Geospatial Big Data Analytics: Opportunities & Challenges
Geospatial Big Data Analytics: Opportunities & Challenges in Present & Future Modes of Operation
Abstract: Big data and big data analytics have emerged as prevalent topics today in business and academia, promising a wide range of benefits and insights generated through the analysis of vast and varied datasets. The potential to create an enhanced understanding of consumer and corporate opportunities, through the extraction of trends and patterns, results in many business opportunities but also presents numerous challenges. Increased emphasis is now being placed on the use of geospatial datasets extracted from location-based services to supplement big data derived from other sources. Geospatial big data refers to “geo-enriched” data; that is, data that is supplemented with a geographic component, and when contextualised, layered with additional levels of detail, and analysed, provides some form of “location intelligence”. This talk will reflect on the opportunities and challenges of location intelligence, drawing out the ethical considerations relevant to both present and future modes of operation. GIS is no longer simply bounded by satellite and aerial photography geotagged with demographic market data; rather it has become an integral part of Artificial Intelligence and Machine Learning algorithms, resulting in the fusion of human-centered data for the prediction of behavioural patterns and trends through a variety of operational scenarios facilitated by a variety of emerging technologies inclusive of IOT, biometrics, location-enabled apps, and augmented reality.
http://sdss2021.spatial-data-science.net/
Spatial Data Science Symposium 2021
Spatial and Temporal Thinking in Data-Driven Methods
December 13-14, 2021 - Virtual / Online
Citation: Roba Abbas and Katina Michael [Keynote], 2021, “Geospatial Big Data Analytics: Opportunities & Challenges in Present & Future Modes of Operation”, Spatial Data Science Symposium 2021: Spatial and Temporal Thinking in Data-Driven Methods, December 13-14, 2021 - Virtual / Online, http://sdss2021.spatial-data-science.net/