De Gemeente Amsterdam wil een meldingsplicht voor sensoren in de publieke ruimte. Iedere organisatie die sensoren in de buitenruimte plaatst zou vanaf het najaar moeten melden waar die sensoren staan. Dit is nuttig om meerdere redenen. Allereerst omwille van transparantie en om de discussie over nut en noodzaak van al die sensoren om ons heen diepgaand te kunnen voeren. Of om te zien welke gegevens die nu door private organisaties worden verzameld, eventueel ook voor een gedeeld publiek belang kunnen worden gebruikt.
Amsterdam gaf eerder al een voorbeeld dat navolging verdient met de start van een algoritme-register, en dit sensorenregister lijkt me een uitstekende aanvulling.
(Defect) reclamebord op Utrecht Centraal Station dat ik in 2018 fotografeerde, met een ondoordachte camera in de publieke ruimte om aandacht voor de advertentie te meten. Burgerlijk verzet plakte de camera af.
Cory’s approach provides agency, the standard smart city approaches tend to take it away.
the idea of an Internet of Things that treats people “as sensors, not things to be sensed” — a world where your devices never share your data with anyone else to get recommendations or advice, but rather, where all the inanimate objects stream data about how busy they are and whether they’re in good repair, and your device taps into those streams and makes private recommendations, without relaying anything about you or your choices to anyone else.
As I’ve often written, the most important thing about technology isn’t what it does, but who it does it to, and who it does it for. The sizzle-reels for “smart cities” always feature a control room where wise technocrats monitor the city and everyone in it — all I’m asking is that we all get a seat in that control room.
This week’s heat wave is breaking records across Europe including here in the Netherlands. So I’ve kept an eye on the temperature in our garden. Our sensor is part of a city wide network of sensors, which includes two sensors nearby. Of the three sensors, ours indicates the lowest temperature at 36.8 (at 16:45), the other two hover just under 40 and at 41.8 respectively. Such differences are caused by the surroundings of the sensor. That ours is the lowest is because it’s placed in a very green garden, while the others are out on the street. In our completely paved and bricked up courtyard the temperature is 42.1 in the shade, due to the radiation heat of sun and stones. Goes to show that greenery in a city is key in lowering temperatures.
Three sensors in our neighbourhood, ours is in the middle, showing the lowest temperature. Note that the color scale is relative, for these 3 sensors running from 36.6 to 41.8.
In the past days since our return from France the temperature has been steadily rising, as per the graph below (which currently ends at the peak of 36.8 at 16:45). Staying inside is the best option, although the also increasingly higher lowest temperatures (from 15 to above 20) mean that the nights are slowly becoming more uncomfortable as the outside temperature will stay above the in house temperature during most or all of the night.
UPDATE as of 26/7 June noon, here you can see how the night minimum jumped 5 degrees in 24 hours, bringing it above the in house temperature for the entire night, except a brief moment around 6 am. At noon the maximum for the day before is already nearly reached.
The way to make this graph yourself is
Go to meetjestad.net/data, where you can select various data types and time frames. Our sensor is number 51, and I selected a time frame starting at July 19th at midnight. This allows me to download the data as CSV.
The data in that download is Tab separated, not comma,when you select a comma to be used as decimal point.
The file contains columns for the sensor number and its latitude and longitude, that are not needed as this is data for just one sensor. Likewise, empty columns for measurement values for which my sensor kit doesn’t contain sensors, such as particulate matter, can be removed. Finally the columns for battery level and humidity are also not needed on this occasion.
With the remaining columns, time and temperature it is easy to build the graph. In this case I replaced the timestamps with sequential numbers, as I intend to make a sparkline graph with it later.
The quality of information households in local governments is often lacking.
Things like security, openness and privacy are safeguarded by putting separate fences for each around the organisation, but those safeguards lack having detailed insight into data structures and effective corresponding processes. As archiving, security, openness and privacy in a digitised environment are basically inseparable, doing ‘everything by design’ is the only option. The only effective way is doing everything at the level of the data itself. Fences are inefficient, ineffective, and the GDPR due to its obligations will show how the privacy fence fails, forcing organisations to act. Only doing data governance for privacy is senseless, doing it also for openness, security and archiving at the same time is logical. Having good detailed inventories of your data holdings is a useful instrument to start asking the hard questions, and have meaningful conversations. It additionally allows local government to deploy open or shared data as policy instrument, and releasing the inventory itself will help articulate civic demand for data. We’ve done a range of these inventories with local government.
1: High time for mature data governance in local and regional government
Digitisation changes how we look at things like openness, privacy, security and archiving, as it creates new affordances now that the content and its medium have become decoupled. It creates new forms of usage, and new needs to manage those. As a result of that e.g. archivists find they now need to be involved at the very start of digital information processes, whereas earlier their work would basically start when the boxes of papers were delivered to them.
The reality is that local and regional governments have barely begun to fully embrace and leverage the affordances that digitisation provides them with. It shows in how most of them deal with information security, openness and privacy: by building three fences.
Security is mostly interpreted as keeping other people out, so a fence is put between the organisation and the outside world. Inside it nothing much is changed. Similarly a second fence is put in place for determining openness. What is open can reach the outside world, and the fence is there to do the filtering. Finally privacy is also dealt with by a fence, either around the entire organisation or a specific system, keeping unwanted eyes out. All fences are a barrier between outside and in, and within the organisation usually no further measures are taken. All three fences exist separately from each other, as stand alone fixes for their singular purpose.
The first fence: security
In the Netherlands for local governments a ‘baseline information security’ standard applies, and it determines what information should be regarded as business critical. Something is business critical if its downtime will stop public service delivery, or of its lack of quality has immediate negative consequences for decision making (e.g. decisions on benefits impacting citizens). Uptime and downtime are mostly about IT infrastructure, dependencies and service level agreements, and those fit the fence tactic quite well. Quality in the context of security is about ensuring data is tamper free, doing audits, input checks, and knowing sources. That requires a data-centric approach, and it doesn’t fit the fence-around-the-organisation tactic.
The second fence: openness
Openness of local government information is mostly at request, or at best as a process separate from regular operational routines. Yet the stated end game is that everything should be actively open by design, meaning everything that can be made public will be published the moment it is publishable. We also see that open data is becoming infrastructure in some domains. The implementation of the digitisation of the law on public spaces, requires all involved stakeholders to have the same (access to) information. Many public sector bodies, both local ones and central ones like the cadastral office, have concluded that doing that through open data is the most viable way. For both the desired end game and using open data as infrastructure the fence tactic is however very inefficient.
At the same time the data sovereignty of local governments is under threat. They increasingly collaborate in networks or outsource part of their processes. In most contracts there is no attention paid to data, other than in generic terms in the general procurement conditions. We’ve come across a variety of examples where this results 1) in governments not being able to provide data to citizens, even though by law they should be able to 2) governments not being able to access their own data, only resulting graphs and reports, or 3) the slowest partner in a network determining the speed of disclosure. In short, the fence tactic is also ineffective. A more data-centric approach is needed.
The third fence: personal data protection
Mostly privacy is being dealt with by identifying privacy sensitive material (but not what, where and when), and locking it down by putting up the third fence. The new EU privacy regulations GDPR, which will be enforced from May this year, is seen as a source of uncertainty by local governments. It is also responded to in the accustomed way: reinforcing the fence, by making a ‘better’ list of what personal data is used within the organisation but still not paying much attention to processes, nor the shape and form of the personal data.
However in the case of the GDPR, if it indeed will be really enforced, this will not be enough.
GDPR an opportunity for ‘everything by design’
The GDPR confers rights to the people described by data, like the right to review, to portability, and to be forgotten. It also demands compliance is done ‘by design’, and ‘state of the art’. This can only be done by design if you are able to turn the rights of the GDPR into queries on your data, and have (automated) processes in place to deal with requests. It cannot be done with a ‘better’ fence. In the case of the GDPR, the first data related law that takes the affordances of digitisation as a given, the fence tactic is set to fail spectacularly. This makes the GDPR a great opportunity to move to a data focus not just for privacy by design, but to do openness, archiving and information security (in terms of quality) by design at the same time, as they are converging aspects of the same thing and can no longer be meaningfully separated. Detailed knowledge about your data structures then is needed.
Local governments inadvertently admit fence-tactic is failing
Governments already clearly yet indirectly admit that the fences don’t really work as tactic.
Local governments have been loudly complaining for years about the feared costs of compliance, concerning both openness and privacy. Drilling down into those complaints reveals that the feared costs concern the time and effort involved in e.g. dealing with requests. Because there’s only a fence, and usually no processes or detailed knowledge of the data they hold, every request becomes an expedition for answers. If local governments had detailed insight in the data structures, data content, and systems in use, the cost of compliance would be zero or at least indistinguishable from the rest of operations. Dealing with a request would be nothing more than running a query against their systems.
Complaints about compliance costs are essentially an admission that governments do not have their house in order when it comes to data.
The interviews I did with various stakeholders as part of the evaluation of the PSI Directive confirm this: the biggest obstacle stakeholders perceive to being more open and to realising impact with open data is the low quality of information systems and processes. It blocks fully leveraging the affordances digitisation brings.
Towards mature data governance, by making inventory
Changing tactics, doing away with the three fences, and focusing on having detailed knowledge of their data is needed. Combining what now are separate and disconnected activities (information security, openness, archiving and personal data protection), into ‘everything by design’. Basically it means turning all you know about your data into metadata that becomes part of your data. So that it will be easy to see which parts of a specific data set contain what type of person related data, which data fields are public, which subset is business critical, the records that have third party rights attached, or which records need to be deleted after a specific amount of time. Don’t man the fences where every check is always extra work, but let the data be able to tell exactly what is or is(n’t) possible, allowed, meant or needed. Getting there starts with making an inventory of what data a local or regional government currently holds, and describing the data in detailed operational, legal and technological terms.
Mature digital data governance: all aspects about the data are part of the data, allowing all processes and decisions access to all relevant material in determining what’s possible.
2: Ways local government data inventories are useful
Inventories are a key first step in doing away with the ineffective fences and towards mature data governance. Inventories are also useful as an instrument for several other purposes.
Local is where you are, but not the data pro’s
There’s a clear reason why local governments don’t have their house in order when it comes to data.
Most of our lives are local. The streets we live on, the shopping center we frequent, the schools we attend, the spaces we park in, the quality of life in our neighbourhood, the parks we walk our dogs in, the public transport we use for our commutes. All those acts are local.
Local governments have a wide variety of tasks, reflecting the variety of our acts. They hold a corresponding variety of data, connected to all those different tasks. Yet local governments are not data professionals. Unlike singular-task, data heavy national government bodies, like the Cadastre, the Meteo institute or the department for motor vehicles, local governments usually don’t have the capacity or capability. As a result local governments mostly don’t know their own data, and don’t have established effective processes that build on that data knowledge. Inventories are a first step. Inventories point to where contracts, procurement and collaboration leads to loss of needed data sovereignty. Inventories also allow determining what, from a technology perspective, is a smooth transition path to the actively open by design end-game local governments envision.
Open data as a policy instrument
Where local governments want to use the data they have as a way to enable others to act differently or in support of policy goals, they need to know in detail which data they hold and what can be done with it. Using open data as policy instrument means creating new connections between stakeholders around a policy issue, by putting the data into play. To be able to see which data could be published to engage certain stakeholders it takes knowing what you have, what it contains, and in which shape you have it first.
Better articulated citizen demands for data
Making public a list of what you have is also important here, as it invites new demand for your data. It allows people to be aware of what data exists, and contemplate if they have a use case for it. If a data set hasn’t been published yet, its existence is discoverable, so they can request it. It also enables local government to extend the data they publish based on actual demand, not assumed demand or blindly. This increases the likelihood data will be used, and increases the socio-economic impact.
More and more new data is emerging, from sensor networks in public and private spaces. This way new stakeholders and citizens are becoming agents in the public space, where they meet up with local governments. New relationships, and new choices result. For instance the sensor in my garden measuring temperature and humidity is part of the citizen-initiated Measure your city network, but also an element in the local governments climate change adaptation policies. For local governments as regulators, as guardian of public space, as data collector, and as source of transparency, this is a rebalancing of their position. It again takes knowing what data you own and how it relates to and complements what others collect and own. Only then is a local government able to weave a network with those stakeholders that connects data into valuable agency for all involved. (We’ve built a guidance tool, in Dutch, for the role of local government with regard to sensors in public spaces)
Having detailed data inventories are a way to start having the right conversations for local governments on all these points.
3: Getting to inventories
To create useful and detailed inventories, as I and my colleagues did for half a dozen local governments, some elements are key in my view. We looked at structured data collections only, so disregarded the thousands of individual once-off spreadsheets. They are not irrelevant, but obscure the wood for the trees. Then we scored all those data sets on up to 80(!) different facets, concerning policy domain, internal usage, current availability, technical details, legal aspects, and concerns etc. A key element in doing that is not making any assumptions:
don’t assume your list of applications will tell you what data you have. Not all your listed apps will be used, others won’t be on the list, and none of it tells you in detail what data actually is processed in them, just a generic pointer
don’t assume information management knows it all, as shadow information processes will exist outside of their view
don’t assume people know when you ask them how they do their work, as their description and rationalisation of their acts will not match up with reality,
let them also show you
don’t assume people know the details of the data they work with, sit down with them and look at it together
don’t assume what it says on the tin is correct, as you’ll find things that don’t belong there (we’ve e.g. found domestic abuse data in a data set on litter in public spaces)
Doing an inventory well means
diving deeply into which applications are actually used,
talking to every unit in the organisation about their actual work and seeing it being done,
looking closely at data structures and real data content,
looking closely at current metadata and its quality
separately looking at large projects and programs as they tend to have their own information systems,
going through external communications as it may refer to internally held data not listed elsewhere,
looking at (procurement and collaboration) contracts to determine what claims other might have on data,
and then cross-referencing it all, and bringing it together in one giant list, scored on up to 80 facets.
Another essential part, especially to ensure the resulting inventory will be used as an instrument, is from the start ensuring the involvement and buy-in of the various parts of local government that usually are islands (IT, IM, legal, policy departments, archivists, domain experts, data experts). So that the inventory is something used to ask a variety of detailed questions of.
We’ve followed various paths to do inventories, sometimes on our own as external team, sometimes in close cooperation with a client team, sometimes a guide for a client team while their operational colleagues do the actual work. All three yield very useful results but there’s a balance to strike between consistency and accuracy, the amount of feasible buy-in, and the way the hand-over is planned, so that the inventory becomes an instrument in future data-discussions.
What comes out as raw numbers is itself often counter-intuitive to local government. Some 98% of data typically held by Dutch Provinces can be public, although usually some 20% is made public (15% open data, usually geo-data). At local level the numbers are a bit different, as local governments hold much more person related data (concerning social benefits for instance, chronic care, and the persons register). About 67% of local data could be public, but only some 5% usually is. This means there’s still a huge gap between what can be open, and what is actually open. That gap is basically invisible if a local government deploys the three fences, and as a consequence they run on assumptions and overestimate the amount that needs the heaviest protection. The gap becomes visible from looking in-depth at data on all pertinent aspects by doing an inventory.
(Interested in doing an inventory of the data your organisations holds? Do get in touch.)
I’ve backed the Kickstarter project Point. Point is a device that on the front-end, in your home, works as a sensor hub and alarm. At the back-end it feeds a machine learning database that learns your patterns, and shows them to you over time. The whole set-up can be controlled by an app (both iPhone and Android). An API is provided, and it can also talk to IFTTT so you can connect your own triggers and devices if you want (and I do).
Point is an existing product, and this Kickstarter is meant to collect pre-orders for the second generation device.
To me the alarm part of the Point devices is less interesting than having a very capable sensorhub in the home. I’ve pre-ordered 4 Points, one for each floor of our home, and one for the garden shed. The sensors it has on board are:
The company behind Point is Minut based in Sweden and China, and they state they take privacy protection seriously. It delivers on this, the company says, by doing a lot of the sensor signal processing locally in the device, and then only forwarding exceptions or events of interest (sudden sound e.g. in dB, though not the sound itself) to the centralised database at Minut. Traffic is encrypted, though the owner of the device has no posession of the keys. So in the event that the company should close up shop, there is the risk of having 4 smart but bricked devices, because there would be no way to read out the data it is sending, nor redirect the data to another database for instance. Given they’ve been around since 2014, this is their second run (and they know all about production and supply chains already) and that Thomas is now an investor and advisor to them, is reassuring. Hopefully over time they realise that true privacy also means I should have full control over how and where the traffic gets send, even if it means foregoing their entire back-end service. By the time the devices get delivered (May, so likely just in time for the Stuff That Matters 2018 Smart Homes unconference), I intend to open a discussion with them on those ‘privacy by design‘ aspects.