The following datasets are going away on March 31, 2020
Below are the standard vector sources provided and information on update frequency.
- DG Catalog - DigitalGlobe image coverage footprints and metadata, Global coverage, updated continuously
- Gazetteer - https://www.usgs.gov/ & http://geonames.nga.mil/gns/html/index.html USGS & NGA GeoNames, Global coverage, data is static
- GBDX Workflow Outputs - Results from on-demand processing within GBDX, results from users' jobs are ingested and updated continuously
- Human Landscape - https://www.digitalglobe.com/products/human-landscape Country level coverage (DG product, new countries and refreshes can be purchased), data is static, although new data is ingested as it becomes available
- Landsat-8 - https://aws.amazon.com/public-datasets/landsat/ Landsat-8 image coverage footprints and metadata, Global coverage, updated continuously
- NaturalEarth - http://www.naturalearthdata.com/ vector boundaries, Global coverage, static, current as of December 2017
- OSM - https://www.openstreetmap.org/ Global coverage, updated hourly
- Sentinel-2 - https://aws.amazon.com/public-datasets/sentinel-2/ Sentinel-2 image coverage footprints and metadata, Global coverage, updated continuously
- Twitter - https://twitter.com Global coverage, updated real time
- Note: We also have the ability for users to contribute their own vectors - that is by definition user dependent and irregularly updated.
Note: Ingest Sources are subject to rapid change, and both the list of available sources as well as the vector count within each listed source may be different than the given examples. Basic principles still apply.
When users know the indexes containing the items they want to query for or retrieve, VectorServices provides the ability to limit operations to specific vector indexes. These calls avoid sending queries to the entire set of vectors, so they tend to take less time to process and return results.
Index Query also supports aggregations. Aggregation capabilities allow users to quickly and easily summarize data based on multiple parameters. Data can be grouped by geohash, date/time, terms, and a number of other aggregation types.
Aggregations are useful for discovering relationships and differences among groups of data. For example, a user could discover the most prolific Twitter users over a set of geohash boxes for the last week by aggregating tweet data by geohash region, time range, and the Twitter user name field. Mixing different aggregation types will give users different views on their data, allowing them to discover relationships they might not have otherwise seen.
In order for users to add a vector item or multiple vector items directly to an index, VectorServices provides user vector generation. Vector generation is useful for direct additions of or updates to vectors, without needing a third party service or a vector submission request form. If the user has a few vectors to ingest into VectorServices, using the write API is the preferred method of ingest.
If, however, the user has a large number of vectors to ingest into VectorServices, the bulk ingest process is the preferred method of ingest.
Updated about a year ago