Between the Poles

Visualizing underground infrastructure for multiple utilities on a single map

The National Underground Asset Register (NUAR) is intended to be a comprehensive digital map of all underground pipes and cables owned and operated by 650 asset owners in England, Wales and Northern Ireland. When deployed it will provide immediate access to UK underground asset data online from handhelds and other devices to qualified excavators and their contractors.  A unique capability that differentiates NUAR from other underground data management systems lies in its ability to combine and visualize underground infrastructure of many different network operators on a single map.

Background to NUAR

In 2018 the Northumbrian Water Group Innovation Festival brought together 20 utilities, telecoms, and local government authorities in Newcastle upon Tyne to voluntarily collaborate in developing a shared underground infrastructure map showing the location of water, wastewater, gas, electricity, telecoms and other underground services provided by the network owners. Later extended to Central London, it became the pilot for what subsequently became the NUAR project sponsored and funded by the Geospatial Commission, part of the UK Government Cabinet Office.

Integrating underground data from different utilities

One key differentiator of the NUAR project is combining and visualizing underground utility data sourced from different utilities on a single map. Globally every utility has developed its own terminology for describing its network facilities, its own ways of graphically symbolizing different equipment, its own choice of base map showing road centre lines and other land base features and its own standards for network documentation.

A major advantage of mapping infrastructure in the UK is that every network operator records the location of their facilities against the same national base map, the Ordnance Survey's MasterMap. In North America in contrast every utility uses a different base map. Conflating utility data to the same base map is a complicated process which inhibits the development of a representation of underground infrastructure on a single map. In the UK the common national base map provides a foundation for displaying data from many utilities on a single base map. But to meaningfully combine and visualize data from different utilities on a single map requires that data providers agree on standards for data elements, terminology, symbology and templates for network documentation.

Developing a harmonized data model

The first version of the data model was developed in conjunction with about 40 utilities who participated in the North East of England and Central London pilots.  Fortunately the development of the Open Geospatial Consortium's open standard for exchanging underground data Model for Underground Data Definition and Integration (MUDDI) coincided with the NUAR pilot. The NUAR project became the first project to implement MUDDI in the real world.  MUDDI supports profiles that enable the MUDDI model to be adapted for different uses cases and countries. For NUAR the UK Excavation Profile was used. Participation in the MUDDI initiative enables the NUAR data model to be openly available to network operators and other organizations who choose to adopt it as a standard for their own data operations.

The central guideline that has guided the development of the model from its inception during the pilot was the need to minimize structural elements that hardwired the model. With a rapidly evolving model it was essential to be able to change different aspects the model without having to reconfigure the entire model as each new utility's data brought new changes. The result was the development of ways of modelling utility networks that differed from what had been used in previous utility models. Examples include defining general feature types that could be qualified using attributes rather than structures making them more flexible, allowing a single feature table to support multiple spatial data types (points, lines and polygons) rather than separate tables for each data type, and implementing codelists separate from the data model making it possible to change them without republishing the model.

During the initial build phase of the project each it was found that new utility data source continued to bring changes to the model. At this point it had become more essential to carefully manage changes resulting from incorporating data sets from new asset owners because of the potential impact on the data that had already been brought into the model.  

As new data is brought into the system, new feature types are discovered, for example, EV charging points, so that over time the data model is becoming more comprehensive with the potential for supporting additional use cases. In addition it is important to support the interaction of NUAR with external systems by making data types general and accessible to multiple systems and incorporating fields for capturing codes unique to the external systems. In the future this will make it possible to support work flows in which network operators' data systems provide direct data updates to the NUAR database.

One of the important realizations during the development of the Ordnance Survey MasterMap was the critical importance of object identifiers (OIDs). As new data flows in to be analyzed and brought into the NUAR model, it is being found that many asset owners have not implemented feature identifiers. One of the long term benefits of the development of the NUAR data model may be instilling in industry data modelling practice the importance of unique feature identifiers.

Progress of NUAR build phase

At the time of Amy Manefield's presentation at the SUMSF conference in March, the NUAR team had engaged with all 250 asset owners in the three initial regions of the North East of England, Wales and London. Data Exploration Agreements (DEA) had been signed with 88 of these utilities. The DEA allowed the data from these utilities to be explored, analyzed and the process of transforming it to be initiated. Of the utilities with DEA agreements 42 had already shared their data with the NUAR team and over a dozen of these data sets had been successfully transformed to the NUAR harmonized data model.

 

This post is based on Amy Manefield's talk at the Subsurface Utility Mapping Strategy Forum (SUMSF). Amy Manefield is NUAR Head of Data Operations at the NUAR project of the Geospatial Commission.

GIS or data science ?

I always look forward to Joe Francica's talks at various events.  At this year's GIS in the Rockies he offered words for the wise in the geospatial world that will be considered by some revolutionary and by others evolutionary.

 

Don't call yourself a GIS analyst, call yourself a data scientist

 

The reasoning behind this is that geospatial technology which used to be an isolated technology, often with its own group within the organization, has become integrated with the broader IT technology.   This translates into IT professionals increasingly knowing how to used geospatial technology along with other IT tools, and GIS professionals including general IT technologies in their quiver.  For example, data science professionals who use SQL databases, Hadoop and Spark, and JSON to manage data are also using spatial extensions such as Oracle Spatial, SQLServer Spatial, PostGIS, GeoMesa and GeoJSON as well as Google Maps.  GIS professionals have broadened their use of general IT tools from SQL databases to non-SQL databases, Python, Java, and Javascript APIs, and HTML5.   Another dimension to this is that whereas geospatial professionals used to focus their attention on data acquisition, their focus nowadays is on data quality, visualization, and analytics - converting data into information.

 

Don't major in GIS, get a degree in forestry, engineering or data science first.

 

The corollary to this is that folks responsible for setting curricula for non-GIS major and professional undergraduate programs should ensure that geospatial technology is included.  For example, nowadays it is essential that architects and engineers be familiar with geospatial technology.  A number of years ago Anne Kemp, then head of GIS at Atkins Global, the design and engineering firm, asked the question ‘BIM isn’t geospatial — or is it?’ and argued that integration of geospatial and BIM was essential to address the challenges of the 21st century.  The need to integrate geospatial and BIM has since rapidly gained traction in the construction industry as evidenced by the partnership developing between GIS and AEC market leaders ESRI and Autodesk.

 

Joe's advice reflects the integration of geospatial technology into general IT that dramatically broadened in scale in 2006 with the release of Google Maps and which since has accelerated driven by modern web applications' ability to integrate data and tools from multiple APIs and data sources.

Citizen geodata science an emerging big data trend

Gartner's latest hype cycle identifies a new trend "citizen data science" that has important implications for the geospatial sector.

At the recent Canadian Institute of Geomatics (CIG) Ottawa Branch Workshop on Big Data: What is it? Technology and Applications, Monica Wachowicz from the University of New Brunswick pointed out a brand new entry Data Science in the Gartner Hype Cycle 2014 that wasn't in the Gartner Hype Cycle 2013. Then in the Gartner Hype Cycle 2015 Data Science and Big Data have disappeared and a new entry with the intriguing name Citizen Data Science has appeared.

2013 Big Data

2014 Big Data and Data Science

2015 Citizen Data Science

What is data science ?

Monica referenced research by Harlan Harris et al “Analyzing the Analyzers” An Introspective Survey of Data Scientists and Their Work which surveyed data scientists to try to determine what data scientist do.  They divided data scientists into five categories based on their self-ranked skill sets; Statistics (which includes spatial statistics), Math/Operations Research, Business, Programming, and Machine Learning/Big Data, and four categories based on their respondents' self-identification; Data Researchers, Data Business people, Data Engineers, and Data Creatives.  

• Data Business people are most focused on the organization and how data projects yield profit.
• Data Creatives excel at applying a wide range of tools and technologies to a problem, or creating innovative prototypes at hackathons.
• Data Developers are focused on the technical problem of managing data — how to get it, store it, and learn from it.
• Data Researchers backgrounds are in statistics or mathematics, 75% of them have published in peer-reviewed journals, and over half have a PhD.

The resulting landscape of data scientists shows a broad range of skills distributed among the self-identified professional categories.  The researchers found that

• a defining feature of data scientists is the breadth of their skills, and their ability to single-handedly do at least prototype-level versions of all the steps needed to derive new insights or build data products.
• the evidence points in favor of a scientific versus a tools-based education for data scientists. 
• 70% of the respondents had at least a Master’s degree. 
• 40% had undergraduate degrees in scientific fields, specifically, physical or social sciences (but not mathematics, computer science, statistics, or engineering).
  

The survey showed that the most successful data scientists are those with substantial, deep expertise in at least one aspect of data science; statistics, big data, or business communication.  The authors concluded that data science is a collaborative and creative field, where the successful professional is able to work with database administrators, business people, and others to get data projects completed in innovative ways. 

What is citizen data science ?

One of the biggest drivers for a new type of data specialist is that there are simply not enough data scientists to satisfy the demand from virtually all sectors of the economy.  Gartner has recommended cultivating “citizen data scientists” which they identify as people on the business side who may have some undergraduate mathematics or social science background and who can be assigned to exploring and analyzing data with the appropriate software tools.  Last year Gartner predicted that the demand for citizen data scientists will increase five times more rapidly than that of the highly skilled data scientists Harlan Harris studied.

Software tools are critical for helping citizen data scientists find real insights and avoid simple statistical mistakes.  For example, a technology that Gartner identified is "smart data discovery" which is a next-generation data discovery capability that provides business users or citizen data scientists with insights from advanced analytics and helps them avoid some of the common statistical pitfalls.

Citizen geodata science

There is at least ancedotal evidence that there has been for some time a trend toward "citizen geodata science".  At the India Geospatial Forum in Hyderabad in 2014, I moderated a session on electric power.   One of the speakers was Arup Ghosh, Chief Technology Officer at Tata Power Delhi Distribution Ltd (TPDDL) who presented his perspective on implementing geospatial technology in a private utility.   The geospatial group at TPDDL had about 60 field personnel and 18 analysts and support staff,  none of which had an educational background in geospatial data and technology.  Twelve were electrical engineers and the rest were people with electric power experience.  All learned geospatial data management and simple analytics "on the fly".

At a recent GoGeomatics Social Jonathan Murphy gave a presentation about his experience working in Northern Alberta preparing terrain for seismic surveys.  Geospatial data and technology was used in all aspects of field operations.   The staff were experienced in seismic surveying, winter drilling programs, wildfire management, and road and facility construction, but had minimal education in geospatial data management and analytics.  They also had picked up enough geospatial knowledge "on the fly" to do their jobs.

These are two examples of what could be called "citizen geodata science" which I suspect is part of Gartner's citizen data science trend and is almost certainly growing more rapidly than traditional "geospatial data science" as geospatial technology is adopted into vertical industries.  The challenge is how to reach these people, perhaps through MOOCs, community colleges, conferences focused on vertical industries and including geospatial technology vendors, presentations and hand-on training, or vendor marketing to help them avoid the common pitfalls of geodata management and geoanalytics.

RICS BIM 2016: April 4th BIM mandate looms for the UK construction industry

At this year's RICS BIM conference the elephant in the room was April 4^th 2016, the deadline for all public construction projects to be BIM Level 2 compliant. This is the culmination of a five year process starting in 2011 focussed on developing a set of documents defining BIM Level 2 and providing best practices for achieving compliance.

This year's conference suggests that for many in the industry they are now wrestling with implementation issues and are reporting a shortage of BIM skills.  They are finding that BIM is really about data and are realizing that data management and analytics are essential to handling and interpreting the huge volume of data that is being generated. A shortage of data scientists to help them to do this has become an acute problem.

Transforming the UK Construction Industry in Five Years

The government's focus from the beginning has been on full life cycle BIM.  Back in 2010/2011, the Government's BIM roadmap was perceived to be very aggressive, mandating BIM Level 2 compliance for all national government projects after just five years. In the UK public projects comprise about 50% of the construction industry so this amounted to radically transforming the UK construction industry in half a decade. Put another way the construction industry had to move from thinking analog to thinking digital in just five years. 

The utility industry has been going through a similar transition in moving to what is called a smart grid and distributed generation.  Some think that this process will take 20 years.  But new technology and a younger generation steeped in digital is stepping in to make this happen much sooner than that.  Energy is going digital more rapidly than expected.  I think it likely that something similar will happen in the construction industry because digital enables all sorts of efficiencies that we often cannot even imagine.  Uber and Airbnb are classic examples.  Both are the largest or second largest companies in their respective industries.  Gartner has forecasted a similar type of company will emerge for managing energy flows in the electric power industry by 2020.

For 2012 the Government's strategy focussed on discovery through pilot projects, notably the Ministry of Justice Cookham Wood project, a £20 mllion project where it is estimated that BIM saved hundreds of thousands of pounds in CAPEX costs.  

At the 2013 conference David Philp,Head of BIM Implementation, Cabinet Office, UK Government, announced PAS 1192-2 a key document supporting the Construction BIM Strategy to achieve Level 2 compliance.  It specified requirements for achieving BIM Level 2, sets set out the framework for collaborative working on BIM enabled projects and provided specific guidance for the information management requirements associated with projects delivered using BIM.  David Philp made it clear that although the initial focus is on the design/build part of the lifecycle with the goal of saving 20% of Capex and reducing the 30% waste in most construction projects, the "largest prize for BIM lies in the operational stages of the project life-cycle".

At the 2014 conference Peter Hansford, Government Chief Construction Advisor, made a link between the overwhelming interest in BIM as represented by the sold-out RICS BIM conference and the industry’s enhanced interest and appetite for BIM. He saw a shift in emphasis from asking ‘’should we do BIM?’’ to ‘’how do we do BIM?’’  This was happengin inspite of the fact that as, Rich Saxon, the UK Government's BIM Ambassador for Growth, pointed out In a panel discussion on the future of BIM that some aspects of BIM Level 2 were still  incomplete. 

Deborah Rowland, Head of Facilities Management, Government Property Unit, Cabinet Office in the UK Government outlined the Government's motivation and plans for a program to improve the post construction handover and operation of newly constructed buildings. She described the Government Soft Landings (GSL) program which was intended to rectify this and was designed to put in place a legal, contractual, and technical framework (based on the BSRIA Soft Landings Framework)  incorporating building information modeling (BIM) to fix the problem by ensuring continuity thoughout the buiiding lifecycle from inception, though design, construction, commissioning, training and handover through to operations and maintenance for 1-3 years after handover.

At the 2015 conference seven key BIM Level 2 documents (PAS 1192:2, PAS 1192:3, BS 1192-4, CIC BIM Protocol, Classification (Uniclass), Digital Plan of Works / Levels of Detail (LoD), Government Soft Landings) which collectively represented the current repository of BIM best practices were announced.  Every speaker at the conference referenced these documents.

Seven major government projects using BIM, worth about £ 10 billion, have maintained records of the savings they have seen from using BIM. The measured benefits of BIM and factors associated with BIM range between15-20 %.   By 2014 the government estimated that it had achieved savings of £ 1.4 billion through BIM and related improvements in the 2013/2014 fiscal year.  But with full lifecyle BIM, the government saw an upside potential of 40%+ with the biggest benefits being realized in the operations phase of the building lifecycle.

This year it was announced that Government statistics show that in 2015 Her Majesty's Government saved GBP 3 billion on these demonstration projects as a result of gains in construction productivity related to BIM. 

Full Life Cycle BIM

But BIM Level 3 is where the gold is.  Level 3 will enable the interconnected digital design of different elements in a built environment and will extend BIM into the operation of assets over their lifetime. It will support the accelerated delivery of smart cities, services and grids. As the AGI has pointed out in a recent Foresight Report integrated BIM and geospatial technology will be fundamental for BIM Level 3.

Is the UK construction industry ready for April 14 2016 ? 

An industry survey in the UK in 2013 reported that 54% of respondents said that they were using BIM on projects.  Most of those not yet using BIM said that they would be within one to two years.  70 % of the respondents reporting using BIM said that BIM has given them a competitive advantage. But this year's conference suggests that most in the industry are doing BIM although for some it is not really clear what BIM Level 2 is and how one demonstrates compliance. 

Data and People

This year's RICS BIM conference was quite different from previous conferences.  The focus was heavily on implementation, not on progress in completing specifications or developing and working with 3D models as in previous RICS BIM conferences.

The event was led off by a panel discussion that set the tone for the rest of the conference - the focus was on data and people rather than 3D BIM models.  The industry people on the panel were patently up to their elbows in real world BIM projects including data, the analytics to make sense of it, and the shortage of the required skills (data scientists and people with BIM skills).

Alex Jones, Interserve, said that within the construction industry there is now a focus on data and how data is used downstream.  He said that clients (owners) are now aware of BIM and that both private and public procurements are asking for BIM.  But he qualified this - they are not so much asking for BIM (as in 3D models) as asking for data that can be used downstream. David Throssel, Skanska, added that deliverables now means data.

In the context of data, Simon Rawlinson, Arcadis UK, gave an example of utilities where smart devices and automated capture has resulted in a database of asset conditions, and which also includes geospatial data for geolocating those assets. This information allows utilities to move forward to condition-based maintenance where prioritizing maintenance and replacement depends on asset condition not the calendar.  Real-time monitoring is being adopted by virtually all industries.  For example, devices are being implanted in structures such as bridges to monitor distortions. He added that the leadership has not yet recognized that sensors and analytics enable a major change that will revolutionize asset maintenance.

Also the new digital economy is disintermediating certain skills. For example, digitalized construction requires fewer layers. Simon cited a KPMG study of disintermediation by technology. As an example, he mentioned data such as that collected by utilities from smart devices that allows owners/clients/utilities to do condition-based maintenance, which can lower field staff requirements significantly.

In the construction industry as a whole one of the major issues that emerged is a shortage of BIM related skills.Simon emphasized that now that we have lots of data, what we need urgently are data scientists to help make sense of it.  He identified this as a major problem because in his experience data scientists are as rare as “hens teeth”. 

David Hancock, UK Government Construction, said that construction industry still thinks analog. Nowadays we can all produce data, but analyzing it is the challenge.  We need people who can analyze to produce actionable items. He also identified BIM skills shortage as a major problem, and repeated that data scientists people are “as rare as hens' teeth.  He suggested that one solution is upskilling – training existing construction/bldg maintenance/utility folks in BIM and data science.  Another may be raiding the U.S. baseball industry where teams such as the Oakland Atheletics starting using analytics several years ago.  "Moneyball" analytics has been recommended as a strategy for the utility sector in the U.S.

In the question period a speaker argued that the 3D BIM model has been "massively overplayed".  BIM is really about data and collaboration, not about 3D. To maximize the benefits of BIM clients  (owners) have to be clear about what the model, and much more importantly the data associated with the model, are going to be used for. For example, if for facilities management (FM) then clients (owners) have to be specific about what data is required for maintaining and operating the building as part of defining deliverables.

BIM Level 2 for BIM Novices

What if you are a small company with no more experience with BIM than discovering what the acronym stands for ?  At RICS BIM 2016 there was a sequence of presentations by a group of mostly quantity surveyors (people who do quantity takeoff), all BIM novices, who came together to try to understand and share openly what BIM Level 2 means practically. (As one speaker pointed out, there is no legal definition of BIM Level 2 compliance.  They called their project KT4BIM, which you can find on LinkeIn. The objective is to answer the questions

• how do we do BIM Level 2

• how do we demonstrate compliance.

The target audience is BIM novices and for both the residential and commercial sectors.  The objective is to share all the material used and developed as part of this project with the industry.  More information about this very important project will be provided in my next blog post.

BIM Level 2 Documents

I've included this list just to show how comprehensive this set of BIM and BIM-related documents is.

Key documents

• BS 1192:2007+A1:2015     Collaborative production of architectural, engineering and construction information
• PAS 1192-2:2013     Specification for information management for the capital/delivery phase of construction projects using Building Information Modelling
• PAS 1192-3:2014      (Corrigendum No. 1) Specification for information management for the operational phase of assets using building information modelling
• BS 1192-4:2014     Collaborative production of information Part 4: Fulfilling employer’s information exchange requirements using COBie
• PAS 1192-5:2015     Specification for security minded building information modelling, digital built environments and smart asset management
• BS 7000-4:2013     Design management systems. Part 4. Guide to managing design in construction
• BS 8536-1:2015     Briefing for design and construction. Code of practice for facilities management (Buildings infrastructure)
• Uniclass 2015     Classification embedded within the NBS Toolkit
• Digital Plan of Work     Delivery plan embedded within the NBS Toolkit
• CIC/BIM Pro first edition 2013     Building Information Model (BIM) Protocol

Supporting documents

• CIC/INF MAN/S first edition 2013    Outline Scope of Services for the Role of Information Management
• Government Soft Landings    "Design for operation" to encourage better outcomes using BIM for built assets during the design and construction stages to ensure value is achieved in the operational lifecycle of an asset.
• AEC (UK) BIM Technology Protocol Version 2.1.1 June 2015    Practical implementation of BIM for the UK Architectural, Engineering and Construction (AEC) industry
• BS ISO 16757-1:2015    Data structures for electronic product catalogues for building services. Concepts, architecture and model.
• BS 8541-1:2012, BS 8541-2:2011, BS 8541-3:2012, BS 8541-4:2012, BS 8541-5:2015, BS 8541-6:2015    Library objects for architecture, engineering an construction

Smart power spatial power

For the first time in a hundred years, the electric power utility industry is undergoing a momentous change. Distributed renewable power generation, especially solar photovoltaics (PV), is introducing competition into an industry that has been managed as regulated monopolies. Consumers with solar PV panels on their roofs (and in not-too-distant future with Tesla batteries in the basement) and companies like Solar City (co-founded by Tesla co-founder Elon Musk) are fundamentally changing the traditional utility business model. A recent report from the Edison Electric Institute (EEI) report refers to disruptive challenges that threaten to force electric power utilities to change or adapt the business model that has been in place since the first half of the 20th century.

As a result, every aspect of the the electric power industry is changing. One of these changes involves the role that geospatial data and technology play in the electricity industry. In the past, geospatial has been a tactical tool — it was (and still is) used in a variety of applications — in outage management, asset management, mobile work- force management, energy density modelling, vegetation management, demand modelling, transmission line siting, substation siting and design, energy performance modelling of buildings, disaster management, and mapping renewable resources, to name just a few. However, with the changes that the industry is undergoing now, geospatial is poised to become a foundation technology for the smart grid.

The Energy Issue of Geospatial World Magazine explores the impact that this momentous change is having on the application of geospatial technology in the electric power utility sector.  Below I'm providing an overview of the material relating to electric power you'll find in this issue.

GIS has been widely used by utilities for years for automated mapping/facilities management, back office records management, asset management, transmission line siting, and more recently for design and construction, energy conservation, vegetation management, mobile workforce management (MWFM), and outage management (OMS). Now, utilities are integrating GIS with automated meter infrastructure (AMI) and supervisory control and data acquisition (SCADA) systems. Intelligent design has crossed over from the office to the field in utilities, also enabled by the capabilities of GIS, says Smith. Geospatial-related analytics (spatial analytics) is seen as one of the key aspects of success for electric utility operations in the smart grid era. Looking for patterns and correlations between different land, weather, terrain, assets, and other types of geodata will be increasingly important for utilities. Power-related analytics with geospatial components include network fault tracing, load flow analysis, Volt/VAR analysis, real-time disaster situational awareness, condition-based maintenance, and vegetation management. The smart grid is all about situation awareness and effective anticipation of and response to events that might disrupt the performance of the power grid. Since spatial data underlies everything an electric utility does, GIS is the only foundational view that can potentially link every operational activity of an electric utility, including design and construction, asset management, workforce management, and outage management as well as supervisory control and data acquisition (SCADA), distribution management systems (DMSs), renewables, and strategy planning.

Peter Batty reports on the major growth in geospatially-enabled Web and mobile applications with a special focus on the open source geospatial community and the significant impact of these technologies in the utility sector. "In general, there are a lot of geospatial open source software components available now that have the capabilities and robustness to be used in serious enterprise applications."  John McDonald, Chairman of the Smart Grid Interoperability Panel has been a firm believer for a long time that geospatial information is part of the foundational platform for smart grid.  SGIP has signed a memorandum of understanding with the Open Geospatial Consortium with the goal of incorporating more geospatial standards into SGIP standards.  Cindi Smith of Bentley goes even further and argues that  “geospatial technology is already a foundational component of electric power utilities’ IT/OT systems.  Smart grid simply brings more focus to the role it can play by virtue of the visibility of smart grid projects and processes in a utility and their need to exploit the vast amounts of data produced by the smart grid." Loek Bakker & Jan van GelderIt of Alliander, a Dutch utility company, describe how essential it has been for Alliander to integrate GIS, ERP and SCADA systems for a correct picture of its assets.  As electric utilities evolve into increasingly data-driven organisations, Jeffrey Pires and G. Ben Binger describe how GIS is fast emerging as the backbone for data management platforms. 

Cities are beginning to develop 3D models of underground infrastructure motivated by new underground remote-sensing technologies and by ROIs of up to of US$21.00 saved for every US$1.00 spent on improving the quality level of subsurface utility information.  Steve Dibenedetto, Senior Geoscientist and Technology Manager, Underground Imaging Technologies (UIT), part of Caterpillar describes new remote-sensing technology for detecting and geolocating in 3D underground utility infrastructure such as Ground Penetrating Radar (GPR) and Electromagnetic Induction (EMI). 

The Indian on-going Restructured – Accelerated Power Development and Reforms Program (R-APDRP) is one of the largest IT initiatives by electric utilities anywhere in the world — in one integrated project, all state-owned distribution utilities in India are building IT infrastructure, IT applications and automation systems.  The programme set out to create baseline data in the form of consumer indexing, GIS mapping and asset mapping. Reji Pillai & C. Amritha assess how GIS can be applied in this context.  

Integrating  geospatial and BIM is a key enabler for energy performance modeling which is a fundamental instrument for reducing the energy consumption and improving the energy performance of new and existing buildings. According to a report from Navigant Research, global zero energy buildings revenue is expected to grow from $629.3 million in 2014 to $1.4 trillion by 2035.

Wolfgang Eyrich of Entegra shares how Entegra’s primtech product, which is designed to help substation designers deliver designs based on integrated product modelling, provides a geographical context to substation designing. 

Matt Zimmerman of Schneider Electric highlights one of Schneider Electric's key techologies "graphic work design" which is integrated geospatial and engineering design (CAD or BIM).  Schneider Electric's geospatial division focuses on developing integrated, location-aware enterprise solutions such as integrated outage management (OMS), customer information system (CIS), GIS, and external weather reporting and forecasting service to help plan crew deployment during a storm.  Matt foresees that location-aware predictive analytics for electric networks is going to be one of the major development areas for utilities in the future.  Brad Williams of Oracle points out that spatial analytics is becoming a key technology for electric utilities because everything a utility does - customers, assets, and operations - involves location. 

One of the biggest challenges that utilities are experiencing is increasing volumes of structured and unstructured data (big data) that is overwhelming traditional enterprise systems.  The structured data comes from smart meters and intelligent electronic devices, and the unstructured data from social networks including Twitter, Google, Facebook and other social applications.  Consumerization of geospatial technology (we are all GPS-enabled sensors) will enable crowd-sourcing new sources of information about electric power networks most of which involves location (big spatial data).

Data science becomes an academic discipline

About a year ago the Obama administration announced a  Big Data Research and Development Initiative to focus on extract knowledge and insights from large and complex collections of digital data.  It included an initial investment of more than  $200 million to improve tools and techniques for accessing, organizing, and extracting knowledge from the huge volumes of data that are available.  The list of ongoing Federal government programs that relate to big data is extensive.

Yesterday at a White House Office of Science and Technology Policy (OSTP) event, the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation announced a $37.8M funding commitment to develop new data science facilities at the University of California Berkeley (UC Berkeley), the University of Washington (UW) and New York University (NYU).

UC Berkeley has announced the creation of the Berkeley Institute for Data Science (BIDS), which is scheduled to begin operating in Spring 2014 in partnership with the UW eScience Institute and the NYU Center for Data Science.

It is fantastic that data and its management and analysis at long last has been recognized as serious enough to be studied in an academic environment.  It will be interesting to see how geospatial data fits into this.

For a compelling open source-oriented perspective on this, take a look at Fernando Perez's blog where he makes the case that open source is a key ingredient in this effort.