Between the Poles

Above and underground infrastructure comprise Rotterdam's open digital twin

The City of Rotterdam is developing a digital twin of the city.  3D vector and image data of the above- and underground infrastructure, zoning regulations and city by-laws, data streams from real-time sensors, applications and simulations will comprise Rotterdam's digital twin. It is envisaged that the digital twin will enable the city to assess the impact of proposed changes in bylaws and regulations by simulating them before they are implemented. Rotterdam, and in fact the whole of the Netherlands have adopted transparency and open data as a pillar of government and are basing 3D models on open national and international standards, intend for the 3D data to be open and accessible to the public, and intend to use open source software wherever possible.

Many cities have 3D models of their aboveground infrastructure; buildings, transportation networks, parks, and other infrastructure typically captured in 2D or 3D imagery from overflights or in 2D GIS maps.  Imagery is not very smart and extracting 3D vector objects such as building footprints, heights, profiles and transportation networks requires sophisticated post-processing.  Even more importantly, underground infrastructure is usually neglected, even though it is recognized that water and wastewater, energy (gas, electric power, and district heating),  and communications (fibre and copper) networks provide the life blood of the city.

Rotterdam has been developing its 3D model, which underpins the digital twin, for several years.  Currently it contains street furniture such as street lights, trees and underground parts of street furniture, a large scale basemap, a complete map of underground networks of cables and pipes, buildings, and non-physical content such as zoning regulations and  bylaws.  The 3D model is kept in sync with data repositories maintained by city departments and others.  In the future it will also include real-time data streams from a variety of sensors. 

The 3D model is open, based on open standards and many of the applications that have been developed for it are open source projects. For example, visualizations rely on Cesium, an open source initiative which has just announced support for visualizing underground utilities, mines, and geological layers. The rationale for the data being based on open standards is that an standards-based environment creates an open data commons where developers can devote their time to developing innovative applications rather than spending time converting data from one format to another.

The Dutch 3D standard is an integration of CityGML Application Domain Extension (ADE) and the existing national IMGeo standard for large-scale representations of roads, water bodies, and land use. The Dutch Kadaster, Geonovum, the Netherlands Geodetic Commission and the Dutch Ministry of Infrastructure and Environment initiated a "3D Pilot NL" to develop a national 3D standard.  Existing intnernational standards including CityGML(Open Geospatial Consortium), IFC (buildingSMART), and others were investigated. The findings of the pilot  resulted in a national 3D standard implemented as a CityGML Application Domain Extension (ADE) that integrates the latest version of the existing Dutch national standard IMGeo 2.0. 

Current applications (virtualcitySYSTEMS, ESRI, IMAGEM and Unity) use the 3D model as input for analytics and visualization. One can imagine many use cases for the data in the Rotterdam 3D model. Among these three use cases have been identified as low-hanging fruit; climate adaptation, viewsheds and energy performance of buildings. 

Much of Rotterdam is below sea level and managing rain water accumulation during heavy rainfalls is a major issue. which affects building basements, subway entrances, low-lying roads, electrical transformers, and other infrastructure.  To address this problem, the concept of "water squares" has been developed and the 3D model used to determine the optimum location for these..  Water squares are open areas where water can safely be allowed to accumulate during heavy rainfalls to avoid overstressing storm sewers. 

In another application of the 3D model data, building data can be filtered and streamed to energy performance applications that provide insights into how buildings perform.

Open standards and open data also enables the sharing of data among cities in the Netherlands.  The Dutch Kadaster has developed an open 3D map of the whole of the Netherlands, with scales ranging from 1:500 to 1:25,000 in collaboration with academic, private sector, and public sector partners.  All data in the 3D map is built on the basis of open standards.  The map was created by combining existing 2D data with the Elevation Model of the Netherlands resulting from a LiDAR survey which covers the whole of the Netherlands with a resolution of eight elevation points per square meter.

Open data and open standards also relate to a critical issue that is of growing importance. Many governments have adopted transparency and open data as a cornerstone of democratic policy.  But often digital twins for smart cities are being developed by private-sector firms or by public-private initiatives.  As William Davies points out in Nervous States, "the IT infrastructure used to build e-geovernment projects or "smart cities" comes from the commercial sector, and the companies that provide it often retain control over the data that results." 

For example, recently a joint Alphabet/Google/Sidewalk Labs and City of Toronto initiative to develop a smart city on Toronto's waterfront foundered and was cancelled.  One of the reasons, it is argued, is that a technology giant such as Google (or Facebook, Amazon, ...) is not a vendor in the traditional sense, but a "surveillance capitalist" that collects and analyzes behavioural data captured from users for its own economic imperatives.  In the context of a smart city the opportunities for data collection from citizens and from systems such as water and wastewater, transit, waste removal, traffic management, and others are immense.  If this data is appropriated and becomes proprietary, it puts a private entity in a position to supplant democratic government, which doesn't have access to this huge volume of data and the analytics which extracts information from it.  Relying on open standards and ensuring that data is open and accessible to the public are essential to avoid citizens' and municipal data becoming proprietary.  Furthermore, using open source software and, I would add, open training data for machine learning applications helps avoid the problem of the analytics also becoming proprietary.

Based on a Webinar by Christian Veldhuis, Advisor City of Rotterdam, Rick Klooster, CCO, FUTURE INSIGHT and Antje Kunze, Director Sales & Marketing, virtualcitySYSTEMS.

Severely restricted access to critical infrastructure information in the U.S.

The Critical Infrastructure Information Act of 2002 of the U.S. Congress provides a mechanism for severely restricting access to proprietary critical infrastructure information voluntarily shared by network operators with the federal government.  It prevents consumers and organizations from accessing this information under the Freedom of Information Act, by a regulatory agency, or as part of civil litigation.  What this means is that a utility, telecom, or other organization can elect to submit proprietary network information including the location of above and below ground facilities to the Department of Homeland Security's critical infrastructure information program (PCII) after which it is protected from access by anyone except certified Federal government employees and contractors.

Introduction

Information about infrastructure in the United States is regulated by an act of Congress and several presidential directives. The earliest federal directive is the 1998 Presidential Decision Directive/NSC-63 Critical Infrastructure Protection that identified the need for a private public partnership, preferably voluntary, to protect critical infrastructure.  The Critical Infrastructure Information (PCII) Act of 2002 followed very closely after September 11, 2001,   The Homeland Security Presidential Directive 7 in 2003 established a national policy for Federal departments and agencies to identify and prioritize critical infrastructure and to protect them from terrorist attacks.  The Presidential Policy Directive 21 (PPD-21), Critical Infrastructure Security and Resilience of 2013, established a national policy on critical infrastructure security and resilience as a shared responsibility among the Federal, state, local, tribal, and territorial governments and public and private owners and operators of critical infrastructure. This directive also clarifies the critical infrastructure-related responsibilities across the Federal Government.  The National Infrastructure Protection Plan (NIPP) 2013 is a plan resulting from Homeland Security Presidential Directive 7. The NIPP's goals are to protect critical infrastructure and key resources and ensure resiliency. The NIPP is based on the 1998 Presidential Decision Directive-63, which identified critical sectors of the economy and identified government agencies to each of them on sharing information and on strengthening responses to attack.

Critical Infrastructure Information Act of 2002

The U.S. Congress established standards for protecting critical infrastructure information through the Critical Infrastructure Information Act of 2002. In response, the Department of Homeland Security (DHS) created the Protected Critical Infrastructure Information (PCII) program to protect private sector infrastructure information voluntarily shared with the government for the purposes of homeland security. The 6 Code of Federal Regulations (CFR) part 29, Procedures for Handling Critical Infrastructure Information; Final Rule, published in 2006, established uniform procedures on the receipt, validation, handling, storage, marking, and use of critical information information voluntarily submitted to DHS. Information submitted by utilities, telecoms and others to the PCII is broadly protected from disclosure. This includes disclosure under the Freedom of Information Act; State, local, tribal, or territorial disclosure laws; regulation or civil litigation.  Only trained and certified U.S. government employees or contractors may use PCII. All users of PCII must have homeland security responsibilities and duties, possess a valid need-to-know, and sign a non-disclosure agreement.  Organizations that can submit proprietary information to the PCII includes privately or publicly owned companies, industry associations and State and local government officials.  Federal government agencies may not submit information to the PCII Program.  Information that is in the public domain cannot be submitted.

Presidential Decision Directive/NSC-63 1998 Critical Infrastructure Protection

Directive NSC-63 identified critical infrastructure whose physical and cyber-based systems were essential to the minimum operations of the economy and government. These included telecommunications, energy, banking and finance, transportation, water systems and emergency services, both governmental and private. Since most infrastructure is privately owned, the protection of critical infrastructure is necessarily a shared responsibility and partnership between owners, operators and the government.  The directive specifically identified interdependence between different utility, telecom and other systems as a growing challenge. Its goal was to create a public-private partnership to enable a closely coordinated effort of both the government and the private sector to avoid increasing government regulation or putting a greater burden on the private sector. To that end it stated that it is preferable that participation by owners and operators in a national infrastructure protection system be voluntary. It also emphasized that privacy rights of consumers and operators of utility, telecom, and other networks must be protected and information handled accurately, confidentially and reliably.

Homeland Security Presidential Directive 7: Critical Infrastructure Identification, Prioritization, and Protection 2003

Homeland Security Presidential Directive 7 establishes a national policy for Federal departments and agencies to identify and prioritize critical infrastructure and to protect them from terrorist attacks. It directs that federal agencies will collaborate with the private sector to encourage the development of information sharing and analysis mechanisms. Its objective is to identify, prioritize, and coordinate the protection of critical infrastructure and key resources and to facilitate sharing of information about physical and cyber threats, vulnerabilities, incidents, protective measures, and best practices.

President issued Presidential Policy Directive 21 (PPD-21), Critical Infrastructure Security and Resilience 2013

This directive establishes national policy on critical infrastructure security and resilience as a shared responsibility among the Federal, state, local, tribal, and territorial governments, and public and private owners and operators of critical infrastructure.  Its objective is to enable information exchange among these organizations by Identifying baseline data and systems requirements for the Federal Government. Federal departments and agencies are directed to ensure that all existing privacy principles, policies, and procedures are implemented consistent with applicable laws and policies.  The Secretary of Homeland Security, in coordination with other Federal departments and agencies, was directed to convene a team of experts to identify baseline data and systems requirements to enable the efficient exchange of information and intelligence relevant to strengthening the security and resilience of critical infrastructure.

National Infrastructure Protection Plan 2013

The National Infrastructure Protection Plan (NIPP) 2013 was mandated by Homeland Security Presidential Directive 7. The latest version of the plan was produced in 2013.  The NIPP's goal is a mechanism for developing coordination between government and the private sector.  As the Nation’s critical infrastructure is largely owned by the private sector, managing risk to enhance security and resilience is a shared priority for industry and government.  The goal is collaborative public-private partnership that operates as a unified national effort, as opposed to a hierarchical, command-and-control structure.  The eighteen sectors identified by DHS as critical infrastructure are: chemical, commercial facilities, communications, critical manufacturing, dams, defense industrial base, emergency services, energy, financial services, food and agriculture, government facilities, healthcare and public health, information, technology, nuclear reactors, materials, and waste, transportation systems, water and wastewater systems.  While it lauds the investment in some critical infrastructure it specifically refers to A National Academy of Sciences report that the Nation’s earlier heavy investment in the design, construction, and operation of critical infrastructure systems—water, wastewater, energy, transportation, and telecommunications—has not been matched with the funds necessary to keep these systems in good condition or to upgrade them to meet the demands of a growing population.

Information about critical infrastructure is vulnerable to unauthorized access that could affect its confidentiality, integrity, or availability. The distribution of such information to those entities that can use it for efficient and effective risk management remains a challenge. It is critical to maintain the availability of information and distribute it to those who can use and protect it properly. This entails being transparent about information-sharing practices; protecting sources and methods; and ensuring privacy and protecting civil liberties, while also enabling law enforcement investigations.  It references the PCII program, authorized by the Critical Infrastructure Information (CII) Act of 2002 and its implementing regulations (Title 6 of the Code of Federal Regulations Part 29).

The National Plan identifies certain lifeline functions that are essential to the operation of most critical infrastructure sectors. These lifeline functions include communications, energy, transportation, and water. To manage critical infrastructure risk effectively, it directs that partners must identify the assets, systems, and networks that are essential to their continued operation including interdependencies. Interdependencies may be operational (e.g., power required to operate a water pumping station) or physical (e.g., co-located infrastructure, such as water and electric lines running under a bridge span) and include infrastructure that requires accurate and precise positioning, navigation, and timing (PNT) data. PNT services are critical to the operations of multiple critical infrastructure sectors and are vital to incident response.

General Data Protection Regulation (GDPR) which enters into effect today covers your location

The General Data Protection Regulation (GDPR) enters into force today.  It is a regulation in EU law on data protection and privacy for all individuals within the European Union and the European Economic Area. It also addresses the export of personal data outside the EU and EEA. The GDPR is quite far ranging. Here are some interesting examples of what it covers. You can take the online test that the BBC prepared (which the material below is derived from) here.  You will notice that location data is included in the personal data covered by the legislation.  Surprising to me is that it covers AI (artificial intelligence) when it is applied to an application.

• GDPR is designed to help people protect and control use of their "personal data". What does that cover?

The UK’s Information Commissioner’s Office defines personal data as: "Information relating to an identifiable person who can be directly or indirectly identified in particular by reference to an identifier." It says this includes "name, identification number, location data or online identifier". Under some circumstances, this can extend to images, and details about your family. Unconnected facts - such as the distance from the Earth to the Moon - would not become personal data just on your say-so.

• A free app that relies on adverts to make money has gathered information about you. Under what circumstances can you forbid it to use the data?

Organizations have six lawful bases for processing personal data: Consent, contract, legal obligation, vital interests, public task or legitimate interest. But whatever the legal basis, you always have the right to object to the continued processing of your personal data if it's for the purposes of direct marketing. If you willingly and explicitly consented to your personal data being used for ads in the past, then apps and others can continue to do so.

• An online chat service wants access to your location and email contacts and is relying on user consent as the legal basis for this.

GDPR says consent means individuals must have real choice and control. So, they need to have a "clear and concise" explanation as to what they are agreeing to, and pre-ticked boxes and other forms of default consent no longer apply.

• Over time you have grown to dislike being part of a social network and decide to quit.

GDPR introduces a right for individuals to demand their personal data be erased under some circumstances. These include situations when its use has been based on their consent. Organizations must respond within a month of receiving the request and should comply without charging a fee unless the request is deemed "manifestly unfounded or excessive".

• You are unconscious after a car crash and require surgery. Can your GP provide your personal medical records to the nearest hospital ?

One basis for processing and sharing an individual’s personal data is that it is necessary to protect their vital interests, which includes saving their life. So, sharing data with a hospital's A&E department for an emergency would count.

• A video streaming service emailed you all the personal data it held about you last week, as requested, but you decide to ask again.

GDPR gives individuals a right to access the personal data held on them under some circumstances. Organizations are encouraged to make this possible via the internet, and are supposed to respond within a month. However, if a user subsequently asks to be sent further copies of their data, then the service involved can charge a "reasonable fee" based on its administrative costs.

• You lose your wallet. Inside is a scrap of paper on which you had written down your work login and current password. Your IT department confirms that an unidentified party entered your account, giving them access to a file containing the names and addresses of 12 police informants involved in a project you are working on.

GDPR introduces a duty to report certain types of data breaches within 72 hours of them being detected, even if all the details are not yet known. If individuals are also put at significant risk, they must also be informed. Failure to comply can entail a fine of up to $23.6 million or 4% of the annual global turnover.

• Your application to work at a restaurant is turned down, and you are told it was rejected by the firm's artificial intelligence system.

GDPR gives an individual the right to challenge decisions made solely on the automated analysis of their personal data if they did not consent to it in advance. Those affected can ask for access to the details on which the decision was based. They also have the right to have a human double-check that a mistake was not made.

• You decide to quit membership of a gym chain to join a rival. You ask for your data in a common machine readable format.

GDPR includes the right to obtain and reuse personal data from one service to another. It applies when the lawful basis for processing the information is consent or contract, and the processing is carried out by automated means (ie not paper files). The data must be provided in a commonly used and machine-readable format.

Massachusetts mandates smart grid for electric power utilities

Massachusetts has not been among the states that have led the smart grid revolution in the United States.  In September 2012 National Grid began a small smart grid pilot involving 5,000 meters in Worcester, Massachusetts.  National Grid is expanding this to 15,000 customers to evaluate integrating home automation, dynamic pricing and distribution automation.  At this point Massachusetts looks like a late adopter, but this is set to change. The Massachusetts Department of Public Utilities (DPU) has decided to aggressively accelerate the deployment of smart grid technologies in the state.

Background

DPU is responsible for ensuring that electric distribution companies provide safe and reliable electric service to customers and expand the deployment of clean energy technologies.  As part of that mandate In October, 2012, the DPU issued a Notice of Investigation (“NOI”) into the modernization of the electric grid with the goal of developing policies to  ensure that electric distribution companies adopt grid modernization technologies and practices.  The expected benefits of grid modernization include improved reliability, reduced operating costs, reducing rate increases and volatility for customers, and empowering customers to better manage their use of electricity.  The NOI also specifically mentions increasing the resiliency of the grid in response to "increasingly extreme weather".  Other benefits of grid modernization that the DPU foresees include providing support for energy efficiency, demand response, distributed generation, storage, electric vehicles (“EVs”), and renewable energy resources.

In November 2012, the DPU organized a workshop attended by over 125 stakeholders which resulted in the formation of a Working Group tasked with providing input to the DPU on approaches to grid modernization over the short, medium, and long terms and with helping the DPU in prioritizing grid modernization investments.

In July, 2013, the Working Group submitted a report to the Department covering on a wide array of grid modernization issues entitled “Report to the Department of Public Utilities from the Steering Committee”.

Enterprise systems required for smart grid include GIS

This report explicitly identifies what it calls network system enablers, enterprise systems that it considers essential for implementing a smart grid. These include GIS in addition to Distribution Management System (DMS)/SCADA, Outage Management System (OMS), Billing System, Metering System and Meter Data Management System (MDMS), and Communication Systems (Fiber, Microwave, Radio, etc.).

The report sees the role of GIS as the repository for asset information.  It feeds system planning models, system operations models, outage management models and work-order/financial systems and can also 

Electricity  distribution companies required to prepare Grid Modernization Plans (GMP)

Based on the report the DPU established four grid modernization objectives;  reduce the effects of outages; optimize demand (which includes reducing system and customer costs); integrate distributed resources, and improve workforce and asset management.

The DPU then proposed requiring each electric distribution company to develop and submit to the DPU a ten-year strategic grid modernization plan within six months.  The GMPs must lay out plans to make measureable progress towards all of these grid modernization objectives.

Smart grid priorities

In its first GMP each power utility must include a comprehensive plan for advanced metering (CAMP). The DPU is prioritizing advanced metering because it sees AMI as a basic platform upon which grid modernization will be developed.  It is being very aggressive in mandating completion of the rollout of AMI with three years of the DPU's approval of each company's GMP.  To support this effort the DPU has already developed a regulatory framework specifically targeted on advanced metering.

The DPU has identified its next top priorities;
(1) time varying rates (time-of-use rates)
(2) cybersecurity, privacy, and access to meter data
(3) electric vehicles.

Internet of things is becoming a reality: Nest Labs acquired by Google

Monday Google announced that it has entered into an agreement to buy Nest Labs for $3.2 billion in cash.

Back in November 2011, I blogged about several ex-Apple iPod engineers who got together to find a new way to change the world, and of all things decided that what the world needed is a high end smart thermostat that you don't have to program manually.   The result was Nest Labs and the Nest thermostat, which is not only stylish as you would expect from anyone with Apple experience, but smarter than any thermostat I have ever seen. There was a very good review in engadget.  According to Nest, The Nest Learning Thermostat programs itself in a week using its six sensors, which track temperature, ambient light, humidity and motion, near and far.  It knows, for instance, that no one is in the house after 8:30 am and that people are back after 5 pm. The Nest also detects if you're at home, even if you're not normally at home at that time, or that you're not when you normally are. You can install multiple thermostats and they can communicate with each other.  It also supports Wifi so you can control it from anywhere on the Internet.  This device could significantly reduce energy usage while maintaining or increasing consumer comfort levels

Since then they have developed a smoke and carbon monoxide detector called Nest Wave, that is also smarter than any of these types of devices.  In Ontario 22% of fires occur where there is no smoke detector or it has been disabled.  Smoke detectors are frequently disabled because they issue a really annoying, strident sound most often when you have burned the toast or overfried the chicken.  Nest Wave is designed to avoid that.  It's called Wave because waving at it will turn off the alarm sound.  Like the thermostat it is smart and automatically configures itself to the room.  If you move furniture and appliances around, it will reconfigure itself.  Best of all in my opinion the device gives you an early warning,  It lights up yellow and speaks with a human voice and tells you where smoke is or that carbon monoxide levels are increasing. This is an earlier warning if there’s a real emergency.  But if you know that you havee just burnt the chicken, you can wave at it and it will subside.  The most important thing is that unlike normal smoke alarms it isn't designed to encourage you to disable it.  I am convinced this device will  prevent fires and save lives.

For both of these devices there are significant benefits for the consumer.

Regulating the internet of things

These devices learn a lot about your habits and even your furniture arrangement.  The are connected via wifi and can be linked to the internet. With the Google acquistion this is the internet of things impacting consumers directly in the home. 

There are clearly privacy issues.   According to Nest,

Our privacy policy clearly limits the use of customer information to providing and improving Nest’s products and services. We’ve always taken privacy seriously and this will not change.

As with geospatial data such as personal location that is now widely available via smart phones, the technology is moving so fast that regulation and legislation haven't been able to keep up with it .  According the the Federal Trade Commission, which is responsible for protecting privacy at the Federal level, the four holy data types are financial data, medical data, data about children, and geographic data.  The first three are treated as very sensitive and there is a legal framework to ensure these data types are protected.  When it comes to geographic data, on the other hand, there is very little by way of policy or legal infrastructure to protect personal geographic data.  Technology, including GPS, GIS and smart phones, has moved so rapidly and regulation and legislation move so slowly that for personal geographic data (where you are and where you have been), the horse has already left the barn and it is too late to close to the door.  I suspect with the broader internet of things including thermostats and smoke detectors, we may be seeing the same situation.  When the legislators and regulators grasp what the technology is capable of, it may be too late to regulate.

FTC enforces consumer privacy: Android app transmitted user phone ID and location to 3rd parties

According to allegations by the FTC the “Brightest Flashlight Free” app which has been downloaded by tens of millions of times by users of the Android operating system, transmitted users’ precise location and unique device identifier to third parties, including advertising networks. In addition, even though the app presented users with the option to not share their information, it was shared automatically no matter what option the user chose.
 
The settlement with the FTC

• prohibits the defendants from misrepresenting how consumers’ information is collected and shared and how much control consumers have over the way their information is used.
• requires the defendants to provide a just-in-time disclosure that fully informs consumers when, how, and why their geolocation information is being collected, used and shared, and requires defendants to obtain consumers’ affirmative express consent before doing so.

Economic value of open data estimated at $3 trillion annually

Open data has been gathering momentum among governments ever since United States Federal Government data was placed in the public domain.  The principle of free and open access to government geospatial data has been adopted by many governments including US Federal, Canada, Japan, South Africa, Brazil, California state and counties, and by the City of Vancouver and other cities.  G8 leaders (Canada, France, Germany, Italy, Japan, Russia, United Kingdom, United States, and the European Union) recently signed a charter on open data.  But being able to access prepared maps and other derived material is just a first step, governments need to provide access to raw geospatial data in commonly used Web-friendly formats.  In May 2013, President Obama issued an Executive Order making open and machine readable the new default for federal government information in the United States. 

A major motivation for open data has been government transparency.  But there is also a strong belief that open data contributes to the economy.  I blogged earlier about a study by a team at Cambridge University commissioned by the Treasury that found that making all Ordnance Survey data (geospatial data)  free would cost the government £12m but bring a net gain of £156m.  Australia and New Zealand have commissioned studies of the contribution of spatial data to the national economy that have concluded that with appropriate government policies the contribution to the GDP, estimated to be on the order of 1%, could be doubled.

McKinsey Global Institute (MGI) have just published a study that seeks to quantify the value of open data by examining applications in seven fields of the global economy: education, transportation, consumer products, electricity, oil and gas, health care, and consumer finance.  Their research found that seven sectors alone would generate more than $3 trillion a year in additional value as a result of open data.

For each of the sectors they looked at, MGI identified ways that open data could create economic value and identified potential barriers to adoption.

Electricity sector

For example in the electricity sector, MGI identified five levers for using open data to improve efficiency across the electricity value chain:

1. improved generation investment
2. efficient generation operations
3. optimized investment in transmission and distribution
4. efficient transmission and distribution operations
5. optimized retail and consumption

By using publicly available data such as siting applications, resource planning documents, and regulatory filings, utilities can streamline the permitting process for new power projects.

ƒƒ By sharing benchmarking data about processes such as maintenance routines and equipment utilization, power companies can optimize the performance of generating operations.

Open data can help optimize investments in transmission and distribution.

Sharing data gathered via “smart grid” technologies can help identify efficiency opportunities in transmission and distribution operations.

Open data makes it possible to compare products and services so that residential and business users make better decisions about which appliances, equipment an electric service to use.

Electric power usage data combined with community-wide data can show customers how their energy use compares with neighbors and peer organizations.

MGI estimates that open data has the potential to add $340 billion to $580 billion of value annually across the electricity value chain.

Economic potential

MGI found that the benefits of open data has a large potential economic value including increased efficiency, new products and services, and a consumer surplus (cost savings, convenience, better products).  The concluded that it benefits consumers more than businesses, by creating price and product transparency as well as new channels to provide feedback that improves the quality of goods and services (including public ones).

But open data involves business risks, including the potential release of negative information; the potential consumer backlash from aggressive open-data use; and the inadvertent release of confidential information.

The barriers to wider use of open data include privacy issues and the need for legal and regulatory frameworks.  To address these issues MGI recommends that governments need to play a central role by developing and implementing policies to mitigate consumer and business concerns about the misuse of open data and to help set standards that will allow the potential economic and social benefits to materialize.

The future of national mapping agencies over the next 5-10 years

Since Google Earth's release in June 2005, national mapping agencies and other government organizations involved with geospatial data and technology have been reasessing their role.  At the Geospatial World Forum (GWF) in Amsterdam last year, Paul Cheung of the United Nations Initiative on Global Geospatial Information Management (GGIM) voiced the concern about their role that many national mapping agencies and other government organizations with responsibility for geospatial information have had since the advent of Google Earth, private data companies like Digital Globe, Geoeye, TeleAtlas and Navteq, crowd-sourced geospatial data like OpenStreetMap, and open data policies adopted by many governments around the world.

The UN-GGIM has just published Future trends in geospatial information management: the five to ten year vision.  A number of experts and visionaries across a range of disciplines of the geospatial community including data collection, academics and major users of geospatial information,  the private sector and the crowdsourced/volunteered geographic information  movement were invited
to contribute their views on the emerging trends in the geospatial world. 

Private sector participation

With respect to the private sector the list of contributors includes senior people at Trimble, Hexagon, ESRI, and Digital Globe/GeoEye, but does not include Google, Microsoft, Nokia/Navteq, or TomTom(TeleAtlas).  I also didn't see anyone on the list from OpenStreetMap.  However, Google, Microsoft Bing, and OpenStreetMap are discusssed in the report.

Trends

Given the large number of people contributing to this report, it is not surprising that it includes a comprehensive set of trends that are impacting national mapping and cadastral agencies (NMCAs),  government bodies responsible for the provision of authoritative geospatial information within a country.

• Sensors - the proliferation of low‑cost, network‑enabled sensors in mobile phones, computers, smart meters or cars.
• Big data - estimated at approximately 2.5 exabytes every day, huge volumes of data are expected to increase demand for geospatial technology  to help to make sense of all this data.
• Linked data -  connecting data to other pieces of data on the Web, providing context and adding value to existing data
• Semantic technologies  - the semantic web as been discussed for years, but there are signs within some verticl industries such as construction that something practical may be emerging
• internet of things - a hyper‑connected network of intelligent objects, I have seen estimates of a trillion objects in the near future
• Cloud - infrastructure as a service (IaaS), platform as a service (PaaS), software as a
  service (SaaS) and data as a service (DaaS)
• Open‑source - expected to continue to gaim momemntum, already a number of NMCAs have  adopted open‑source solutions into some of their services, OGC reference implementations are open source.
• Open standards - In many ways the geospatial community is an model industry in the development and maintenance of open standards, led and coordinated by OGC in partnership with many organisations
• 3D data - the world is moving from two dimensional (2D) to three dimensional (3D) mapping; many municipalities are developing realistic and intelligent city models including buildings and infrastructure; the report projects that 3D is becoming an intrinsic part of core geospatial data, rather than an add‑on as it is now
• 3D visualization -3D visualization technology much of it arising in the gaming industry makes it possible to convey complex concepts, projects, and analytical results to non-technical folks such as politicians and the general public
• 4D - used widely in the construction industry, for example, linking Primavera to a BIM model helps with construction scheduling, 5D is also being  used; the report also foresees 4D increasing over the coming five to ten years in geographic information systems (GIS), not only to understand change that has already taken place, but to also enable predictive modelling of future trends
• Imagery - forecasts centimeter precision in many areas of the globe, focus is likely to be performance and analytics.
• Mobile mapping - street‑level visual information, points of interest (POI) and attribute data in more detail; especially LiDAR (and oblique imagery) which will facilitate the generation of 3D datasets. [I would add ground penetrating radar (GPR) for mapping underground infrastructure]
• Unmanned aerial vehicles (UAVs) - in the civilian sector UAVs are already being used increasingly in a number of sectors
• GNSS - by 2015, there will be over 100 GNSS satellites in orbit. This will enable faster data collection in very challenging environments, with higher accuracy and greater integrity
• Satellite gravimetry - some nations are already taking the step to move away from traditional schemes defined using large‑scale terrestrial observations and base the national vertical reference system on purely gravimetric geoids instead
• Coordinate systems - are becoming more accurately defined as technology and techniques improve; this is critical large scale construction projects, especially in the transportation sector
• Indoor positioning - an emerging frontier, but one that still represents major challenges
• Passive crowdsourcing - anyone with a smart phone, with or without a GPS, is a sensor which is already being used by private companies to derive infirmation about traffic, shopping patterns, and other information; social media make this an even richer source of information
• Active crowdsourcing - OpenStreetMap (OSM) which grew by providing an alternative to an NMCA is the best known example; some NMCAs still find the concept of crowd-sourced data a challenge
• Funding models - funding the collection of geospatial data remains a controversial topic in some countries; one of the major challenges of the next five to ten years for governments will be demonstrating the value pf geospatial data to the national economy as has been done in Australia and New Zealand and as is currrently underway in Canada

Role of governments

Clearly governments have a role in defining policy, for example,  for access to government spatial data such as free and open data or for protecting consumer privacy.

For some vertical industries the report says that "governments will also play a role in many countries in driving, or at least supporting, cross-sector collaboration in areas such as building, construction and public safety, through building information modelling (BIM)..."  This is already occuring in the U.S., U.K., Finland, Netherlands, Denmark, Norway, Hong Kong, and Singapore, where national governments ara mandating or at least strongly encouraging the adoption of BIM for public projects.  It is being driven primarily by efficiency and reducing the cost of infrastructure.

The report argues that "governments have a key role to play in bringing all actors together to ensure that our future society is a sustainable, location-enabled one, underpinned by the sustainable provision and effective management of reliable and trusted geospatial information." 

The challenge for NMCAs in the area of traditional mapping is that in parts of the world, Google. Bing, OpenStreetMaps (see here to compare these data sources), or a local crowd-sourced alternative like Malsingmaps provides more comprehensive, accurate, current and accessible data with less restrictive licensing and at a lower cost than the NMCA.  There are those who argue that in the area of traditional mapping, private and crowd-sourced organizations do a better job at lower cost and that government needs to find a new area of focus where it can provide a service that it would be difficult for anyone else to provide.  Examples are managing authorative data such as the parcel fabric or  mapping undeveloped areas such as Northern Canada or the Amazon interior that may be uneconomic for a commercial concern.

Infrastructure Mapping

An area where government may be uniquely qualified is mapping national infrastructure, much of which has a national security dimension.  In many parts of the world the location of underground infrastructure including electric power, water and wastewater, gas, district heating, and other networks is unknown or poorly known. 

According to national statistics, in the United States an underground utility line is hit on average every 60 seconds.  The total cost to the national economy is estimated to be in the billions of dollars.  The problems is that in most municipalities in North America, for years underground utility lines have been put in the ground not according to plan but wherever it has been easiest and cheapest to build them.  In addition 2D as-builts of underground infrastructure are unreliable.  The result is that in most municipalities the location of underground utiltiies is very poorly known.

In the U.S. access to infrastructure data including location is restricted under Protected Critical Infrastructure Information (PCII) Program. In addition utilties, telecommunications and other organizations responsible for operating infrastructure networks are very often reluctant to share information about their networks for competitive and other reasons.  There are successful examples around the world where government has helped enable a shared ulility network database.

The Victorian Spatial Council has developed a framework for sharing based on licenses and metadata, not only within the Government of Victoria, but also among utilities, telecommunications firms, National Government, local governments, quasi-government, and non-government organizations.

As a practical example, the City of Las Vegas has successfully completed a pilot to develope a 3D model of the City's underground and aboveground infrastructure and is expanding the project to cover more of the City.

There is a growing body of information that indicates that the return on investment (ROI) for investing in accurately geolocating underground infrastructure is positive and substantial.  A pilot in northern Italy estimated a ROI of €16 for every € invested in improving the reliability information about underground infrastructure.  For comparison the ROI for investing in improving information about underground infrastructure in the United States has been estimated to range from $3 to $21 for every dollar invested. 

Economic value of big geospatial data could reach $700 billion/yr by 2020

McKinsey Global Institute published a report Big data: The next frontier for innovation, competition, and productivity that analyzes the state of digital data, how it is used in different domains in both the private and public sectors, and offers suggestions for policy makers. In addition to looking at big data as a whole, the report examines five areas in detail including personal location data.

Real-time location data generated by mobile phones and other devices has created an entirely new set of location-based services from navigation to pricing property and casualty insurance. Personal location data is a new horizontal application domain that includes industry sectors from utilities to insurance to transportation. The growing smart phone market is creating an exponentially growing volume of location-aware data.  McKinsey sees this domain as a hotbed of innovation that has the potential to create significant value for consumers.  For example, use of real-time  traffic information for navigation will create a quantifiable consumer benefit by saving time spent traveling and fuel consumption. McKeinsey expects the economic value these innovations create to far exceed the revenue generated by service providers.

Economic value

Mobile phones with GNSS chips have created a revolution will continue over the next  decade. This personal location domain offers the potential for huge new value creation over the next ten years that McKinsey estimates at more than $100 billion/yr (about 0.14% of world GDP) in revenue to service providers and as much as $700 billion/yr (about 1% of world GDP) in value to consumer and business end users. Realizing this value will require investment in technology, infrastructure, and personnel as well as appropriate government action.

McKinsey's detailed analysis of the major applications of personal location data finds that, in ten years’ time, these applications could create value of $100 billion/yr or more for service providers  primarily from sales of navigation hardware and revenue from LBS, mobile LBS premiums, and geo-targeted advertising.

McKinsey estimates that by 2020 the benefits (difference between willingness to pay and the cost of applications) that customers, consumers and businesses, will enjoy from location-based applications will reach as as $700 billion/yr. Most of this, about 70 %, will be the consumer benefit from time and fuel saved by using GNSS navigation systems including those with real-time traffic information and mobile LBS applications. The remaining 30 % of the benefits will be through location-enabled applications such as geo-targeted mobile advertising.  Smart navigation applications are estimated to provide $500 billion in value to global consumers in time and fuel saved by 2020. Geo-targeted advertising could represent more than 5 percent of total global advertising spending by 2020.

For comparison, a recent study prepared for Google by Oxera Consulting Ltd in the UK estimated global revenues from geospatial products and services to be $150-$270 billion per year.  The geospatial industry is estimated to be growing by 13% per year through 2016.  These numbers were estimated using a different methodology to McKinsey's. The lower estimate of $150 billion/year was estimated using a bottom-up approach based on the Bloomberg BICS classification of companies worldwide and assumptions about allocating the revenue from companies that provide geospatial in addition to other services such as Trimble.  The $270 billion number was estimated by scaling up the US revenue estimated by Boston Consulting Group to come up with a world estimate.

Personal location data

McKinsey includes sources of personal location data that locate an individual to at least within a couple of city blocks in an urban settingin its analysis.

• Credit and debit card payments from cards processed at point-of-sale (POS) terminals and  automated teller machines. In 2008, there were 90 billion to 100 billion transactions of this type worldwide.
• Mobile phones - the location of such devices can be triangulated from cell-tower signals.  In 2011 there were about 6 billion of these.
• Smartphones with GPS - about 600 million devices in 2010 and the number is growing at about 20 % per year. Many smartphones also have Wi-Fi networking capability, which can be linked to a physical location of a Wi-Fi network.

In the future McKinsey expects indoor location to become more important.  They mention Shopkick, which allows merchants to track their customers from when they walk into a store, and Path Intelligence, which can track foot traffic within malls or amusement parks.

McKInsey's research estimates that the global pool of personal location data was at least 1 petabyte in 2009 and that this pool is growing by about 20 % a year. 

Location data generated by smartphones is growing at double the average rate of growth for all location data.  Asia is the leading region for the generation of personal location data because so many mobile phones are in use there. In 2010 the regions with the largest number of cell phone users were

• China 800 million
• India 650 million
• North America 300 million.

Applications of personal location data

McKInsey has identified three major categories of applications of personal location data.

• Location-based applications and services for individuals including smart routing, automotive telematics, and mobile-phone based location services
• Organizational use of individual personal location data that includes geo-targeted advertising, electronic toll collection, insurance pricing, and emergency response.
• Macro-level use of aggregate location data that includes urban planning and retail business intelligence.

Barriers to use of personal location data

Concerns about privacy and security was identified by McKinsey as a major barrier to future expansion of the use of personal location data. Laws are unclear about who owns that data and how it can be used.  McKinsey believes that a policy and legal framework that clearly describes the permissible and prohibited use of these data would be beneficial for all stakeholders.

For policy makers, McKinsey sees that a priority is incentives for innovation are in place including developing an up-to-date framework for intellectual property rules and rights, funding R&D in potential breakthrough areas, and ensuring that the infrastructure, including spectrum policies, is optimal.

Methodology

By looking at the value chain of location-based applications, McKinsey examined both the amount of data generation and the potential to create value.  McKInsey estimated the amount of personal location data generated in each category of application orsource device and in different geographic regions using the installed base of devices, usage behavior, and the frequency with which data are generated. To assess the potential value that data can generate, McKinsey estimated the economic value of each application to end users—individual consumers, enterprises, and government organizations.

Privacy and personal geographic data

At the Creating the Policy and Legal Framework for a Location-enabled Society conference in Boston, Kirk Goldsberry, who is a Visiting Scholar at the Center for Geographic Analysis at Harvard, gave a fascinatng presentation with the help of two of his students on the topic of personal geographic data and privacy.

According the the Federal Trade Commission, which is responsible for protecting privacy at the Federal level, the 4 holy data types are

• Financial data
• Medical data
• Data about children
• Geographic data

Goldsberry's main point was that the first three are treated as very sensitive and there is a legal framework to ensure these data types are protected.  When it comes to geographic data, on the other hand, there is very little by way of policy or legal infrastructure ro protect personal geographic data.

As an example he cited the iPhone.  About two years ago it was disclosed that iOS devices maintain a database that potentially could be used to track the movements of users. In response Steve Jobs in an email said that "We don't track anyone. The info circulating around is false."

Without the user's explicit consent iOS doesn't record GPS location, but does record the location of cell towers and Wi-Fi access points, which can be used to create a clear general track of a user's movements. 

To show what was possible with this iOS database, two of Goldsberry's students voluteered to carry an iPhone (with location disabled) for a couple of weeks.  Goldsberry used the iOS database recording cell towers and Wi-Fi access points to put together maps that showed pretty clearly where the students had been including long distance trips and their daily routines.  For example, one student had travelled to Seattle for a conference and had stayed in a hotel.  The student's location at and around the hotel and conference were not hard to discern.  The student went to a Mariner's game and from the location determined through the iOS database it was very clear that the student was at Safeco Field.

Limited legal and policy protection for personal geographic data

I interpreted what Goldsberry showed as not targetting Apple specifically, but that there is very little policy and legal protection for personal geographical data.

A number of speakers made the point that technology, including GPS and GIS, has moved so rapidly and regulation and legislation move so slowly that for personal geographic data, the horse has already left the barn.  It is too late to close to the door. 

Experiment

There is the risk that hasty legislation or regulation on privacy and personal geographic data could prevent or inhibit the very real social benefits the technology is capable of delivering.  The approach that several speakers recommended is to enable governments to experiment, so that regulation or legislation on privacy of personal geographic data is able to optimally walk the fine line between protecting privacy on one hand, and enabling the social beneifts the technology is capable of on the other.

I commend Kevin Pomfret, Excecutive Director of the Center for Spatial Law and Policy, for organizing such an important conference at such a critical time for geospatial technology.