An ounce of information is worth a pound of data.
An ounce of knowledge is worth a pound of information.
An ounce of understanding is worth a pound of knowledge. — Russell Ackoff
The smart city, in its purest essence, concerns the melding and entwinement of urban infrastructure and digital sensing technologies. Expanding upon my overt simplification, Andrea Caragliu, Chiara Del Bo & Peter Nijkamp, in their much cited article Smart Cities in Europe, “believe a city to be smart when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance”. The premise is thus, use the data produced by ICT embedded infrastructure to make informed and wise decisions about how to run a city. But how does the binary ones and zeros of data equate to the wisdom required to fuel sustainable urban growth?
Over the years, those in information management have often referred to the DIKW hierarchy. DIKW stands for data, information, knowledge and wisdom and the smart city as a concept taps into this notion. Russell Ackoff, the contested founder of this concept, stated that “data are symbols that represent the properties of objects and events. Information consists of processed data, the processing directed at increasing its usefulness”. Data, on its own, is useless. It requires context to make it in any way meaningful. Take an accelerometer, the data produced by moving it represents the amount of g-force exerted upon it, which is represented numerically. If you produced a ream of figures in a spreadsheet without any other information, you would have, well, a ream of figures in a spreadsheet; as much use as a chocolate teapot. Yet, if you stated that this data emanated from an earthquake sensor which sensed the seismic activity of the ground below, then you have useful information — for your data is paired with the context from which it represents. No longer does your spreadsheet represent random numbers, but the Earth’s movement.
Now, according to Ackoff, “information is contained in descriptions, answers to questions that begin with such words as who, what, when, where, and how many. Knowledge is conveyed by instructions, answers to how-to questions”. So, with our earthquake sensing example, the question could be ‘how seismically active has the ground underneath our sensor been in the last year’, and to answer it we need to look no further than the aforementioned spreadsheet. This information answers the question posed and thus produces knowledge, but it doesn’t answer why there is seismic activity near our sensor. This is where understanding — an extra layer added by Ackoff to the DIKW hierarchy — “is conveyed by explanations, answers to why questions”. The earthquake sensor would have, most likely, been installed owing to a lived history of seismic activity in the region. In this example, let’s say the sensor is installed in Los Angeles, which just happens to be a city with over 100 small fault lines — which would explain the recorded seismic activity. Just like that, you have understanding.
As Ackoff states, “information, knowledge, and understanding enable us to increase efficiency, not effectiveness […] Effectiveness is evaluated efficiency. It is efficiency multiplied by value, efficiency for a valued outcome. Intelligence is the ability to increase efficiency; wisdom is the ability to increase Effectiveness.”
Therefore, to produce smart urban wisdom, you need raw data paired with context which can be used to answer ‘how-to’ questions which are then linked with the understanding of why the action — which produced the data — first took place, you then take this understanding, apply it to the city in an effective way which has demonstrable benefits. To round off our earthquake example, the California Geological Society produced a map of all the fault lines under LA, giving potential insight to residents regarding their proximity to a fault line, and therefore their risk of being impacted by seismic activity.
However, the DIKW hierarchy has been critiqued by numerous academics, but within the smart city domain it seems to fit pretty well. Martin Frické of The University of Arizona identifies several issues with the hierarchy in his aptly titled 2009 paper The knowledge pyramid: a critique of the DIKW hierarchy. I am going to focus on one specific issue raised in this essay — the type of knowledge the DIKW hierarchy produces.
According to Alvin Goldman, there are two types of knowledge; strong and weak. “Strong knowledge covers justified-true-beliefs and justified-true (community-accepted) statements. Weak knowledge is like strong knowledge except that the justification component is omitted.” So, strong knowledge is justified and weak knowledge isn’t. Yet, what justifies belief? Goldman, states that “justified belief is belief produced by processes that reliably lead to true beliefs” — scientific method and data collection are both clear examples of this. Therefore, strong knowledge is justified by being backed up by objective fact. The question is whether our perceptions of a city represent the truth?
I ask this because the majority of the information we possess, certainly pertaining to our lived experience of a city, is weak knowledge. For instance, I could choose to not go to a certain pub because it has a bad reputation. I’ve never been, but I know people who have and they thought it was as rough as toast. So, I choose to eschew it and go to my usual haunt. I have no objective interpretation of the suitability of this pub for how I experience, or for that matter, don’t directly experience that space is mediated through my interpretation of reality, an interpretation that would almost certainly be at odds with the regulars of this particular establishment. In short, our perceptions of a city are not formed by ‘justified’ sources of belief, but rather our experience, and to a certain extent what we are told by others.
Data didn’t tell me about the essence of this pub, but rather the opinion of another human being. Now you could argue that this opinion could be seen as data, but the opinion of another person isn’t reducible down to what Ackoff would describe as “symbols that represent the properties of objects and events”. No, an opinion is a form of wisdom, for it is rooted in the answering of a ‘why-question’; this doesn’t mean the wisdom will be wise, for instance your friend could be craft beer swilling a snob. This trivial example does, however, highlight the manner in which we formulate our perceptions of the cities we find ourselves in. We build our viewpoint upon personal experiences and the views of others; not data.
However, this is not to say that strong knowledge fed to us vis-a-vis the smart city is out of place in the urban realm. Strong knowledge is particularly useful, for instance, when formulating resilience plans or in emergency responses. During a recent interview I carried out for my research into urban resilience, my respondent — a member of the Cambridgeshire and Peterborough Local Resilience Forum (CPLRF) — stated that during a recent bout of flooding — where 30,000 homes were at risk — the CPLRF had to ascertain who would need assistance in the case of evacuation. Their response to this conundrum was to use the local council’s data regarding residents who need help with putting their bins out — owing to being old, injured, disabled etc. — used that data set (knowledge) and converted it into actionable wisdom. Ultimately, only a handful of residents were evacuated, but it shows the obvious benefit of having data-reinforced ‘strong’ knowledge in operating a city.
The smart city is often framed as a cold and inhuman, top-down imposition upon our lives. This is a rather simplistic view, but not entirely without merit. The role of data collection in our everyday lives is here to stay and it does serve a purpose for the average citizen. For instance, many who visit a new city will use Google Maps to find their way around and, perhaps more importantly, prevent them from getting lost which, in the confines of this essay, is an example of strong knowledge being utilised for a beneficial reason. In my mind, the issue with the ‘techno-centric’ view of the smart city is it deals solely in strong knowledge at the expense of the weak. This is incompatible with how the vast majority of urban residents live their lives. Sure a web of sensors can provide information which could make decision making more accurate, but what if the data is flawed in some way? The harsh lessons learned by the cybernetics movement of the 1960s are still relevant today.
The solution, like almost anything, appears to lie in a balance. Weak knowledge could certainly humanise the blunt rationality of data-driven decision making, and strong knowledge could provide significant and important information to the citizenry. Perhaps, it is this blending of strong and weak knowledge (which maybe produces healthy knowledge… who knows) which will enable the smart city to deliver the promises of informed decision making, but not at the expense of the civil liberties or the democratic ideals cherished by most urbanites.
By Will Brown
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