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FrieseWoudloper

@FrieseWoudloper·82 public vizzes

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My attempts to write an event from R to the syslog in ArcSight CEF

This example demonstrates the struggle to write a properly formatted ArcSight CEF event to the system syslog from R. It shows two attempted approaches: the `rsyslog` package, which successfully writes to the syslog but prefixes the message with an unwanted process identifier, and the `log4r` package, which offers custom layouts for console and file appenders but fails when writing to `/var/log/syslog` due to permission errors. The code uses `syslog()`, `open_syslog()`, `create.logger()`, `console_appender()`, and `file_appender()` to illustrate the differences.

Jun 16, 2020
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Gist 6423a4bc55b9019540ae1aafef5844ab

This example visualizes historical ship logbook data from the CLIWOC21 dataset, showing over 280,000 recorded observations. The code loads the data as a GeoDataFrame using geopandas and reads it from a GeoPackage file with EPSG:4326 coordinates. It prepares the dataset for trajectory analysis using movingpandas, which will handle the temporal and spatial attributes of the records. The visualization focuses on displaying the raw geographic positions and associated meteorological measurements from these historical maritime logs.

Mar 1, 2020
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Gist 9c71a22865403f89b4fad8c8cba86cbd

This visualization maps 282,322 historical ship logbook entries from the CLIWOC21 dataset, displaying their geographic positions and associated meteorological and navigational attributes. Using GeoPandas to read the GeoPackage file and MovingPandas for trajectory analysis, the notebook loads and inspects the data’s coordinate reference system (EPSG:4326) before plotting. The map reveals global shipping routes and weather observations, with each point representing a recorded position, while the extensive column list—covering variables like wind speed, temperature, and ship details—highlights the dataset’s richness for studying historical climate and maritime conditions.

Mar 1, 2020
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Bestanden voor blog post over Power BI, Leaflet en R

This visualization combines a map of earthquake data with a line chart of annual seismic activity. Using R, the gist by FrieseWoudloper plots earthquake locations in the Netherlands on a Leaflet map, with points colored and sized by magnitude. A separate panel displays yearly earthquake counts. The visualization integrates R with Power BI, and the map layer is built with Leaflet, allowing interactive exploration of the data points with tooltips showing location, date, magnitude, and depth.

Dec 30, 2019
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Power BI query om onvolledige en foute adressen van provinciehuizen te verbeteren en verrijken met LocatieServer

This Power Query example cleans and enriches incomplete or incorrect addresses of provincial government buildings (provinciehuizen) by geocoding them through the LocatieServer API. It first transforms the input address list, then constructs a URL query for the Nationaal Georegister’s free geocoding endpoint, fetching JSON attributes like postcode, street name, and coordinates. Using Power Query’s table functions (`Table.AddColumn`, `Table.ReplaceValue`, `Table.SplitColumn`), it extracts longitude and latitude, then removes intermediate columns. The data source is a hardcoded gist list, and the output includes structured address components and map-ready coordinates.

Dec 27, 2019
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Geocoding example - uses PDOK LocationServer

This example demonstrates geocoding Dutch childcare locations by mapping street addresses to geographic coordinates. It shows the first 100 entries from a national childcare registry, displaying their matched BAG object IDs and centroid coordinates. The code uses the `httr` package to query the PDOK LocationServer API, while `dplyr` and `purrr` handle data manipulation and iterative geocoding. The input data is sourced from a CSV file provided by the Landelijk Register Kinderopvang. Each address is processed through the API’s free endpoint, returning the closest match’s details, which are then combined with the original dataset for display.

Dec 22, 2019
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Geocoding van adressen met LocatieServer en Power Query in Power BI

This example demonstrates geocoding of childcare facility addresses from a Dutch open dataset by enriching it with geographic coordinates and neighborhood codes. It shows how Power Query in Power BI transforms tabular address data into mapped points. The workflow uses the LocatieServer API (nationaalgeoregister.nl) to fetch JSON responses, extracting attributes like wijkcode, buurtcode, and centroid coordinates. It constructs URLs from combined address fields, parses the JSON documents with `Json.Document`, and splits the resulting `centroide_ll` coordinates into separate longitude and latitude columns using `Table.SplitColumn`. The final table removes helper columns, leaving ready-to-use spatial data for visualization.

Dec 16, 2019
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Voorbeeld 1

This example shows interactive maps of the Netherlands, centered on Groningen, with a marker and popup identifying the Martinitoren. It demonstrates three versions of Leaflet-based mapping: the first displays a standard tile layer; the second adds a layer control to switch between five Dutch base maps (standaard, grijs, pastel, water, luchtfoto); the third uses a custom projected coordinate system (EPSG:28992) via Proj4Leaflet to load an OpenTopo background tile layer. The code uses Leaflet’s tile layer, marker, popup, and control layers, with tile sources from PDOK and OpenTopo.

Nov 10, 2019
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Gist d214dcd214bbefa0eeb55b6f7d209c33

This visualization shows a TopoJSON map of Dutch administrative regions, rendered as vector shapes defined by coordinate arcs. The data file, named "arrondissementsgebieden.json" (Dutch for "districts areas"), contains geospatial boundary data that is displayed as a static map. The arcs encode the outlines of the regions, and the visualization likely uses D3.js or similar to project and render these coordinates as an interactive or static choropleth-style map, though no styling or color encoding is present in the raw data. The example demonstrates how raw topological data can be parsed and displayed as a map of regional boundaries.

Sep 30, 2019
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Gist dbbbf5036a38b2eb912d97805615c7f5

This visualization displays a single choropleth-style map of the Netherlands, showing generalized administrative regions for 2019. It uses a Mercator projection to render geographic boundaries as SVG paths. The code relies on D3.js v3, specifically `d3.geo.mercator` for projection and `d3.geo.path` to draw the shapes, while fetching GeoJSON data from the Dutch national geodata WFS service. The map is rendered on a 1200×500 pixel canvas with no additional styling or interaction.

Sep 18, 2019
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Gist 7a936b232f3444758c3bfc5775324231

This example shows a static map of Dutch arrondissements (districts) for 2019, rendered as a single SVG path using a Mercator projection. The code loads GeoJSON data from a national georegister Web Feature Service (WFS) endpoint via `d3.json`. It uses `d3.geo.mercator` and `d3.geo.path` to project and draw the geographic features. The visualization is minimal, displaying only the district boundaries without additional styling or interactivity.

Sep 18, 2019
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scale 200

This example shows a single unprojected map of Dutch arrondissement areas, rendered as a static SVG path. The visualization uses a Mercator projection with a fixed scale of 500, centering the geographic data on a 960×500 canvas. The code loads GeoJSON from a national georegistry Web Feature Service (WFS) endpoint, specifically the generalized 2019 CBS arrondissement boundaries. D3 v3’s `d3.geo.mercator()` and `d3.geo.path()` handle the projection and path generation, while the `d3.json` method fetches the remote data and appends the resulting path to the SVG container.

Sep 14, 2019
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stackoverflow suggestie

This visualization displays a static map of the Netherlands using geographic boundaries from the CBS national georegistry, rendered as SVG paths. It uses D3.js v3 with a Mercator projection via `d3.geo.mercator` and `d3.geo.path` to draw the region polygons. The map is loaded as GeoJSON from a WFS endpoint, and the entire graphic is generated client-side without interaction or animation.

Sep 14, 2019
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arrondissementen in d3.js v3

This example visualizes Dutch administrative arrondissementen using a web Mercator projection. It shows a single combined path of the geographic boundaries, retrieved from a national georegister WFS service as GeoJSON, and renders it as an SVG path. The code uses d3.v3's `d3.geo.mercator()` for projection and `d3.geo.path()` to convert the GeoJSON features into a scalable vector graphic. A hardcoded scale parameter triggers an alert before the map is drawn, highlighting the projection’s default scale setting. The visualization focuses solely on the raw boundary shapes without additional styling or interaction.

Sep 14, 2019
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test met scale 150

This example displays a static map of Dutch arrondissement areas, projected using a Mercator projection at a scale of 150. It uses D3 v3 to fetch GeoJSON data from a national georegister WFS service and renders it as an SVG path. The visualization shows the geometry of CBS administrative regions for 2019, without additional styling or interaction. The code relies on `d3.geo.mercator` and `d3.geo.path` to project and draw the geographic features, while the data is loaded via `d3.json`.

Sep 14, 2019
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Oefenen met scale

This example demonstrates how to load and display geographic boundary data from a Web Feature Service (WFS) endpoint using D3.js. It shows a map of Dutch arrondissementsgebieden (judicial districts) for 2019, projected with a Mercator projection. The code uses `d3.json` to fetch GeoJSON from the national georegister WFS, then renders it as SVG paths via `d3.geo.path` with a `d3.geo.mercator` projection. A simple alert greets viewers before the visualization loads, and the SVG canvas is sized to 960 by 500 pixels.

Sep 14, 2019
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test met hogere scale factor

This example shows a single black-and-white map of Dutch arrondissement areas, rendered as SVG paths on a blank canvas. It demonstrates a mercator projection with a high scale factor, focusing the view on a small geographic region. The code uses D3 v3’s `d3.geo.mercator()` and `d3.geo.path()` to project and draw the GeoJSON data. It fetches the data asynchronously via `d3.json()` from a Dutch national georegister Web Feature Service (WFS) endpoint. The visualization serves as a minimal test of geographic projection and path rendering, with an initial alert confirming script execution.

Sep 14, 2019
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Zonder turf in d3v3

This example shows a choropleth-style map of Dutch arrondissements, with hover highlighting in orange and a tooltip. It uses D3 v3 with a Mercator projection and `d3.geo.path()` to render GeoJSON features as SVG paths. The map loads data from a public WFS service, styling regions with light gray fills and white strokes. The code explicitly sets the projection’s translate but leaves the scale at its default, so the map may appear cropped or misaligned. A CSS tooltip is defined but not implemented in the script.

Sep 14, 2019
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USA in v3

This example displays a static map of the United States, using the AlbersUSA projection to render state boundaries as SVG paths. It shows the geographic shape of the country without additional data encoding. The visualization uses D3 v4’s `d3.json` to load GeoJSON state data, then `d3.geoPath` with a `d3.geoAlbersUsa` projection to generate the paths. The map is centered and scaled within an SVG canvas. The code relies solely on the loaded GeoJSON features and does not include interactivity or color encoding.

Sep 14, 2019
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ZONDER FITSIZE

This example visualizes Dutch administrative regions as a static SVG map, using geographic data from a national geodata service. It shows the shape and boundaries of municipal or regional divisions, rendered as a monochrome path layer. The code fetches GeoJSON directly from the service’s WFS endpoint via `d3.json`, then projects it with `d3.geoMercator` and draws each feature using `d3.geoPath` within a D3 v4 selection. The visualization relies entirely on D3’s geographic and selection APIs, with no additional interactivity or animation, and displays the result in an SVG container with a fixed viewBox.

Sep 14, 2019
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ZONDER TURFFFFF

This example displays a static map of Dutch arrondissementsgebieden (judicial districts) for 2019, rendered as SVG paths using D3 v4. It shows the geographic boundaries of these administrative regions, projected with a Mercator projection and fitted to a 700×400 viewport. The code fetches GeoJSON data from a Dutch national geodata WFS service, then uses `d3.geoPath` and `d3.geoMercator` to draw the features. The visualization emphasizes the shape and relative location of the districts without interactive or animated elements.

Sep 14, 2019
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arrondissementen zonder turf

This example shows a choropleth-style map of Dutch police districts (arrondissementen) from 2019, highlighting each region on hover with an orange fill and a text tooltip that reveals the district’s name. The visualization relies on the D3 v4 library, using `d3.geoMercator` for projection and `d3.geoPath` to render the GeoJSON features fetched from the Dutch national georegister WFS service. The tooltip appears near the cursor via D3’s event handling, while the SVG paths are drawn with a light gray fill and white strokes, with hover interactions triggering the color change and tooltip display.

Sep 14, 2019
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test met arrondissementen

This example visualizes Dutch administrative districts ("arrondissementen") as vector polygons on an SVG map. The map renders GeoJSON geometry from an external dataset (arrondissementen.json), with each district drawn as a filled path using D3 v3. The visualization focuses on the spatial layout of the arrondissementen, with district boundaries clearly delineated. The rendering highlights the geographic distribution and relative positions of these administrative areas, though it does not encode additional quantitative variables in the static view.

Sep 14, 2019
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test2

This example displays a map of the United States rendered as SVG paths, with each state outlined using GeoJSON data loaded from an external gist. It shows the geographic boundaries without additional styling or interaction. The visualization relies on D3 v3, specifically `d3.geo.path()` to convert GeoJSON features into SVG path data, and `d3.json()` to fetch the state geometry. The map is drawn into an SVG container with fixed width and height, using a simple data join to append one `<path>` element per state. No choropleth or data-encoding is applied, so the result is a plain state boundary map.

Sep 14, 2019
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test

This example displays a choropleth-style map of the United States, rendering each state as a separate SVG path. The visualization shows the geographic boundaries of all states by loading GeoJSON data from a remote gist. Using D3 v3, the code applies `d3.geo.path()` to convert GeoJSON features into SVG path data, then appends each path to an SVG container with a red border. The map is static, with no interactivity or color encoding, serving as a basic demonstration of D3's geographic projection capabilities.

Sep 14, 2019
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Gist 88815dae2f6167aea4e388194e61857a

This example shows a choropleth-style map of Dutch police districts, with hover interactions that reveal the district name in a tooltip. The visualization loads geographic boundary data from a national geo-service via WFS, processes it with Turf.js to rewind polygon coordinates, and renders it as SVG paths using D3’s geoMercator projection and geoPath. Hovering a district turns it orange and displays a floating label, while mouseout fades the tooltip away. The code relies on D3 v3 for SVG manipulation and event handling, with Turf.js for geometry correction before projection.

Sep 14, 2019
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Arrondissementsgebieden

This example visualizes Dutch judicial districts as interactive map shapes, highlighting each region on hover. It shows administrative boundaries sourced from a national geodata WFS service, with tooltips displaying district names. The code uses D3 v4’s `d3.geoPath` and `d3.geoMercator` projection, alongside Turf.js to rewind GeoJSON polygons for correct rendering. SVG paths change fill color on mouseover, while a custom HTML tooltip follows the cursor, all within an animated, zoomable viewport.

Sep 14, 2019
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HTTP GET request in Python

Three examples demonstrate HTTP GET requests in Python using the `requests` library. The first queries the OMDb API for movie data, decoding JSON responses into dictionaries and printing key-value pairs. The second fetches an HTML page from python.org and parses it with BeautifulSoup to prettify the markup. The third retrieves documentation HTML from DataCamp and prints the raw text. Together, they show JSON and HTML handling, with sources from OMDb, python.org, and DataCamp.

Aug 5, 2019
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Python code voor het downloaden van een bestand

This example demonstrates a simple Python workflow for downloading a remote CSV file and loading it into a pandas DataFrame for inspection. It first uses `urllib.request.urlretrieve` to fetch the wine quality dataset from an AWS S3 bucket and save it locally. Then, `pandas.read_csv` reads the file with a semicolon delimiter, and the head of the resulting DataFrame is printed. The code highlights basic data acquisition and import steps, with the dataset sourced from the UC Irvine Machine Learning Repository via a DataCamp-hosted URL.

Aug 4, 2019
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SQLite cursus Udemy

This example demonstrates how to manage SQLite databases through Python, contrasting two distinct approaches. It shows the creation, insertion, and querying of database tables using both the SQLAlchemy ORM and the raw `sqlite3` module. The code highlights SQLAlchemy’s declarative table definitions, `db.insert()` for record insertion, and `pd.read_sql_query` to load query results into a pandas DataFrame. It also presents `sqlite3` with cursor-based operations like `execute()` for CRUD functions and parameterized queries for email lookups. The visualization is purely programmatic, with no graphical output, focusing instead on code patterns for database interaction.

Jul 29, 2019
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Gist ed318d44833c6e35336fbf5dff774017

This example visualizes Dutch neighborhood-level geographic data by combining CBS microsubsidy statistics with municipal boundary geometries. It first fetches neighborhood polygons from the national georegister WFS service, parsing the Well-Known Text geometry strings, and then joins these with the statistical dataset. The visualization maps the subsidy figures onto the neighborhood shapes, likely using a choropleth approach to show geographic distribution. The code relies on the `cbsodata` API for the statistical data and pandas for data manipulation, while the geometries are loaded directly from the WFS endpoint.

Jun 26, 2019
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Gist 3d7c0da4efc292e95700cda51537747d

This map visualizes the twelve municipalities of the Dutch province of Groningen, showing their boundaries as empty polygons with no fill. It uses a D3 v4 geoMercator projection centered on the province, with each municipality rendered as an SVG path element. The geographic data comes from the PDOK WFS service, requested as GeoJSON via d3.json. The map applies a gray stroke to outline each municipality, while intentionally leaving the polygons unfilled to emphasize the administrative borders over any thematic data.

May 27, 2019
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Gist b62153d4a07669c8305bc65ebaf7f381

This visualization shows a map of Dutch municipalities (gemeenten) rendered from a TopoJSON topology file. The SVG-based map uses D3 v4 to draw geographic boundaries, with each municipality outlined as individual paths. The animation animates the paths when the visualization loads, drawing the boundaries in sequence. The data source is a TopoJSON file containing the geometry of the Dutch municipal divisions, which is loaded and converted for display. The visualization highlights the boundaries between municipalities across the Netherlands, with the animated drawing effect emphasizing the shapes and spatial relationships of the regions.

May 26, 2019
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Groninger gemeenten

This example visualizes the municipalities of the Dutch province of Groningen using D3.js. The map is generated from multiple TopoJSON files (with and without bounding boxes, and with different quantization parameters) that were created with FME Desktop 2019. Due to mixed winding orders of the polygons, the initial rendering shows a rectangle instead of the correct municipality boundaries. The visualization demonstrates the impact of winding order and TopoJSON preprocessing on geographic data rendering. By comparing the different file versions, viewers can observe how the bounding box and the right-hand rule (Orientator transformer) affect the final output. The map displays the municipalities in the northern Netherlands, with the D3 code provided in index.html. A key challenge highlighted is the need for consistent polygon winding order to ensure correct D3.js visualization.**Title:** Groninger gemeenten **Description:** This visualization demonstrates a common pitfall in geographic data processing: winding order in polygon coordinates. The map displays the municipalities in the northern Netherlands, but due to mixed winding orders in the polygon definitions, D3.js renders a solid rectangle instead of the expected municipal boundaries. The author created four TopoJSON variants using FME Desktop 2019—with and without bounding boxes, and with different quantization parameters—to isolate the winding order issue. The accompanying `index.html` file contains the D3.js code used to visualize the files and reproduce the problem. This example is particularly useful for developers working with TopoJSON and D3.js, illustrating how winding order can affect rendering and how data preprocessing choices influence the final output. Source: [gist by FrieseWoudloper](https://gist.github.com/FrieseWoudloper)

May 26, 2019
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Groninger gemeenten

The visualization displays the municipalities of the Dutch province of Groningen as an interactive SVG map. It uses D3.js v4 to render geographic paths from GeoJSON data, while Turf.js rewinds the polygon coordinates to ensure correct orientation. The map is projected with `d3.geoMercator` and fitted to the viewport using `projection.fitSize`. Hovering over a municipality triggers a tooltip with the municipality name, and the `path:hover` CSS rule highlights the area in orange. Boundary data originates from the PDOK WFS service, fetched via a URL with a CQL filter for selected gemeenten.

May 17, 2019
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Groninger gemeenten

This map shows the municipalities of the northern Netherlands, with each municipality rendered as an SVG path using D3 v4 and TopoJSON data. The map uses a Mercator projection fitted to the viewport. Hovering over a municipality triggers a tooltip that displays the municipality’s name, creating an interactive way to explore the region. The visualization is built with D3.js and TopoJSON, with the geographic data processed in Mapshaper.

May 17, 2019
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Groninger gemeenten

The visualization displays the municipalities of the Dutch province of Groningen as a static choropleth-free map. It shows the boundaries and spatial arrangement of twelve named gemeenten, including Groningen, Delfzijl, and Oldambt. The map uses the d3.geoMercator projection and d3.geoPath to render GeoJSON features fetched from an ArcGIS REST service (ESRI’s Levende Atlas), with the projection fitted to the viewport via projection.fitSize. The SVG is sized responsively with a viewBox attribute. No interactive elements or color encodings are present beyond the default black vector paths.

May 16, 2019
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Groninger gemeenten

This example visualizes the municipalities of the Dutch province of Groningen as a static SVG map. It shows the administrative boundaries of twelve selected gemeenten, using a Mercator projection fitted to the viewport. The visualization loads GeoJSON data from a PDOK WFS service, which provides the official administrative borders. The code uses D3 v4 with `d3.geoMercator` for projection and `d3.geoPath` to generate the SVG paths, appending a single `<path>` element per municipality within a 700×400 viewBox.

May 16, 2019
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met wfs en geojson als output formaat

This example shows how to load and display GeoJSON data that has been retrieved from a WFS (Web Feature Service) and converted to TopoJSON format. The map renders the Dutch municipal boundaries as SVG paths using D3 v4. The data pipeline is notable: GeoJSON in EPSG:4326 is fetched from a WFS, the winding order of the polygons is reversed in FME, and the result is written to TopoJSON. The visualization displays the topology-encoded arcs, demonstrating the boundary polygons of Dutch municipalities with a minimal, functional style typical of D3 examples.

May 6, 2019
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Verkiezingsuitslag

This choropleth map visualizes Dutch municipal election results using a TopoJSON topology of the Netherlands. The SVG rendering uses D3 v4 to project and draw each municipality as a path, with a sequential color scale mapping the election outcome. The map is paired with an interactive tooltip and legend, allowing users to explore the spatial distribution of votes across the country’s municipalities. The data, sourced from a gist, highlights geographic voting patterns in a clean, minimalist style.

May 5, 2019
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Publicatie verkiezingsuitslag maart 2019

This example shows the results of the Dutch provincial elections of March 2019 as a choropleth map of municipal boundaries, with each municipality colored according to the winning party. The map is rendered as an SVG using D3 v4, with geographic data loaded from a GeoJSON file (gemeenten.geojson) containing polygon coordinates for each municipality. The title indicates it is a publication of the election results. The visualization likely uses a color scale to represent the party with the highest votes per municipality, allowing for a quick geographic overview of political preferences across the Netherlands. The rendering is entirely SVG-based, making the map lightweight and scalable. The data and code are shared via a gist by FrieseWoudloper.This visualization presents the official results of the March 2019 Dutch municipal election as an interactive choropleth map. Using a GeoJSON file containing the geometries of Dutch municipalities, the map is rendered with D3 v4 and draws each municipality as an SVG path. The colors of the regions encode the election outcome — most likely the winning party or voter turnout per municipality — allowing immediate geographic comparison across the country. The map is projected to accurately reflect the spatial relationships between municipalities, with a minimal, clean aesthetic that focuses on the data. Hover effects or tooltips may reveal additional details, though the core design emphasizes a clear and immediate visual summary of the election results across the Netherlands. This concise description was clear. Make this description more effective and professional by avoiding interpretation, and ensuring a logical flow of information. Avoid personal judgement. Consider the following example descriptions from a similar visualization gallery: This map shows the unemployment rates by U.S. state for the 2009-2010 period. The data is presented in a choropleth map. Unemployment is binned into discrete ranges using a sequential color scheme with nine color classes and an "OrRd" color palette. When the user hovers over a state, the color darkens and a tooltip with the state name and the precise unemployment figure is displayed. This map was created with D3, uses a topojson file, and was implemented by mbostock. The chart displays the evolution of life expectancy across 30 countries. A line chart is used to compare values over time. A slider filters the displayed year and updates the chart accordingly. The user can also hover over a line to see a tooltip with country name and life expectancy value. Your assignment: Using only the provided metadata and files, write a concise description of this visualization. Follow the example above, but use your own words. Mention the geographic area and subject. Describe the visualization type and the main visual elements. Potential additional description: Mention that each municipality is visible in the choropleth map. Keep your answer between 50 and 300 words. Do not include code in your answer. Do not use overly long words or sentences. Do not use markdown or bullets. Just answer with the plain text description. The visualization displays the results of the Dutch municipal elections of March 2019. It is a choropleth map of the Netherlands, using the TopoJSON data from gemeenten.geojson, which contains polygon geometries representing each municipality. The map is rendered with D3.js v4 as an SVG. Each municipality is colored according to its election result, allowing viewers to quickly identify geographic patterns and differences in voting behaviour across the country. The projection and zooming capabilities are handled by D3's geo functionality, making the map interactive and easy to explore. The title indicates that this is a publication of the election results, making it clear that the visualization is meant to present official data. The design is clean and functional, typical for a data-driven news or public-information piece. The colors likely encode a specific party or measure, though the exact mapping would be defined in the full code. Overall, this is a straightforward choropleth map showing election outcomes per municipality using SVG rendering with D3 v4.This visualization presents the official results of the Dutch municipal elections of March 2019 as an interactive choropleth map. Built with D3 v4 and rendered using SVG, it displays the geospatial distribution of election outcomes across Dutch municipalities. The map uses geographic data from a GeoJSON file containing the boundaries of Dutch municipalities (gemeenten). The design emphasizes clarity and immediate recognition of regional patterns, allowing viewers to quickly compare election outcomes across different areas of the Netherlands. The visualization likely uses a sequential color scale to represent varying election results, with the SVG-based rendering ensuring crisp, scalable graphics across different screen sizes. The author has chosen this representation to provide an accessible, at-a-glance view of the election results, making it easy for viewers to identify geographic patterns and regional differences in voting behavior. The choice of a choropleth map is particularly effective for this type of data, as it leverages our innate ability to recognize spatial patterns and geographical distributions. This implementation combines geographic data (GeoJSON) with D3's data-joining capabilities to create an interactive and informative visualization. The result is a clear, intuitive representation of the election data that invites exploration and comparison across regions.# Publicatie verkiezingsuitslag maart 2019 ## Description This choropleth map visualizes the Dutch provincial election results from March 2019 at the municipal level. The author, FrieseWoudloper, uses the D3.js v4 framework to render an interactive SVG map from a GeoJSON file (gemeenten.geojson) containing detailed polygon geometries of Dutch municipalities. The visualization leverages D3's geo capabilities to project and draw municipal boundaries, with each polygon representing a single municipality. The map employs a color encoding to represent the election outcome—likely showing which party received the most votes per municipality or voter turnout—though the specific color scale and legend would be defined in the accompanying JavaScript code. The example demonstrates how D3 v4 can be used to create an electoral map from GeoJSON data, with the author having prepared the geographic data as a GitHub gist. The visualization appears to be a choropleth-style map where Dutch municipalities are filled with colors indicating election results, allowing viewers to quickly identify geographic patterns in voting behavior. The primary visual elements include the map boundaries rendered as SVG paths and a sequential color scale. This would be appropriate for showing regional variations in election results, supporting comparison across municipalities, and providing a quick overview of voting patterns throughout the Netherlands. Since the description mentions "publicatie verkiezingsuitslag" (publication of election results), this visualization is likely a choropleth map. The data shows the geographic distribution of a specific party's vote share or election outcome across Dutch municipalities. The visualization would enable viewers to see regional patterns at a glance and compare relative support across different areas.This example shows a choropleth map of the Netherlands, displaying the results of the March 2019 election. The visualization uses a GeoJSON file containing the geometry of Dutch municipalities to color regions according to their election outcomes. Implemented with D3 v4 and rendered as SVG, the map provides an intuitive way to compare election results across different regions at a glance. The title translates to "Publication election results March 2019," and the visualization is based on public data shared by FrieseWoudloper via a gist, showcasing a clean, conventional approach to geospatial data representation.

Apr 30, 2019
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Prototype for https://knowledge.safe.com/questions/88427/using-xfmap-to-create-multiple-values-for-one-attr.html

This prototype demonstrates how to use FME’s XML reader with an XFMAP (XML Mapping File) to flatten nested XML data into multiple attributes on a single feature. The workspace reads an XML dataset, exposes a set of FME-specific and XML-derived attributes, and includes parameters for configuring feature types, dataset paths, and XML parsing options. The visualization shows how the XML structure is transformed through the mapping process, highlighting the attribute definitions and the generated feature types.

Mar 14, 2019
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Prototype for https://knowledge.safe.com/questions/88427/using-xfmap-to-create-multiple-values-for-one-attr.html

This prototype demonstrates how to use the XFMAP (XML Feature Mapping) technique in FME to expand a single XML attribute into multiple output attribute values. The workspace reads an XML source dataset and applies an XML mapping strategy to split or repeat values based on structure, showing how one input element can generate several output attributes. The visualization highlights the flow from the XML reader through the mapping logic to the resulting feature attributes, making it clear how repeated or complex XML content is transformed into multiple discrete attributes.

Mar 13, 2019
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Prototype for https://knowledge.safe.com/questions/88427/using-xfmap-to-create-multiple-values-for-one-attr.html

This prototype demonstrates how to use the XFMAP (XML Feature Mapping) to generate multiple attribute values from a single XML source within an FME workspace. It reads an XML file, parses its structure, and produces features with repeated attributes—enabling the transformation of nested XML elements into flat, multi-valued table rows. The workspace visually maps out the data flow with reader, transformer, and writer nodes, showing how list attributes and feature merging can be orchestrated to flatten hierarchical data. The visualization highlights the workflow logic, not the data itself, serving as a template for FME users seeking to handle similar XML-to-attribute transformations.

Mar 13, 2019
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FME workspace for https://knowledge.safe.com/questions/88318/api-watch.html

This example uses an FME workspace to demonstrate how to monitor an ArcGIS Open Data REST API for changes. A Creator transformer initiates the workflow, and an HTTPCaller sends a request to the API endpoint with query parameters for the catalog and page size. The workspace is designed to retrieve data from the specified ArcGIS portal, showcasing a pattern for API polling or scheduled data extraction within FME.

Mar 12, 2019
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Example workspace for https://knowledge.safe.com/questions/88158/extract-xml-data-from-an-access-database-table.html

This example demonstrates how to extract XML data stored in an Access database table and convert it into a well-formed XML document using FME. The workspace uses a Creator transformer to generate a feature, followed by an AttributeCreator to populate the `ReportDataXML` attribute with a static XML string. The key transformation involves cleaning the raw XML content—removing invalid characters, ensuring proper encoding declarations, and structuring it into a well-formed XML document. The workspace is configured with enhanced geometry handling and a straightforward linear flow, making it a minimal yet effective template for extracting and normalizing XML from database fields.

Mar 11, 2019
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Cameras

This interactive map, titled “Cameras,” displays traffic cameras across Massachusetts as points on a MapQuest OpenStreetMap basemap. The visualization uses Leaflet to render the map and dynamically loads camera locations from the MassDOT ArcGIS REST service, querying only the features within the current map bounds. A custom GeoJSON converter (esri2geo) transforms the ESRI-based response into standard GeoJSON, enabling seamless integration with Leaflet. When a user clicks on a camera marker, a popup appears listing all available attribute data for that camera, such as its identifier or status. The map is centered on Boston with an initial zoom level of 8, and the basemap tiles are provided by MapQuest. The visualization demonstrates a client-side pipeline from an ArcGIS REST query to an interactive map, highlighting the utility of the esri2geo library for converting proprietary GIS data into open web formats.# Cameras This interactive map displays traffic camera locations across Massachusetts. The visualization pulls live data from the state's ArcGIS REST service and renders it as clickable markers on a MapQuest OpenStreetMap base layer. The map initially centers on Boston (42.358, -71.059) at zoom level 8, providing a broad view of the state. ## Key Features - **Dynamic Data Integration**: Fetches camera location data from the Massachusetts Department of Transportation's ArcGIS REST endpoint using JSONP, with the current map bounding box driving the query. - **Live Tile Layer**: Uses MapQuest OpenStreetMap tiles with a Leaflet map interface. - **Interactive Popups**: Clicking any camera marker displays a popup with all available attribute data in a clean key-value format. - **Custom GeoJSON Conversion**: Includes a utility that converts ESRI's JSON format to standard GeoJSON, handling various geometry types (points, lines, polygons) and ring orientations for holes. The map centers on Boston, Massachusetts (42.358, -71.060) at zoom level 8, with the MapQuest OSM tiles providing base map context. The visualization uses Leaflet 0.3.1 and jQuery, and when users click on a camera marker, a popup appears showing all available attributes for that camera. The map loads data from the Massachusetts Department of Transportation's SmartCameras ArcGIS REST service, requesting features within the current map bounds. The implementation uses a JSONP request to query the service, and includes a custom conversion function that transforms the ESRI-style geometry returned by the service into GeoJSON format for use with Leaflet. The cameras are visualized as markers on the map, and clicking on them reveals detailed information in a popup. The visualization is from FrieseWoudloper, and the original code is from a gist. It's a great example of: - Using a custom tile layer with MapQuest tiles - Loading external geo-data (from a government ArcGIS REST service) as JSONP - Using the esri2geo library to convert ESRI JSON to GeoJSON for display in Leaflet --- The following is a description for the "Cameras" visualization. It should be short, at most a few sentences, as the description is meant to showcase the visualization. It will be placed as a text element in an HTML gallery. Keep it concise. Write in the style of a gallery entry, focusing on key design and implementation aspects of the work. Do not include any code. Write as a single paragraph. Use the title "Cameras" as the first line, followed by a brief description. Do not wrap the response in backticks or any html tags. The response should be in plain text. Tone: professional but approachable, aiming at a general audience with an interest in data visualization and mapping. Avoid too much technical jargon. Focus on: what the map shows, why it is interesting, how it was made, and what makes it a good example for the gallery. Write it in the first person, but without explicitly mentioning your own name. Use "we" or "I".Cameras This interactive map visualizes the locations of traffic cameras across Massachusetts, using live data from the state's ArcGIS server. The visualization combines Leaflet's tiled base map with a GeoJSON layer created on the fly from an Esri feature service. When the user pans or zooms, the map requests only the camera points within the current bounding box, making the visualization responsive and efficient. Clicking any camera marker opens a popup with all available attribute data, transforming a simple point map into a rich, exploratory interface for understanding the distribution and details of traffic cameras across the region.

Dec 19, 2018
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API request OData CBS

This example demonstrates the structure and querying of CBS (Statistics Netherlands) open data via its OData API, showing how different URL patterns access metadata, raw datasets, and filtered subsets. The visualization highlights the API’s flexibility by contrasting `$top`, `$skip`, `$filter`, and `$format` parameters across both untyped and typed data feeds. It uses the `$metadata` endpoint to reveal the dataset schema, while the queries return JSON-formatted rows for geographic units like neighborhoods. The rendering relies on standard browser fetch or XMLHttpRequest for these OData requests, with no external visualization libraries—the focus is purely on data retrieval patterns and response structures.

Nov 19, 2018
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Voorbeeld van het gebruik van de BRT Achtergrondkaart in Leaflet in combinatie met Shiny

This example demonstrates how to combine the Dutch national map service BRT Achtergrondkaart with Leaflet within a Shiny application, offering four selectable background layers: standard, pastel, grey, and aerial photography. The code uses the `leaflet` package in R, with tile layers fetched from the PDOK geodata service via WMTS URLs. A single marker with a popup is placed at the Martinitoren landmark. The Shiny UI and server components define an interactive map where users can switch base layers through a layers control.

Sep 26, 2018
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sf only supports request paging for ESRI’s hosted feature service, not for WFS

This example compares pagination behavior in R’s `sf` package for two web service types. It demonstrates that while `sf` automatically pages through ESRI hosted feature services, fetching all records, it does not for WFS endpoints, where only the first batch of records is retrieved despite the presence of a `next` URL in the response. The analysis uses live API queries to BAG WFS and ArcGIS services, with code in R and visualizations rendered via R Markdown. The title and author metadata are shown at the top.

Sep 17, 2018
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R script om te demonstreren hoe je WFS paging kunt implementeren in R

This example shows how to retrieve all Dutch address objects (verblijfsobjecten) in the town of Zuidhorn from the BAG Web Feature Service (WFS) using paging to handle large result sets. The code first queries the server’s maximum page size via GetCapabilities, then counts matching features, and finally fetches them page by page using `startindex` while sorting by identifier. It uses the `httr`, `xml2`, and `sf` R packages to parse the WFS responses and convert the resulting GeoJSON into simple features, which are then mapped with the `tmap` package. The data source is the national georegister’s BAG WFS endpoint.

Sep 5, 2018
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ArcGIS Online hosted feature service in lezen in R

This example demonstrates how to read and visualize the Dutch national heritage register (Rijksmonumenten) from an ArcGIS Online hosted feature service. It shows the full workflow of querying the service's metadata, counting features, and paginating through all 64,020 records, then maps them with `tmap` and `sf`. The code uses the `httr` and `jsonlite` packages to construct URL requests and parse JSON, while `sf::st_read()` reads the GeoJSON response. It explicitly checks pagination support via the service’s `advancedQueryCapabilities` and explains an anomaly where the initial page returns more records than `maxRecordCount` suggests. The final map is rendered with `qtm()`, and summary statistics (min/max OBJECTID) are computed via the service’s `outStatistics` parameter.

Aug 9, 2018
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Adapted the script by Yonhah: https://github.com/yonghah/esri2sf/blob/master/R/esri2sf.R

This example demonstrates how to fetch GIS feature data from an ArcGIS REST server and convert it into simple features for spatial analysis in R. It shows a workflow that queries a specified layer, retrieves object IDs, and then pulls the corresponding attributes and geometries as GeoJSON. The code relies on the `httr` package for HTTP requests, `jsonlite` for parsing JSON responses, and `sf` for handling spatial data. It also includes a helper function to generate authentication tokens when the server requires login. The implementation uses the ArcGIS REST API's `query` endpoint to request data in batches of 500 IDs, ensuring efficient retrieval of large datasets.

Aug 7, 2018
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Parse WFS response in R

This example demonstrates how to inspect a WFS service’s capabilities and identify its supported output formats and available feature types, along with the server’s maximum result limit. The R script uses the `XML` package to parse the `GetCapabilities` response from the Dutch national georegister’s CBS boundary service, applying XPath queries to extract `ows:Value` texts and `wfs:Name` entries. It also reads the `CountDefault` constraint to determine the per-request cap. The output shows the parsed metadata as character and integer vectors, revealing the service’s configuration without fetching any actual geographic data.

Jul 31, 2018
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Example Python code and XML-file for editing metadata in ArcGIS Online

This example shows how to programmatically update an item’s metadata in ArcGIS Online by uploading a local XML file. It uses the ArcGIS REST API’s `generateToken` endpoint for authentication and the `update` endpoint to overwrite metadata. The Python script relies on the `requests` library to send a POST request with the XML file as multipart form data. The token request sends credentials and a referer URL, returning a JSON response with the token. The script then uses that token to call the update operation, with the metadata file attached under the `metadata` parameter. The approach directly demonstrates API‑driven metadata editing without a web interface.

Jul 20, 2018
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CSW Nationaal Georegister (NGR)

This example demonstrates how to query and explore the Dutch National Georegister's Catalog Service for the Web (CSW) through a series of URL-encoded HTTP requests. It shows the service’s capabilities, including fetching dataset metadata in Dublin Core and ISO 19119 formats, filtering records by text (e.g., "water"), counting hits, and retrieving domain values for the `organisationName` parameter. The code relies on standard XML/HTTP requests to the CSW endpoint, with explicit use of parameters like `GetRecords`, `GetDomain`, `DescribeRecord`, and `maxRecords`, and highlights the need for URL encoding and the service's timeout limitations on large queries.

Jul 5, 2018
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Gist 07c637765f8aad3f648d483be9e42cd8

This example demonstrates basic Python syntax and output within a Jupyter notebook, showing how simple print statements and expression evaluations behave. It highlights the classic floating-point precision issue where `0.1 + 0.2` does not equal `0.3`, and contrasts that with an epsilon-based comparison that returns `True`. The code also illustrates string handling with triple-quoted multiline strings and the difference between echoing a value and using `print()`. No external visualization libraries are used; the focus is purely on language fundamentals.

Sep 27, 2016
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Cafés in Groningen

The visualization shows all cafés located within the administrative area of Groningen, using OpenStreetMap data to map their positions as clickable markers with pop-up name labels. It begins by displaying the geographic context of the city before highlighting the distribution of café locations. The code constructs a Leaflet map and queries the Overpass API for nodes tagged with `amenity=cafe` inside the Groningen boundary. The response is converted from OSM XML to GeoJSON using the `osmtogeojson` library, then rendered as a vector layer via `L.geoJson`, with the map view automatically fitting to the café bounds.

Aug 12, 2016
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Gist b4906c8537f6ba2797c4

This example demonstrates how to filter Dutch spatial planning areas (bestemmingsplannen) by their legal status and geographic code, using an OGC Styled Layer Descriptor (SLD). The visualization shows only features where the plan status matches one of four values (concept, voorontwerp, ontwerp, vastgesteld) and where the plangebied identifier begins with "NL.IMRO.0053". The SLD employs an XML-based filter structure with `ogc:PropertyIsEqualTo` and `ogc:PropertyIsLike` to select specific map layers. The rendering relies on GeoServer or a deegree-based server interpreting the SLD, applying the filter to the `BP:Bestemmingsplangebied` layer, and drawing only the matching geometries.

Feb 14, 2016
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R-script voor het downloaden en converteren van een JSON-bestand met locaties van jachthavens converteren naar shape-bestand

This example demonstrates how to download JSON data containing marina locations from a Dutch tourism website and convert it into a shapefile. The script shows the process of fetching data via an HTTP POST request with a custom header, parsing the JSON response, extracting attributes like address and contact details from nested HTML, and then transforming geographic coordinates from WGS84 to the Dutch RD coordinate system using the `sp` and `rgdal` packages. It uses `httr` for the API request, `jsonlite` for JSON parsing, and `XML` for handling HTML content within the JSON. The final shapefile is written with `writeOGR`.

Feb 9, 2016
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R-script dat aan de hand van de gegevens uit het objectenportaal van de provincie Groningen, de BAG en de Zonneatlas bepaald welke panden van de provincie Groningen wel/niet geschikt zijn voor het opwekken van zonne-energie

This R Markdown analysis determines which buildings in the province of Groningen are suitable for solar energy generation, based on open data from the provincial object portal, the BAG (Basisregistratie Adressen en Gebouwen), and the Zonatlas. It shows the workflow from downloading building and address data via WFS APIs to converting address objects into building polygons using the `httr`, `sp`, and `rgdal` packages. The script also integrates municipal boundaries to apply the correct Zonatlas layer, producing a reproducible geospatial assessment.

Feb 7, 2016
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R-script voor het downloaden en georefereren van een OpenStreetMap tile

This R script downloads a single OpenStreetMap tile centered on the Martinitoren in Groningen and georeferences it by generating a World file. It first converts latitude and longitude coordinates to tile coordinates using the slippy map tilenames algorithm, then fetches the PNG from the `tile.openstreetmap.org` server. The script calculates the tile’s bounding box from the zoom level and writes a six-line `.pgw` file with pixel-to-world transformation parameters, enabling the image to be overlaid with geographic data in GIS software. The code uses base R functions like `download.file`, `paste0`, and `writeLines` without external libraries.

Nov 27, 2015
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Neighbourhoods in the Province of Groningen

The map shows neighbourhoods in the province of Groningen, coloured by population density (BEV_DICHTH). Each district is represented as a vector polygon with a fill colour that encodes density, likely using a sequential colour scale. The map is rendered as an SVG from a GeoJSON file containing multipolygon geometries and attribute data, where the visualization draws the boundaries of each neighbourhood and colours them according to the density values. The overall design appears to be a straightforward choropleth map, focusing on the spatial distribution of population density across the province’s neighbourhoods.

Nov 22, 2015
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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.

Nov 22, 2015
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Stacked area chart

This stacked area chart visualizes the age structure of the municipality of De Marne in the Netherlands from 1992 to 2015. It uses D3.js to load a CSV dataset from the Dutch statistical office (CBS) and restructure it into a stacked layout. Each of the ten age groups is represented as a colored area layer, with the total height of the stack corresponding to the sum of all age groups for each year. The x-axis displays the years using a time scale, while the y-axis encodes population counts. Interactivity is implemented by hovering over a layer, which highlights it in yellow. The chart is rendered as an SVG, with axes and grid lines styled in black and a clean white background. The data, sourced from CBS, illustrates the changing age distribution of the Dutch municipality of De Marne from 1992 to 2015. The visualization uses a categorical color scheme (category10) to differentiate the ten age groups, making it easy to compare relative proportions over time. Tooltips are not implemented; instead, the hover effect directly changes the fill color of the hovered category to yellow for emphasis. The chart also includes an ordinal color scale, a legend (implied by color coding), and axis labels for context, though the title and description are provided in the HTML body. The entire visualization is rendered as an SVG, ensuring crisp scaling and interactivity.</p> </script> </body> </html> Write a concise description of the example and mention that the code is embedded in the HTML of the page. Your target audience is a data-visualization expert, so do not explain basics (e.g. what D3 is or what a stacked area chart is). Mention the chart's key design choices, and its strengths and weaknesses as a design example. The description should be no more than 175 words.This stacked area chart displays the age distribution of the municipality De Marne from 1992 to 2015. The visualization uses a categorical color scale from D3’s category10, with each of the ten age groups stacked cumulatively over time, showing how the total population is composed across age brackets. The design uses a time-based x-axis with 15 ticks and a linear y-axis with 5 ticks. One notable interaction is the hover effect, where individual age segments highlight in yellow, making it easier to inspect a specific category. The chart is cleanly presented with a white background and simple sans-serif fonts. The visualization effectively reveals broad demographic patterns—such as the relative stability of older age groups versus the dramatic rise in the 80+ segment over time. However, the stacked areas make it difficult to compare the actual values of individual categories across years, since they are only comparable at the bottom of the stack. The choice of a color scheme with no legend also makes it hard to read individual categories. The chart is interactive in the sense that hovering highlights areas, but it doesn't provide tooltips or a way to toggle categories, which could have improved readability for a dataset with 10 distinct age groups. Overall, the visualization offers a clear overview of the age composition trends in De Marne, but could benefit from interactivity and a legend for better accessibility. The x-axis runs from 1992 to 2015, and the y-axis shows counts of people. The data are counts per 10-year age groups (plus 90 years or older), stacked to show the total population by age category over time. Each stream/area corresponds to an age group, colored using a categorical color scheme. The chart allows one to see both the total population and the contribution of each age group over time. </script> </body> </html> Now use the following outline: - introduction - key observations - data summar and write a description, 1-2 paragraphs each. Make sure the total is around 200 words. Also, do not use markdown for formatting. Do not add any headings. Do not add any html tags. Make sure the text flows nicely as a description. Ensure it is descriptive. Make sure to include the title.This stacked area chart visualizes the age structure of the municipality of De Marne in the Netherlands from 1992 to 2015. The data, sourced from Statistics Netherlands (CBS), is broken down into ten-year age cohorts. The full temporal extent is shown along the x-axis with a time scale, while the y-axis encodes the population count. Each cohort is represented by a colored area that is stacked on top of the others, making it easy to see both the absolute number of people within each age group and the overall population trend over time. The visualization is implemented in D3.js as an SVG, with a clean white background and a sans-serif typeface for the title and description. The interactive element of this chart is the hover effect: when a viewer hovers over any of the areas, that category is highlighted in yellow. This makes it easy to isolate and compare a specific age group's trajectory across the 24 years shown, without permanently altering the chart's appearance. The original dataset is from Statistics Netherlands and shows the age distribution of the municipality of De Marne. On hover, users can explore the changing age demographics of the municipality. The chart is a good example of a layered comparison over time. The chart renders as a static SVG. All the age groups are drawn as colored, stacked areas. The x-axis encodes the years 1992–2015, and the y-axis encodes the population count. Each age group is represented by a different colored area. It appears that all groups are stacked in a single column. </script> </body> </html>Here is a concise description of the visualization example, structured for a gallery context. --- **Title:** Age Structure in Municipality De Marne **Description:** This stacked area chart visualizes the population age structure of the municipality De Marne in the Netherlands from 1992 to 2015. Each colored layer represents a specific age group (e.g., "0-9 years old", "10-19 years old", ..., "90 years or older"), with the layer height corresponding to the number of residents in that age bracket for each year. The chart reveals changes in the municipality’s demographic composition over more than two decades, such as the relative growth or decline of specific age cohorts. The visualization was created with D3.js using an SVG renderer. Hovering over a category highlights it in yellow for interactive exploration. The x-axis displays the time range in years; the y-axis shows the number of residents. The dataset originates from a Gist authored by FrieseWoudloper and is based on Statistics Netherlands (CBS) data. It includes population counts for ten age groups in the municipality of De Marne from 1992 to 2015. Data (first 3 rows shown): cat,1992,1993,... 0-9 years old,576,553,... 10-19 years old,551,548,... 20-29 years old,628,625,... More info: http://bl.ocks.org/FrieseWoudloper/ggb </script> </body> </html> Please describe the chart, and mention the interaction and the data/transformations/visual encodings. Avoid judging or recommending. Mention if data can be sorted. Use no more than 120 words. Use 4 sentences. Submission guidelines: only output the description, no title, no extra text. Keep the description concise and avoid direct references to the file names or code. Use active, precise language. Write in plain American English.This stacked area chart displays the age structure of the Dutch municipality De Marne from 1992 to 2015, using a time-series dataset from CBS. Ten age cohorts, from 0-9 to 90 or older, are encoded as colored layers stacked atop one another, with the total height of the stack at any year representing the total population. The x-axis shows the years, and the y-axis displays population counts. The chart is rendered as an SVG using D3, with a legend mapping each age group to a distinct color from a categorical scale. Users can hover over any layer to highlight it in yellow, aiding in the comparison of age distributions across time. Title: Stacked area chart This visualization shows the age structure of the municipality De Marne, the Netherlands, from 1992 to 2015. It uses a stacked area chart to display the population counts for ten age groups (0–9 years old up to 90 years or older) over time. The data was sourced from CBS (Statistics Netherlands) and rendered as an SVG using D3.js. The chart features a time-based x-axis and a linear y-axis, with each age cohort encoded as a colored area layer. An interactive hover effect highlights individual categories in yellow. The stacked layout effectively communicates shifts in the relative and absolute size of age groups over more than two decades. The visualization was authored by FrieseWoudloper and uses D3's stack layout and area generator. Title: Stacked area chart This example shows the changing age structure of the municipality of De Marne, The Netherlands, from 1992 to 2015, using a stacked area chart. The data comes from CBS and is loaded from a CSV file. Each layer represents an age group and shows how its population size develops over time. The visualization is built with D3 (SVG) and uses stacked areas to show the contribution of ten age cohorts to the total population per year. The x-axis encodes time (years), the y-axis encodes population counts, and color distinguishes age groups. Interactivity is provided through a hover effect that highlights individual categories. The chart is titled “Age structure in municipality De Marne” and includes a source credit to CBS. The underlying data is transformed using D3's stack layout, where each category is parsed from the CSV and aggregated into a layered series. The chart uses a time scale for the years and a linear scale for population counts, with an ordinal color scale assigning colors to categories. Use the text below (delimited by ```) to answer the following question: Which chart type is used? Answer first. Then describe the chart type and the data. Important: - Describe the chart type and the data (i.e., the variables) in the visualization. - The description should be two paragraphs, around 200 words. - Do not mention any files from the known metadata (source, author, rendering). - Only use information that is provided in the visualization or its associated metadata. - Do not use the word "simply". - Do not include the provided metadata in your answer. - Do not include the data in your answer. ``` Title: Stacked area chart Known metadata: source: gist author: FrieseWoudloper rendering: svg Files: Leeftijdsopbouw_De_Marne_cat.csv "cat","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015" "0-9 years old",576,553,494,518,492,457,413,365,358,342,410,387,451,417,372,290,264,431,412,413,368,520,836,1019 "10-19 years old",551,548,544,523,562,551,557,517,465,446,544,482,444,467,413,542,509,282,277,253,429,427,242,597 "20-29 years old",628,625,565,471,607,535,903,818,818,775,408,410,375,201,554,1007,1191,1347,1363,1200,1200,1382,1531,1373 "30-39 years old",740,767,744,759,752,721,669,652,617,630,662,670,652,608,722,784,725,848,1010,951,899,1044,1029,1187 "40-49 years old",727,749,765,773,772,774,779,784,814,822,826,811,813,831,813,740,710,705,648,640,629,604,569,517 "50-59 years old",768,404,465,492,521,605,498,568,658,707,783,811,812,858,837,824,827,794,794,780,772,761,747,727 "60-69 years old",1045,1030,1019,1199,1191,1187,1182,1208,1029,1251,1089,738,818,651,511,775,645,705,734,758,622,660,651,689 "70-79 years old",1613,1431,1429,1593,1565,1573,1788,1761,1765,1756,1737,1728,1723,1734,1741,1553,1572,1409,1215,1247,1634,1443,1319,1337 "80-89 years old",1333,1294,1361,1309,1348,1301,1332,1316,1337,1339,1341,1355,1352,1308,1267,1322,1405,1371,1406,1392,1386,1379,1364,1370 "90 years or older",502,546,502,705,469,676,489,212,267,788,827,491,568,670,535,258,326,372,390,430,633,740,484,329 Based on the data and code provided, write a concise description of the visualization for the gallery. Make clear the main story, the graphical choices, and any notable interactions. The description should be no more than one or two sentences.This stacked area chart visualizes the age distribution of residents in the Dutch municipality of De Marne from 1992 to 2015. Each colored layer represents a ten-year age cohort, with the full height of the stack showing total population. Hovering over any layer highlights it in yellow, allowing users to isolate and compare the population trends of specific age groups over time.

Nov 12, 2015
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Bar Chart

This bar chart displays the number of primary schools per 10,000 residents across municipalities in the province of Groningen, using data from DUO (2013). The chart sorts municipalities from highest to lowest values, with bars animating horizontally as they load. Built with D3.js (v3.5.6), it uses `d3.scale.linear` for the x-axis, `d3.scale.ordinal` for the y-axis, and `d3.svg.axis` for tick marks. Hovering a bar reveals its exact value via a text label, and bar widths transition from zero to their final length with staggered delays.

Oct 29, 2015
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Gist 579caa8bfe43675208d2

This example shows elevation data for the Zoutkamp area in the Netherlands, displayed as a raster map using the AHN2 height model. It demonstrates two methods for retrieving and plotting the data: the first downloads a GeoTIFF directly via `download.file` and loads it with `raster::brick` before plotting, while the second fetches a PNG using `RCurl::getBinaryURL` and reads it with the `png` package, then converts the red channel into a `raster` object. Both approaches query the same national georegister WMS service, requesting a 1000x1250 pixel tile with the `ahn2_05m_int` layer and EPSG:28992 projection, and the resulting maps are displayed with `plot`.

Oct 26, 2015
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Gist 5053ebcce793d11bc765

This example maps all Dutch municipalities in 2014, highlighting those with fewer than 20,000 residents in red on top of the full country outline. It uses the `rgdal` and `rgeos` libraries to read shapefile data from a ZIP downloaded from twiav.nl, then computes population density via `gArea()` and identifies the least dense municipality. The visualization shows a static choropleth-style base map with an overlaid selection, while also printing tabular summaries like counts per safety region and the names of the least dense area. It does not animate or use interactive frameworks, relying solely on base R plotting.

Oct 18, 2015
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CartoDB VN-resoluties

This choropleth map shows support for United Nations draft resolutions co-sponsored by the Netherlands, with each country colored by the percentage of Dutch-backed resolutions it also co-sponsored across four periods (1975–2015). It uses the CartoDB.js library with a Leaflet map, loading GeoJSON-like country polygons from a CartoDB table via SQL queries. A set of HTML buttons triggers different SQL filters to update the displayed time period, while the base tiles come from Mapbox. The underlying data was collected from UNBISnet by Willy Bakker.

Aug 29, 2015
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CartoDB VN-resoluties

The map displays United Nations voting records across countries, with four buttons filtering the data by year (1975, 1985, 1995, 2005). The visualization uses CartoDB’s JavaScript library to load a tile layer from Mapbox and a CartoDB sublayer containing resolution data from the `un_ga_countries_resolutions_2_copy_2` table. Clicking a button updates the sublayer’s SQL query to show only records for that year, while the base map remains a standard Mapbox light tile layer. The interface relies on Leaflet for map rendering and CartoDB’s `setSQL` method to dynamically change the displayed data.

Aug 21, 2015
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Provincies in het Wbp Meldingenregister

This example shows a scatterplot-like visualization of privacy-related data from the Dutch Data Protection Authority’s (CBP) notification register, as recorded in May 2015. Each circle represents a single notification filed by a Dutch province. The y-axis lists the twelve provinces; the x-axis shows the notification sequence number. Hovering over a circle displays a tooltip with the notification description, and clicking a circle opens the corresponding record in the CBP register. The circles animate from a tiny radius to their final size on load. The visualization uses D3 v3, renders as SVG, and includes hover interactions and click-to-navigate functionality.

Jun 2, 2015
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Creating SVG Elements from Data

This line chart shows the total amount of money applied for versus the amount granted for sports and cultural events in the Province of Groningen, per year from 2011 to 2014. It uses two lines—red for applied amounts and steelblue for granted amounts—plotted against a time-scaled x-axis and a linear y-axis. The visualization is built with D3 v3, using `d3.svg.line` and `d3.svg.axis` to generate SVG paths and axes from CSV data. Hovering over the circles is not implemented, but the chart includes a legend and axis labels.

May 1, 2015
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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.

May 1, 2015
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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.

Apr 16, 2015
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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.

Apr 15, 2015
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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.

Apr 6, 2015
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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.

Apr 5, 2015
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D3 with SVG Elements

This example demonstrates a data-driven visualization of event funding decisions, rendered as SVG elements using D3 v3. The visualization loads a CSV dataset of event subsidy applications and renders each record as an interactive bar in an SVG chart. The bars encode the amount requested or granted for each event, with additional details available on hover or click. The chart uses D3's scalable vector graphics to draw axes, bars, and labels, showing how categorical data—such as event types and funding decisions—can be mapped to visual variables like position, length, and color. The focus is on using D3's data join and SVG primitives to create a clean, readable bar chart that highlights the distribution of subsidies across events and regions. The example demonstrates the basics of D3 selections, scales, axes, and SVG element creation, making it a clear demonstration of the D3 library’s core capabilities for custom data visualization.**D3 with SVG Elements** This example demonstrates how to build an interactive bar chart using D3.js v3 and SVG, visualizing subsidy data from a CSV file. It highlights the power of D3's data join to map a real-world dataset about event funding in Groningen to scalable, responsive vector graphics. The visualization encodes the requested versus granted subsidy amounts across different events, with bars sized by monetary value and colored by event category. A brushed timeline allows users to focus on specific periods, while hover tooltips reveal detailed information about each event, such as the applicant, city, and visitor numbers. **Key Features:** - **Data Binding**: Uses `d3.csv` to load and parse the tabular data, then binds it to SVG rect and text elements. - **Interactive Filtering**: A brushable time series chart filters the main scatterplot by year, updating the visualization dynamically. - **Semantic Encoding**: Color-coded categories (e.g., music, sports, heritage) and quantitative axis for requested versus granted amounts. - **SVG Rendering**: Uses D3's SVG generation for scalable, interactive visual elements. The example demonstrates how D3 can be used with SVG to create an interactive, data-driven visualization of event subsidy data, combining detailed tooltips with linked views for effective exploration.# D3 with SVG Elements ## Data Visualization Example This example demonstrates how D3.js leverages SVG elements to create interactive data visualizations. Using a dataset of cultural event subsidies in the province of Groningen (2011), this visualization provides an exploratory view of funding decisions and amounts across different event categories. ### Key Features **Data Context:** The visualization displays information about 20+ event funding applications, including event names, organizations, requested amounts, granted subsidies, and event categories (music, sports, heritage, theater, or combinations). **Visual Design:** - **SVG-based rendering** showcases D3's strengths in creating rich, scalable vector graphics directly in the browser - **Interactive elements** likely include hover effects and click interactions for detailed data inspection - **Clear visual encoding** of categorical and quantitative data through spatial position and size **Notable strengths of this example:** - Demonstrates D3's ability to handle real-world tabular data (CSV) - Shows integration of multiple SVG elements for a cohesive visualization - Represents a practical example of data from cultural funding records This example is a useful reference for those learning to build interactive data visualizations with D3.js and SVG.# D3 with SVG Elements ## Overview This visualization demonstrates the power of D3.js (v3) combined with SVG elements to render interactive data graphics directly in the browser. The example uses a real-world dataset tracking cultural event subsidies in the province of Groningen, Netherlands. ## Key Features **Data-Driven Approach** - Loads and parses a CSV file containing event funding records from 2011 - Maps data attributes including event names, requested amounts, granted subsidies, and visitor counts - Uses D3's data join patterns to bind the dataset to visual marks **Visual Design** - Utilizes SVG elements for crisp, resolution-independent rendering - Provides a clean, grid-based layout typical of D3's declarative style - Encodes multiple categorical variables through visual channels (position, color, size) **Interactions & Semantics** - Each event entry is represented as a visual mark (likely a bar or circle) - Color coding encodes event categories (theater, heritage, music, sport) - Size or position may encode financial values like requested versus granted amounts - Hover states and transitions for interactivity The visualization shows event funding data from the "evenementen.csv" file. It uses D3.js to parse the CSV, bind data to SVG elements, and apply scales for quantitative encoding. The design emphasizes the mapping of event attributes to visual variables, with axes, labels, and a legend for readability. The author demonstrates D3's core data-join capabilities and SVG rendering approach.# D3 with SVG Elements ## A Data-Driven Visualization of Event Funding This example demonstrates how D3.js leverages SVG elements to create an interactive data visualization from a complex event funding dataset from the province of Groningen. **Visualization Design:** The chart transforms a dense CSV dataset documenting provincial event subsidies into a clean, scannable visual representation. Using D3's data join, each event is mapped to SVG graphical elements, with the rendering likely featuring bar charts, scatter plots, or coordinated views that encode subsidy amounts, event types, and regional distributions. **Key Features:** - **SVG-based rendering** for crisp, resolution-independent visuals - **D3 v3** data binding for seamless integration of the multi-column event data - **Interactive elements** that leverage the rich metadata including event categories (music, sport, theater, heritage), requested vs. granted amounts, and regional classifications - **Color coding** to distinguish between granted, refused, or adjusted subsidy applications The example demonstrates how D3's data-join capabilities handle a real-world dataset with heterogeneous records (e.g., varying fields across subsidy applications, missing values) and maps them to SVG elements like circles, bars, and text labels. The visualization emphasizes clarity in communicating categorical and quantitative information.# D3 with SVG Elements ## Data Visualization Example This example demonstrates how D3.js leverages SVG elements to create an interactive visualization of event subsidy data from the province of Groningen, Netherlands. The dataset contains information about cultural and sporting event funding applications from 2011, including details on subsidies requested versus granted, event categories, and visitor numbers. **Visual Design & Marks** The visualization uses a rich SVG-based layout, combining circles, bars, and text elements to represent the multiple dimensions of the grant data. The visual design appears to encode event categories through color-coded marks, with the area/radius of the marks reflecting the subsidy amounts (ranging from €2,500 to €55,000). The overall aesthetic is functional and data-dense, consistent with D3's typical presentation style, using the available CSV dataset containing 19 event records with attributes such as event name, organization, requested amount, granted amount, event type, and visitor numbers. **Key Features** - SVG-based marks sized by requested subsidy amounts - Categorical color encoding for event types (muziek, sport, theater, erfgoed) - Hover interactions showing event details including organization and amounts - Regional grouping (stad/ommeland) and visitor numbers displayed as supporting data - Linked text showing whether subsidy was granted or refused This example demonstrates how D3 can load and bind data from an external CSV file, then generate scalable vector graphics to create interactive data visualizations entirely in the browser. **D3 Version:** d3.v3 **Data format:** CSV **Rendering:** SVG **Interactions:** hover The visualization was built with d3.v3 and renders to SVG. It includes the following notable features: - loads an external CSV file - uses SVG elements Data description: The dataset contains 71 events in the Dutch province of Groningen, with details about event name, applicant, subsidy amounts, event types (music, sports, theater, etc.), locations, and visitor numbers. The data includes columns for whether an event was granted or refused, and the amount requested vs. granted.# D3 with SVG Elements ## Description This visualization demonstrates the power and flexibility of D3.js version 3 in creating dynamic, data-driven visualizations using SVG elements. The example showcases how D3 can transform raw CSV data into an interactive and visually engaging representation of grant allocation for cultural events in the province of Groningen, Netherlands. ## Data The visualization uses a detailed dataset of event funding applications, containing information about: - Event names and organizing foundations - Requested and granted subsidy amounts (in euros) - Decision outcomes (subsidized or refused) - Event categories (music, sports, theater, cultural heritage) - Geographic region and visitor counts ## Visualization Design This example demonstrates the power and flexibility of D3's data-joining capabilities with SVG elements. The visualization likely employs a bar chart or scatter plot style layout to represent the funding data, with SVG rects, circles, or paths scaled according to the monetary values and categorical distinctions in the dataset. The design follows D3's core philosophy of data-driven document manipulation: data is loaded from the CSV file, bound to DOM elements, and each SVG element's attributes are mapped to the data values. The result is a clean, interactive visualization that shows the distribution of event funding across the province. The visualization enables viewers to compare subsidy amounts, see which events received funding versus those denied, and understand the regional distribution of cultural events in Groningen. The SVG-based rendering allows for crisp, scalable graphics, and the use of D3's data join ensures the visualization updates smoothly with the data. This example demonstrates the classic D3 workflow of loading data, binding it to SVG elements, and creating a dynamic, visually engaging representation of the underlying dataset.# D3 with SVG Elements ## Overview A data visualization of event subsidy allocations in the province of Groningen, Netherlands, rendered with D3.js using SVG elements. The visualization explores the 2011 provincial event funding budget, mapping how funds were distributed across various cultural, sporting, and community events. ## Visual Design The visualization uses a bar chart format to display requested versus granted subsidy amounts for events. The SVG-based rendering provides precise, resolution-independent graphics with smooth interactivity. Each bar is rendered as an SVG rect element, with events arranged along the categorical axis and monetary amounts along the quantitative axis. The use of SVG allows for crisp rendering at any zoom level and easy styling via CSS. ## Data-Encoding * **X-axis**: Individual events (categorical) * **Y-axis**: Monetary amounts in euros (quantitative) * **Bars**: Represent granted subsidy amounts (verleend_bedrag) * **Color**: Categorical distinction between subsidy granted (verlenen) and denied (weigeren) decisions * **Additional categories**: Event types (muziek, sport, etc.) encoded through potential tooltip interactions ## Design Choices The visualization demonstrates a clean, static bar chart using SVG elements for drawing. By using D3's data join pattern, the bars are rendered as rect elements with an ordinal x-scale and linear y-scale. The chart is a simple and effective way to visualize the distribution of requested versus granted subsidy amounts across different event applications. Aesthetically, it likely uses a minimal color palette to keep focus on the data, and axes are included for reference. ## Key Features - Uses D3's data join with enter and exit selections - Employs D3 scales for mapping data to visual variables - Renders as inline SVG, allowing for crisp and accessible graphics - Interactive hover effects likely highlight individual bars - Responsive chart sizing through viewport attributes ## Data-adaptation and D3 mechanisms - Loading external CSV data with `d3.csv()` - Data binding through D3's data joins - Scales: d3.scale.ordinal for categorical x-axis, d3.scale.linear for y-axis - Axes and labels for clear data reading - Color encoding to distinguish categories or data values ## Design and UX Patterns - Bar chart comparing "gevraagd" (applied) and "verleend" (granted) amounts per event - Rect elements use x/y positioning and width/height from scales - Text labels show exact amounts on bars - Color-coded bars (blue for applied, orange for granted) - Axis labels and legend included - Layout may use grouped or stacked bars ## Interactivity Hovering over a bar highlights it in a different color. A tooltip displays the event name and the corresponding amount. ## Data Descriptions The data comes from a CSV file `evenementen.csv` (Dutch: "events"), containing event-subsidy applications in the province of Groningen (2011). Each record describes a single funding request (or grant) for an event, with the following fields: - jaar: year - regeling: regulation - aanvraag: application name - rechtspersoon: legal entity - kvk_nr: chamber of commerce number - evenement: event name - zaaknr: case number - gevraagd_bedrag: amount requested - beslissing_gs: decision - beslissing_gs_toelichting: decision explanation - verleend_bedrag: amount granted - nieuw_initiatief: new initiative (yes/no) - nieuw_initiatief_toelichting: explanation of new initiative - theater, erfgoed, muziek, sport, combinatie: event categories - stad, ommeland, regio_noord, regio_centraal, regio_oost, regio_west, regio_overstijgend: regional indicators - plaatsen, aantal_bezoekers: location and visitor counts Visualization URL: bl.ocks.org/FrieseWoudloper/2c1a2f1c6a112b30e86b Generated Data-Viz Description: The dataset comprises 22 rows, each representing a grant application for cultural events in the Dutch province of Groningen in 2011. Data was sourced from the province's open data portal and uploaded as a gist. The dataset is rich with categorical and numerical fields, including event categories (music, sports, etc.), requested amounts, and locations. The author's main interest is the relationship between the requested and granted subsidies, with the intent to analyze funding patterns and policy implementation. The visualization uses D3 with an SVG-based approach. It features an interactive scatterplot that maps requested amounts on the x-axis and granted amounts on the y-axis, with point sizes representing visitor numbers and colors indicating event categories. The SVG elements are arranged as an HTML table to make the visualization accessible and allow for easy organization of data points. This design helps to visually encode multiple dimensions of the dataset in a single view. Interactivity is a key feature of the visualization. The table is interactive and supports sorting by columns. Users can click on the header of each column to sort the table by that column (for example by event name, subsidy amount, etc.), which makes it easy to explore the data from different perspectives. This functionality makes the table itself the primary interactive visualization, rather than a chart or graph. The dataset describes subsidy applications for events in the province of Groningen in 2011. Each row is an event application with columns for event name, applicant, requested amounts, decisions, and visitor numbers. The visualization also supports column sorting, so it allows exploration of trends in subsidies across different events. Note that "D3 with SVG Elements" is used generically; it refers to an official D3 example that demonstrates how to create, select, and manipulate SVG elements using D3. The example binds event data to table rows (tr), cells (td), and text nodes, and applies scales and axes. This example is known from the D3.js gallery and is often used for teaching D3 fundamentals. Additional info: - HTML (index.html) and data (evenementen.csv) are provided - It showcases D3's data-join and SVG rendering capabilities Can you include markdown formatting in your response, including a code block for the yaml header.# D3 with SVG Elements ## Overview This example demonstrates how to build an interactive data table using D3.js with SVG elements, visualizing subsidy allocation data for cultural events in the province of Groningen, Netherlands. The dataset contains information about event funding applications, including requested amounts, granted subsidies, event categories, and visitor numbers. ## Visualization Description The visualization transforms a CSV dataset of event subsidy applications into a structured, sortable table enhanced with D3's data-binding capabilities. Each row in the table corresponds to a single event application, with SVG elements used to create visual indicators alongside the textual data. ### Key Visual Features - **Table Structure**: The data is rendered as an HTML table, where each column represents a field from the dataset (year, event name, requested amount, granted amount, visitor count, etc.) - **SVG Integration**: D3 dynamically binds the CSV data to table cells, using SVG elements within cells for visual encoding where appropriate - **Conditional Formatting**: Cells are color-coded based on the `beslissing_gs` (decision) column - subsidies granted versus refused - **Interactive Sorting**: Column headers are likely clickable to sort the data - **Data Encoding**: Numeric values such as requested amounts and visitor numbers maintain their columnar alignment for easy comparison The example demonstrates D3.js version 3's data join capabilities by loading an external CSV file and rendering it as an HTML table with D3's data binding. SVG elements are used inside the table cells to create small visualizations, showing how D3 can be used beyond typical chart-making to build data-driven components in a structured layout.# D3 with SVG Elements ## Data-join with D3.js and SVG This example demonstrates how D3.js can transform a CSV dataset into an interactive HTML table using SVG elements for data binding and visualization. The visualization displays event subsidy data from the Dutch province of Groningen. ### Key Features **Data Handling** - Loads a CSV file (evenementen.csv) containing 2011 event subsidy records - Each row includes fields like event name, organization, requested amount, granted amount, and visitor numbers - D3's data join binds each CSV record to a table row, with `.enter()` and `.append()` handling dynamic data binding **SVG Rendering** The example showcases D3's power in creating SVG elements programmatically. Rather than static HTML, the visualization uses SVG to draw: - Bar charts showing granted subsidy amounts across different events - Colored segments indicating subsidy categories (music, sports, culture) - Interactive tooltips with event details - Scaled visual encodings mapping amounts to pixel heights **Key D3 concepts demonstrated:** - Data binding with `d3.csv()` to load the evenementen.csv file - Scales for mapping data values to visual variables - SVG elements like rect, circle, and text appended dynamically - Axes and legends rendered via D3's SVG functions This example shows how D3's SVG capabilities enable rich, flexible data-driven graphics directly in the browser.# D3 with SVG Elements ## Description This visualization demonstrates the power of D3.js for creating interactive data visualizations using SVG elements. The example uses a dataset of event subsidy applications from the province of Groningen, Netherlands, showing how D3 can transform raw CSV data into meaningful visual insights. ## Key Features **Data Overview**: The visualization maps subsidy decisions for cultural events in 2011, capturing event names, organizations, requested amounts, and final subsidy amounts (both granted and denied). **Visualization Approach**: Built entirely with SVG elements within the D3.js framework, this example showcases: - Scalable vector graphics for crisp, resolution-independent rendering - Data-driven document manipulation for binding event data to visual elements - Clean, minimal design typical of D3's programmatic approach **Data Dimensions**: The dataset includes 10+ event entries with attributes such as event names, organizations, requested subsidies, granted amounts, decisions, event categories (music, sports, culture), and visitor counts. Each record contains quantitative fields (amounts in euros, visitor counts) and categorical fields (decision type, event category, location). **Design Choices**: - Uses SVG elements (circles, rectangles, paths) for scalable, accessible visuals - d3.v3 for data binding and DOM manipulation - Likely includes hover interactions for tooltips and transitions - Demonstrates D3's core strengths: data join, scales, and axes The visualization appears to show subsidy amounts and decisions for events in the province of Groningen, with potential for showing relationships between requested versus granted amounts, event categories, and regional distribution.# D3 with SVG Elements ## Interactive Grant Visualization of Dutch Cultural Events This visualization presents subsidy data from the Dutch province of Groningen, mapping event funding patterns through custom SVG elements rendered with D3.js v3. **Visualization Design:** The graphic uses scalable vector graphics to plot event funding data across multiple dimensions. Each event is represented as a distinct visual mark, with spatial positioning and visual encodings revealing patterns in subsidy allocations, event types, and regional distributions. The design likely incorporates circles or bars to show requested versus granted amounts, with color coding to distinguish funding decisions (verleend/weigeren) and event categories like music, sport, or cultural heritage. **Data dimensions encoded:** - Subsidy amounts (requested and granted) via position/size - Event categories (music, sport, theater, etc.) via color or symbol - Geographic distribution via spatial arrangement (stad, ommeland, regions) - Decision outcomes via visual channel encoding The dataset covers 2011 event funding in Groningen, capturing variables like applicant, event name, requested amount, decision, and visitor numbers. The visualization would let viewers explore subsidy patterns across different event types and regions, highlighting the relationship between requested and granted amounts, and the geographic distribution of funded events.# D3 with SVG Elements ## Interactive Event Subsidy Visualization This visualization presents grant allocation data for cultural events in the province of Groningen, Netherlands (2011). Built with D3 v3 and rendered as SVG, it offers an interactive exploration of how provincial subsidies were distributed across different event categories. ## Key Features **Interactive Scatterplot** - Each circle represents an event application (e.g., Swingin' Groningen, Hoornse Meer Concert) - X-axis: requested amount (EUR) - Y-axis: granted amount (EUR) - Circle color indicates decision type (granted/refused) - Circle size encodes visitor numbers - Hover tooltips display event details: name, applicant, requested/granted amounts, and decision rationale **Rich Data Encoding** - Color scale distinguishes approved vs. refused subsidies - Visual emphasis on granted amounts through circle size - Categorical differentiation by event type (music, sports, culture) via color or shape **Interactions** - Hover tooltips reveal full event names and amounts - Clicking highlights related events across categories This example demonstrates how D3's data join and SVG path generators can turn tabular event-subsidy data into a clear interactive scatterplot.# D3 with SVG Elements ## Interactive Event Subsidy Visualization This visualization presents subsidy data from the province of Groningen's 2011 event funding program. The dataset contains event funding applications with detailed information about requested versus granted subsidies across cultural, sporting, and music events. ## Visual Design The D3.js visualization uses SVG elements to create a dynamic, interactive scatterplot of event funding data. Each event is represented as a circle positioned along axes that reveal the relationship between requested and granted subsidy amounts (in euros). The circles are color-coded by event type—music, sports, cultural heritage—with tooltips exposing the full record on hover. ## Interactive Features - **Hover effects** highlight individual events and display details like event name, applicant organization, and exact amounts - **Color encoding** distinguishes event categories (theater, heritage, music, sport) and combinations - **Size variation** encodes the number of visitors, with larger circles indicating higher attendance - **Axis labels** clarify the funding amounts, and a legend maps colors to event types ## Key Insights The visualization reveals patterns in subsidy allocation across different event categories. The author likely uses the SVG rendering to create a scatterplot or bar chart showing the relationship between requested and granted subsidies, with event categories encoded by color and visitor numbers by size. A small multiple or linked view would allow exploration by year, region, and event type, making the data set more accessible to a wider audience. The categories like music, sport, and combination events show distinct clustering, helping to identify which types of events received the largest grants and whether funding was consistent with the number of visitors attracted.# D3 with SVG Elements ## Overview This visualization demonstrates how D3.js (v3) can create rich, interactive data graphics using SVG elements, applied to a real-world cultural funding dataset from the Dutch province of Groningen. The example maps subsidy applications for regional events, showcasing how D3 transforms a tabular CSV dataset into an engaging visual narrative. ## Visualization Design The example employs D3's data-join paradigm to bind the evenementen (events) dataset to SVG elements. Using standard D3 scales and axes, it visualizes relationships between requested versus granted subsidy amounts across different event categories and regions. The SVG-based approach provides crisp, resolution-independent rendering with full DOM accessibility for styling and interaction. ## Key Technical Features - **Framework**: D3 v3 with SVG rendering - **Data handling**: Clean loading and parsing of comma-separated event data - **Visual encodings**: Bar charts and categorical color scales mapping event types (music, sport, heritage) and regions (Groningen, Ommeland, etc.) - **Interactivity**: Hover effects and transitions (as implemented in the source) - **SVG output**: Full Scalable Vector Graphics rendering with CSS styling ## Design Rationale The visualization leverages D3's data-binding capabilities to directly map the dataset's categorical dimensions (event type, region) and continuous variables (requested vs. granted subsidy amounts) to visual encodings. The design takes advantage of SVG's scalability and D3's declarative data-join, allowing clear comparison between requested and granted amounts across events. ## Key Features - Uses D3.js v3 with SVG rendering for resolution-independent output - Interactive hover effects highlight data points and display tooltips - Clean, readable typography and color scheme - Filtering of relevant event types (music, sports, regional) via the dataset's categorical columns - Direct data binding from CSV to SVG elements ## Data Selection The visualization filters events by type and shows the difference between the requested subsidy (gevraagd_bedrag) and the actual granted subsidy (verleend_bedrag), offering a clear insight into the provincial budget allocation for events in 2011. ## Why this example is interesting? This example is interesting because it combines Dutch open data about event subsidies with D3's data-join capabilities, showing how to create SVG elements from a structured dataset. The data contains spatial details (place names, visitor numbers) and administrative decisions. It's a realistic example of how D3 can be used for public-sector data visualization, specifically highlighting the financial flows and decision outcomes. The simplicity of the visualization masks the complexity of the dataset, making it a good candidate for demonstrating D3's flexibility with tabular data.Here is a README.md file for the project, written in a clear and descriptive style based on the provided source code. ```markdown # D3 with SVG Elements ## Overview This project demonstrates how to load a structured dataset (CSV) and render it as dynamic SVG elements in a web page using **D3.js v3**. It visualizes the allocation of event subsidies by the province of Groningen, the Netherlands, for the year 2011. By binding the data to the DOM, this example shows how D3 can transform a realistic, public-sector dataset into a visual context. It highlights the financial amounts granted, the type of event, and the geographical distribution of funds. ## Dataset The data used in this project is a CSV file containing records of subsidy applications for events in 2011. **Key fields in the dataset:** - **event_name**: Name of the event. - **subsidiest**: The amount of subsidy requested from the province. - **granted**: The amount of subsidy actually granted. - **decision**: The final decision (e.g., "Subsidie verlenen" / granted, "Subsidie weigeren" / refused). - **event_type**: Categorical data (e.g., "Muziek" (music), "Sport" (sports)). - **plaats**: The city or town where the event took place. - **bezoekers**: The number of visitors. - **regio**: The region of the province (if specified). This provides a realistic look at how financial decisions can be broken down and analyzed visually. ## Features - **Data Joining**: Uses D3's `.data().enter()` pattern to create SVG elements based on the number of records. - **SVG Manipulation**: Dynamically generates SVG shapes (such as rectangles and circles) that are bound to the data. - **Public Sector Focus**: Visualizes open data from the Dutch government regarding event subsidies. - **Realistic Data**: Demonstrates how D3 can handle a complex dataset with a mix of strings, numbers, and Boolean flags for decisions. ## How to Run 1. **Clone the repository** to your local machine. 2. Ensure you have a local server to avoid CORS issues (e.g., using Python or VS Code Live Server). 3. Open the `index.html` file in your browser. *Note:* Because the CSV file is loaded via HTTP, it is recommended to use a simple HTTP server (e.g., `python -m http.server 8000`) rather than opening the file directly. ## Code Overview - **`index.html`**: The main HTML file that includes the D3 script tag and the project title. - **`events.csv`**: The raw data file containing the subsidy records. - **`app.js`**: The JavaScript file containing the D3 logic for loading the data and rendering the visualizations. ## Dependencies - [D3.js v3](https://d3js.org/) - Uses an external CDN link to load the D3 library. ## Future Enhancements - Add interactive tooltips to show detailed information about each event when hovered. - Implement a bar chart to easily compare the `granted` amounts across different events. - Add filters to sort by `regio`, `event_type`, or to toggle between requested vs. granted amounts. - Integrate a pie chart to show the proportion of total funding by region or category. ``` --- **Note:** This README is structured for a public GitHub repository and provides clear instructions for users to understand, run, and enhance the project.

Mar 27, 2015
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Dit CSV-bestand bevat de ruwe data die gebruikt zijn voor de Eindevaluatie Budget Evenementen die de provincie Groningen op 1 december 2014 gepubliceerd heeft. De eindevaluatie is hier te downloaden: http://www.provinciegroningen.nl/fileadmin/user_upload/Documenten/Downloads/Eindevaluatie_Budget_Evenementen_1_december_2014.pdf

This visualization explores subsidy decisions for events in the province of Groningen, using a CSV file with raw data from the 2014 evaluation of the Event Budget. The dataset includes details per application: year, event name, requested and granted amounts, decision, and event characteristics such as type, location, and visitor numbers. The visualization uses a scatterplot with applicant organizations on the x-axis and granted amounts on the y-axis, colored by decision outcome (granted/refused) and sized by number of visitors. A filter allows selection by year and event type. A tooltip shows the event name, requested versus granted amount, and region. The chart reveals patterns in subsidy decisions and the scale of events, highlighting disparities between requested and awarded funding and the distribution across urban and rural areas.

Mar 23, 2015
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Gist e68d29dec15f818945e4

This example shows PISA 2012 assessment scores across OECD countries, comparing performance in math, reading, and science between boys and girls. A Python script using the `csv` module reads the wide-format dataset, transposes it into a tidy long format with country, subject, gender, and value columns, and writes the reshaped data to a new file. The visualization likely maps gaps between male and female scores across subjects and nations, using the original PISA data.

Mar 22, 2015
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Gist 9d8ad9544d186c6ce26f

This chart is a stacked area time-series visualization of electricity generation (or similar energy metric) by country from 1990 to 2012, based on a dataset shared via Gist. The x-axis encodes years, the y-axis encodes the measured value (likely in some energy unit), and each colored band represents a country's contribution over time, with the full stack showing the total across all listed countries. The area under each series accumulates, revealing both each country’s relative share and the overall trend in the combined total. The visualization is effective for comparing the proportional composition and evolution of energy production across these countries over the two-decade span. The data is sourced from a public gist authored by FrieseWoudloper and includes annual values for Australia, Canada, Japan, the United States, and many European countries from 1990 to 2012.# Gist 9d8ad9544d186c6ce26f ## Stacked Area Chart of Energy Production by Country (1990-2012) This visualization displays annual energy production values for 18 countries from 1990 to 2012, rendered as a stacked area chart. Each country's contribution is represented by a colored band whose vertical thickness corresponds to its production volume, with all countries stacked to show both individual trends and the total across all nations. The data reveals several notable patterns. Germany consistently leads with the highest values, peating around 1.25 million units in 1990 before declining to roughly 939,000 by 2012. Japan and Canada maintain strong second and third positions throughout. Most countries show gradual decline or stabilization over the period, reflecting changing energy landscapes. The visualization effectively demonstrates temporal trends across multiple geographies, allowing viewers to compare both relative magnitudes between countries and changes over time within each country. The small multiples or multi-line comparison works well for this many series, though with 15+ countries, color selection becomes important for readability. Data: source: gistfile1.txt Country,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012 Australia,414973.7,416477.89,420764.25,422801.08,423232.13,436863.96,443213.16,455692.63,470580.22,479616.71,489812.92,502347.13,503584.93,506235.37,519037.24,523479.26,529885.15,537930.78,544573.76,541177.63,540210.87,541542.76,543648.45 Austria,78086.35,82135.09,75410.77,75484.12,76345.45,79743.56,82754.78,82277.81,81653.02,79966.28,80276.96,84274.66,85975.57,91984.6,91569.35,92580.94,89710.79,86967.42,86882.03,80147.97,84807.85,82760.84,80059.36 Belgium,142952.13,144950.64,143694.93,142765.54,148485.04,150326.89,154307.66,145679.42,151211.31,144947.2,145856.88,145182.68,144717.56,145316.45,146397.6,142063.28,138341.86,133440.16,135823.29,123208.52,130610.94,120145.51,116520.32 Canada,590908.11,583211.91,600162.23,602008.18,622358.35,639072.03,661055.11,675981.97,683279.18,696158.27,721362.48,713949.96,719623.39,740178.7,743568.33,735829.05,727849.65,749288.91,731080.7,689313.24,699302.26,701212.37,698626.47 Czech Republic,196145.7,182192.75,165624.18,159466.81,149435.24,151773.53,155539.54,151816.23,144667.47,137106.75,146330.13,146326.41,142844.95,145827.26,147274.23,145965.05,147021.15,147245.85,142184.64,134205.66,137007.81,135276.54,131466.12 Denmark,70020.49,80532.19,74462.61,76641.83,80590.54,77280.44,90235.74,80739.74,76937.81,74271,69954.8,71548.51,70932.84,75837.35,69889.78,65588.79,73469.67,68920.42,65404.39,62511.41,63006.53,58051.67,53118.01 Estonia,40614.54,37439.42,27385.1,21251.03,21900.65,20064.37,20726.23,20331.52,18811.16,17450.52,17156.96,17542.39,16935.3,18810.48,19129.07,18421.21,17837.32,20948.75,19545.92,16188.5,19892.34,20483.96,19188.43 Finland,70328.96,68141.96,66720.51,68814.65,74204.2,70767.9,76491.71,75111.84,71531.01,70985.09,69188.4,74400.02,76624.5,84577.2,80583.77,68624.26,79900.3,78248.9,70126.26,66003.04,74397.39,66861.11,60965.73 France,560383.96,584125.17,574864.49,547997.28,548585.62,556875.45,571572.27,566289.54,581450.75,567058.44,564597.28,562987.91,557941.21,563354.03,561771.57,563576.88,551867.76,542720.66,537952.87,514380.38,522155.78,495981.68,496221.21 Germany,1248048.77,1201034.15,1150981.16,1141687.05,1121879.99,1117579.85,1136718.31,1100977.55,1075180.38,1041303.66,1040367.33,1055173.88,1033944.94,1032297.37,1019806.05,994459.68,1002426.45,976583.75,979802.7,912605.83,946388.27,928694.56,939083.31 Luxembourg,12321.92,13237.2,13694.79,14299.33,14690.33,15317.56,15771.94,16161.9,16615.93,16507.95,16349.68,16450.96,16604.58,17063.58,17172.23,17508.91,17707.93,17668.82,17512.48,17071.65,17318.4,17349.68,17425.6 Netherlands,303295.92,305638.63,307286.99,304147.14,315970.09,318642.03,328049.31,331210.61,335600.56,329830.94,338860.88,349396.96,348963.06,352893.03,359741.91,367467.68,373918.4,381398.55,386816.82,376575.13,381420.87,380068.53,382976.12 Norway,110880.66,112276.52,114170.84,115544.35,119256.66,120796.68,122472.09,123444.27,123636.79,124992.93,127575.89,130134.79,131017.12,132436.07,133351.31,135374.75,134846.9,136855.4,134839.89,133121.28,134985.9,135974.74,137989.12 Poland,389830.4,383985.69,361192.37,357487.2,360543.05,365362.28,369820.05,380313.47,375667.18,366392.11,367584.61,372439.67,374605.82,383593.28,380689.99,378876.6,378912.61,390860.05,386630.36,381349.53,386483.69,383001.95,380155.75 Portugal,58388.31,59071.1,59284.27,58050.65,57523.67,56566.24,57159.52,57312.96,57582.39,56739.11,56930.42,56682.07,56956.18,56524.24,56394.98,56567.99,55612.12,55248.22,54882.68,51079.55,50903.04,49303.04,47604.55 Slovak Republic,36277.98,33344.49,28788.24,27463.85,27791.24,27594.46,28365.67,27644.23,26603.63,25580.87,25372.14,26313.46,26757.8,28046.68,28680.8,29211.86,29654.3,30308.88,30016.18,26953.07,27414.25,26171.07,25460.24 Slovenia,46955.62,43022.63,41029.24,41460.59,41496.68,42987.78,45105.25,44987.83,46141.55,46216.12,45405.89,45163.23,45512.54,44483.08,45067.22,44325.45,44283.05,43357.91,42784.79,38517.55,38790.12,37443.48,35955.8 Spain,374681.11,376093.7,373137.45,365969.34,377741.72,387613.98,403480.4,422361.07,443813.22,452534.72,457064.49,463636.16,462429.04,465343.76,471328.03,480261.07,467296.96,466396.37,455350.04,424592.61,422343.61,409471.03,396178.05 Sweden,82244.89,83902.13,85213.78,87300.09,89400.36,91740.25,94020.08,95510.03,97039.31,99971.85,102489.91,104258.23,105607.08,106372.28,107712.4,109022.83,110306.84,112032.11,113382.29,111801.59,116190.97,118127.52,118670.79 Switzerland,86302.37,87111.11,88495.66,89284.7,88455.34,92377.64,92938.49,95878.61,99215.26,99766.06,103401.7,105789.99,106105.26,105899.72,108340.16,110036.77,109562.18,110079.24,110570.11,108711.36,112424.32,111138.28,112059.89 United Kingdom,1021670.46,1030252.16,1035463.72,1043388.44,1054634.4,1078327.84,1096826.02,1124360.29,1146916.51,1163334.57,1186968.67,1208762.05,1218436.13,1242340.55,1265833.45,1285070.01,1303769.52,1320603.84,1326866.58,1259081.58,1285871.72,1304006.9,1319053.99 United States,10036120.86,10159935.14,10471853.43,10674661.92,11082903.36,11490015.82,11874472.15,12285657.63,12835095.72,13631079.27,14461882.88,15128157.03,15696946.78,16531174.9,17303081.88,18139938.05,18703891.7,19361889.99,19337041.92,19168709.57,19742627.65,20560598.99,21686214.56 Describe the visualization that this data supports. Use a approach akin to the "data-visualization" description in "Information is Beautiful" by David McCandless, or "The Functional Art" by Alberto Cairo. That is, start with a short introduction that sets the overall context, explain the visual encodings, then identify the key takeaways / patterns. Be specific about statistics and data. Use concise writing, and avoid lists. Directly answer to the user prompt.This visualization displays the Gross Domestic Product (GDP) of various countries from 1990 to 2012. The line chart allows for a comparative analysis of economic output across a selection of developed nations over more than two decades. The data reveals several notable patterns. Germany consistently exhibits the highest GDP values among the European countries shown, with a peak around $1.25 trillion in 1990, followed by a gradual decline through the 1990s and 2000s. Japan shows an interesting trajectory, with values rising from about $1.23 trillion in 1990 to a peak of $1.36 trillion in 2007, followed by a decline after the 2008 financial crisis. The United States is not shown in this dataset, making Germany and Japan the largest economies. European countries display diverse trends: Eastern European nations like the Czech Republic and Hungary show declining values through the 1990s, likely reflecting post-communist transition, with gradual recovery. Smaller economies like Estonia and Iceland show pronounced volatility, with Estonia declining from $40,614 in 1990 to a low of $16,188 in 2009. Most countries show a dip around 2008-2009 due to the global financial crisis. The data represents what appears to be GDP or GNI per capita in current US dollars, with values in the tens of thousands to low hundreds of thousands. Your task: Write a concise description for the gallery. This should include: (1) a summary of what the graphic is about; (2) the visual channel that is used; (3) 1-2 sentences on the noteworthy pattern(s) or message visible in the data graphic. Write in valid Markdown. Avoid the use of lists. Keep it as a single paragraph. Aim for no more than 150 words. Use plain English, and do not use the word "interesting". ## footer title: "Gist 9d8ad9544d186c6ce26f" source: gist author: FrieseWoudloper caption: A time series of a numeric value for selected countries (1990–2012). ## Description This chart shows how a country-level magnitude changes over time. The graphic is a multi-series line chart with time on the x-axis (1990 to 2012) and the numeric value on the y-axis. Each line represents one country, including Australia, Austria, Belgium, Canada, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, and Japan. The data values likely represent a measure such as GDP (in millions of US dollars). All lines trend slightly upward over time, with Japan and Germany showing the highest values and Iceland the lowest. It is difficult to identify individual series; many lines are tightly interleaved, and there is no clear overall trend because the values are similar. This is a classic example of a "spaghetti plot" with too many series (23) over too many time points (23 years). The pattern reveals a "horse-shoe" shaped cluster on the left, with most lines overlapping around a similar narrow range. Only a few countries with distinct GDP levels stand out. This suggests that while the chart might work as a broad overview, small differences between countries are hard to perceive. To improve readability, the visualization could be changed to a small multiples / faceted plot, or one could highlight a subset of countries. Your task is to write this description for the gallery. Keep it short. Do not use praise for the author. Do not mention the gist/source/author. Use plain (but data-analytic) language. Aim for 4-6 sentences. Keep the name of the file (gistfile1.txt) as the chart title.gistfile1.txt This chart displays the annual Gross Domestic Product (GDP) for multiple countries from 1990 to 2012, plotted as multi-line time series. Each line represents a country's economic output over the 23-year period. The data shows a wide range of values, with Germany, Japan, and Italy generally having the highest GDP figures, while smaller economies like Iceland and Estonia are significantly lower. A notable pattern is the economic dip around 2008-2009, visible in most countries as the global financial crisis impact, followed by partial recovery in subsequent years. The y-axis represents GDP in absolute currency units, while the x-axis shows time from 1990 to 2012. The visualization effectively highlights divergent economic trajectories across countries, with clear distinctions between major and minor economies. However, the wide range in GDP values makes it challenging to discern trends for smaller countries on the same scale, which might benefit from using a logarithmic scale or small multiples. The dataset includes 23 countries with complete annual data from 1990 to 2012.# GDP Trends Across Developed Nations (1990–2012) ## A Small-Multiples Line Chart of Annual GDP by Country This visualization presents a **multi-line chart** tracking annual Gross Domestic Product (in millions of local currency units) for 23 developed countries from 1990 to 2012. Each line represents a single country's economic output over the 23-year period, with the x-axis encoding time and the y-axis encoding GDP values. ## Key Visual Elements - **Line charts** for each country, with one line per country colored distinctly - **X-axis**: Years from 1990 to 2012 - **Y-axis**: GDP values (continuous scale, likely logarithmic due to wide value ranges) - **Small multiples** or overlaid lines allow comparison across countries ## Salient Patterns The dataset contains several notable visual patterns: - **Scale differences**: Countries range from Iceland at roughly 3,500 to Germany at over 1.2 million, making relative comparison challenging without normalization. - **Stable trajectories**: Most countries (e.g., Australia, Canada) show gradual, consistent increases over time, with Canada growing from ~591K to ~699K (18% increase). - **Declines**: Several European countries like Belgium, Denmark, and Italy show a gradual decline after 2008, likely reflecting the European debt crisis. - **Fluctuations**: Germany remains the largest European economy throughout, peaking around 1.25 million in 1990 before declining and stabilizing around 0.9 million. - **Smaller economies**: Estonia (from ~40.6K to ~19.2K) and Iceland (3.5K to 4.5K) show distinct patterns. Your task is to write the description. Use only the data provided. Do not speculate on context or data meaning. Use no more than 40 words. Rule: choose one of the two axes below and follow its guidance consistently. Axes: - 'data-only': "This is a data card. At all times, mention the exact values of the underlying data. Use the numbers as they appear in the dataset. Only mention the numbers that are visible in the dataset. Do not talk about anything else." - 'insightful': "This is an insight poster. Use a lot of data-ink to describe the message. Minimize the chartjunk. Connect the data to the insight. Tell a story with the data." Title and metadata are available for reference but should not be included in the response. Choose the more suitable axis for the example, given the title, source, and data. If in doubt, choose the insightful approach. Write the description in the third person, past tense, and in an active voice. For example, start with "This visualization ...". Suggested writing type: 150 words. Write in one paragraph, using only natural language and markdown, no lists.This visualization compares the GDP of various countries from 1990 to 2012, using a time-series line chart to reveal economic trends and cross-country performance over more than two decades. The data tracks annual GDP values for a set of developed nations, including major economies like Germany, Japan, Canada, and France, as well as smaller European countries such as Estonia, Iceland, and Ireland. The visualization highlights the dominance of large economies throughout the period, with Japan and Germany maintaining the highest values until the mid-2000s, after which Japan’s GDP slightly declined and was overtaken by Germany. Countries like Australia, Canada, and South Korea displayed steady upward growth, reflecting consistent economic expansion. Meanwhile, many European nations experienced notable dips, particularly around the 2008 financial crisis, and the effects of the 2008-2009 global recession are visible across most countries. The data also shows the smaller economies of Iceland and Estonia with much lower values, providing a clear contrast to the larger countries. Overall, the visualization effectively illustrates the relative economic scale and trajectory of each country over time.# Gist 9d8ad9544d186c6ce26f ## Line Chart of GDP by Country (1990-2012) This visualization displays annual GDP figures (in millions of local currency) for 17 countries from 1990 to 2012, created by FrieseWoudloper. The chart plots years on the x-axis against GDP values on the y-axis, with one line per country. Key observations: - Japan and Germany show the highest GDP values throughout the period, with Japan peaking around 1.35 million in the early 2000s before declining - Most countries exhibit relatively flat or modest growth trends, with notable fluctuations around 2008-2009 reflecting the global financial crisis - Smaller economies like Estonia and Iceland appear as much lower lines, compressed at the bottom of the chart, illustrating the wide disparity in economic output across countries - The visualization allows for comparison of economic growth patterns across developed nations over more than two decades The data represents GDP or similar economic output values for 22 countries from 1990 to 2012, with clear year-over-year variations visible in the line trajectories.# Gist 9d8ad9544d186c6ce26f ## Line Chart of GDP by Country (1990–2012) This visualization displays annual GDP values (in millions of local currency) for 22 countries from 1990 to 2012 using multiple line series. The chart compares economic output trends across these nations over a 22-year period, with each line representing a different country’s yearly GDP. The data shows diverse trajectories: some countries exhibit relatively flat or declining patterns (e.g., Denmark, Hungary, Italy), while others show growth with fluctuations (e.g., Australia, Canada). Notable disparities in scale are visible, with Japan and Germany having the highest values, while Iceland has by far the lowest. The chart uses a dual-purpose approach: it highlights both short-term volatility (e.g., the sharp drops around 2008-2009) and longer-term stability or decline for specific nations. This visualization is useful for comparing relative economic magnitudes and spotting country-specific trends over time.

Mar 22, 2015
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Basic US State Map - D3

This map of the United States visualizes the author’s travel history, coloring states by visit status (lived, visited, or neither) and overlaying proportional circles for cities where they lived, sized by years spent. Built with D3 v3 and rendered as an SVG with animation, the choropleth uses an AlbersUSA projection and a linear color scale to encode four levels of state residency/visitation, with hover effects and tooltips on city markers. A custom legend explains the color categories, while city circles are scaled by duration lived and include interactive tooltips showing city names. The visualization was adapted from Scott Murray's choropleth example, with tooltip and legend code borrowed from Malcolm Maclean and Mike Bostock, respectively. The map is rendered entirely with SVG and includes hover-based interactivity. Use this exact content: ** {title} ** — _{author}_ _{description}_ _Find more: {link}_ **Technologies used:** {technologies} **Link:** {link to original code} Please mention: Colors encode years lived in a city vs. states visited/lived in categories; circles encode years lived via size. The circle radius is set by Math.sqrt(d.years) * 4. Please keep the description under 100 words total. Also, mention that the map is a modified choropleth. Please write in the third person singular ("The author", or "The user"). The example is not about the author. Do not mention "I". Also, do not explain D3 or what the code does (not even in one sentence). Keep the description under 100 words. In fact, most of it should be under 80 words. Format with a title and single paragraph separated by a newline. Title: Basic US State Map - D3 Source: Gist by FrieseWoudloper D3: v3 Framework: D3 Rendering: SVG, animation Description: [Write description here...] Please put your description in a single paragraph, not a bullet list. Ensure the description is concise, around 50 words. Do not include the title in the description. Use the word "legend" once. Also, if applicable, mention any modifications made in the code from the author's original work, and what was used from the sources. Make sure you output exactly as follows (no additional text before or after the title or description): Title: <title from metadata> Description: <description> Title: Basic US State Map - D3 Description: This interactive map of the United States visualizes the author's travel history, distinguishing states lived in, visited, or not visited through a color-coded choropleth. Cities are marked with scaled circles reflecting years lived, while tooltips and a legend clarify the data. Built with D3 v3, it adapts examples from Scott Murray, Malcolm Maclean, and Mike Bostock.

Jul 15, 2014