The following post is influenced by Dan Slee’s excellent post on Augmented Reality and the future of local government communications.
The blog argues that transparency data mapped to location and context can be used for augmented decision making. What this means is that as more data is released, people will develop applications based on a decision algorithm will allow people to visualize multi-criteria decision making. However, the concept is more than using an algorithm or using a GIS system to help with making a decision. Instead, it is taking data linking it to location and context so these can be mapped into an augmented reality to support an algorithm decision making application.
Hedge funds have been using algorithms to augment their decision making for over a decade. For even longer, companies have been mining their customer or client data with data mining systems to gain an advantage over rivals. What is new is how the algorithms and other data (especially big data and GIS data) are being used for decision making. Companies need to be able to make multi-criteria decisions and they often turn to decision modelling that allows them to assess the choices they confront so they can choose the “best” option based on the criteria and data they have available.
The decision models can take into account context and location in the decisions. 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.
In time, these decision technologies will filter into local government through the use of transparency data. The transparency agenda contains most of the information used for decisions within local government and if we extrapolate these, 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.
The following example shows how decision making can use the transparency data. It illustrates, in a basic way, the potential that is available in transparency data, when linked 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. At the moment, 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 one information perspective.
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 et al. 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 et al’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.
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. 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? Is it in a bad neighbourhood?
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. 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.
The final stage is to use the three dimensions of transparency for decision making. The proximity issues or relationships between data sets could then be organised by 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 both in 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.
- For the first time ever, scientists have made monkeys smarter using brain implants. Could you be next? [Futurism] (io9.com)
- Resistance to Making Decisions based upon Data (datascience101.wordpress.com)
- Creating a Data Driven Culture (bostinno.com)
- The untapped potential of augmented reality (digitaltrends.com)