mongodb geospatial tutorial

100%

mongodb geospatial tutorial

✓ Published0🌍 Public

Last edited Apr 9, 2019

Created on Apr 9, 2019

This example demonstrates MongoDB’s geospatial query capabilities for locating restaurants within New York City neighborhoods. It shows how to find a neighborhood containing a given point, count restaurants inside that neighborhood, and filter restaurants by proximity using spherical distance calculations. The code uses MongoDB’s `2dsphere` indexes, `$geoIntersects`, `$geoWithin`, `$centerSphere`, and `$nearSphere` operators against imported GeoJSON datasets (`neighborhoods.json` and `restaurants.json`). It also illustrates the earth’s radius in kilometers and miles for distance thresholds.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 877801

This example demonstrates a debugging session for a geospatial query on a Portland crime database using MongoDB. The visualization is not a traditional chart but rather a log of command-line interactions in the MongoDB shell, revealing a critical bug in the `geoNear` command with spherical geometry. The author, Caged, queries crime records at a specific latitude/longitude coordinate and discovers that `$centerSphere` queries return no results due to a "spherical distance would require wrapping" error, while a `geoNear` command returns records at a distance of 1.803 even for the exact same location. The gist highlights the inconsistency and potential bug in MongoDB's spherical distance calculations, where identical coordinates yield non-zero distances. The visualization would likely plot these crime locations on a map, with a note or highlight on the unexpected distance value, illustrating the importance of accurate geospatial querying and the quirks of early MongoDB versions.

CCaged

71% match

Loading thumbnail…

neighborhood mongo

Neighborhood mongo maps Portland neighborhood polygons stored as GeoJSON in MongoDB, rendered on a zoomable, panning map. Each polygon is drawn as an SVG path using a geographic projection, with neighborhood boundaries overlaid on muted base tiles. Hovering highlights a district and reveals its name, while the geo-coordinates in the data drive the shape and position of each region. The visualization demonstrates how spatial querying and rendering of GeoJSON features can be handled directly from a MongoDB document store.

CCaged

70% match

Loading thumbnail…

Created on Cognitive Class Labs

This example shows the process of segmenting and clustering neighborhoods in New York City. It maps each neighborhood to its latitude and longitude coordinates, then uses the Foursquare API to fetch common venue categories per neighborhood. The code applies k-means clustering to group neighborhoods into clusters and visualizes the results on an interactive Folium map, with each cluster color-coded. The underlying neighborhood dataset is downloaded from a public web source, and the map rendering relies on Folium’s mapping library.

DDAVINDER

63% match

Loading thumbnail…

nyc zip code gejson

This example shows a map of New York City ZIP code boundaries, with each postal code drawn as a filled polygon. The map uses d3.geoPath to render the GeoJSON features from a gist, which contains both the polygon geometry and property data such as borough and neighborhood name. The visualization colors each district and includes a longitude/latitude attribute for each ZIP code, enabling geographic context. The GeoJSON file supplies all boundary coordinates and metadata directly, so the rendering relies on d3’s geographic projections to display the polygons in a flat, interactive layout.

JJn1532

62% match

Loading thumbnail…

New York Centroid Fixed

This example demonstrates a workaround for a known issue in d3-geo where computing the centroid of a geographic feature collection can produce inaccurate results. It visualizes New York City neighborhoods on a Mercator projection, with orange and red dots marking computed centroids. The code uses `d3.geoCentroid` and `d3.geoPath` to project and render the data. A fix is applied by including the centroids as a MultiPoint feature in the projection's `fitExtent` calculation, ensuring the map is properly scaled and centered on the data.

PPhilippe Rivière

61% match

Loading thumbnail…

nyc_map

This visualization displays the borough boundaries of New York City as a blue SVG map, rendered using D3 v4’s geoMercator projection and geoPath. The map is centered on coordinates [-73.94, 40.70] and drawn from GeoJSON data. Three red circles are overlaid at fixed coordinate points, though their placement is inconsistent due to a coding error that uses the same value for both x and y positions, causing the markers to appear along a diagonal line rather than at their intended locations. The example demonstrates loading GeoJSON, projecting geographic features, and appending SVG elements.

VVictorHom

61% match

Loading thumbnail…

nyc_map

This visualization displays the five boroughs of New York City as a blue SVG map, built using a GeoJSON dataset of borough boundaries. The map is rendered with a Mercator projection, centered on coordinates [-73.94, 40.50], and scaled to fit the 960×1160 viewport. Red circles mark four specific locations, including points in Manhattan and Queens, overlaid on the borough polygons. The example demonstrates how to load GeoJSON, compute a projection, and draw geographic features using D3 v5's geoPath and geoMercator.

VVictorHom

61% match

Loading thumbnail…

DOITT_MUSEUM_01_13SEPT2010

This map displays the locations of museums and cultural institutions across New York City’s five boroughs, with each point labeled by name. The visualization is built using sample GeoJSON data from a gist, which is parsed and rendered as a point layer. The code employs D3.js spatial methods, specifically `d3.geoPath` for drawing map boundaries and `d3.geoCircle` for projecting individual museum coordinates, creating an interactive point-based overview of the city’s cultural landscape.

JJn1532

60% match