Ricardo A. Pasquini
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January 21, 2020 by admin 3
Coding Notes, Uncategorized

Reshape of lat and lon coordinates in MongoDB, using the aggregate pipeline

Reshape of lat and lon coordinates in MongoDB, using the aggregate pipeline Summary To transform a large number of documents in a MongoDB collection with spatial data, for example: {lat: -58.1, lon: -34.2} to a GeoJson format, recognizable by MongoDB spatial analysis functions, for example: {type: ”Point”, location: [- 58.1, -34.2]} It seems advisable to use the aggregation framework: db.tweets.aggregate ([{$project: {location: {type: "Point", coordinates: ["$lon", "$lat"]}}}, $ out: {$out: "newcollectionname"}] );   The problem An usual task in the database in MongoDB may be to prepare the data for spatial tasks. As I described in the previous post, it is necessary to have the data in the compatible format, in this case as a GeoJson. For example, if we are working with points: {type: ”Point”, location: [- 58.1, -34.2]} {type: ”Point”, location: [- 58.1, -34.2]} That is, specifying a "type" key that specifies that it is a point, and then a "location" key with an array of the coordinates pair: longitude and latitude (in that order!) If working with polygons: {type: "Polygon", coordinates: [[[0, 0], [3, 6], [6, 1], [0, 0]]]} The problem with my data is that it was in the following format and I needed to do…

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Recent Posts

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AMM apps bienes publicos causal inference classification conda COVID-19 criptomonedas cryptocurrencies defi econometrics economía de mercados Ethereum Exportar resultados Export output Financial inclusion financiamiento cuadrático fraud geopandas Geospatial analysis Gitcoin h3 hexagons Households Finance Indebtedness Jupyter Loops machinelearning Mercado de Alquileres MongoDB negocios precision proyectos ingeniería public goods pymongo python quadratic funding recall Regression roc-curve scalability Stata Tablas Tables ubuntu