Between the Poles

Geospatial analytics for the smart grid

At the Distributech 2013 conference, the largest electric power distribution conference in North America with attendance of about 9600, earler ths year in San Diego, if there was one consistent message, it was that smart grid has become mainstream - every utilty is doing something related to smart grid. 

I was interested in how gespatial was being used for smart grid applications by vendors at Distributech.  At the conference there were something on the order of 400 talks and panels.  Of these only a few explicitly mentioned geospatial or GIS so you might have concluded that geospatial was not playing a very important role in smart grid.  But the reality is quite different.  I talked directly to about 18 companies, mostly vendors, whom I asked about their use of geospatial technology.  All but two said that they were using geospatial technology in some capacity.

Geospatial analytics

One of the most interesting from the perspective of the application of geospatial technology for analytics was Tantalus Systems.  Tantalus provides utilities with an intelligent communications infrastructure supporting smart grid. Tantalus' network solutions rely on wired, wireless, or a combination for bidirectional communications.  Tantalus can integrate with well-known GISs like ArcGIS, or alternatively can provide an open source geospatial solution.

RF network analysis

Geospatial technology is used by Tantalus customers in several different capacities.  The first is to help identify problems or misalignment of resources in the radio frequency (RF) and electric grid network.  Visualizing the RF and distribution network geographically enables customers to identify and resolve problems in the RF network.   According to Dave Kauffman, Senior Product Manager for Applications and Interfaces at Tantalus, “GIS brings an entirely new dimension to problem solving for utilities. Before, our customers had to try to understand the source of problems by looking at tables and reports, but they found that with the geospatial view, the source of the problem, typically obstructions, became much easier to identify and correct."

Predictive maintenance

Tantalus customers also use geospatial analytics to look for patterns in outages by overlaying historical outage events on the distribution network and maps showing soil types, weather patterns, and traffic density patterns. Using this type of analytical approach can identify patterns that make it possible to identify equipment that is susceptible to failure and correct the problem before it actually fails.

Reducing risk in Volt/VAR

One of the new areas where GIS is just beginning to be be applied is voltage maps, a new application that Dave is very excited about. When a utility is implementing Volt/VAR at a substation to reduce load, voltages reported by smart meters can be mapped geographically in real-time across the entire distribution network in the form of isovolt maps. This makes it possible to identify areas of low voltage in real time, which is critical because the voltage cannot be allowed to drop below a tolerance mandated by the regulator in order to avoid potential damage to utility and consumer devices.

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.

Estimating the economic and financial impact of poor data quality

I have blogged on numerous occasions about data quality issues relating to infrastructure data at utiltiies including geolocational accuracy, most recently here.  

There is an interesting compilation of statistics in an InsightSquared infographic relating to the cost  of poor data quality in general, not just for utilities.

• The cost of bad or dirty data exceeds $600 billion for US businesses annually.
• Poor data across businesses and the government costs the US economy $3.1 trillion a year.

But it's worthwhile remembering David Loshin's (The Practitioner's Guide to Data Quality Improvement) perspective about the statistics relating to data quality.  He cited some widely known statistics available in 2011

• Tom Redman’s 1998 article in Communications of the ACM said he was “aware of three proprietary studies that yielded estimates in the 8–12% of revenue range.”
• In 1999, Larry English suggested that “Based on numerous cost analyses, the typical organization may see from 15 to 25 percent of its revenue go to pay the costs of information scrap and rework.”
• In 2002, Tom Redman claimed that “Poor data quality costs the typical company up to twenty percent of revenue.”
• IN 2003, TDWI produced a report estimating that “that data quality problems cost U.S. businesses more than $600 billion a year.”
• More recently Larry English said “Poor information quality costs organizations 20-35% of operating revenue wasted in recovery from process failure and information scrap and rework.”

Loshin concluded that

1. There are few (if any) published papers on actual case studies providing tangible details about the cost of poor data quality.
2.  Academic notes and books base their numbers on estimates, “proprietary studies,” accumulations from survey responses, or extrapolation from other estimates of the “cost of quality.”
3. In the absence of tangible evidence of actual costs, according to the experts the costs seem to be rising, from a low of 8% of revenue in 1998 to 20-35% of operating revenue in 2009.  I'm not sure if that means that the cost of poor data quality is rising or that our understanding of the impact of poor data quality has expanded.

Some other interesting items in the InsightSquared infographic

• Data quality best practices boosts revenue by 66 %.
• If the median Fortune 1000 company were to increase the usability of its data by 10%, company revenue would be expected to increase by $2.02 billion.

Infrastructure excellence competition

Infrastructure-excellence.com is hosting the second annual competition showcasing Excellence in Infrastructure— highlighting the best projects done in 2012/2013. The winners will be given over US$10,000 in prizes by the competition sponsors.  The competition is open to anyone 21 years and older who is planning, designing, building, and managing infrastructure projects.  Project types may include the following:

• Transportation (roads and highways, rail, airports, or bridges)
• Land development (commercial sites, subdivisions, public parks, or recreation)
• Water (distribution, water resources, dams and levees, or wastewater)
• Energy (electric and gas distribution, electric transmission, and substation design)
• Urban planning

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 Trace 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.

Solar PV on the verge of becoming a disruptive technology in the electric power industry

I blogged previously about a presentation that, Rod Nikula,  VP Power Supply at Wright-Hennepin Cooperative Electric Assn (WH) in Minnesota, gave at this year's NRECA conference that hit on a very sensitive topic among power utilties.  Companies like Solar City, a full service solar PV energy company,  offer a financially attractive solar package (that does not involve the local power utility) once the power rate from the local utility reachs 14 cents/kWh or above.  Solar City offers a unique leasing plan that requires no money down.  It offers a flat rate for 20 years, and payments are made against savings from the monthly utility bill. 

According to Rod Nikula, Solar City is taking 600 homes off the California power grid every month.  Mark Vogt, CEO of WH, put two and two together and concluded that "We better find a way to be in the home generation business,"  or face a serious drop in revenue.

In a report by John Farrell at the Institute for Local Self-Reliance, Rooftop Revolution: Changing Everything with Cost-effective Local Solar, the levelized cost of solar PV power is compared with the cost of power from local utilities.  The report conclues that right now solar PV delivers power more cheaply than the local power utility for 10% of residential demand in California, Connecticut, Hawaii, New Hampshire, and New Jersey. 

By 2022 it is projected, assuming continued decreases in the cost of solar panels, that solar power will deliver power more cheaply for 10% of residential demand in 49 states, the only exception being Washington State which has the lowest power rates in the United States.  Furthermore according to another report Commercial Rooftop Revolution, solar PV installed by businesses is projected to be competitive with at least 10% of commercial power demand in the United States by 2022. 

As ponted out in a recent article in IEEE Spectrum, Farrel's asumptions are fairly conservative - the most important assumption is that the driver behind these projections are technology and industrial efficiency.  Farrell also assumes that utility power rates will continue to rise about 2% per year.  Most importantly, he does not include government subsidies in his projections of the cost of solar PV.

Farrel's conclusion (both of his reports have the word "revolution" in their titles) is that solar PV is poised to dramatically change the electric power industry as we know it.  Wright-Hennepin Cooperative Electric Assn (WH) is one of the utilities that has recognized this trend and is trying to do something about it, by getting into the residential solar PV business.

Location-enabled Society: Mapping tweets using real-time big data technology and privacy

In a very appropriately titled session Taking Society's Pulse in Real-time at the Creating the Policy and Legal Framework for a Location-enabled Society conference in Boston, one item really got people's attention focussed on the issues that arise from combining location with personal data. TweetMap is a technology that visualizes a large number of tweets on a map of the world. This is not the first time I have seen tweets mapped.  But what really got people's attention is that a speaker showed how to drill down to your neighbourhood and see what the people next door are tweeting about.  Of course you can do this without a map by simply following your neighbors on Twitter.  But somehow mapping it makes it seem more intrusive, even invasive to some people. In another demonstration, Tweetmap was used to show how a person sailing in the Caribbean could be tracked as they tweeted from their boat.

To be tracked geospatially on Twitter requires that you explicitly agree to geolocation tracking for Twitter on your iPhone or Android device.  For example, on an iPhone the geolocation option allows you to make your location known to others. Apps such as Facebook or Twitter for iPhone can report your location as well as the messages you post.  The iPhone requires that these applications obtain your permission before they can be enabled.  Apparently only about 2-4% of smart phone users, mostly younger folks, agree to this.  After seeing a Tweetmap drill down, I would expect that some people might have second thoughts about agreeing to the gelocation option. And in the not too distant future this will be possible in real-time on inexpensive hardware with open souce software.

TweetMap is an instance of MapD, a massively parallel database platform being developed through a collaboration between Todd Mostak, (a researcher at MIT), and the Harvard Center for Geographic Analysis (CGA).  Currently the tweet database starts on 12/10/2012 and ends on 12/31/2012 and contains 125 million tweets.  The database can be queried by time, space, and keyword.  The ultimate objective is to be able to query billions of tweets live in real-time.  In this case real-time means from tweet to tweet on a map in under a second.

MapD is a general purpose SQL database that can be used to provide real-time visualization and analysis of very large data sets.  MapD uses commodity Graphic Processing Units (GPUs) to parallelize compute intensive tasks such as querying and rendering very large data sets on-the-fly.  MapD runs on inexpensive hardware ( ~$1000) and is intended to be open source.

Open source geospatial lab announced at University of Southampton

The Open Source Geospatial Foundation (OSGEO) has announced the formation of the Open Source Geospatial Laboratory (OSGL) at the University of Southampton in the UK..

In 2011, the International Cartographic Association (ICA) signed a Memorandum of Understanding (MoU) with the OSGeo to develop global collaboration opportunities for academia, industry and government organisations in the field of open source geospatial software and data.

The Open Source Geospatial Laboratory is supported by the GeoData Institute and Geography and Environment at the University of Southampton and brings together staff from various disciplines including Engineering, Geography, Electronics and Computer Science.

The laboratory will conduct open source research for geospatial software and data, using open source geospatial software and data as tools and contributing to the development of tools, standards and data through research. The lab will seek to exploit new opportunities for research, often in partnership with other OSGLs and members of the ICA-OSGeo initiative. The Laboratory will also deliver open source geospatial training programs.

Creating the Policy and Legal Framework for a Location-enabled Society

On Thursday and Friday of this week the Centre for Spatial Law and Policy is co-hosting a conference with Harvard University's Center for Geographic Analysis (CGA), Berkman Center of Internet and Society and Belfer Center for Science and International Affairs. The conference, titled "Creating the Policy and Legal Framework for a Location-enabled Society" will be live-streamed and combines leaders from many of the diverse aspects of the geospatial community (many of whom are members of the Centre) with internationally-recognized academics and researchers from Harvard and other top universities.

If you have some time on Thursday and Friday  you can watch/listen to the conference over the web.  Details will be posted here.

Global smart grid investment reached $36.5 billion in 2012

Memoori, a UK research firm, has released  a report “The Smart Grid Business 2012 to 2017”, which analyzes global smart grid-related sales. Its research has identified some interesting trends in the current smart grid market.

Globally

In the last 3 years Memoori estimates that the world smart grid-related sales has more than doubled from $16.2 billion in 2010 to $36.5 in 2012. 40% of this is smart meters

Most of this business is going to the major international suppliers.   26 companies shared most of the sales in 2012, but they represent only 1% of the population of suppliers.

Over the past 6 years mergers and acquisitions have grown from $134 million in 2007 to $10.6 billion in 2011 and $19.5 billion in 2012.

Memoori forecasts demand for pure smart grid products and systems will be worth about $2 trillion over the next 20 years.

In the last 3 years mergers and acquistions have been driven by strategic buys, particularly by the major global electrical transmission and distribution supplier companies.

Strategic buys as % of deals

• 2010 73%
• 2011 75%.
• 2012 60%

In 2012 Memoori says that significant acquisitions were made by companies external to the electrical transmission and distribution industry, such as IT and communications companies, who are interested in getting a foothold in the smart grid business.

U.S.

The U.S. represents about 20 percent of the smart grid market.   Europe is about 10 percent.

Smart grid investment in the U.S. was approximately $7 billion in 2012, including the refurbishment business.