Joy Diversion

Saturday 19th May 12.00 – 17.00
Federation House
Manchester M4 4BF and then wherever

Register here

Calling all ramblers, explorers and meanderers. Surveyors, cartographers and inquisitors – people who look up to the rooftops and down into the culverts. Join us for an afternoon of mapping, exploring and wandering in Central Manchester and Salford.

Often viewed as a functional place of work, retail and leisure, our city centre bounded by Trinity Way, Great Ancoats Street and the Mancunian Way is imbued with history, iniquity, celebration and endeavour. Let us go out and find what’s out there, discover the forgotten spaces, create stories and map our city.

As well as exploring, we will also introduce you to mapping with OpenStreetMap; the people-powered map of the world.

We will propose expeditions to uncharted territories, revisits to previously explored places, strange meanderings and any other diversions that people fancy.

Adventurers will be split into parties and encouraged to map, photograph, document and bring back their findings to share with everyone.

If people want to delve deeper there should be an opportunity to do further research on the places discovered.

You will need:

Yourself
Comfortable walking shoes or what ever you need to get around
Weather appropriate clothing (hopefully sun cream rather than waterproofs!)
A phone: with a camera would be advantageous

The event is open to all, although minors need to be accompanied.

In addition to this event, the day will also include a short annual general meeting of the OSM UK community interest company. For more information on the AGM and to register click here

If you would like to help out on the day, let us know by email hello[a]opendatamanchester[.]org[.]uk or contact us on Twitter @opendatamcr

Safiya Umoja Noble – Algorithms of Oppression

Tuesday 8th May 18.00 – 20.00
Federation House
Manchester
M4 4BF

Register here

In her recent best-selling book Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem. Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of colour, specifically women of colour- and contributes to our understanding of how racism is created, maintained, and disseminated in the 21st century.

Safiya Umoja Noble

Dr. Safiya U. Noble is an assistant professor at the University of Southern California (USC) Annenberg School of Communication. She is the recipient of a Hellman Fellowship and the UCLA Early Career Award.

Noble’s academic research focuses on the design of digital media platforms on the internet and their impact on society. Her work is both sociological and interdisciplinary, marking the ways that digital media impacts and intersects with issues of race, gender, culture, and technology design. Her monograph on racist and sexist algorithmic bias in commercial search engines is entitled Algorithms of Oppression: How Search Engines Reinforce Racism (NYU Press). She currently serves as an associate editor for the Journal of Critical Library and Information Studies, and is the co-editor of two books: The Intersectional Internet: Race, Sex, Culture and Class Online, and Emotions, Technology & Design and several articles and book chapters. Safiya holds a Ph.D. and M.S. in Library and Information Science from the University of Illinois at Urbana-Champaign, and a B.A. in Sociology from California State University, Fresno with an emphasis on African American/Ethnic Studies. She is a partner in Stratelligence, a firm that specializes in research on information and data science challenges, and is a co-founder of the Information Ethics & Equity Institute, which provides training for organizations committed to transforming their information management practices toward more just, and equitable outcomes.

Supported by MMU and The Federation in partnership with The Omidyar Network and Co-op Foundation

Deprivation vs Political Representation dataset

Over the last couple of weeks we have been putting together data on local political representation and deprivation at a Local Super Output Area level. This data is put together using LSOA 2011 to Ward to Local Authority District 2016 with the English Indices of Deprivation 2015 Lookup in England from the Office of National Statistics and Ministry of Housing, Communities and Local Government respectively, and local councillor affiliation data manually entered from the 326 council tax and business rates raising local authorities of England.

The open data can be found here and we will update it after the local elections in May – please read the README before using.

You can read more about how the dataset was constructed here

Data for Good #1 Understanding where we live

Tuesday 24th April 18.30 – 20.30
Federation
Federation Street, Manchester M4 4BF

Register on Meetup here

There is a huge amount of data that is collected by the UK Government and others that describes the communities in which we live. This data informs policy decisions at a national and local level. Datasets such as the Indices of Multiple Deprivation have been described as the ‘billion pound dataset’ because of its importance.

Outside of the world of data analysts and academia these datasets are relatively unknown, yet they can be incredibly useful to anyone who is interested in their communities, wants to develop evidence for funding applications or is thinking of developing a business in a certain area.

Data for Good #1 follows on from our So you think you know your country? events and gives deeper insight into some of the data available. The seminar will introduce the world of statistical geography and some of the datasets and tools you can use.

As the event is going to be more hands on, access to a laptop would be advantageous, but not essential.

IF YOU LIKE YOUR STATISTICAL GEOGRAPHY AND WANT TO HELP OUT – GET IN TOUCH – THE MORE PEOPLE WHO CAN HELP THE MORE PEOPLE WE CAN HELP

Deprivation vs political control

View full image here

The Indices of Deprivation is a hugely useful collection of datasets in England. Commissioned by the Ministry for Housing, Communities and Local Government, and developed by OCSI, the indices allow us to understand relative deprivation of neighbourhoods across the country. The indices are used by many different organisation to support decision-making, from determination of funding for schools, to charitable grant-giving. Some even call it the billion pound dataset.

In the context of these indices, deprivation is a measure representing a range of factors, from quality housing, to air quality, educational attainment to crime. The indices tend to be published every 4 years or so, the last in England was 2015. Similar indices are also published for Wales, Scotland, and Northern Ireland.

England is split into 32,844 geographical units, each of which has a population of around 1,500 people. These are called lower super output areas (LSOAs). Each of these LSOAs is scored according to a range of factors, and then each LSOA is ranked according to all the others (ie 1 is the most deprived, 32,844 is the least deprived).

At Open Data Manchester, we believe that it is important that everyone is aware that this dataset exists, and that they have the opportunity to understand it and use it. Because of this, we are embarking on a series of events which will look at the indices of deprivation in detail, to help people understand what the data says about the area that they live in, and how they might be able to use that data to challenge local decision-making, to support their own business development, help with applications for grant-funding, etc.

While there will be events, workshops and training to come, we wanted to make something that visualised the dataset, to help kickstart the conversations. So we came up with this poster, that we’re calling a lava lamp plot.

To interpret the charts, local authorities are arranged according to the rank of average ranks of LSOAs in that area. For this measure, Manchester is the most deprived local authority, and Hart in Hampshire is the least deprived.The shape of each chart is derived from the number of LSOAs in each area that fall into each vigintile (quantiles of 5% – a more granular version of a decile). This distribution curve is smoothed, mirrored, and filled in according to the political party in control of the local authority council. The shape of the curve shows the distribution of LSOAs by deprivation – the fatter the bottom, the more deprived LSOAs there are; the fatter the head, the fewer deprived areas there are.

We’ll do a couple more posts over the coming weeks that look at the technical details for how we made the poster. We got the deprivation data from OpenDataCommunities, and the political control data from the Local Government Information Unit. The tools that we used to manipulate the data were R, RStudio, ggplot, and Adobe Illustrator. Code is available at this GitHub repo.

We are producing an edition of A2 Fine Art prints printed on heavyweight paper suitable for framing for £30 inc VAT. If you would like one of these please get in touch at julian[a]opendatamanchester[.]org[.]uk with the subject line Fine Art Prints. The A2 image can also be downloaded here

Manchester Youth Hack # 8 The Open Data Manchester Challenge

Manchester Youth Hack is a two day coding competition where young people get to try coding in a friendly environment with professional mentors. The next event is 24-25 March.
If you are interested in getting involved more information can be found here

The challenge

On three floors of Federation House there are sensors measuring temperature, humidity and movement producing a dense dataset of many thousands of data points. The data allows the performance of the building to be monitored to save energy and money, but what else is it showing? Something has happened and no-one is quite sure what it is.

We will be making available two weeks of sensor data from the sensor network that has been created as part of our Knowable Building Framework. What will the data reveal?.

So you think you know your country – who owns the land?

Tuesday 27th March 18.30 – 20.30
Federation
Federation Street, Manchester M4 4BF

Register on Meetup here

In the second event of our “So you think you know your country” series we look at land and property ownership, public space and rights of way with Guy Shrubsole and Morag Rose.

Our towns and cities often have a complex patchwork of rights and ownership associated with them. Rights of way are often undefined, public space is contested and ownership of land hidden behind secretive shell companies and investment vehicles. The release of data from the Land Registry last year cast light on patterns of ownership across England. This data has been mapped and explored by Anna Powell-Smith and Guy Shrubsole – Who owns England? https://whoownsengland.org/2017/11/14/the-companies-corporate-bodies-who-own-a-third-of-england-wales/

Within this complex interplay of rights, ownership and access exists the experience of the city — for the people are the city¹ — and it is through the human stories and experience that the city comes to life. The LRM (Loiterers Resistance Movement) http://www.thelrm.org/index is a Manchester based collective of artists, activists and urban wanderers interested in psychogeography, public space and the hidden stories of the city. LRM founder Morag Rose recently completed her thesis on women’s experiences of walking in Manchester.

¹ Coriolanus Act III Scene I – William Shakespeare http://www.bartleby.com/70/3631.html#235

Knowable Building Framework – building consent – Rennes workshop

En français

The Knowable Building Framework is a consent building framework for the release and sharing of data from sensor data captured in buildings. The Rennes workshop took place on the 18th January with 22 participants who had an interest in energy, internet of things, environment and civic technology. The meeting took place at Telecom Bretagne (IMT Atlantique).

The focus of the discussion was broadly split into technology- looking at the types of sensors being deployed in the pilot project and the data being created- and human- how the data could be used to affect behavioural change and the implications of having a dense sensor network in a building.

Technology

The Knowable Building Framework pilot in Manchester is located in an office building with sensors on a number of floors. The sensors measure temperature, humidity and motion on two floors and another floor also measuring light. The sensors are discrete and battery powered using a LoRaWAN (Long Range Wide Area Network) to send data to an intelligence platform that displays measurements on a dashboard. The information can then be used to determine time of activity within a building and how different areas are used; temperature variation over time allowing the ability to adapt heating so that it is more inline with occupancy; and humidity within areas which can be used to identify underlying issues with ventilation and damp. The light sensor modules allow the detection of room lighting.

Placement of sensors

Placement of the sensors within the building was based upon the need for the system to optimally collect data. Zones on floors, entrance areas, meeting rooms are covered. Importantly the sensors were placed in what could be considered common usage spaces i.e. spaces that were owned and operated by the building company for the benefit of all tenants and users. Areas where a tenant had exclusive access were excluded.

Preliminary insight gained from the network

A small number of sensors has already been installed and we can see from the online interface that useful data is already being created. Even at this stage we can see start to see how the building functions over the course of a number of days. We can deduce when the disparate heating systems kick in, which areas have a thermal lag, when cyclists use the storage and shower facilities and the areas that are more humid than others. As the sensor network becomes denser we will be able to see how different areas get used offering the ability to heat certain areas based upon demand.

This all looks very positive from a building management ‘mechanical’ perspective but we already can see that buildings don’t operate in isolation of the people who inhabit and work within them. At this early stage we can see how certain rooms and areas are used more regularly than others. We can start to infer patterns of activity and behaviours of the small number of people who operate the buildings on behalf of tenants. During the day the building is awash with activity that creates a certain anonymity, but out of hours, areas get cleaned, security personnel check spaces and these show up as isolated trigger events within the building. It does not take a big leap of the imagination to realise that a building optimisation platform could also be used as an instrument of surveillance.

Discussion

There was broad agreement by the group that creating open data from sensor data was a good thing, especially if it could be used to benchmark buildings and enable a greater understanding of how buildings of different types behaved.

Ownership of data was seen as being important. Who is the owner of this data – the building owner or the people who inhabit or work in the space? It was highlighted that there is a relationship between ownership of the data and the power/agency people have within the space. So if you are a building owner you are more likely to feel that the data that you collect is yours as it is your building and a private space even though some of this data may be used to identify individuals. This is analogous to CCTV in that people in CCTV monitored buildings generally have very little say or knowledge of how the systems are operated other than ‘its for your security and safety’, how video is stored or who it is shared with.

Identification of need is really important with creating a sensor network or else initiatives are prone to poor application of technology with the creation of unusable data. There is also a tendency to over collect data or collect the wrong type of data. It was suggested that the forthcoming GDPR regulations might have a bearing on how data is collected within buildings and public spaces.

A criticism of the Knowable Building Framework pilot in Manchester is that the implementation has been undertaken with only the consent of the building managers who can see tangible benefit in seeing how the building operates with little consideration of its occupants.

The group identified that we need to explore how we build networks consensually as this would then be a precursor to building consent. Consensus assumes a number of things that potentially could be challenging for the implementation of sensor networks these might be:

  1. An understanding of the sensor environment’s purpose and how the data will be used
  2. The technology and what it measures
  3. What the safeguards are so that the technology is not abused
  4. Representing concerns if the technology is considered to be abused
  5. Understanding of data sharing and open data

The data that is collected from the sensors in its raw form is a rich sense of information but sharing the data as open data has risks. There are different levels of granularity that could help data owners share data but care would need to be taken in order to identify the appropriate level of aggregation and anonymisation. There is a trade-off between usefulness and privacy.

There was a some discussion on the value of the data created and if it could be used as a commodity to draw down preferential deals for building owners and building users alike. This could have some kind of leverage regarding power suppliers and building insurers.

There exist different classes of stakeholders within the building and it is important that any consent mechanism acknowledges them. These classes roughly break into; owners and management; occupants and users and; maintenance and operational.

Data needs to be available – or at least the intelligence that is derived from the data so that occupants and users of the building can understand for themselves how the building works. This would help in demystifying the technology and make it less threatening but also help people make changes to their own behaviours.

Visualisation could have a big part to play in this process as good visualisation and design enable understanding. By making the data available there is also the possibility to run events around the data to create new ways of creating information and understanding. Rennes has used the Data Remix (a form of hack event) format successfully for a number of challenges and it is suggested that one could be run that includes teams from Rennes in the future.

A further write up of the whole day (in French) can be found here

Faire un compte rendu à partir de l’atelier de travail

« Knowable Building Framework » est un modèle d’ouverture et de partage de données issues de capteurs qui collectent des données au sein des bâtiments. L’atelier de travail à Rennes a eu lieu le 18 janvier 2018 avec la participation de 35 personnes ayant une forte appétence pour les sujets énergétiques, l’internet des objets (IoT), l’environnement et l’appropriation de la technologie par les citoyens.

La rencontre s’est tenue à Telecom Bretagne (IMT Atlantique).

La discussion s’est essentiellement focalisée sur deux volets :

D’abord technologique – il a été question de chercher des types de capteurs déployés dans le projet pilote et les données créées – et d’autre part Humain – comment les données pourraient être utilisées pour induire un changement comportemental et les implications d’avoir réseau de capteurs dense dans un bâtiment.

Technologie

Le projet pilote « Knowable Buildings Framework » à Manchester est situé dans un espace de Co-working contenant plusieurs capteurs répartis sur un certain nombre d’étages. Les capteurs mesurent la température, l’humidité et les mouvements sur deux niveaux et sur un autre niveau, les capteurs incluent également la luminosité.

Les capteurs sont discrets et sont alimentés par des batteries LoRaWAN (Long Range Wide Area Network) pour envoyer des données à la plateforme intelligente qui affiche les mesures sur un tableau de bord.

Les informations peuvent ensuite être utilisées pour déterminer l’activité au sein du bâtiment et la manière dont les différentes zones sont utilisées.

La variation des températures au fil du temps permet d’être en mesure d’adapter le chauffage afin que ce dernier soit plus en adéquation avec l’occupation de l’espace. 

Les informations relatives à l’humidité dans les espaces peuvent être utilisées pour identifier des problèmes sous-jacents tels que des problèmes de ventilation ou d’humidité.

Les modules de capteurs de lumière peuvent être utilisées pour détecter une présence afin d’éclairer la pièce.

L’emplacement des capteurs

Le placement des capteurs dans le bâtiment est basé sur le besoin pour le système de collecter de manière optimale les données. Les espaces au niveau des étages, les zones d’entrée et les espaces de réunion sont couvertes pour les besoins de l’expérimentation.

Point important, les capteurs sont placés dans ce que l’on pourrait considérer comme des espaces communs, c’est à dire les espaces qui appartiennent et sont exploitées par l’opérateur pour le bénéfice de tous les locataires et utilisateurs du bâtiment. Les zones où les locataires avaient un accès exclusif ont été exclues.

Aperçu préliminaire issu du réseau

Un petit nombre de capteurs ont déjà été installés et nous pouvons voir à partir de l’interface en ligne que des données utiles ont déjà été créées. Même à ce stade, nous pouvons voir comment fonctionne le bâtiment en l’espace de quelques jours. Nous pouvons voir les variations de température du système de chauffage, les zones avec un décalage thermique et les zones qui sont plus humides que d’autres.

Au fur et à mesure que le réseau de capteurs se densifie, nous pouvons voir comment différentes zones sont utilisées, offrant la possibilité de chauffer certaines zones en fonction de la demande.

Tout cela semble très positif d’un point de vue « mécanique » de la gestion du bâtiment, mais nous pouvons déjà constater que les bâtiments ne fonctionnent pas isolément des personnes qui y habitent et qui y travaillent.

A ce stade précoce, nous pouvons voir comment certaines pièces et zones sont utilisées plus souvent que d’autres. Nous pouvons commencer à déduire des modèles d’activité et de comportement d’un petit groupe de personnes qui exploitent le bâtiment.

Pendant la journée, le bâtiment est inondé d’activités qui créent un certain anonymat, mais en dehors des heures d’ouverture, les zones sont nettoyées, le personnel de sécurité vérifie les espaces et ceux-ci apparaissent comme des événements déclencheurs isolés dans le bâtiment.

Il ne faut pas faire un gros effort d’imagination pour se rendre compte qu’une plateforme d’optimisation des bâtiments pourrait également être utilisée comme un instrument de surveillance.

Discussion

Le groupe a été largement d’accord pour dire que la création de données ouvertes à partir des capteurs a été une bonne chose, surtout si elle pouvait être utilisée pour comparer les bâtiments et permettre une meilleure compréhension du comportement des bâtiments de différents types.

La propriété des données était considérée comme importante. Qui est le propriétaire de ces données – le propriétaire du bâtiment ou les personnes qui habitent ou travaillent dans le bâtiment – Il a été souligné qu’il existe une relation entre la propriété des données et le pouvoir que les personnes ont dans l’espace.

Ainsi, si vous êtes propriétaire d’un bâtiment, vous avez plus de chances de penser que les données que vous collectez vous appartiennent, car il s’agit de votre bâtiment et d’un espace privé, même si certaines de ces données peuvent être utilisées pour identifier des individus.

De manière analogue aux caméras de surveillance dans la mesure où les personnes se soucient peu de la manière dont la vidéo est stockée ou avec qui elle est partagée, dans les bâtiments, les personnes ont généralement peu de choses à dire ou à savoir sur le fonctionnement des systèmes.

L’identification des besoins est vraiment importante avec la création d’un réseau de capteurs pour éviter que les initiatives soient sujettes à une mauvaise application avec pour conséquence, la création de données inutilisables.

Il y a aussi une tendance à trop recueillir de données ou à collecter le mauvais type de données. Il a été suggéré que les règlements RGPD à venir pourraient avoir une incidence sur la façon dont les données sont collectées dans les bâtiments et les espaces publics.

Une critique du projet pilote « Knowable Building Framework » à Manchester est que la mise en œuvre a été entreprise avec juste le consentement des gestionnaires de l’immeuble qui peuvent voir un avantage tangible sur le fonctionnement du bâtiment. Ces derniers n’ont cependant pas pris la peine de recueillir l’avis des occupants du bâtiment.  

Le groupe a identifié que nous avons besoin d’examiner la manière dont nous construisons les réseaux de manière consensuelle, car cela serait précurseur de la construction du consentement.

Un consensus suppose un certain nombre de choses qui pourraient potentiellement être difficile à mettre en place pour la mise en œuvre des réseaux de capteurs. Ils pourraient s’agir de :

  1. La compréhension de l’objectif de l’environnement du capteur et de la façon dont les données seront utilisées.
  2. La technologie et ce qu’elle mesure.
  3. Des garanties pour que la technologie ne soit pas utilisée à mauvais escient.
  4. La représentation des préoccupations si la technologie est dévoyée.
  5. La compréhension du partage de données et des données ouvertes.

Les données collectées à partir des capteurs dans leur forme brute sont riches en informations, mais le partage des données en tant que données ouvertes présente des risques. Il existe différents niveaux de granularité qui pourraient aider les propriétaires de données à partager des données, mais il faudrait prendre soin d’identifier le niveau approprié d’agrégation et d’anonymisation. Il y a un compromis entre l’utilité et la vie privée.

Il y a eu une discussion sur la valeur des données créées et si elles pouvaient être utilisées comme une marchandise pour tirer des accords préférentiels pour les propriétaires de bâtiments. Cela pourrait avoir une sorte de levier en ce qui concerne les fournisseurs d’énergie et les assureurs du bâtiment.

Il existe différentes catégories de parties prenantes dans le bâtiment et il est important que tout mécanisme de consentement les reconnaisse. Ces parties prenantes sont pleinement impliquées dans le bâtiment. Nous avons les propriétaires et les gestionnaires ; les occupants et utilisateurs et la maintenance et les opérationnels.

Les données doivent être disponibles – ou au moins les renseignements dérivés des données afin que les occupants et les utilisateurs du bâtiment puissent comprendre par eux-mêmes comment fonctionne le bâtiment. Cela aiderait à démystifier la technologie et la rendrait moins menaçante, mais aiderait aussi les gens à modifier leurs propres comportements.

La visualisation pourrait jouer un grand rôle dans ce processus, car une bonne visualisation et un bon design permettent la compréhension. En rendant les données disponibles, il est également possible d’exécuter des événements autour des données afin de créer de nouvelles façons de créer l’information et de la comprendre.

Rennes a utilisé avec succès le format Data Remix (sous le format d’un hack event) pour relever un certain nombre de challenge et il est suggéré d’en faire un dans le futur en incluant les équipes de Rennes.

Vous trouverez c-dessous des informations complémentaires en français de notre atelier de travail, sur le lien suivant.

Open Data Manchester secures funding to develop its work

Open Data Manchester has secured investment from Omidyar Network to develop its programme of advocacy, training and events through 2018.

In November 2017, Open Data Manchester became a Community Interest Company, setting in stone its core mission to promote a fairer and more equitable society through the development of intelligent and responsible data practice in Greater Manchester, nationally and internationally. This has allowed it to develop a more coherent and ambitious programme, and the ability to secure funding for its work. At present it is developing a framework for the consent and sharing of sensor data through its Knowable Building Framework project funded through the Open Data Institute.

Linda Humphries, a member of the Open Data Manchester CIC board said: “Being part of the Open Data Manchester community for over 5 years, I’ve seen the opportunities it has opened up, connecting people who then work together towards a common aim. This funding from the Omidyar Network will ensure that we can go on making these connections, growing skills and sharing insight, so that people in the community can use data or build tools and services that encourage citizens to better understand and influence their villages, towns and cities.”

The investment from Omidyar Network will enable Open Data Manchester to employ staff and develop its programme from its base within The Federation, Manchester.

Omidyar Network has traditionally supported projects citizen engagement and governance projects in Central and Eastern Europe. As well as Open Data Manchester it is supplying grants to:
The Federation, a co-working space and community of digital innovators in Manchester, in collaboration with the Co-op Foundation.
Campaign Bootcamp, a nation-wide initiative to empower early-stage activists by providing them with the skills, confidence and resilience to run effective campaigns.
The Bristol Cable, a citywide media co-operative focused on investigative journalism.

Established in 2010 to promote and support the use of open data for the benefit of everyone, Open Data Manchester has promoted and run regular events and programmes. In 2010 Open Data Manchester became the first organisation to secure the release of public transit schedules as open data in the UK and then went on to develop a number of programmes over the years that looked critically at how data was being used. From sector specific events around transportation and health, to programmes looking at data and democracy.

Building consent

En français

The Knowable Building Framework sets out to create a consent framework for sharing building performance data from a network of sensors installed within a Victorian office building in the centre of Manchester.

The development of low-cost connected sensors coupled with the advent of low power wide area networks (LPWAN) specifically Long Range Wide Area Networks (LoRaWAN) creates the ability to monitor remote and hard to reach assets that would otherwise be too difficult or too expensive to operate and maintain.

Within the domain of building management the opportunity to retrofit passive sensors into older buildings offers the ability to understand how buildings operate over time. Giving building managers the ability to implement control measures and promote behavioural change of the buildings users – saving money and reducing environmental impact. The sensors that the Knowable Building Framework are installing measure temperature, humidity, movement and light with building managers abe to analyse the output from the sensors using an online dashboard.

Although the application of sensors in buildings may not be particularly novel, the sharing of data to allow a better understanding of building usage either within organisations or at a city level offers the potential of creating a more holistic picture of energy usage.

The idea of sharing data even if it is not shared as open data can seem daunting to many organisations and the development of a consent framework seeks to help data owners understand the data that they hold, both technically and contextually. It identifies perceived and real risk and suggests possible mitigations. Through enhancing understanding the framework hopes to make it easier for data owners to consent to data release. With some analysts predicting over 50 billion connected devices by 2020 the prospect of a confusing mess of siloed and conflicting data sources adhering to dubious technical standards is very real.

Building owners and management are only one class of stakeholders when it comes creating consent. On the face of it the temperature, humidity, movement and light may seem innocuous and are part and and parcel of understanding the use of a building, but there is a danger that the data could be used outside its original purpose. Within the first few days of sensors being installed the data revealed patterns of usage that could infer the activity of individuals. During the working day this may not be a problem but for the people who maintain offices out of hours it would not be a leap of the imagination to think that sensors could be used as a method of surveillance. These issues are not unique to office spaces and similar challenges lie within public spaces and the urban built environment.

At this point we are starting to identify three classes of stakeholders within the consent framework:

  • Building owners and management – those that have the ability to use the data for analysis and can make final decisions on data release
  • Building users – individuals and companies who pay for the use of the space
  • Building operatives – individuals employed for maintenance, cleaning and security.

Any consent framework needs to understand the concerns of these stakeholders and propose approaches to address them.

To attend our next workshop in Manchester on the 25th January click here

The Knowable Building Framework is being developed by Open Data Manchester along with its partners Sensorstream, Things Manchester and Rennes Metropole funded by the Open Data Institute.

Construire le consentement

«The Knowable Building Framework» vise à créer un modèle de consentement pour le partage des données de performance des bâtiments à partir d’un réseau de capteurs installés dans un immeuble contenant des espaces de travail dans le centre de Manchester.

Le développement des capteurs connectés à bas coûts,  couplés avec l’avènement du LPWAN ( Low Power Wide Area Networks), notamment du LoRaWAN (Long Range Wide Area Networks) offre la possibilité de surveiller des actifs distants et difficiles à atteindre qui autrement, seraient trop difficiles à exploiter ou trop cher à entretenir.

Dans le domaine du management des bâtiments, l’opportunité de moderniser les capteurs passifs dans des bâtiments anciens offre la possibilité de comprendre comment les bâtiments fonctionnent dans le temps. Donner aussi la possibilité aux gestionnaires de bâtiments d’implémenter des mesures de contrôle et de promouvoir un changement comportemental des utilisateurs du bâtiment – Faire une économie de coûts et réduire l’impact environnemental-

Les capteurs installés mesurent la température, l’humidité, la lumière et les mouvements pour ensuite analyser les performances du bâtiment à l’aide d’un tableau de bord en ligne.

Bien que l’utilisation de capteurs dans le bâtiment ne soit pas particulièrement nouveau, le partage de données provenant de ces capteurs  permet une meilleure compréhension de l’utilisation des bâtiments au sein des organisations ou au niveau des villes et offre la possibilité de créer une image plus holistique de l’utilisation de l’énergie.

L’idée de partager de données même si elles ne sont pas en mode “open data” peut sembler inquiétant pour plusieurs organisations, et le développement d’un modèle de consentement vise à aider les propriétaires de données à comprendre les données qu’ils détiennent aussi bien d’un point de vue technique que contextuel.

Il identifie les risques perçus et réels et suggère de possibles atténuations. En améliorant la compréhension, le modèle espère faciliter le consentement des propriétaires de données à la diffusion de leurs données.

Certains analystes prédisent  près de 50 Milliards d’appareils connectés d’ici 2020 et la perspective de voir un désordre des sources de données qui seraient cloisonnées et conflictuelles respectant peu les normes techniques est très sérieuse.

Les propriétaires des bâtiments et les gestionnaires représentent une partie des parties prenantes quant il est question de créer le consentement.

A première vue, la température, l’humidité, les mouvements et la lumière peuvent sembler banal mais sont parties intégrantes de la compréhension de l’utilisation d’un bâtiment. Cependant, il y a le risque que l’utilisation de la donnée soit dévoyée.

Dans les premiers jours où les capteurs furent installés, les données ont révélé des schémas d’utilisation susceptibles de comprendre l’activité des individus.

Pendant la journée de travail, cela peut ne pas poser de problème mais pour les personnes qui travaillent en dehors des heures d’ouverture (personnel de sécurité et de nettoyage), ce n’est pas difficile d’imaginer que les capteurs pourraient être utilisés comme une méthode de surveillance. Ces problèmes ne sont pas spécifiques aux espaces de bureaux et des défis similaires apparaissent aussi dans les espaces publics et dans l’environnement urbain.

A ce stade, nous commençons à identifier trois classes de parties prenantes dans le cadre du consentement:

  • Les propriétaires de bâtiment et les gestionnaires, ceux qui ont la possibilité d’utiliser les données afin de les analyser et qui sont en mesure de décider ou pas de la publication des données récoltées
  • Les utilisateurs du bâtiment, les personnes ou les entreprises qui payent pour utiliser les espaces.
  • Le personnel employé pour la maintenance, le nettoyage et la sécurité du bâtiment.

Tout cadre de consentement doit comprendre les préoccupations des parties prenantes et apporter des réponses pour y remédier.

«The Knowable Building Framework» est développé par Open Data Manchester, financé par Open Data Institute, avec ses partenaires Sensorstream, Things Manchester et la Métropole de Rennes.