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.
The United Nations Committee of Experts on Global Information Management (GGIM) recently organized a meeting of thought-leaders and visionaries in the geo-spatial world to identify the future of this space over the next 5-10 years. These experts came up with some 80+ individual predictions. I’ve included some of the more interesting ones below.
- The use of Unmanned Aerial Vehicles (UAVs) as a tool for rapid geospatial data collection will increase.
- 3D and even 4D geospatial information, incorporating time as the fourth dimension, will increase.
- Technology will move faster than legal and governance structures.
- The link between geospatial information and social media, plus other actor networks, will become more and more important.
- Real-time info will enable more dynamic modeling & response to disasters.
- Free and open source software will continue to grow as viable alternatives both in terms of software, and potentially in analysis and processing.
- Geospatial computation will increasingly be non-human consumable in nature, with an increase in fully-automated decision systems.
- Businesses and Governments will increasingly invest in tools and resources to manage Big Data. The technologies required for this will enable greater use of raw data feeds from sensors and other sources of data.
- In ten years time it is likely that all smart phones will be able to film 360 degree 3D video at incredibly high resolution by today’s standards & wirelessly stream it in real time.
- There will be a need for geospatial use governance in order to discern the real world from the virtual/modelled world in a 3D geospatial environ-ment.
- Free and open access to data will become the norm and geospatial information will increasingly be seen as an essential public good.
- Funding models to ensure full data coverage even in non-profitable areas will continue to be a challenge.
- Rapid growth will lead to confusion and lack of clarity over data ownership, distribution rights, liabilities and other aspects.
- In ten years, there will be a clear dividing line between winning and losing nations, dependent upon whether the appropriate legal and policy frameworks have been developed that enable a location-enabled society to flourish.
- Some governments will use geospatial technology as a means to monitor or restrict the movements and personal interactions of their citizens. Individuals in these countries may be unwilling to use LBS or applications that require location for fear of this information being shared with authorities.
- The deployment of sensors and the broader use of geospatial data within society will force public policy and law to move into a direction to protect the interests and rights of the people.
- Spatial literacy will not be about learning GIS in schools but will be more centered on increasing spatial awareness and an understanding of the value of understanding place as context.
- The role of National Mapping Agencies as an authoritative supplier of high quality data and of arbitrator of other geospatial data sources will continue to be crucial.
- Monopolies held by National Mapping Agencies in some areas of specialized spatial data will be eroded completely.
- More activities carried out by National Mapping Agencies will be outsourced and crowdsourced.
- Crowdsourced data will push National Mapping Agencies towards niche markets.
- National Mapping Agencies will be required to find new business models to provide simplified licenses and meet the demands for more free data from mapping agencies.
- The integration of crowdsourced data with government data will increase over the next 5 to 10 years.
- Crowdsourced content will decrease cost, improve accuracy and increase availability of rich geospatial information.
- There will be increased combining of imagery with crowdsourced data to create datasets that could not have been created affordably on their own.
- Progress will be made on bridging the gap between authoritative data and crowdsourced data, moving towards true collaboration.
- There will be an accelerated take-up of Volunteer Geographic Information over the next five years.
- Within five years the level of detail on transport systems within OpenStreetMap will exceed virtually all other data sources & will be respected/used by major organisations & governments across the globe.
- Community-based mapping will continue to grow.
- There is unlikely to be a market for datasets like those currently sold to power navigation and location-based services solutions in 5 years, as they will have been superseded by crowdsourced datasets from OpenStreetMaps or other comparable initiatives.
Which trends have the experts missed? Do you think they’re completely off on any of the above? The full set of predictions on the future of global geospatial information management is available here as a PDF.
I’ve been meaning to blog about this project since it was featured on BBC last month: “Mobile Phones Help to Target Disaster Aid, says Study.” I’ve since had the good fortune of meeting Linus Bengtsson and Xin Lu, the two lead authors of this study (PDF), at a recent strategy meeting organized by GSMA. The authors are now launching “Flowminder” in affiliation with the Karolinska Institutet in Stockholm to replicate their excellent work beyond Haiti. If “Flowminder” sounds familiar, you may be thinking of Hans Rosling’s “Gapminder” which also came out of the Karolinska Institutet. Flowminder’s mission: “Providing priceless information for free for the benefit of those who need it the most.”
As the authors note, “population movements following disasters can cause important increases in morbidity and mortality.” That is why the UN sought to develop early warning systems for refugee flows during the 1980’s and 1990’s. These largely didn’t pan out; forecasting is not a trivial challenge. Nowcasting, however, may be easier. That said, “no rapid and accurate method exists to track population movements after disasters.” So the authors used “position data of SIM cards from the largest mobile phone company in Haiti (Digicel) to estimate the magnitude and trends of population movements following the Haiti 2010 earthquake and cholera outbreak.”
The geographic locations of SIM cards were determined by the location of the mobile phone towers that SIM cards were connecting to when calling. The authors followed the daily positions of 1.9 million SIM cards for 42 days prior to the earthquake and 158 days following the quake. The results of the analysis reveal that an estimated 20% of the population in Port-au-Prince left the city within three weeks of the earthquake. These findings corresponded well with of a large, retrospective population based survey carried out by the UN.
“To demonstrate feasibility of rapid estimates and to identify areas at potentially increased risk of outbreaks,” the authors “produced reports on SIM card move-ments from a cholera outbreak area at its immediate onset and within 12 hours of receiving data.” This latter analysis tracked close to 140,000 SIM cards over an 8 day period. In sum, the “results suggest that estimates of population movements during disasters and outbreaks can be delivered rapidly and with potentially high validity in areas with high mobile phone use.”
I’m really keen to see the Flowminder team continue their important work in and beyond Haiti. I’ve invited them to present at the International Conference of Crisis Mappers (ICCM 2011) in Geneva next month and hope they’ll be able to join us. I’m interested to explore the possibilities of combining this type of data and analysis with crowdsourced crisis information and satellite imagery analysis. In addition, mobile phone data can also be used to estimate the hardest hit areas after a disaster. For more on this, please see my previous blog post entitled “Analyzing Call Dynamics to Assess the Impact of Earthquakes” and this post on using mobile phone data to assess the impact of building damage in Haiti.