The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data
Scientific datasets from global-scale earth science models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. Water data are frequently stored as multidimensional arrays, also called gridded or raster data, and span two or three...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/13/15/2066 |
| _version_ | 1797525148179365888 |
|---|---|
| author | Riley Chad Hales Everett James Nelson Gustavious P. Williams Norman Jones Daniel P. Ames J. Enoch Jones |
| author_facet | Riley Chad Hales Everett James Nelson Gustavious P. Williams Norman Jones Daniel P. Ames J. Enoch Jones |
| author_sort | Riley Chad Hales |
| collection | DOAJ |
| description | Scientific datasets from global-scale earth science models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. Water data are frequently stored as multidimensional arrays, also called gridded or raster data, and span two or three spatial dimensions, the time dimension, and other dimensions which vary by the specific dataset. Water engineers and scientists need these data as inputs for models and generate data in these formats as results. A myriad of file formats and organizational conventions exist for storing these array datasets. The variety does not make the data unusable but does add considerable difficulty in using them because the structure can vary. These storage formats are largely incompatible with common geographic information system (GIS) software. This introduces additional complexity in extracting values, analyzing results, and otherwise working with multidimensional data since they are often spatial data. We present a Python package which provides a central interface for efficient access to multidimensional water data regardless of the file format. This research builds on and unifies existing file formats and software rather than suggesting entirely new alternatives. We present a summary of the code design and validate the results using common water-related datasets and software. |
| first_indexed | 2024-03-10T09:07:41Z |
| format | Article |
| id | doaj.art-de1918909fa74e03b2bcbf254074a387 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T09:07:41Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-de1918909fa74e03b2bcbf254074a3872023-11-22T06:19:53ZengMDPI AGWater2073-44412021-07-011315206610.3390/w13152066The Grids Python Tool for Querying Spatiotemporal Multidimensional Water DataRiley Chad Hales0Everett James Nelson1Gustavious P. Williams2Norman Jones3Daniel P. Ames4J. Enoch Jones5Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USADepartment of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USADepartment of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USADepartment of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USADepartment of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USADepartment of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USAScientific datasets from global-scale earth science models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. Water data are frequently stored as multidimensional arrays, also called gridded or raster data, and span two or three spatial dimensions, the time dimension, and other dimensions which vary by the specific dataset. Water engineers and scientists need these data as inputs for models and generate data in these formats as results. A myriad of file formats and organizational conventions exist for storing these array datasets. The variety does not make the data unusable but does add considerable difficulty in using them because the structure can vary. These storage formats are largely incompatible with common geographic information system (GIS) software. This introduces additional complexity in extracting values, analyzing results, and otherwise working with multidimensional data since they are often spatial data. We present a Python package which provides a central interface for efficient access to multidimensional water data regardless of the file format. This research builds on and unifies existing file formats and software rather than suggesting entirely new alternatives. We present a summary of the code design and validate the results using common water-related datasets and software.https://www.mdpi.com/2073-4441/13/15/2066multidimensional datatime series dataraster datagridded datagrids |
| spellingShingle | Riley Chad Hales Everett James Nelson Gustavious P. Williams Norman Jones Daniel P. Ames J. Enoch Jones The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data Water multidimensional data time series data raster data gridded data grids |
| title | The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data |
| title_full | The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data |
| title_fullStr | The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data |
| title_full_unstemmed | The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data |
| title_short | The Grids Python Tool for Querying Spatiotemporal Multidimensional Water Data |
| title_sort | grids python tool for querying spatiotemporal multidimensional water data |
| topic | multidimensional data time series data raster data gridded data grids |
| url | https://www.mdpi.com/2073-4441/13/15/2066 |
| work_keys_str_mv | AT rileychadhales thegridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT everettjamesnelson thegridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT gustaviouspwilliams thegridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT normanjones thegridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT danielpames thegridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT jenochjones thegridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT rileychadhales gridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT everettjamesnelson gridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT gustaviouspwilliams gridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT normanjones gridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT danielpames gridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata AT jenochjones gridspythontoolforqueryingspatiotemporalmultidimensionalwaterdata |