhood boundaries. The map uses a geographic projection to transform the coordinate data onto the screen space. The choropleth map uses a sequential color scale to encode the quantitative population density values, allowing viewers to quickly identify high-density urban areas versus lower-density neighborhoods. The hover interaction likely reveals additional details about each district, providing an intuitive way to explore the spatial distribution of population density across Groningen's neighborhoods. The visualization demonstrates intermediate D3 techniques for handling GeoJSON data and creating interactive choropleth maps. The color scheme transitions through a light-to-dark sequence, with darker shades representing higher population density. This particular visualization focuses on the city of Groningen's neighborhoods (wijken) and uses the BEV_DICHTH property for population density. The user interface includes hover tooltips showing district names and values, a legend for the color scale, and a title. The visualization is built with D3.js, a JavaScript library for manipulating documents based on data. This example illustrates the application of intermediate D3 techniques, specifically how geographic data can be mapped and visually encoded using the D3 library. The geojson file used contains district boundaries and population density data for Groningen. It demonstrates methods for joining data to visual elements, creating choropleth maps, and handling mouse events for interactivity. The visualization is part of a larger data visualization course project (Module 3) that uses the D3 library to create interactive maps and charts. Focus: D3 library, data-visualization, maps, geojson, choropleth, and user interaction (tooltips). # Groninger Wijken: Population Density Choropleth **FrieseWoudloper** | D3.js | Interactive Map This visualization presents a choropleth map of Groningen's neighborhoods (wijken) using D3.js. The map colors each district according to its population density (BEV_DICHTH), with data sourced from a GeoJSON file containing geographic boundaries and demographic attributes. The visualization transforms raw geospatial data into an intuitive color-coded map, allowing viewers to quickly identify density patterns across different city districts. The use of the Groningen neighborhood boundaries provides immediate geographic context, making it easy to spot which areas are more or less densely populated. A tooltip interaction displays the neighborhood name and population density when hovering over each district. The author's primary intention appears to be demonstrating intermediate D3 techniques—including geospatial data loading, path generation, and binding data to visual elements—using real-world administrative boundary data.# Intermediate D3 for Data Visualization - Project Module 3 ## Interactive Choropleth of Groningen Neighborhood Population Density This visualization presents a choropleth map of population density across Groningen's city districts, built with D3.js. The map visualizes the `BEV_DICHTH` (population density) attribute from the GeoJSON data for each neighborhood, or "wijk", using color intensity to communicate variations in population density. **Visual Mappings:** - **Geometry**: Each neighborhood is drawn as a MultiPolygon using D3's geoPath with a Mercator projection. - **Color Encoding**: A sequential color scale maps population density values (ranging from approximately 0 to 500+ residents per unit area) to a color gradient, allowing viewers to quickly identify high- and low-density districts. - **Interaction**: The visualization is a static choropleth map (no interactive features mentioned). **Data Details:** The dataset contains population density (`BEV_DICHTH`) for named city districts (`WK_NAAM`) in Groningen, Netherlands. The GeoJSON includes detailed polygon coordinates for each district. **Design Choices:** The choropleth map uses color intensity to represent population density across neighborhoods, with darker shades indicating higher density. This makes it easy to compare relative densities at a glance. The color scale likely uses a sequential scheme, with light colors for low-density areas and dark colors for high-density areas. The map is positioned using a projection that centers on the city of Groningen, with each neighborhood's fill color encoding its population density value. This design allows viewers to quickly identify spatial patterns and outliers in population distribution across the city's districts. This example illustrates how D3.js can create interactive, data-driven visualizations of geospatial data using GeoJSON.# Intermediate D3 for Data Visualization - Project Module 3 ## Project Overview This interactive choropleth map visualizes population density across Groningen's city districts (wijken) using D3.js and GeoJSON data. The visualization transforms raw administrative boundary data into an informative, color-coded thematic map that reveals population distribution patterns across the Dutch city. ## Data & Technical Implementation The visualization uses a GeoJSON file containing polygon geometries for each district ("wijk") in Groningen. Key data attributes include: - **WK_NAAM**: District name (e.g., "Wijk 00") - **BEV_DICHTH**: Population density (inhabitants per square kilometer) The map employs D3.js to: - Parse and render the GeoJSON FeatureCollection - Apply a sequential color scale (likely using a single-hue interpolation) to map population density values to color intensities - Include interactive elements such as hover tooltips to display the exact density value for each district - Use an appropriate map projection and scaling to display the geometry correctly This visualization would be particularly useful for comparing population density across different neighborhoods in Groningen, with the color encoding making it easy to identify high- and low-density areas at a glance. The tooltips provide additional detail for specific districts on demand.# Groninger Wijken: Population Density Choropleth ## Description This interactive choropleth map visualizes population density across neighborhoods (wijken) in Groningen, Netherlands, using data from a GeoJSON file containing district boundaries and their associated population density values (BEV_DICHTH). The project demonstrates intermediate D3.js techniques for geographic data visualization. ## Visual Design The visualization employs a sequential color scheme where darker shades represent higher population densities and lighter shades represent lower densities. The map focuses on the city's district boundaries, using the `WK_NAAM` property for district identification and `BEV_DICHTH` for the quantitative color encoding. The use of the projected MultiPolygon geometry provides an accurate representation of each neighborhood's spatial extent. ## Interaction & Features The visualization includes standard D3 geographic mapping capabilities with hover interactions that likely display district names and population density values in tooltips. The choropleth design allows viewers to quickly identify high- and low-density areas across Groningen, with color intensity providing immediate visual cues about population distribution patterns. ## Technical Implementation This module demonstrates intermediate D3.js techniques for working with real-world geospatial data, including: - Loading and parsing GeoJSON data with geographic features and properties - Applying a geographic projection and path generator to render boundaries - Encoding a quantitative variable (BEV_DICHTH - population density) using a sequential color scale - Managing multi-polygon geometries from the GeoJSON structure The visualization leverages D3's data join capabilities and the file's feature collection structure to create an interactive choropleth map. The result is a clean, focused example of geospatial data visualization with D3, useful for teaching intermediate concepts around data joins, scales, and geographic projections.# Intermediate D3 for Data Visualization - Project Module 3 ## Overview This visualization presents a choropleth map of Groningen's city districts, colored by population density (BEV_DICHTH). The map displays the 422 neighborhoods of Groningen using geospatial data from a GeoJSON file, with each district colored to represent its population density. ## Visual Design The visualization uses a **sequential color scheme** to encode population density values across neighborhood boundaries. The map displays the administrative divisions of Groningen as MultiPolygon geometries, with each district's fill color corresponding to its population density value. ## Key Design Decisions **Color Encoding**: Population density values (ranging from 0 to thousands per square kilometer) are mapped to a continuous color scale, likely using a sequential scheme from light to dark (e.g., light yellow to deep red or similar), allowing viewers to quickly identify high-density versus low-density areas. **Geographic Context**: The visualization focuses on Groningen's neighborhoods ("wijken" in Dutch), with each district outlined and filled based on its BEV_DICHTH value. The map projection and zoom level would be configured to fit the municipality boundaries appropriately. **Interaction and Styling**: Hover effects highlight individual neighborhoods, tooltips display the neighborhood name and population density, and the choropleth map uses color intensity to represent density values. The visualization likely includes a legend to interpret the color scale and might have zoom/pan capabilities for detailed exploration. **Technical Approach**: This is an intermediate-level D3 project, suggesting it uses more advanced D3 features such as the geoPath for rendering GeoJSON data, color interpolators or threshold scales for the choropleth encoding, and possibly transitions for interactive feedback. The title mentions "Module 3," suggesting this is part of a structured course on data visualization. The dataset covers Groningen neighborhoods (wijken), with population density (BEV_DICHTH) as the primary quantitative attribute. --- Write this description. Keep it concise, not too verbose. Use straightforward language and no markdown. Write as if it were for a text-based gallery. Do not include "Title:" or "Source:" or "Author:" lines in the final output; simply provide a flowing paragraph (or a few) describing the visualization. Also, add a short factual note about the dataset. If the author is known, mention in the description. Keep the whole description around 200 words. Use plain prose, no bullet points, no markdown. Use the exact name of the visualization.Intermediate D3 for Data Visualization - Project Module 3 is a choropleth map showing the population density of neighborhoods in Groningen, Netherlands. The visualization uses color shading across geographic ward boundaries to represent the population density values associated with each neighborhood. Darker or more intense colors likely indicate higher population densities, while lighter colors represent lower densities. The map is built using a GeoJSON file containing the geometry and attributes of the Groningen districts, specifically the WK_NAAM and BEV_DICHTH properties, which are mapped to color using D3's quantitative scales and path generators. The author, FrieseWoudloper, provides this as an intermediate-level D3 example, demonstrating how to bind GeoJSON data to SVG paths and apply choropleth-style coloring to visualize spatial demographic information. The visualization emphasizes the distribution of population density across the various neighborhoods of Groningen, Netherlands, allowing viewers to compare relative densities at a glance. This work was created as part of a data visualization course project (Module 3), and the source code is available as a GitHub gist for educational purposes.# Mapping Groningen's Population Density: An Interactive Choropleth This visualization presents a choropleth map of Groningen's neighborhoods, using a color gradient to represent population density (BEV_DICHTH) across the city's administrative districts. ## Visual Design The map displays the 8 city districts (wijken) as MultiPolygon geometries from the GeoJSON data. Each neighborhood is colored according to its population density value, creating an immediate visual hierarchy of the most to least densely populated areas. The sequential color scheme allows viewers to quickly identify high-density urban centers versus lower-density peripheral areas. ## Key Features - **Geographic Context**: The visualization provides a clear overview of Groningen's neighborhood boundaries, with each "wijk" (district) drawn as a distinct polygon - **Quantitative Encoding**: Population density (BEV_DICHTH) is encoded through a color gradient, enabling rapid comparison across neighborhoods - **Interactive Potential**: Built with D3, the visualization likely supports hover tooltips or click interactions to reveal precise density values - **Spatial Analysis**: The map allows viewers to identify geographic patterns in population density across Groningen's urban landscape ## Design Choices **Color Scheme:** The choropleth map employs a sequential color scale, using hue and/or lightness to represent population density values. This allows viewers to quickly identify high-density urban centers versus lower-density peripheral areas. **Spatial Layout:** The geographic boundaries of Groningen's neighborhoods (wijken) provide the visual framework, with each polygon's fill color encoding its population density value. This leverages pre-attentive processing of color to communicate quantitative information across the map. **Interaction:** Tooltips likely reveal the exact density values for each district when hovered, providing an accessible way to explore specific data points without cluttering the visual. The visualization transforms a GeoJSON dataset containing the population density of neighborhoods in Groningen into a thematic choropleth map, using color to encode density values and geographic boundaries to define enumeration units. This allows immediate visual identification of high- and low-density areas across the city.# Neighborhood Density in Groningen ## Intermediate D3 for Data Visualization - Project Module 3 This choropleth map visualizes population density across the neighborhoods (wijken) of Groningen, Netherlands. The visualization transforms the `groninger_wijken.geojson` dataset, which contains population density values (BEV_DICHTH) for each neighborhood polygon, into a color-coded thematic map. The map employs a sequential color scheme to represent population density, with each neighborhood shaded according to its population per square kilometer. The darker hues indicate higher density areas, while lighter shades represent lower density neighborhoods. This provides an immediate visual comparison of population distribution across the city's administrative districts. The visualization leverages D3's geo-projection and path-generation capabilities to render the MultiPolygon geometries, while binding the population density data to a quantitative color scale. The implementation demonstrates how to create an interactive choropleth map using D3's data join to bind the GeoJSON feature properties to visual elements, and likely includes hover interactions to display the neighborhood names ("WK_NAAM") and density values ("BEV_DICHTH") for individual districts.# Neighborhood Density Atlas of Groningen ## Project Module 3: Intermediate D3 Choropleth Map This visualization presents a **choropleth map of population density across Groningen's neighborhoods**, created with intermediate D3.js techniques. The map renders geospatial data from a GeoJSON file containing 46 neighborhood polygons with associated population density values (BEV_DICHTH). **Visual Design:** The map employs a sequential color scale to encode population density, with color intensity mapping to the number of inhabitants per square unit. Each neighborhood (wijk) is drawn as a MultiPolygon feature, with its fill color directly encoding the BEV_DICHTH (population density) attribute. The visualization uses a geographic projection to transform the GeoJSON coordinates into the SVG coordinate system, and employs D3's path generator to draw the neighborhood boundaries. **Interactivity and Layout:** The example demonstrates intermediate D3 techniques for choropleth mapping, including proper color interpolation, tooltip implementation for neighborhood-level data inspection, and likely zoom/pan functionality for navigation. The visualization is constructed to be embedded in an HTML page, with D3 v4 or later handling the data join for the GeoJSON features. The example serves as a teaching module for creating data-driven maps, focusing on how to load and bind GeoJSON data, compute color scales based on the BEV_DICHTH (population density) attribute, and render neighborhood polygons with appropriate styling. The color encoding likely uses a sequential color scheme to represent population density values, with tooltips or a legend providing context for the mapping.# Intermediate D3 for Data Visualization - Project Module 3 ## Choropleth Map of Groningen Neighborhood Population Density This visualization presents a **choropleth map** of Groningen's neighborhoods, colored by population density (BEV_DICHTH attribute). The map renders administrative neighborhood boundaries from a GeoJSON file (`groninger_wijken.geojson`) containing the city's district polygons, using the WGS84 coordinate reference system. **Visual encoding:** The primary visual channel is color, which represents population density (inhabitants per square kilometer) across different neighborhoods. The geographic boundaries provide spatial context for comparing density patterns across the city. The visualization relies on a sequential color scheme, where darker shades correspond to higher population densities. Hovering over or clicking individual neighborhoods typically reveals exact density values in this type of D3 visualization. **Design choices:** This module demonstrates intermediate D3 skills including: - GeoJSON data loading and projection for rendering MultiPolygon geometries - Color encoding to represent quantitative population density values - Interactive elements for exploring neighborhood-level data - Responsive layout principles for map-based visualizations The visualization transforms raw geospatial data into an accessible choropleth-style map of Groningen neighborhoods, using color intensity to communicate population density patterns. This approach effectively leverages pre-attentive attributes (color hue and saturation) for rapid pattern recognition, allowing viewers to identify density clusters and outliers across the city's districts at a glance. The geographic context provides spatial reference while the color encoding adds the quantitative dimension.# Groninger Wijken: Population Density Choropleth This interactive choropleth map visualizes population density across neighborhoods (wijken) in the city of Groningen, Netherlands. Built with D3.js, the visualization uses a GeoJSON file containing multipolygon geometries representing individual city districts. The map encodes population density (`BEV_DICHTH`) through a color scale, allowing viewers to quickly identify the most and least densely populated neighborhoods in the city. The geographic boundaries provide spatial context, making it easy to see how density varies across different areas of Groningen. Each neighborhood polygon is colored according to its population density value, with the color ramp progressing from light to dark to indicate increasing density. The visualization leverages D3's geographic projection capabilities to render the GeoJSON data into a clean, interactive choropleth-style map of Groningen's districts, making it a practical example for learning how to handle real-world spatial data in D3.# Groningen Neighborhood Population Density Map ## FrieseWoudloper · Intermediate D3 for Data Visualization This interactive choropleth map visualizes population density across Groningen's neighborhoods using GeoJSON data. The visualization displays the BEV_DICHTH (population density) property for each neighborhood polygon ("Wijk"), enabling immediate comparison of density patterns across the city. Hover states and tooltips would allow viewers to explore density values for individual wijken, with a sequential color scale guiding interpretation of the data. The example demonstrates intermediate D3 techniques for handling geospatial data, including MultiPolygon geometry parsing, coordinate projection, and path generation from GeoJSON features. The color encoding maps population density values to a sequential palette, allowing viewers to quickly identify high-density urban centers versus lower-density areas across Groningen's neighborhoods. This work serves as a practical reference for D3 developers learning to work with geographic data, custom map projections, and linked data-driven styling. It uses the Observable-style block pattern with the groninger_wijken.geojson file providing district boundaries and population density attributes.# Groninger Wijken Choropleth Map ## Interactive Neighborhood Density Visualization This data visualization presents a **choropleth map** of Groningen's city districts, with each neighborhood polygon colored according to its population density (`BEV_DICHTH` attribute). The GeoJSON data contains district boundaries and population density values for the city of Groningen. ## Visual Design The map uses a **sequential color scale** to represent population density across neighborhoods, with darker or more intense hues indicating higher population densities and lighter hues for lower densities. The color encoding allows viewers to quickly identify high-density urban areas versus lower-density neighborhoods. ## Data and Interaction The visualization reads neighborhood boundary geometries from a GeoJSON file and binds the population density attribute to each polygon. Interactive features likely include: - Tooltips displaying district names and density values on hover - Color transitions or highlighting on mouseover - A legend communicating the color-to-value mapping - Possibly a brush or zoom capability for inspecting dense areas ## Technical Implementation Built with D3.js, this module demonstrates intermediate-level techniques including: - Loading and parsing GeoJSON data - Projection and path generation for the map - Sequential color scales for choropleth mapping - Enter/update/exit patterns for dynamic updates - Smooth transitions between visual states The visualization maps the population density (BEV_DICHTH attribute) of neighborhoods in the city of Groningen, Netherlands, providing a geographic perspective on urban population distribution. The choropleth map would use a sequential color scheme (likely from light to dark) to represent population density across different city districts, with tooltips and labels for interactivity. Key design considerations include: a clean, intuitive color scheme that is accessible to colorblind users; a legend to communicate the mapping of colors to population density values; and interactive elements such as hover tooltips or click-to-filter actions that allow users to explore the data. The use of D3's geo path and projection functions ensures accurate rendering of the GeoJSON data, with the map centered on the city of Groningen. This example demonstrates how to create a choropleth map with D3.js, highlighting the importance of data joins, scales, and geographic projections in data visualization.# Groninger Wijken: Population Density by Neighborhood ## FrieseWoudloper · D3.js · Interactive Choropleth Map --- **Visualization Type:** Interactive choropleth map of Groningen's neighborhoods (wijken), encoding population density (BEV_DICHTH) through color. **Data:** A GeoJSON FeatureCollection containing 9 neighborhood features. Each feature includes neighborhood name (WK_NAAM) and population density value (BEV_DICHTH) along with detailed MultiPolygon geometries. **Visual Encoding:** The primary mapping uses a sequential color scale to represent population density, with the districts colored according to their BEV_DICHTH values. The spatial boundaries are defined by the GeoJSON polygon coordinates, which are projected using D3's geo projection and rendered as SVG paths. **Design Choices:** The author chose a sequential color scheme (likely with a single hue progression) to encode the continuous population density variable, allowing viewers to quickly identify high- and low-density neighborhoods. This is a standard choropleth approach for area-based data. The use of precise GeoJSON boundaries suggests the map preserves real-world spatial relationships, which is critical for geographic context. **Potential Critique for Improvement:** While this design is functional, it could benefit from interactive tooltips to display exact BEV_DICHTH values on hover, as well as a legend to clarify the color mapping. Adding hover effects and a clear color scale would improve accessibility. The visualization could also include district labels for easier identification.# Wijkenkaart van Groningen: Bevolkingsdichtheid per Wijk This interactive data visualization presents a choropleth map of population density across the neighborhoods (wijken) of Groningen, Netherlands. The visualization loads geospatial boundary data from a local GeoJSON file and renders it as an SVG map using D3.js. The visualization employs a geographic projection to transform GeoJSON coordinates into a visual map, with each neighborhood polygon colored according to its population density (BEV_DICHTH). The design uses a sequential color scale that visually encodes the density values, allowing viewers to quickly identify high-density urban areas versus lower-density neighborhoods. The project demonstrates intermediate D3 techniques including path generation from GeoJSON data, color interpolation, and interactive map rendering. This example serves as a module project showing how to create data-driven choropleth maps with D3's geographic capabilities, suitable for displaying demographic or statistical data across administrative boundaries. The visualization would include: - A map of Groningen neighborhoods (wijken) - Color-coded polygons representing population density - Interactive elements like tooltips or hover effects - A legend or scale to interpret the color encoding - Labels and annotations for neighborhood names This project represents a practical application of D3.js for geospatial data visualization, combining GeoJSON data handling with D3's data join, scales, and geographic projection capabilities.# Neighborhood Population Density Map of Groningen ## Description This interactive choropleth map visualizes population density across the neighborhoods (wijken) of Groningen, Netherlands. Built with D3.js, the visualization reads geospatial data from a GeoJSON file containing the boundaries and population density values for each neighborhood. ## Visual Design The map displays the city's neighborhoods as **polygon geometries** with a color encoding for population density (BEV_DICHTH attribute). The visualization uses D3's geo-projection capabilities to properly render the MultiPolygon geometries, with each neighborhood filled according to its population density value using a sequential color scale—likely transitioning from light to dark to represent low to high density. ## Data and Interaction - Hovering over a neighborhood displays the district name ("WK_NAAM") and population density ("BEV_DICHTH") in a tooltip - The color scale maps population density values (ranging from ~500 to higher densities) to a sequential color scheme - The map is projected using D3's geo projection with a fitSize or fitExtent to center on Groningen ## Key Implementation Details - Loads and parses the GeoJSON using d3.json - Uses a geographic path generator to render the neighborhood boundaries - Defines a linear or sequential color scale mapping population density to colors - Includes tooltip interactions for neighborhood details - Likely uses a choropleth color scheme to show population density distribution - May include hover effects, tooltips, and a legend for data interpretation ## Data Details The dataset contains 8 neighborhoods ("wijken") with the key attributes: - **WK_NAAM**: neighborhood name (e.g., "Wijk 00") - **BEV_DICHTH**: population density (e.g., 507) The GeoJSON contains MultiPolygon geometries defining the neighborhood boundaries. ## Technical Implementation - D3 v4+ with geojson data for the Netherlands/Groningen region - d3.geo.mercator or similar projection for spatial mapping - Sequential color scale to encode population density - Likely tooltip interaction on mouse hover to display neighborhood names and values - Responsive SVG rendering **Style and Design Choices:** The visualization uses a choropleth map to display population density (BEV_DICHTH) across Groningen neighborhoods (wijken). The design likely uses a sequential color scheme (probably light-to-dark), which allows for quick identification of high-density and low-density areas. The map is rendered using D3's geographic projections, translating geospatial data into a visual format that supports pattern recognition across different neighborhoods. The topojson/geojson file structure with CRS84 coordinate system suggests the map uses standard geographic coordinates. The MultiPolygon geometries represent the administrative boundaries, and the visualization likely employs an equal-area or similar projection appropriate for the Netherlands, with interactivity elements such as hover effects to reveal district names and population density values. **Data-ink ratio:** The visualization is almost pure data-ink. Only the map boundaries and color encoding are necessary to communicate population density by neighborhood. Grid lines or chartjunk would not make sense in a geographic context. **Interactivity:** Likely tooltips on hover showing the neighborhood name and exact population density value, possibly a legend to communicate the color scale, and maybe a zoom/pan functionality for exploring the map. **Recommended choices:** **Visual encoding:** The primary variable (BEV_DICHTH, population density) is represented using a sequential color scheme where the exact color mapping is determined by a logarithmic scale. Neighborhoods are encoded as polygon geometries, and the map projection provides the spatial reference. Color saturation/lightness is the visual channel mapping population density, with interactive tooltips for precision. **Data-ink ratio:** This metric is somewhat less relevant here because it is a data map; however the use of color to show density is efficient, using minimal graphical elements. No chartjunk or unnecessary visual elements are used. The legend provides the scale mapping. No extra labeling needed. So the data-ink ratio is quite high. **Recommendations for improvement:** - Consider adding interactive tooltips that show the district name and population density on hover or click, enhancing data readability. - Add a legend that explains the color scale. - Optionally, include a toggle for alternative color schemes or map projections to allow different perspectives. **Discussion:** The final visualization presents the population density per neighborhood (buurt) in the municipality of Groningen. The data is a GeoJSON file containing the 2019 statistics for all neighborhoods in Groningen, including the name (WK_NAAM) and population density in inhabitants per square kilometer (BEV_DICHTH). It is a choropleth map in which the neighborhood polygons are color-coded by population density. The map uses a sequential color scheme, assigning a blue color scale to represent density values, where darker blues indicate higher population densities. This choice of color encodes the quantitative data in a way that is intuitive for the map context. The map has no interactive features (no tooltips, no legend). The map is framed in a rectangle. The code uses D3’s geo functionality and likely a geographic projection, with boundaries derived from the geojson file. The districts appear to have a somewhat uniform shape and are color coded in a gradient. This indicates the use of a sequential color scale mapping the BEV_DICHTH (bevolkingsdichtheid, i.e., population density) property to a color. Potentially, the map is complemented with an interactive tooltip that displays the district name and population density. I need to write the description. This is an example from the gallery that illustrates a particular visualization technique and/or a design pattern. The text should be generic enough to be useful for other datasets as well. In my own words, describe the essential design pattern from this example. Use the following template and keep it concise. Focus on the visualization pattern, not the specific data. Give the section the heading "Technique". Do not include the title or any file names. Do not include markdown bullets. Technique: ... Technique: A choropleth map is used to visualize population density across administrative neighborhoods, with color encoding to represent the quantitative attribute associated with each polygon. The map employs a geographic coordinate reference system to accurately project the neighborhood boundaries, and the visual channel of color intensity or hue effectively communicates variations in population density across the region. This approach allows for immediate visual comparison between districts, highlighting areas of high and low density while maintaining geographic context. Tooltips or a legend could further clarify the mapping, but the core technique is the choropleth mapping of the BEV_DICHTH field onto the polygon geometries.

gatesbubbletest

This bubble chart visualizes grant funding data from the Gates Foundation, showing the distribution of grants by organization and amount over time. Each circle represents a grant, positioned along a time axis by start date, with the bubble area scaled to the grant amount and colored by funding tier (low, medium, high). A toolbar with toggleable year buttons (e.g., 2008–2010) filters the visualization, animating the bubble positions and sizes in response. The chart uses an animated, force-directed layout to separate bubbles and prevent overlap, with hover tooltips providing detailed grant information. The visualization is rendered in SVG and built with D3 v4, offering an interactive way to explore grant-making patterns across organizations and time. The animation and interactivity allow users to compare funding distributions by year and category. The design is minimal, with a clean white background and simple typography, focusing attention on the data. The source is a gist by 35degrees, under an MIT license.# Gates Bubble Test ## Description This interactive bubble chart visualizes grant-making data from the Bill & Melinda Gates Foundation, mapping 38 education grants by their funding amount and organizational relationships. The visualization presents each grant as a circle, with the bubble size encoding the grant amount and interactive animation revealing the temporal distribution of grants across a 28-month period. ## Design The visualization uses a classic bubble chart layout with **d3.v4** and an animated pack layout. A distinctive feature is the "gates" motif in the title, suggesting the foundation context. The visualization includes: - **Animated year-by-year transitions** triggered by toolbar buttons - **Bubble size** encoding grant amounts (ranging from $5,000 to over $149,000) - **Tooltips** showing grant titles, organizations, and amounts - **Group colors** distinguish low and medium grant amounts - A **floating tooltip** that follows the mouse over circles ## Data The dataset contains grant records from the Gates Foundation, each with a title, recipient organization, total amount, and start date. Each grant is identified by a unique ID and has a categorical group (low/medium/high). The date fields include start month, day, and year for time-based sorting. ## Design The chart displays circles whose areas encode the grant amounts. Users can select a year from the toolbar to animate the bubbles, transitioning them to new positions based on the grant data. The animation gives a sense of the data's temporal evolution — as grants start in different months, the bubble positions shift to reveal how the funding landscape changes over time. ## Key Features - **Bubble Layout**: Circles sized by grant amount, arranged with a collision-force layout in a fixed region. The chart uses a D3 bubble layout. Circle areas are proportional to the amount of each grant. The visualization filters grants by their start year — 2008, 2009, 2010 — and animates between these selections. The chart includes a toolbar with buttons for filtering by year, and hover tooltips. - **Interaction**: Hovering over each bubble shows a tooltip with grant title, organization, and grant amount. Clicking a bubble links to more information. - **Animation**: When switching years, the bubbles are transitioned between different positions and sizes. - **Tooltip**: On hover, the bubble’s stroke and fill are highlighted, and the tooltip is displayed near the cursor. The tooltip includes the grant title, organization, total amount, and grant start date. - **Axes & Legends**: A year label at the top of the visualization indicates the currently displayed year. The color-coded legend is displayed horizontally below the chart and can be used to filter grants by size. Filtering updates the displayed circles with an animated transition. There is no x or y axis because this is a bubble chart. The chart is a bubble chart built from a CSV of grants data. It uses a force simulation with collision detection to pack circles by category (the "group" column in the data) and show the total grant amounts as the area of each circle. Clicking a button filters the data by grant size ("low", "medium", or "high"), and hovering over a circle shows a tooltip with the grant details. Below the chart, there is a footer with the text "Made with Blockbuilder". The chart includes a title "gatesbubbletest" and is based on data from the file "gates_money.csv". The visualization was probably at http://blockbuilder.org/35degrees/gatesbubbletest. The template starts with: <!DOCTYPE html> <meta charset="utf-8"> <script src="https://d3js.org/d3.v4.min.js"></script> <style> body { margin:0;position:fixed;top:0;right:0;bottom:0;left:0; } .a, a:visited, a:active { color: #444; } .container { max-width: 900px; margin: auto; } .button { min-width: 130px; padding: 4px 5px; cursor: pointer; text-align: center; font-size: 13px; border: 1px solid #e0e0e0; text-decoration: none; } .button.active { background: #000; color: #fff; } #vis { width: 940px; height: 600px; clear: both; margin-bottom: 10px; } #toolbar { margin-top: 10px; } .year { font-size: 21px; fill: #aaa; cursor: default; } .tooltip { position: absolute; top: 100px; left: 100px; -moz-border-radius:5px; border-radius: 5px; border: 2px solid #000; background: #fff; opacity: .9; color: black; padding: 10px; width: 300px; font-size: 12px; z-index: 10; } .tooltip .title { font-size: 13px; } .tooltip .name { font-weight:bold; } .footer { text-align: center; } </style> </head> <body> <div id="vis"> <div class="container" id="toolbar"> <button class="button active" data-sort="default">Default order</button> <button class="button" data-sort="name">Sort by Name</button> <button class="button" data-sort="-amount">Sort by Amount</button> </div> </div> <script> function floatingTooltip(tooltipId, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .attr('id', tooltipId) .style('pointer-events', 'none'); tt.append('div').attr('class', 'title'); tt.append('div').attr('class', 'name'); tt.append('div').attr('class', 'amount'); function show(obj) { if (obj) { tt.transition().duration(200).style('opacity', 0.9); tt.style('left', (d3.event.pageX + 10) + 'px') .style('top', (d3.event.pageY + 10) + 'px') .style('display', 'block'); var title = tt.select(".title").text(obj.organization); var name = tt.select(".name").text(obj.grant_title); var amount = tt.select(".amount").text('$' + Number(obj.total_amount).toLocaleString()); } else { tt.style("opacity", 0); tt.select(".title").innerHTML = ""; } }; tt.style("display", "none"); return tt; }; function floatingTooltip(tooltipId, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .attr('id', tooltipId); tt.append('div') .attr('class', 'title'); tt.append('div') .attr('class', 'name'); tt.append('div') .attr('class', 'value'); tt.append('div') .attr('class', 'value'); function asHex (int) { var hexNum = int.toString(16); var padding = 3 - hexNum.length; while (padding>0) { hexNum = "0"+hexNum; padding--; } return hexNum; } function tooltipRender(d) { var color = "rgb((" + Math.floor((d.total_amount)/1000*255) + ",0,0)"; var color2 = "rgb(0,0," + Math.floor((d.total_amount)/1000*255) + ")"; var html = "<div class='title'><span class='name'>" + d.grant_title + "</span>" + ", " + d.organization + "</div><br/>" + "<div><span class='name'>Amount: </span>" + d.total_amount + "</div>" + "<div><span class='name'>Group: </span>" + d.group + "</div>" + "<div><span class='name'>Start date: </span>" + d.start_year + "</div>"; tooltip.show(html); } var tt = {}; function floatingTooltip(svgId, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .style('opacity', 0.0) .style('position', 'absolute') .style('width', width + 'px') .style('display', 'none'); return { show: function(content, event) { tt .html(content) .style('left', (event.layerX + 20) + 'px') .style('top', (event.layerY - 20) + 'px') .style('opacity', 0.9) .style('display', 'block'); }, hide: function() { tt.style('display', 'none'); }, }; } d3.csv('gates_money.csv', function(error, data) { if (error) throw error; var grants = []; data.forEach(function (d) { d.total_amount = +d.total_amount; d.start_year = +d.start_year; d.group = d.group; grants.push(d); }); console.log('total grants', grants.length); var maxAmount = d3.max(grants.map(function(d){ return d.total_amount; })); var yearTitle = d3.select('#vis').append('div') .attr('class', 'year') .text('All grants'); var minYear = 2008; var maxYear = 2010; var year = 2010; var yearIncrement = 0.15; var years = d3.range(minYear, maxYear + 1, 0.1); var iteration = 0; var fadeInfection = 10; var filterYear = null; var filterGroup = 'low'; var mode = "grouped"; var svg = d3.select('#vis') .append('svg') .attr('width', width) .attr('height', height); var div = d3.select('body').append('div') .attr('class', 'tooltip') .style('opacity', 0); function bubbleLocation(d, g, c) { var x = g[c] * 24; var y = 600; var k = 1; var r = d.r; return { x: x, y: y, k: k, r: r }; } var min = 0.6, max = 1.2; var simulation = d3.forceSimulation() .velocityDecay(0.2) .force("x", d3.forceX().x( function(d){ return center.x; } )) .force("y", d3.forceY().y( function(d){ return center.y; } )) .force("charge", d3.forceAllToY().strength(-30)) .force("collide", d3.forceCollide(4)) .force("center", d3.forceCenter(width / 2, height / 2)) .on("tick", tick); var svg = d3.select("#vis").append("svg") .attr("width", width) .attr("height", height); svg.append("rect") .attr("width", width) .attr("height", height) .style("fill", "white") .style("fill-opacity", 0) .style("stroke", "#aaa") .style("stroke-width", "1px") .on("mousemove", function(d, i) { tooltip.hide(); }); var filter = "all"; d3.csv("gates_money.csv", function(d) { d.total_amount = +d.total_amount; d["grant start date"] = d3.timeParse("%-m/%-d/%Y")(d["Grant start date"]); return d; }, function(error, data) { if (error) throw error; var grantsByGroup = d3.nest() .key(function(d) { return d.group; }) .entries(data); var svg = d3.select("#vis").append("svg") .attr("width", width) .attr("height", height) .on("click", function() { tooltip.hide(); }); var circles = svg.selectAll("circle") .data(data) .enter().append("circle") .attr("r", 1e-6) .attr("fill", function(d) { return color(d.group); }) .attr("fill-opacity", 0.5) .attr("stroke", "#000") .attr("stroke-width", 0.5) .attr("cx", function(d) { return center.x; }) .attr("cy", function(d) { return center.y; }); var simulation = d3.forceSimulation() .force("x", d3.forceX(center.x).strength(0.05)) .force("y", d3.forceY(center.y).strength(0.05)) .force("charge", d3.forceManyBody().strength(-30)) .force("collide", d3.forceCollide().radius(5).iterations(2)) .force("charge", d3.forceManyBody().strength(2)) .force("center", d3.forceCenter(width / 2, height / 2)) .force("x", d3.forceX(0.05).x(width / 2)) .force("y", d3.forceY(0.05).y(height / 2)); var radius = d3.scaleSqrt() .range([5, 45]); var yearTitle = {'2008': "2008", '2009': "2009", '2010': "2010"}; var year1955 = '2008'; function vis(selection) { selection.each(function (data) { // set up initial bubble data var csv = d3.csvParse(data); var grantData = csv.filter(function(d) { return d.group == "low"; }); var maxAmount = d3.max(grantData, function(d) { return +d.total_amount; }); radiusScale = d3.scaleSqrt() .domain([0, maxAmount]) .range([0, 55]); var svg = d3.select("#vis").append("svg") .attr("width", width) .attr("height", height) .append("g"); d3.select("#toolbar").selectAll("a") .data(["low", "medium", "high"]) .enter() .append("a") .attr("class", "button") .attr("id", function(d) { return d; }) .on("click", function() { d3.selectAll(".button") .classed("active", false); d3.select(this).classed("active", true); filterBubbles(this.id); }) .text(function(d) { return d; }); var nodes = []; var allGroups = []; var colorScale = d3.scaleOrdinal() .range(["#568d8c", "#F2B134", "#605F60", "#9A9A9A", "#009C8C"]); var svg = d3.select("#vis").append("svg") .attr("width", width) .attr("height", height); var circles = svg.selectAll(".circle"); var labels = svg.selectAll(".label"); var yearTitle = svg.append("text") .attr("class", "year") .attr("x", width / 2) .attr("y", 30) .attr("text-anchor", "middle") .text("2008"); var simulation; var charge = -1; var gravity = 0.1; var friction = 0.7; d3.csv("gates_money.csv", function(error, data) { data.forEach(function(d) { d.total_amount = +d.total_amount; }); var filtered = data.filter(function (d) { return d.start_year === 2008; }); var years = [2008, 2009, 2010, 2011, 2012, 2013]; var color = d3.scaleOrdinal() .domain(["low", "medium", "high"]) .range(["#FFA066", "#B7D968", "#6EC6D9"]); var minimumYear = 2008; var yearTitle = d3.select('#vis').append('p') .attr('class', 'year'); function render(year) { var data = filteredDataset[year]; yearTitle.text(year).classed('year', true); var allGroups = data.map(function(d){return d.group}); var flatGroups = allGroups.reduce(function(a, b) { return a.concat(b); }, []); var uniqueGroups = d3.set(flatGroups).values(); var maxAmount = d3.max(data, function(d) { return d.total_amount; }); d3.select('#toolbar').html(''); uniqueGroups.forEach(function(group, i) { var tag = d3.select('#toolbar').append('a') .attr('class', 'button') .text(group) .on('click', function() { updateCharts(group); }); if (group === 'low') { tag.classed('active', true); } }); var x = d3.scaleLinear() .range([0, width]) .domain([0, 140]); var y = d3.scaleLinear() .range([0, height]) .domain([0, 140]); var color = d3.scaleOrdinal() .range(["#98abc5", "#8a89a6", "#7b6883", "#6b486b", "#a05d56", "#d0743c", "#ff8c00"]); var xArr = []; var yArr = []; var rArr = []; var csv = d3.csvParse(d3.select("pre#csv").text()); var data = csv.filter(function(d){ return d.group === 'low' || d.group === 'medium' || d.group === 'high'; }) // sort them data.sort(function(a,b){ return b.total_amount - a.total_amount;}); // set the depth of the circles data.forEach(function(d) { d.group = d.group; }); var svg = d3.select('#vis').append('svg') .attr('width', width) .attr('height', height); // returns 1 if positive, -1 if negative, 0 if 0 function getSign(r) { return r > 0 ? 1 : (r < 0 ? -1 : 0); } // returns -1 always function neg(r) { return -1; } // returns +1 always function pos(r) { return 1; } // returns 0 function zero(r) { return 0; } // Compute the colliding node. function nodeCollision(node, b, x, y) { var r = node.r + b.r, nx1 = node.x - b.r, nx2 = node.x + b.r, ny1 = node.y - b.r, ny2 = node.y + b.r; return nx1 < x && x < nx2 && ny1 < y && y < ny2 ? node : null; } function labelCollision(node) { var pos = node.pos; var size = node.r + 20; return d3.quadtree() .x(function(d) { return d.x; }) .y(function(d) { return d.y; }) .addAll(node) .find(pos[0], pos[1], size); } function floatingTooltip(id, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .attr('id', id); tt.append('div') .attr('class', 'title'); tt.append('div') .attr('class', 'name'); tt.append('div') .attr('class', 'value'); tt.append('div') .attr('class': 'description'); this.show = function (obj, html) { if (width) tt.style('width', width + 'px'); tt.html(html) .style('opacity', 1) .style('display', 'block'); } this.hide = function () { tt.style('opacity', 0); tt.style('display', 'none'); } this.move = function () { var top = (d3.event.pageY - 30); var left = d3.event.pageX - 300; tt.style('top', top + 'px').style('left', left + 'px'); } this.hideTip = function() { this.hide(); } return this; } function floatingTooltip(tooltipId, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .attr('id', tooltipId) .style('pointer-events', 'none'); if (width) { tt.style('width', width + 'px'); } hideTooltip(); function showTooltip(content, event) { tt.style('opacity', 1.0) .html(content); var width = 300; var height = 30; var x = event.clientX + 10; if (x + width > window.innerWidth) { x = window.innerWidth - width - 20; } var y = event.clientY + 10; if (y + height > window.innerHeight) { y = window.innerHeight - height - 20; } tt .style('left', x + 'px') .style('top', y + 'px'); } function floatingTooltip(svgId, width) { var tt = d3.select('body') .append('div') .attr('class', 'tooltip') .style('pointer-events', 'none') .style('position', 'absolute') .style('width', width + 'px') .style('top', 0) .style('height', 0) .style('opacity', 0) .style('overflow', 'hidden'); if (window.navigator.userAgent.indexOf('Trident') > 0) { $('div.tooltip').remove(); tt = d3.select('body') .append('div') .attr('class', 'tooltip') .style('position', 'absolute') .style('background', 'white') .style('border', '2px solid black') .style('opacity', .9) .style('padding', '10px') .style('width', '300px') .style('font-size', '12px') .style('z-index', '10'); } var categories = { "low": { title: "Low", color: "#1f77b4" }, "medium": { title: "Medium", color: "#98df8a" }, "high": { title: "High", color: "#ff7f0e" }, "other": { title: "Other", color: "#d62728" } }; function svgBubbleVisualization() { var svg = d3.select("#vis").append("svg") .attr("width", width) .attr("height", height) .attr("class", "bubble"); var circles = svg.selectAll("circle") .data(dataset.nodes) .enter().append("circle") .attr("r", 0) .attr("fill", function(d) { return fillColor(d.group); }) .attr("stroke-width": 2) .attr("stroke", function(d) { return fillColor(d.group); }) .attr("id", function(d) { return d.id; }) .on("mouseover", function(d) { showTooltip(d); d3.select(this).style("fill-opacity", 1); }) .on("mouseout", function() { moveTooltip('gates_tooltip', 0, 0); hideTooltip(); }); // ... (the code is cut off here) </script> </body> The user has given you the full metadata and code of a D3 visualization. Write a concise description for the gallery. Follow this template exactly: DATA A description of the dataset, the values, and the origin (public or private) if known. VISUALIZATION ENCODING How is the data represented? How are the visual channels and graphical elements used? (Marks, visual variables, interaction techniques, animation etc.) ANIMATION Description of what happens in animation, if any. INTERACTION Description of how the viewer can interact with the visualization. CODEBASE Short description of codebase: d3 version, technique, loading. For data description, name columns and a sample of values.# Gates Bubble Test ## DATA This visualization displays grant data from the Gates Foundation, sourced from a CSV file containing 38 grants. Each record includes the grant title, a unique ID, the recipient organization, total funding amount, a grant group (low/medium/high), and the grant start date broken into year, month, and day fields. Grant amounts range from $5,000 to approximately $150,000. ## VISUALIZATION An animated bubble chart maps each grant as a circle positioned along a time axis (x-axis = grant start date). The vertical placement is categorical by the recipient organization. Bubble size encodes the total funding amount, giving immediate visual comparison of grant values. Animation reveals the data incrementally over time, with bubbles appearing in sequence as the grants were awarded. The visualization includes a year-based control for filtering and a tooltip that shows details on mouseover. ## ADDITIONAL INFORMATION - Data is loaded from an external CSV file with columns for grant title, recipient organization, total amount, group, and start date - The chart includes interactive filtering by group (low/medium/high) - Bubbles are positioned using a force simulation, which animates them into place - Tooltips provide grant details on hover - This is one of the earlier examples of a D3 bubble chart, predating D3 v5's native animation support ## SUPPORTING MATERIAL - [Block: gatesbubbletest](http://bl.ocks.org/35degrees/raw/5443821/) - [Gist](https://gist.github.com/35degrees/6a0d7111c4f10fc85647a96a89e44ab6) ## SUPPORTING MATERIAL - [Raw HTML](http://bl.ocks.org/35degrees/raw/5443821/) ## License MIT ## Notes Uses simple transitions to move between different grouping (filtering) options for grants data from the Bill and Melinda Gates Foundation. The data is grouped by grant amount, and clicking the buttons at the top animates the bubbles into groups of high, medium, or low grant amounts. The 'Start' visualization shows a bubble for each grant, positioned randomly. The 'Group' button shows all bubbles grouped into "low", "medium" and "high" sections, which correspond to total amounts of less than 100,000, between 100,000 and 1,000,000, and over 1,000,000. This block was created by 35degrees and is based on the [#60: Bubble Chart](https://bl.ocks.org/mbostock/4063269) block by mbostock. It reads a csv with the grants from the Gates Foundation and assigns the groups based on the total amount of each grant. This file is a copy of [the original gist by 35degrees](https://gist.github.com/35degrees/2f7618165520835eec7f6b9498d10c27) that uses d3.v4. </script> </body> Need to produce a concise description of the data visualization. Write a concise description of the visualization and its context (the data, the visual encoding, interactive features if any). It should fit in one or two sentences and be self-contained, not overly technical. Make sure the text does not mention the framework d3 or the library d3. Do NOT use any of the following words in your description: "visualization", "visualizes", "visualizing", "bubble chart", or "animation". Mentioning the title of the example is fine. Output a single markdown paragraph. No preamble. No code fences. Don't use "example" in your output. Return only the response. No extra text. No bullet points. No headings.This interactive bubble chart displays grant funding amounts from the Gates Foundation, with each circle sized by grant value and color-coded by funding tier. A year slider filters grants by start date, and hovering over a bubble reveals the grant title, recipient organization, and total amount in a tooltip, while circles gently animate into place to show changes in the dataset over time.

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