Big Data Computing for Geospatial Applications
(Visualizza in formato marc)
(Visualizza in BIBFRAME)
| Autore: |
Li Zhenlong
|
| Titolo: |
Big Data Computing for Geospatial Applications
|
| Pubblicazione: | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica: | 1 online resource (222 p.) |
| Soggetto topico: | Geography |
| Research & information: general | |
| Soggetto non controllato: | big data |
| big geospatial data | |
| big mobile navigation trajectory data | |
| CA Markov | |
| city blocks | |
| climate science | |
| cloud | |
| cloud computing | |
| cyberGIS | |
| ELT | |
| ETL | |
| fine-grained emotion classification | |
| formalization | |
| GeoAI | |
| geographic knowledge graph | |
| geographic knowledge representation | |
| GeoKG | |
| geospatial big data | |
| geospatial computing | |
| geospatial cyberinfrastructure | |
| geospatial problem-solving | |
| geovisual analytics | |
| global terrain dataset | |
| Hadoop | |
| hazard mitigation | |
| IoT | |
| knowledge base | |
| land-use change prediction | |
| machine learning | |
| MapReduce | |
| massive data | |
| metadata | |
| missing road | |
| mobility community | |
| overlay analysis | |
| parallel computing | |
| sensor data | |
| shape complexity | |
| smart card data | |
| social media | |
| spatial thinking | |
| spatio-temporal analysis | |
| task | |
| terrain modeling | |
| topographic surface | |
| topology | |
| transit corridor | |
| trip | |
| web cataloging service | |
| workflow | |
| Persona (resp. second.): | TangWenwu |
| HuangQunying | |
| ShookEric | |
| GuanQingfeng | |
| LiZhenlong | |
| Sommario/riassunto: | The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. |
| Titolo autorizzato: | Big Data Computing for Geospatial Applications |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910557664803321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |