Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.

ESSI2.16

Addressing global challenges demands analytics over a variety of heterogeneous data sets across the globe. An increasing number of data collectors and providers make their data available online. Volunteered geographic information and citizen science projects (e.g. https://luftdaten.info, https://openSenseMap.org) further increase the volume and variety of the set of available data. However, these data sets are provided in different formats or accessed via different APIs or only support certain dialects of wider standards. The automatic meaningful integration of these data sets is often hindered due to semantic and structural differences between data and poor meta(data) quality. Also the size of the current data sets calls for solutions to combine data sets without moving the data from one to the other silo or to a processing platform where applicable.

Standardisation and evolving best practices are in place to overcome these issues. However, these are regularly adapted on a case to case basis and many systems following the same purpose exists for different or even the same domain. This session aims at synchronizing efforts and learn from success stories and experiences made across domains towards an easier integration of spatio-temporal data sources for spatial data analytics. Besides the technical integration, also the meaningful integration for different spatial and temporal support or measurement scales is an important part of this process.

This session will contribute to the FAIR data principle in especially increasing the Interoperability and Reusability of data sets. This session calls for abstracts on infrastructure, software, statistical models and applications that address cross-domain data integration supporting spatial analytics.

Fields of interest (but not limited to):
- Spatial Statistics
- Spatial Data Science
- Spatial Data Analytics
- Cloud Based Processing
- Semantic Mappings
- Distributed Computing
- Cloud Storage
- History, Versioning and Synchronisation of Data Sets
- Meaningful Automated Analytics
- Data fusion: official Statistics with Geoinformation
- Sensor Web

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Convener: Benedikt GrälerECSECS | Co-conveners: Anusuriya DevarajuECSECS, Matthes Rieke
Addressing global challenges demands analytics over a variety of heterogeneous data sets across the globe. An increasing number of data collectors and providers make their data available online. Volunteered geographic information and citizen science projects (e.g. https://luftdaten.info, https://openSenseMap.org) further increase the volume and variety of the set of available data. However, these data sets are provided in different formats or accessed via different APIs or only support certain dialects of wider standards. The automatic meaningful integration of these data sets is often hindered due to semantic and structural differences between data and poor meta(data) quality. Also the size of the current data sets calls for solutions to combine data sets without moving the data from one to the other silo or to a processing platform where applicable.

Standardisation and evolving best practices are in place to overcome these issues. However, these are regularly adapted on a case to case basis and many systems following the same purpose exists for different or even the same domain. This session aims at synchronizing efforts and learn from success stories and experiences made across domains towards an easier integration of spatio-temporal data sources for spatial data analytics. Besides the technical integration, also the meaningful integration for different spatial and temporal support or measurement scales is an important part of this process.

This session will contribute to the FAIR data principle in especially increasing the Interoperability and Reusability of data sets. This session calls for abstracts on infrastructure, software, statistical models and applications that address cross-domain data integration supporting spatial analytics.

Fields of interest (but not limited to):
- Spatial Statistics
- Spatial Data Science
- Spatial Data Analytics
- Cloud Based Processing
- Semantic Mappings
- Distributed Computing
- Cloud Storage
- History, Versioning and Synchronisation of Data Sets
- Meaningful Automated Analytics
- Data fusion: official Statistics with Geoinformation
- Sensor Web