Dan Slee’s excellent post on Augmented Reality and the future of local government communications encouraged me to extrapolate the idea to a wider decision making framework. In a sense, data will become three-dimensional. A good introduction to the role of augmented reality is this article.
What is three-dimensional data?
The challenge for many organisations is to visualize and make multi-criteria decision-making. For example, how will building a bridge at location Y affect traffic flows on road X and what will the economic effect be on business in area Z would be a multi-criteria decision. The concept, though, is more than using an algorithm or using a GIS system to help with making a decision. Instead, it is taking data and putting it within a context that can be accessed, potentially in real-time, to help people understand what is being done in an area. The three dimensions are the data itself, the location and the context, we can then put the information into an augmented reality that can support decision making by councils as well as residents.
One way to illustrate the idea is to consider winter weather complaints or service requests. In the past, we may have logged the call and noted it on the CRM. The next step would be to map the location by linking the CRM to the GIS system within the organisation. The next steps are to put that information into a real-time context either regarding decisions made about the service or with the delivery of the service. The next step would be to map the information in real time by linking it to RFID or GPS devices embedded within the snow response vehicles. The authority could see where their fleet is deployed and manage that against the number and location of complaints as well as the road priority. If the Information was published, then the public could see how the council was providing the service in real time.
Location and Context computing is coming to a decision next to you
The decision models are being refined to take into account context and location. We can see location awareness decision making based on Radio Frequency Identification (RFID) systems. In these systems the position and location of the object helps inform decision-making. We can see this on some of the automated loading terminals at ports where computer driven cranes and container vehicles do the offloading. Context specific decision making relies on the change within the context to inform the decision maker about best options available. An example of context awareness would be see in large scale medical emergencies where deploying medical emergency professionals needs to be deployed in response to situational changes.
We can see the shape of the future. In that sense, we are at an inflection point within the field. We are moving beyond the first dimension of linking data to place. The second dimension is to link the data to a context. The third dimension is to link to a relationship with the service user. The next generation of transparency then moves beyond these three dimensions to link it to augmented decision making.
Here is another way that the linked data and transparency data can be placed into an augmented reality for the service user to decide. At a basic level, it illustrates the potential from linking transparency data to location and context.
How will this work in practice? How do you Choosing a restaurant?
If you arrive in a strange city, or if you fancy going to a different restaurant than your usual location, you need to find a “good” restaurant. If you wander around the city, you may come to a restaurant and not know whether it is “good” or “bad”. In the future, you will able to point your phone (tablet (or Google goggles) at the restaurant in an augmented reality to help decide whether the restaurant is “good”. The building blocks of that process already exist.
Now, we have a lot of transparency information that is mapped to allow us to find a restaurant. For example, we can see on Google maps the nearest restaurants. We can see their latest food reviews. All of this information can be found through our browser. However, this only tells us part of the story from the perspective of one information channel.
Transparency 1.0 Scores on the doors
In many ways, the information on Google maps represents transparency 1.0. From a local government perspective, transparency agenda can be seen information such as Scores on the Doors. Scores on the Doors has been a successful transparency project. The food hygiene rating, based on the local authority inspection, helps the public make an informed choice about where they are eating.
As Archon Fung pointed out the Los Angeles County scores on the doors, which was an effective and sustainable transparency project had many benefits. First, it helps to improve decision making about food establishments. Second, it supported the regulatory framework by reducing the chances of food poisoning. Third, it creates an incentive for restaurants to improve their food hygiene. The same transparency in other areas should have a similar benefit. Although Fung’s later research showed that transparency projects may not be sustainable unless they have the right regulatory support, it is clear that transparency is important for improving the civil society.
Transparency 2.0 Show me the inspection reports.
The second dimension, which is occurring now, is that people begin to request more detail behind the scores on the doors. Instead of wanting to know what the restaurant scored, they want to know why. They begin to pull information out of the authority by asking for more information beyond what is published or pushed. In response, local government can publish a short summary of the reason a restaurant has scored less than top marks. In that way, each person visiting can assess whether the restaurant has improved and they can then make a better-informed decision
The first dimension of transparency is for the government to publish more information. The second dimension of transparency is to get more information from government. Instead of being a passive consumer, the public makes demands for information. In this way, the public are actively shaping the transparency information being provided. The third dimension of transparency goes beyond the first two dimensions to relate the data to a context or locate it within a relationship.
Transparency 3.0: What is the context or location relationship?
The third dimension of transparency, which exists to some degree already, is to place the restaurant into a context and to locate it in a relationship with other data. On Google maps, you locate a restaurant, but you cannot relate that to anything else. For example, you cannot tell if the neighbourhood is “good” or “bad”. In that sense, crime reports and Anti-social behaviour reports are not filtered into the search engines. In the scores on the doors, you can filter by the number of stars a restaurant has received, but you cannot see the context of the neighbourhood. The third dimension of transparency would take the first two dimensions and locate them within a wider context. For example, you would be able to see service ratings (both formal (critics and reviewers) and informal (customers, complaints, and compliments)). You could also see location within context, its socio economic status, crime statistics or reports.
Augmented Decision-making: Beyond transparency.
The three dimensions of transparency can then be placed in an augmented reality for decision-making. You point your phone (or tablet) at a building and you are fed the proximity issues or relationships between data sets according to a predetermined algorithm. For example, you could find out if the head chef has left he restaurant to improve your decision-making. Or you could put in your decision criteria and the algorithm could work out the best fit, based on your criteria, then map these against your location and see what is best fit for time and money. You could also follow the chef of your choice.
The next revolution in computing, based on context and location, will let us see a place through its proximity (or relation) to other data such environmental information but also within a wider geo-spatial context. We can see the data and the place differently. When we map this data, beyond the physical location, against its relationship to other data, our decisions will be augmented. There is still work to be done to achieve this goal, but it is within our grasp.
What is exciting about this work is much of the secondary information that is not captured by local government and users are creating transparency as they interact with locations. In the future, we could capture that secondary contextual information through the same tools that are being developed for social media regarding online reputation and brand management. In that sense, we could have applications or bots that automatically capture this data from the social media sphere and then repackage it for augmented reality devices that are scanning the area.
I would be interested in your views on the topic and the ideas of where this may develop.