Test of SunCalc in SQL

cdb_latlng

This example defines a set of PostgreSQL functions for converting latitude and longitude values into spatial geometries. It shows how to construct valid 4326-point geometries from numeric, floating-point, or text inputs, including support for comma-style decimal separators. The code uses PostGIS functions such as `ST_SetSRID` and `ST_MakePoint` to build the geometry, along with custom helpers like `CDB_DecimalDegreeLiteral` for parsing text coordinates. It handles both individual lat/lng arguments and a single space-separated coordinate string, making it a flexible utility for geospatial data preprocessing in SQL.

61% match

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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Fun SQL statements for CartoDB

This example shows how to explore and analyze tornado and county data in CartoDB using SQL statements. It demonstrates basic queries for filtering, sorting, and aggregating records, followed by spatial functions that inspect geometry types, compute areas, distances, and intersections. The code uses PostGIS functions such as `ST_Area`, `ST_Distance`, `ST_Intersects`, and `ST_DWithin`, alongside CartoDB-specific helpers like `CDB_LatLng`. It also includes updates to add tornado counts and density per county, highlighting how SQL can drive interactive map visualizations.

Minimum spanning tree in SQL... just because

This example demonstrates how to compute a minimum spanning tree entirely within a PostgreSQL database using custom SQL functions and aggregates. It processes spatial geometry data, calculating distances between points and iteratively building the tree by selecting the shortest edges while avoiding cycles. The code defines custom data types and PL/pgSQL functions to handle the algorithm, ultimately returning the tree’s edges with their associated geometries and costs. It showcases an unconventional approach to graph algorithms, relying solely on SQL’s set operations and array manipulation rather than external graph libraries.

CartoDB SQL tricks: Various things that as a SQL-newbie I had to search for >10 minutes to find answers for.

This example demonstrates a collection of SQL recipes for solving common spatial data problems in CartoDB, ranging from merging neighboring county polygons with ST_Union to creating great circle lines via ST_MakeLine and ST_Segmentize. It shows practical techniques like updating geometries with conditional CASE statements, extracting coordinates with ST_X and ST_Y, stripping HTML tags using regexp_replace, and formatting zip codes with to_char. The code also covers performance strategies for large updates and querying spatial relationships with st_contains. Each snippet targets PostGIS functions within the CartoDB platform, emphasizing data preparation and geometric transformations.

Aandrewbt

@mourner's suncalc for heroku... moved to https://github.com/RandomEtc/suncalc-api

This example demonstrates a lightweight HTTP API for querying solar calculations, wrapping the SunCalc JavaScript library. It exposes two endpoints—`/times` and `/position`—that accept latitude and longitude parameters, along with an optional date, and return the computed sunrise, sunset, and solar position data. Built with Express 2.5.8, it validates query parameters using custom middleware and supports JSONP callbacks for cross-domain requests. The server runs on Node.js, using the Procfile for Heroku deployment, and serves results as plain JSON responses.

RRandomEtc

Gist 1353003

This example demonstrates a spatial SQL query that computes raster statistics for a custom polygon region. It uses PostGIS functions to sum pixel values, count pixels, and calculate the average value across a 10x10 grid of sub-pixels within each raster tile. The query filters raster tiles intersecting the target polygon and then intersects translated grid cells against the same polygon. The code relies on `ST_Value`, `ST_Intersects`, `ST_Translate`, `ST_MakeEnvelope`, and `generate_series` to perform the analysis, extracting data from the `carbon_query_test_s` table.

56% match