A 10 Year Vision: Future Trends in Geospatial Information Management

The United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) recently published their second edition of Future Trends in Geospatial Information Management. I blogged about the first edition here. Below are some of the excerpts I found interesting or noteworthy. The report itself is a 50-page document (PDF 7.1Mb).

The integration of smart technologies and efficient governance models will increase and the mantra of ‘doing more for less’ is more relevant than ever before.

There is an increasing tendency to bring together data from multiple sources: official statistics, geospatial information, satellite data, big data and crowdsourced data among them.

New data sources and new data collection technologies must be carefully applied to avoid a bias that favors countries that are wealthier and with established data infrastructures. The use of innovative tools might also favor those who have greater means to access technology, thus widening the gap between the ‘data poor’ and the ‘data rich’.

The paradigm of geospatial information is changing; no longer is it used just for mapping and visualization, but also for integrating with other data sources, data analytics, modeling and policy-making.

Our ability to create data is still, on the whole, ahead of our ability to solve complex problems by using the data.  The need to address this problem will rely on the development of both Big Data technologies and techniques (that is technologies that enable the analysis of vast quantities of information within usable and practical timeframes) and artificial intelligence (AI) or machine learning technologies that will enable the data to be processed more efficiently.

In the future we may expect society to make increasing use of autonomous machines and robots, thanks to a combination of aging population,  rapid technological advancement in unmanned autonomous systems and AI, and the pure volume of data being beyond a human’s ability to process it.

Developments in AI are beginning to transform the way machines interact with the world. Up to now machines have mainly carried out well-defined tasks such as robotic assembly, or data analysis using pre-defined criteria, but we are moving into an age where machine learning will allow machines to interact with their environment in more flexible and adaptive ways. This is a trend we expect to  see major growth in over the next 5 to 10 years as the technologies–and understanding of the technologies–become more widely recognized.

Processes based on these principles, and the learning of geospatial concepts (locational accuracy, precision, proximity etc.), can be expected to improve the interpretation of aerial and satellite imagery, by improving the accuracy with which geospatial features can be identified.

Tools may run persistently on continuous streams of data, alerting interested parties to new discoveries and events.  Another branch of AI that has long been of interest has been the expert system, in which the knowledge and experience of human experts  is taught to a machine.

The principle of collecting data once only at the highest resolution needed, and generalizing ‘on the fly’ as required, can become reality.  Developments of augmented and virtual reality will allow humans to interact with data in new ways.

The future of data will not be the conflation of multiple data sources into a single new dataset, rather there will be a growth in the number of datasets that are connected and provide models to be used across the world.

Efforts should be devoted to integrating involuntary sensors– mobile phones, RFID sensors and so on–which aside from their primary purpose may produce information regarding previously difficult to collect information. This leads to more real-time information being generated.

Many developing nations have leapfrogged in areas such as mobile communications, but the lack of core processing power may inhibit some from taking advantage of the opportunities afforded by these technologies.

Disaggregating data at high levels down to small area geographies. This will increase the need to evaluate and adopt alternative statistical modeling techniques to ensure that statistics can be produced at the right geographic level, whilst still maintaining the quality to allow them to be reported against.

The information generated through use of social media and the use of everyday devices will further reveal patterns and the prediction of behaviour. This is not a new trend, but as the use of social media  for providing real-time information and expanded functionality increases it offers new opportunities for location based services.

There seems to have been  a breakthrough from 2D to 3D information, and  this is becoming more prevalent.   Software already exists to process this information, and to incorporate the time information to create 4D products and services. It  is recognized that a growth area over the next five to ten years will be the use of 4D information in a wide variety of industries.

The temporal element is crucial to a number of applications such as emergency service response, for simulations and analytics, and the tracking of moving objects.   4D is particularly relevant in the context of real-time information; this has been linked to virtual reality technologies.

Greater coverage, quality and resolution has been achieved by the availability of both low-cost and affordable satellite systems, and unmanned aerial vehicles (UAVs). This has increased both the speed of collection and acquisition in remote areas, but also reduced the cost barriers of entry.

UAVs can provide real-time information to decision-makers on the ground providing, for example, information for disaster manage-ment. They are  an invaluable tool when additional information  is needed to improve vital decision making capabilities and such use of UAVs will increase.

The licensing of data in an increasingly online world is proving to be very challenging. There is a growth in organisations adopting simple machine-readable licences, but these have not resolved the issues to data. Emerging technologies such as web services and the growth of big data solutions drawn from multiple sources will continue to create challenges for the licensing of data.

A wider issue is the training and education of a broader community of developers and users of location-enabled content. At the same time there is a need for more automated approaches to ensuring the non-geospatial professional community get the right data at the right time.  Investment in formal training in the use of geospatial data and its implementation is still indispensable.

Both ‘open’ and ‘closed’ VGI  data play an important and necessary part of the wider data ecosystem.