B

Brian Hulbert

@bmh5227·18 public vizzes

Loading thumbnail…

CO2 Emissions Per Year Final

This interactive choropleth map visualizes historical CO2 emissions, population, and GDP for countries from 1850 to 2023, with a color gradient from green to red indicating value magnitude. Users can hover over countries to view specific data, adjust the year via a slider or scroll wheel, and switch between displayed parameters. The visualization uses React with D3 libraries including `geoNaturalEarth1` for projection, `geoPath` for map rendering, `sliderBottom` for the year control, and `interpolateRgb` for color scaling. Data comes from the Our World in Data CO2 dataset, loaded from a CSV file.

Loading thumbnail…

CO2 Emissions Per Year V6

This interactive choropleth map visualizes global CO₂ emissions, population, and GDP by country from 1750 to 2023. Users can scrub through years using a slider, hover over countries for tooltips, and switch between the three variables. The visualization uses D3.js for map projection, color interpolation, and slider controls, React for state management, and loads data from filtered_data.csv. Countries are colored with a green-to-red gradient reflecting the selected metric, with the legend adapted to the chosen variable’s unit.

Loading thumbnail…

CO2 Emissions Per Year V5

This interactive choropleth visualizes global CO2 emissions per country from 1850 to 2023, with a draggable year slider and a green-to-red gradient legend. Users can hover over countries to view precise emission values in a tooltip, and click the legend to filter countries by specific emission ranges. Built with React and D3.js, it uses `geoNaturalEarth1` projection, `geoPath`, and `sliderBottom` to render the map and controls. The visualization loads historical data from a CSV file and allows switching between emissions, population, and GDP metrics.

Loading thumbnail…

Global Tons of CO2 Emissions Per Year V4

This interactive choropleth map displays global CO2 emissions per country from 1850 to 2023, using a green-to-red color scale to represent emission magnitudes. Users can hover over countries to see specific emission values via tooltips, hover over the color legend to highlight matching countries, and adjust the year slider to filter data by year. The visualization is built with React and D3.js, utilizing `d3.geoNaturalEarth1` and `d3.geoPath` for map rendering, and `d3.sliderBottom` for time navigation. It loads data from a CSV file and world atlas GeoJSON, with colors interpolated using D3's `interpolateRgb` function.

Loading thumbnail…

Global Tons of CO2 Emissions Per Year V3

This choropleth map displays annual CO2 emissions per country from 1850 to 2023, colored on a green-to-red gradient with a legend, year slider, and hover highlighting for specific country values. The visualization uses React state to manage selected years and moused-over countries, while D3’s `geoNaturalEarth1` projection, `geoPath`, and `geoGraticule` render the world map. A D3 `sliderBottom` control adjusts the year, triggering color updates derived from the country’s emission value relative to the global min/max range.

Loading thumbnail…

Global Tons of CO2 Emissions V2

This visualization shows global CO2 emissions on a world map from 1850 to 2023, using color intensity to represent emission levels. A slider enables year selection, while a rectangular legend maps the green-to-red gradient to millions of tons. The map is rendered with React and D3, using `d3.geoNaturalEarth1` and `d3.geoPath` for geographic projection. Emission data comes from a CSV file, and the color scale applies a fifth-root transformation to visually differentiate lower values.

Loading thumbnail…

Global CO2 Emissions V1

This choropleth map visualizes global CO2 emissions by country from 1850 to 2023, with darker reds representing higher emission values. A slider lets users scrub through years, updating country fill colors dynamically. The visualization uses React for state management, D3’s `geoNaturalEarth1` projection with `geoPath` for rendering world geography, and `sliderBottom` from `d3-simple-slider` for the year control. Data comes from a CSV subset of the Our World in Data CO2 dataset, with countries lacking data shown using a diagonal slash pattern.

Loading thumbnail…

CO2 emissions Prototype

This choropleth map visualizes CO2 emissions by country for the year 2023. Countries are colored on a red intensity scale, with darker shades indicating higher emissions, while countries lacking data appear with a gray diagonal pattern. The visualization loads geographic path data from a static SVG file and emission data from a CSV, matching country names to their corresponding SVG paths. The map updates after both datasets load, applying fill colors based on emission values relative to a fixed maximum. The implementation uses React with d3-select for DOM manipulation and d3.csv for data parsing.

Loading thumbnail…

Project prototype v1

This interactive dashboard displays CO₂ emissions over time as a scatter plot, with each point colored by country using `d3.scaleOrdinal` and `d3.schemeCategory10`. A button toggles between the scatter plot and a world map rendered from an external SVG file. The scatter plot uses `d3.scaleLinear` scales and axes from `d3.axisBottom` and `d3.axisLeft`, while the map loads via `fetch` and applies randomly generated colors to regions. Data comes from a CSV file loaded with `d3.csv`.

Loading thumbnail…

CO2 emissions vs Year Project prototype v1

This interactive visualization toggles between a scatter plot of CO2 emissions over time and a world map. The scatter plot uses D3 scales and axes to render yearly emissions data from a CSV file, while the map displays a static SVG of world regions with randomly assigned colors. A React button switches between the two views.

Loading thumbnail…

CO2 emissions vs Year Project prototype v1

This prototype visualizes global CO₂ emissions changes over time using a choropleth world map. It loads yearly emission values per country from a CSV and maps them to a white-to-green color scale applied to SVG country paths. The React app manages the map state, while D3 v7 utilities handle CSV loading, color scaling, and the rendering logic via the `d3` library’s `select`, `scaleLinear`, and `csv` modules.

Loading thumbnail…

CO2 Emissions Project prototype v1

This prototype displays a static world map loaded from an SVG file, with the map scaled to fill the viewport. The visualization currently renders the map without data encodings or interactive elements. Built with React and D3 v7, the `viz.js` component fetches the `world.svg` file, injects its content into the SVG group, and sets the width and height attributes. Additional utility files define axis and label rendering functions, but they are not invoked in this version. The prototype serves as a base for future CO2 emissions data layering.

Loading thumbnail…

Fork of Circles with D3

This example plots polynomial functions as a series of red circles. The visualization calculates y-values for a given set of polynomial constants across a range from -10 to 10, then scales the resulting points to fit the screen dimensions. Using D3 v7’s `select` API and the `.join()` operation for efficient SVG element creation, each circle is positioned and rendered with a fixed radius and 70% opacity. By modifying the `polynomial_constants` array or the `range_min`/`range_max` values in the code, different curves can be displayed.

Loading thumbnail…

Fork of Basic Scatter Plot with React & D3 Emissions

This example uses a React app to render a basic scatter plot of CO2 emissions over time. The chart displays data points for various countries and regions from 1960 onward, sourced from a CSV file. The visualization is created using D3.js version 7, with the `select`, `csv`, `scaleLinear`, and `extent` APIs. A React `useEffect` hook loads the data and calls a custom `viz` function to render circles, mapping the year to the x-axis and emission value to the y-axis.

Loading thumbnail…

Fork of Loading Data - CO2 Emissions

This example shows raw historical CO2 emissions data from a Kaggle dataset, displaying each row as a JSON-formatted object in a `<pre>` element. The visualization begins with a loading state, then fetches and parses the CSV file using the Fetch API and D3’s `csvParse`. It converts the `year` and `value` columns to numbers and stores the array of country-level records in the app’s state, which is rendered via D3’s `select` and `join` methods.

Loading thumbnail…

Fork of Loading Data - Crop Yield

This example demonstrates loading and displaying crop yield data from a CSV file, showing raw parsed values for crops like maize and potatoes across multiple years in Afghanistan. The visualization initially renders a JSON representation of the dataset, then transitions to a structured table after the asynchronous fetch completes. Using `d3.csvParse` and `d3.select`, the code reads the remote CSV, converts numeric fields such as `Year_Code` and `Value` to numbers, and updates the DOM with the processed data. The state management via `setState` triggers re-rendering as data moves from an initial `undefined` state through `'LOADING'` to the final parsed array.

Loading thumbnail…

Fork of Loading Data - Lifestyle Health

This visualization demonstrates how to load and parse a CSV dataset using D3 v7, displaying the raw parsed data as JSON in a `<pre>` element. The example uses `csvParse` to convert survey data about lifestyle and health metrics, explicitly coercing numeric fields with the unary `+` operator. A state management pattern handles asynchronous data loading, showing a 'LOADING' placeholder before the fetched dataset appears. The code relies on `select` and `join` to render the data, sourced from a Kaggle dataset on disease risk from daily habits.

Loading thumbnail…

Fork of Circles with D3

This example visualizes a polynomial function as a series of fixed-size red circles plotted across a green background. The circles are positioned by evaluating the polynomial y = x² + 3x + 2 at negative increments between -10 and 10, then scaling both coordinates to fit the container dimensions. Using D3 v7, the code selects and joins circle elements via `select().selectAll().data().join()`, applying `cx`, `cy`, `r`, `fill`, and `opacity` attributes. The rendering is computed directly from polynomial constants rather than external data.