Convert Messy Spreadsheet of Berlin Street Names to GeoJSON

100%

Convert Messy Spreadsheet of Berlin Street Names to GeoJSON

✓ Published0🌍 Public

Last edited Mar 13, 2018

Created on Mar 13, 2018

This visualization converts a messy CSV file of Berlin street names into a GeoJSON FeatureCollection. The XQuery script uses `csv:parse` and `map:merge` to clean the data, expanding abbreviations like "dr" to "doctor" and normalizing gender fields. It then calls the Mapbox Geocoding API via `http:send-request` to retrieve geometry for each street address, bounded to Berlin’s coordinates. The resulting features are assembled with `array` and output as JSON, ready for mapping.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist bdb4994137a7b0f75652f01bb4c170d2

This visualization converts a tab-separated dataset of Stolpersteine memorial stones into an interactive GeoJSON FeatureCollection. It shows each victim’s name, address, deportation, murder, and death details as a point location on a map. The XQuery script parses the TSV file using `csv:parse` with a tab separator, then constructs a GeoJSON structure with point geometries. It uses `fetch:text` to load the remote data and `json:serialize` to output the final GeoJSON, which can then be rendered by a mapping library.

CCliffordAnderson

72% match

Loading thumbnail…

Identify GeoJSON errors in Mapping Berlin

This example identifies errors in GeoJSON data for a map of Berlin by piping a Cloudant view response through `jq` to extract point features and run `geojsonhint`. The command constructs a FeatureCollection from the database rows, then validates the resulting JSON structure and geometry. The visualization shows which features fail validation, using the command-line tool to highlight malformed coordinates or properties. The process relies on a gist-hosted script that queries the Cloudant database and pipes the output through `jq` before passing it to `geojsonhint` for error detection.

CCliffordAnderson

70% match

Loading thumbnail…

Berlin Wall GeoJSON converted from Open Street Maps

This map displays the former course of the Berlin Wall as a single, continuous polygon, derived from OpenStreetMap data. It visualizes the wall’s intricate path through the city, with the GeoJSON geometry rendered as a filled shape. The code parses the FeatureCollection and draws the polygon using a geo-projection library, likely `d3.geoPath`, directly mapping the geographic coordinates to the canvas. The visualization relies on the multipolygon relation from the OSM dataset, preserving the wall’s true layout without simplification or embellishment.

CCliffordAnderson

70% match

Loading thumbnail…

Gist 9af806e8acc168aa10c06099d668b711

This example presents an interactive map of historical Berlin locations from 1947, using GeoJSON point features to mark sites like the Synagogue Fasanenstrasse and Charlottenburg Gate. Each marker opens a popup with period photographs and captions, rendered through a Mapbox-like interface. The visualization relies on a custom marker system with symbol and color properties, while the underlying tour.json data drives the geographic placement and content. The implementation uses standard web mapping libraries to handle GeoJSON rendering and marker interactions, showing how archival image collections can be geolocated and explored.

CCliffordAnderson

68% match

Loading thumbnail…

Mapping Berlin Feature Collection Examples

This example shows a GeoJSON feature collection used to map a single Berlin location, the Krematorium Berlin-Wedding, with associated photographic data. It demonstrates the JSON syntax for a `FeatureCollection` containing a point geometry with properties such as title, series, and an embedded YouTube image URL. The code uses standard GeoJSON formatting with a `Point` geometry and `coordinates` array, which would be rendered using a library like `d3.geoPath` or Leaflet, though no visualization library is directly invoked here.

CCliffordAnderson

67% match

Loading thumbnail…

test

This example visualizes a single point feature from a GeoJSON file, marking the location of Friedrichstraße in Berlin. The map uses D3’s `d3.geoPath` to render geographic outlines, while the point itself is drawn with SVG markers. The feature’s properties include an image carousel with historical photos, toggled by user interaction. The code relies on D3’s data binding and event handling to update the displayed images, and the geographic coordinates are parsed from string values in the GeoJSON. The rendering approach uses a standard Canvas or SVG projection, though the specific projection library is not shown. The source is a gist by CliffordAnderson, with data embedded directly in the file.

CCliffordAnderson

66% match

Loading thumbnail…

Gist 73100dba40164b493edf7af63649a498

This map shows a guided tour of Berlin in 1947, with markers placed at historical sites like the Fasanenstrasse Synagogue and Charlottenburg Gate. Each marker includes photographic images and descriptive text retrieved from the GeoJSON data. The visualization uses d3.geoPath to render the map and d3.forceSimulation to position the markers, with the tour data loaded from a local JSON file.

CCliffordAnderson

65% match

Loading thumbnail…

Gist c34929ea07077c99b9cb6e8326253b9e

This map-based visualization plots historical sites from a 1947 tour of Berlin, using a GeoJSON FeatureCollection to place markers with associated photographs and descriptions. The code relies on Leaflet for map rendering and marker display, with custom styling applied through marker properties like color and symbol. Each point opens a popup containing historical images from Wikimedia Commons, pairing wartime and contemporary photos to show how locations have changed. The tour data is organized by series and includes coordinates for landmarks such as the Technical University and Flakturm Tiergarten.

CCliffordAnderson

65% match