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@ -24,4 +24,23 @@ If you use JSONP, the 200 HTTP code is always returned so the JavaScript client
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### CORS Support
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All the endpoints, which might be accessed using a web browser, add CORS headers and allow OPTIONS method.
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All the endpoints, which might be accessed using a web browser, add CORS headers and allow OPTIONS method.
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### Map Tile Rendering
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Map tiles create the graphical representation of your map in a web browser. The performance rendering of map tiles is dependent on the type of geospatial data model (raster or vector) that you are using.
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- **Raster**: Generates map tiles based on a grid of pixels to represent your data. Each cell is a fixed size and contains values for particular map features. On the server-side, each request queries a dataset to retrieve data for each map tile. The grid size of map tiles can often lead to graphic quality issues.
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- **Vector**: Generates map tiles based on pre-defined coordinates to represent your data, similar to how basemap image tiles are rendered. On the client-side, map tiles represent real-world geometries of a map. Depending on the coordinates, vertices are used to connect the data and display points, lines, or polygons for the map tiles.
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**Note:** By default, CARTO uses vector graphics for map rendering. Please [contact us](mailto:support@carto.com) if you need raster rendering enabled as part of your requirements.
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### Mapbox Vector Tiles (MVT)
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[Mapbox Vector Tiles (MVT)](https://www.mapbox.com/vector-tiles/specification/) are map tiles that store geographic vector data on the client-side. Browser performance is fast since you can pan and zoom without having to query the server.
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CARTO uses a Web Graphics Library (WebGL) to process MVT files. This is useful since WebGL's are compatible with most web browsers, include support for multiple client-side mapping engines, and do not require additional information from the server; which makes it more efficient for rendering map tiles.
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**Tip:** You can process MVT files with the [`ST_AsMVT` PostGIS function](https://postgis.net/docs/manual-dev/ST_AsMVT.html) with the [Maps API Windshaft renderer](https://github.com/CartoDB/Windshaft/blob/1000x/lib/windshaft/renderers/pg_mvt/renderer.js).
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@ -11,7 +11,7 @@ Begin by instantiating either a Named or Anonymous Map using the `layergroupid t
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##### Definition
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```bash
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GET /api/v1/map/static/center/{token}/{z}/{lat}/{lng}/{width}/{height}.{format}
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GET /api/v1/map/static/center/{token}/{z}/{lat}/{lng}/{width}/{height}.{format}{{?}extra_options}
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```
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##### Params
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@ -57,6 +57,9 @@ Note: you can see this endpoint as
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```bash
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GET /api/v1/map/static/bbox/{token}/{west},{south},{east},{north}/{width}/{height}.{format}`
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```
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#### Extra options
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* Layer: List of layers to be shown in the image (by default `all`), for example `?layer=0,1`.
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#### Named Map
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@ -10,7 +10,7 @@ Aggregation is available only for point geometries. During aggregation the point
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When no placement or columns are specified a special default aggregation is performed.
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This special mode performs only spatial aggregation (using a grid defined by the requested tile and the resolution, parameter, as all the other cases), and returns a _random_ record from each group (grid cell) with all its columns and an additional `_cdb_features_count` with the number of features in the group.
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This special mode performs only spatial aggregation (using a grid defined by the requested tile and the resolution, parameter, as all the other cases), and returns a _random_ record from each group (grid cell) with all its columns and an additional `_cdb_feature_count` with the number of features in the group.
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Regarding the randomness of the sample: currently we use the row with the minimum `cartodb_id` value in each group.
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@ -18,7 +18,7 @@ The rationale behind having this special aggregation with all the original colum
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#### User defined aggregations
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When either a explicit placement or columns are requested we no longer use the special, query; we use one determined by the placement (which will default to "centroid"), and it will have as columns only the aggregated columns specified, in addition to `_cdb_features_count`, which is always present.
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When either a explicit placement or columns are requested we no longer use the special, query; we use one determined by the placement (which will default to "centroid"), and it will have as columns only the aggregated columns specified, in addition to `_cdb_feature_count`, which is always present.
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We might decide in the future to allow sampling column values for any of the different placement modes.
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@ -185,3 +185,80 @@ This is the minimum number of (estimated) rows in the dataset (query results) fo
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]
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}
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```
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### `filters`
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Aggregated data can be filtered by imposing filtering conditions on the aggregated columns.
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Each condition is represented by one or more parameters:
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* `{ "equal": V }` selects an specific value of the aggregated column.
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* `{ "not_equal": V }` selects values different from the one specified.
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* `{ "in": [v1, v2, v3] }` selects any value from a list.
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* `{ "not_in": [v1, v2, v3] }` selects any value not in a list.
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* `{ "less_than": v }` selects values strictly less than the one given.
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* `{ "less_than_or_equal_to": v }` selects values less than or equal to the one given.
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* `{ "greater_than": v }` selects values strictly greater than the one given.
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* `{ "greater_than_or_equal_to": v }` selects values greater than or equal to the one given.
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One of the *less* conditions can be combined with one of the *greater* conditions to select a range of values, for example:
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* `{ "greater_than": v1, "less_than": v2 }`
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* `{ "greater_than_or_equal_to": v1, "less_than": v2 }`
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* `{ "greater_than": v1, "less_than_or_equal_to": v2 }`
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* `{ "greater_than_or_equal_to": v1, "less_than_or_equal_to": v2 }`
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For a given column, multiple conditions can be passed in an array; the conditions will logically ORed (any of the conditions have to be verifid for the value to be selected):
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* `"myvalue": [ { "equal": 10 }, { "less_than": 0 }]` will select values of the column `myvalue` which are equal to 10 **or** less than 0.
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In addition, the filters applied to different columns are logically combined with AND (all the conditions have to be satisfied for an element to be selected); for example with the following `filters` parameter we'll select aggregated records which have a `total_value` > 100 **and** a category equal to "a".
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```json
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{
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"total_value": { "greater_than": 100 },
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"category": { "equal": "a" }
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}
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```
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Note that the filtered columns have to be defined with the `columns` parameter, except for `_cdb_feature_count`, which is always implicitly defined and can be filtered too.
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#### Example
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```json
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{
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"version": "1.7.0",
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"extent": [-20037508.5, -20037508.5, 20037508.5, 20037508.5],
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"srid": 3857,
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"maxzoom": 18,
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"minzoom": 3,
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"layers": [
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{
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"type": "mapnik",
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"options": {
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"sql": "select * from table",
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"cartocss": "#table { marker-width: [total]; marker-fill: ramp(value, (red, green, blue), jenks); }",
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"cartocss_version": "2.3.0",
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"aggregation": {
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"placement": "centroid",
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"columns": {
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"total_value": {
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"aggregate_function": "sum",
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"aggregated_column": "value"
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},
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"category": {
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"aggregate_function": "mode",
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"aggregated_column": "category"
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}
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},
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"filters" : {
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"total_value": { "greater_than": 100 },
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"category": { "equal": "a" }
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},
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"resolution": 2,
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"threshold": 500000
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}
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}
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}
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]
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}
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```
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@ -31,7 +31,7 @@ The value of this attribute can be `false` to explicitly disable aggregation for
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// object, defines the columns of the aggregated datasets. Each property corresponds to a columns name and
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// should contain an object with two properties: "aggregate_function" (one of "sum", "max", "min", "avg", "mode" or "count"),
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// and "aggregated_column" (the name of a column of the original layer query or "*")
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// A column defined as `"_cdb_features_count": {"aggregate_function": "count", aggregated_column: "*"}`
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// A column defined as `"_cdb_feature_count": {"aggregate_function": "count", aggregated_column: "*"}`
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// is always generated in addition to the defined columns.
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// The column names `cartodb_id`, `the_geom`, `the_geom_webmercator` and `_cdb_feature_count` cannot be used
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// for aggregated columns, as they correspond to columns always present in the result.
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