Beyond SDI – Arup Dasgupta (Geospatial World)
Spatial Data Infrastructure has progressed from a data centric concept through distributed computing, web enablement, cloud and other technologies to a point where it has reached a crossroad. Should it remain as a data-centric distributed system for use in governance or should it acknowledge the disruptive changes that are taking place in spatial information and related computing and analytic technologies?
In 2010 a provocative article “The creative destruction of GIS” by Ed Parsons of Google brought out how the combination of the web and remote sensing resulting in Google Earth and Google Maps opened up GIS technologies to a much larger community of spatial planners and managers including the common person. The article asserted that while GIS and related technologies and applications would not disappear, the mainstreaming of these technologies would result in a paradigm shift in the way we look at GIS.
In the same manner there is a need for a paradigm shift in the approach to SDI which can not only benefit from mainstreaming but also from the disruption in data acquisition, processing, analytics and applications.
The sources of data have grown manifold. Satellite remote sensing has seen a major disruption with the advent of small satellite constellations. Aerial remote sensing has been disrupted by Drones. Mobile and in situ sensors as well as voluntary and opportunistic citizen sensors have added a whole new dimension to data gathering. The net result is that the data volume has overwhelmed the capabilities of data storage. SDIs need to look at how to manage this volume. GIS which form the core of SDI has to move away from data centric analysis to analysis centric data management which will extract the salient information and store that rather than the data itself. An analogy is the data compression used in digital video where only a few key frames are retained and only changes between these frames are recorded.
Another major issue particularly where data is volunteered or collected opportunistically is curation of the data. This is a cumbersome process which can be speeded up with judicious application of Big Data Analytics.
SDI analytics has been limited by static data. Today data changes have become dynamic because of anthropogenic activity and uncertain because of climate change brought on by these activities. Analytics are needed to track the changes in the natural environment due to climate change and to be able to predict the future trend and its impact on the human condition. Sentiment analytics is a newly emerging field which tracks in near real time the impact of human activities and natural disasters on humans. Such analytics has to depend on Machine Learning and Artificial Intelligence. Even soil data which has always been considered as static with long term variability has a branch called Predictive Soil Modelling based on statistical models which now seeks to model the soil based on the impact of human activity and natural forces using Machine Learning.
While the current SDIs can produce excellent maps and reports how does it impact the next step – implementation, monitoring and acceptability? In the area of Smart Cities one of the key factors is citizen ideas and acceptability. Yet in most implementations we only find lip service being paid to this core requirement. Interactivity involving the citizens is not new. Apart from VGI there are many systems which use the citizen as a sensor without their knowledge by opportunistically harvesting data from transactions and social media posts. What is becoming increasingly clear is that citizens want an opportunity to be able to influence planning processes and even monitor the activities. Web 2.0 is old hat but can we think of SDI 2.0 where citizens can interactive participate in the development processes?
To paraphrase Ed Parsons, it is not that SDI will cease to exist or that the base data collected over time will lose value but by mainstreaming the SDI processes and incorporating disruptions, the value of SDI will multiply manifold.
Managing Editor, Geospatial World