Novel Tools to Improve the Management of Spatial Data Quality in the Context of Ecosystem and Biodiversity Monitoring. GI_Forum 2013 – Creating the GISociety|
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3 février 2016
- handle: 10670/1.czcy2y
- GOid: 0xc1aa5576_0x0032b4af
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Bruno Marcos et al., « Novel Tools to Improve the Management of Spatial Data Quality in the Context of Ecosystem and Biodiversity Monitoring. GI_Forum 2013 – Creating the GISociety| », Elektronisches Publikationsportal der Österreichischen Akademie der Wissenschafte, ID : 10670/1.czcy2y
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The growth in data availability and in the number of users from non-spatial disciplines has increased the concern with spatial data management and spatial data quality. This reality contextualizes the need and importance of data quality management throughout the spatial data cycle related to data editing and sharing in the context of knowledge networks. The development of standard concepts, procedures and tools may foster important advances in the improvement of spatial dataset production, use and management practices. The implementation of environmental monitoring programs implies a growing number and diversity of users with specific capabilities and responsibilities. In this context, spatial data quality evaluation and management promotes communication, optimizes processes of analysis and spatial modelling, and in this sense improves research and political and technical decisionmaking and action. This paper focuses on the methods and tools currently available to evaluate the external quality of datasets for environmental and ecological monitoring. A novel framework is presented that allows integrating and interconnecting spatial data quality evaluation with metadata geoportals in WebGIS platforms, facilitating evaluation by users with often limited expertise in this field. The advances achieved in this research highlight the relevance of developing capacities for different users to improve data collection, data models, spatial data processing and modelling, but also the need to inform and report on spatial data quality using adequate tools.