UCR STAR is built to serve the geospatial community and facilitate the finding of public geospatial datasets to use in research and development. The main goal is to allow the researchers worldwide to unleash the true value of the datasets that are available all over the web. To this end, we make all the datasets active by placing them on a map. This helps the researchers in deciding which datasets they can use for their research by exploring them on the map before downloading. The UCR STAR does not own any of these datasets, rather, all datasets are attributed to their original creators and we give them the full credit for sharing these datasets. UCR STAR can serve data publishers by providing the visualization and by increasing the visibility of all these datasets.

The UCR STAR archive is built as a service to the community and we will be happy to serve more datasets for the benefit of the research community. When you add your dataset to the archive, you get the following benefits.

• Easier access and better visibility for your dataset
• Scalable visualization that will drive better access
• You can embed the visualization into your own project homepage without the need to install any additional software (See example below)
• Drive more traffic to your project page
• STAR redirects the downloads to your website so you can still track the downloads

When you add your dataset to STAR, you will get an interactive visualization like the one below which you can embed into your own website.

First, you need to make sure that your dataset fits the scope of the STAR archive. In short, it has to have a primary spatial (geographical) component in it. In other words, it can be visualized on a map. If it has a geographical location, e.g., longitude and latitude, then it should fit well.

To submit your request to add a dataset to UCR-Star, please fill in the following form.

To cite the UCR STAR website, please use the following BibTeX.  Copy BibTex

UCR STAR is built by the Big Data Lab at UC Riverside. Here are some of the papers related to UCR STAR.

UCR-STAR

UCR-STAR is described in the following paper:
Tin Vu, Saheli Ghosh, Mehrad Amin Eskandard, and Ahmed Eldawy, "UCR-STAR: The UCR Spatio-Temporal Active Repository." In SIGSPATIAL Special 11, (2), pages 34–40, 2019, DOI>10.1145/3377000.3377005

Da Vinci

The visualization is provided through the Da Vinci project for interactive scalable visualization.
Saheli Ghosh and Ahmed Eldawy, "AID: An Adaptive Image Data Index for Interactive Multilevel Visualization." In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019), in Macau SAR, China, April 8-12 2019

R*-Grove

The scalable indexes are built using the R*-Grove index.
Tin Vu and Ahmed Eldawy, "R-Grove: Growing a Family of R-trees in the Big-Data Forest," In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2018), in Seattle, WA, November 6-9, 2018

Synopses

For interactive exploration, the data has to be summarized into small synopses that can fit in memory.
A. B. Siddique, Ahmed Eldawy, and Vagelis Hristidis. "Comparing Synopsis Techniques for Approximate Spatial Data Analysis" To appear in the 45th International Conference on Very Large Data Bases (VLDB 2019) in Los Angeles, California - August 26-30, 2019
A. B. Siddique and Ahmed Eldawy, "Experimental Evaluation of Sketching Techniques for Big Spatial Data." In Proceedings of the ACM Symposium on Cloud Computing, (SoCC'18), In Carlsbad, CA, October 11-13, 2018
Harry Chasparis and Ahmed Eldawy "Experimental Evaluation of Selectivity Estimation on Big Spatial Data". In Proceedings of the 4^th ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2017, in Chicago, IL, May 2017.

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If you have any questions or suggestions, please feel free to contact the team at feedback@ucrstar.com