Gist 4063106

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Last edited Nov 13, 2012

Created on Nov 13, 2012

This example visualizes the density distribution of bicycle data from the `table_2007_2009_bicy` dataset, dividing points into five quantile-based buckets. It shows how the maximum point count and density vary across these buckets, revealing spatial clustering patterns. The SQL code uses CartoDB functions such as `st_snaptogrid` and `CDB_XYZ_Resolution` to aggregate points into grid cells at a given zoom level, then calculates density per cell and assigns quartiles via `ntile(5)`. The final output summarizes each bucket’s maximum values, providing a statistical overview of the underlying geographic data.

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The visualization maps New York City’s census tracts, coloring each by the density of public bicycle racks. It first simplifies tract geometries using PostGIS’s `ST_SimplifyPreserveTopology`, then counts rack intersections with `ST_Intersects` to populate a numeric column, normalizing the count by `shape_area` via SQL. The final map, rendered with CartoDB’s default styling, displays the normalized rack density as a choropleth, revealing spatial patterns in bike infrastructure across neighborhoods.

Aandrewxhill

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Create a density grid from geometries. For use in CartoDB

This example creates a density grid from point geometries using PostGIS and CartoDB, aggregating points into square cells via `ST_SnapToGrid` and returning cell envelopes with an associated value. It shows both a simple count of points per cell and a version that applies aggregation methods like MAX, MIN, SUM, or AVG to a specified numeric column, such as restaurant ratings or view counts. The functions generate a `CDB_DensityGridPair` type with geometry and float value, enabling thematic mapping of spatial density. An example query uses the grid to visualize cuisine ratings across New York restaurants.

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Gist 6114786

This example implements a private torque-style time-series visualization for geospatial data using PostgreSQL and PostGIS. It defines custom SQL functions that aggregate point features into web-Mercator grid cells, grouping counts by time steps and returning arrays of values and dates for rendering. The code uses `ST_SnapToGrid`, `CDB_XYZ_Resolution`, and `CDB_XYZ_Extent` to tile data at a given zoom level, with two variants: a standard response for instantaneous counts and a cumulative response that includes expiration dates for each cell. A metadata function returns the spatial bounds and time range from the source table, supporting both timestamped and numeric columns.

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Gist 1198897

This visualization demonstrates the geographic distribution of the 2010 U.S. Census data, mapping population counts and related demographic metrics across states. Built with Ruby and CartoDB importer, it renders interactive choropleth maps using color-coded polygons to represent data values, allowing users to explore spatial patterns in the dataset. The visualization leverages Google Spreadsheet integration for data input and PostgreSQL for storage, with map rendering handled through CartoDB’s geospatial processing pipeline.

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Gist 89811213f98645c51fa8

Using CartoDB’s SQL API and PostGIS functions, this notebook explores Gilt purchase data aggregated by zip code. It shows a series of interactive maps, including a bubble map of average session time and choropleths of purchase rates, built from geocoded postal points and polygon tables. The workflow demonstrates nested SQL subqueries to join transaction aggregates with geometries, spatial filtering with `ST_Intersects` and `ST_Buffer` around New York City, and on-the-fly centroid generation via `st_centroid` for a target audience map. Geocoding and tile rendering rely on CartoDB’s default web-mercator projection.

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CartoDB Point Clustering

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CartoDB Point Clustering

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Gist e049813cea7c84ccf54d681eb78088b5

The visualization displays a set of SQL query results from a weather and bike-share database, showing data such as the hottest day by zip code, trip counts by station, shortest trip duration, and average trip duration by end station. It uses Python with SQLite queries to extract the data, then renders the results in a web-based chart. The code references the `sqlite3` library for database access and likely employs a JavaScript charting library like D3.js to plot the aggregated values, though the specific rendering API is not fully visible in the provided snippet.

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