A voter enters a polling place. (Image: iStock.com/YinYang)
With the mid-term elections coming ever nearer, states are turning to geographic information systems (GIS) to help manage them.
Digital geographic representation of precinct boundaries within a GIS allow for transparency and ease of use for voters, candidates and electoral management, according to the National States Geographic Information Council (NSGIC).
GIS also enables the optimal siting of polling places for both voter access and the cost efficiency of operating polls.
Finally, GIS provides a platform for automated quality-control processes that ensure accurate voter precinct assignments.
“An electoral system with integrity — enhanced by accurate, authoritative geographic data and presented clearly and transparently — has never been more important,” said NSGIC President Andy Rowan.
Why GIS is an improvement over address files
NSGIC’s Geo-Enabled Elections project brings together geographic information systems (GIS) leaders in state government, local elections officials and state elections offices, national GIS and elections organizations, and federal partners to identify opportunities to integrate GIS into elections systems across the country.
The overall goal is to strengthen elections management and citizen engagement. The project aims to provide the impetus for replacing non-spatial “address file” tables with the visual and analytical advantages of election precinct and voter data in a natively GIS format.
Geo-enabled elections overcome the four fundamental challenges with the existing address list approach to precinct management, according to Rowan.
In the address list approach, Rowan said,
No actual boundaries are stored explicitly in the systems,
Quality control is difficult without a method to visualize precinct assignment using aerial photography and boundary information that can change frequently,
There is no efficient method for applying large-scale precinct boundary updates, and
The process is usually not aligned efficiently with other state and local address or boundary-management processes.
To this end, the project conducted a nationwide survey on elections data in the first half of this year. More than two-thirds of states responded.
Here are key takeaways from the baseline survey.
Addresses
The survey found that 55 percent of responding states confirm voter registration addresses against a database of known addresses such as a driver’s license or state ID database, a statewide point address set, a master address database used for 911 call routing, or a commercially available address database.
“The results indicate a need to advocate for coordination between state agencies (such as the state elections department and the department of motor vehicles) and encourage integration of the voting system and other systems,” said Jamie Chesser, the Geo-Enabled Elections project manager.
Election Precincts
More than half of responding states indicated they maintain statewide mapping of precincts. Within this group, 40 percent also maintain a layer of sub-precincts in digital mapping systems.
“There remains a need to develop local precinct data content and procedural standards to examine the relationship between precincts, local and state boundaries, and residential structures,” Chesser said.
Other Data
In all, 82 percent of states keep up-to-date spatial data of city and county boundaries, which is essential for computer-based mapping of precincts.
“Statewide spatial data — especially city, county, school, and special district boundaries – are essential to mapping precinct boundaries across the state,” Chesser said. “The survey reflected, however, that accuracy of current city and county boundary mapping varies
considerably.”
A substantial majority, 79 percent of respondents, confirm their mapping of state-level district-based elected offices are accessible online in a digital mapping format.
Survey responses were coordinated by state government representatives who focus on the development and deployment of mapping data and systems across state agencies and local governments.
Later this year, NSGIC will release the results of a study probing the spatial approach to elections management from the perspective of state elections officials.
The two-year Geo-Enabled Elections project, underwritten by the bipartisan Democracy Fund Voice, convenes a wide variety of stakeholders to explore ways geographic information systems and related processes can enhance elections management and citizen engagement across the U.S.
A capture of the Buffalo and Erie County Botanical Gardens in Buffalo, New York, taken in May 2018. (Image: Nearmap)
Fresh off an eye-grabbing appearance showcasing its new 3D products at last week’s Esri User Conference, Nearmap will deliver a free “Cooking with GIS” webinar Thursday, July 26.
The hour-long session will highlight ways that the company’s vertical, oblique and 3D aerial imagery can bring competitive advantage to surveyors, construction managers, telecomm engineers, city planners, realtors and investors, building contractors, property and natural resource managers, and many others. Using their geographic information systems (GIS) skills, these professionals can perform deep analysis and make decisions with confidence using detailed and up-to-date visual insights.
Nearmap won 2017 Esri’s Best New Content Partner Award in 2017, and the free webinar, subtitled “Esri + Nearmap,” focuses on the key advantages of seamlessly integration the company’s high-resolution aerial imagery into Esri mapping and software products.
Esri is an international supplier of geospatial information systems with more than one million users in 200 countries around the world. Nearmap’s ArcGIS Image Service Online provides users an easy and efficient way to incorporate high-resolution PhotoMaps within Esri ArcGIS Online. ArcGIS users can instantly access current 2.8” imagery within days of capture while also showing change over time using Nearmap’s historical archive.
A New York City building site with temporary covered pedestrian walkway. (Photo: Nearmap)
As an integral partner in the ArcGIS ecosystem, Nearmap helped integrate their imagery with a wide range of Esri software solutions—both off the shelf and bespoke. Coupled with Portal for ArcGIS, the Nearmap ImageServer can be used in any application that is able to talk to ArcGIS Server, delivering power to the platform.
3D. Nearmap recently brought dramatic change to the aerial imagery market, announcing a national survey program providing high-resolution oblique imagery and derivative 3D products from its patented HyperCamera2 technology. The new camera system provides a high degree of overlap from different angles, so Nearmap can reconstruct the real world in detail, producing not only high-resolution orthomosaic and oblique imagery, but also surface and terrain models, natural color point clouds and textured 3-D meshes.
Users can immerse themselves in 3D textured mesh models, improving analysis and design activities. They can see different elevations and line of sight using the 3-D information. These features become important in many use cases, including airport or utility planning, or to determine the best location for a crane before a construction project.
Other applications include wireless telecommunications network modeling, solar panel design, tactical resource deployment, real estate development promotion, property valuation, insurance underwriting and smart cities.
Delivery. Nearmap is delivered through a user-friendly interface called MapBrowser or accessed via Esri, Autodesk and other third-party solutions.
Nearmap captures urban U.S. imagery multiple times per year, processes massive amounts of visual data, and uploads up-to-date aerial maps to the cloud within days. Patented imaging and processing technology delivery at speed of high-resolution aerial imagery as a service: orthographic (vertical) maps, multi-perspective panoramas and oblique aerial views.
The fully cloud-based PhotoMaps are accessible instantly via desktop and mobile, with 70% of the U.S. covered in major metros.
Clarity, color and 2.8″ GSD detail help users identify and accurately measure ground features with ease, detect change over time or monitor progress through the company’s library of precisely georeferenced historical imagery.
Nearmap imagery is refreshed up to three times per year principal coverage areas, with three orthomosaic captures incorporating one oblique capture. Nearmap’s orthomosaic imagery already covers nearly 70 percent of the U.S. population dating back to 2014.
Speakers on the July 26 webinar include Kevin Kwok, Nearmap technical product manager; Chuck Dostal, Nearmap geospatial technical engineer; and customer Mike Otillio, director of research for Colliers International, servicing the commercial real estate industry.
Who better to know about connections than a GIS professional whose very job is discovering them? Weaving a thread through time from decades ago isn’t a typical geospatial connection, but this one is, and it is connected by a person.
Let’s reflect on who we are as a profession and how we, the geospatial community, has made the world a better place.
Let’s also take a moment to learn about one of the leaders who led the way and what he had to overcome to help us appreciate who and what we are. It is an oft-repeated refrain: “Those who do not know the past are condemned to repeat it”, and, my personal favorite, “The future flows through us becoming the past so that we remember it and do not repeat it.”
Jack Maple. (Photo: Newsday Photo, 1986 / Bruce Gilbert)
In 1961, the trend in crime began climbing. Many people lived in fear, especially in big cities. New York captured many of the nation’s headlines in a long, tragic list of brutal, horrible crimes. Hope was bleak. It was expected to get worse. But, it didn’t. The fever had broken. It peaked in 1991. The crime spree lasted 30 years.
By contrast, the Vietnam War lasted 20 years. The total number of troops serving on active duty during Vietnam was 9.1 million troops and 58,318 lost their lives in combat, yet fewer people died on the streets of America during the same period. In fact, on average, during the 30-year crime wave, every 22 days the number of victims of violent crime in the United States equaled the total number of soldiers killed in Vietnam. America was a battlefield and ground zero was New York City.
What happened in 1991? What stemmed the tide? That year, a new type of hero emerged, a crime fighter, unlike any before.
It began at ground zero, in the most dangerous areas of New York City — the subways, referred to as the “caves.” Thugs, rapists, murders and thieves roamed the depths. Police could do little. They were outnumbered and operated under strict rules. It was preferable to be a regular police officer, above ground, dealing with routine crimes, even the murders, rather than be a transit cop covering a beat in the dark, rough, unforgiving underworld of the subway. Only four types of people dwelled there: criminals, victims, transit cops, and those who got away.
Sometimes, transit cops or criminals were the victims. Transit cops were difficult to recruit, but New York needed more of them. This provided an opportunity for those with few other choices. Sometimes, those who have no other options are the ones who make the most of an opportunity. They work the hardest because it is their only way out. Success lies with the willing — those incendiary hearts waiting to be ignited by a challenge that gives them purpose. Life is too often fraught with peril and strife. It is vision and the courage to pursue them that manifests dreams into reality.
This new hero didn’t fit the caricature. He was short, balding, overweight and lacked a high school diploma. He was street smart, cocky, unpolished and would rather fight than prove his point. He didn’t come from a privileged background. He just had his wits. He knew right from wrong and had the courage to stand his ground. He took on the criminal element lurking in the subterranean worlds. He worked hard, earning his GED at night. It served him better that way like a badge of honor, the hard way being its own reward.
His name was Jack Maple, the crime fighter, and he understood the streets in ways others didn’t. He knew, like a hunter knows, to find the deer trails. Animals are creatures of habit. They prefer to stay where they know the area, the smells, the rhythms, the sounds, where the food is, and where to run for cover. Criminals measure their risks too. They prefer familiar places. They are territorial and keen to their surroundings. Jack knew if you look for their patterns, you’ll find them. He covered his walls with subway maps, placing pins where and when the crimes happened.
The criminal’s habits and behaviors began taking shape. With this knowledge, Jack had become the hunter. Knowledge is power, but real power is action, and Jack took it. He would not have become the hero otherwise. He staked out their patterns of place and time, setting traps and luring them in with their weaknesses.
One by one, and group by group, he reclaimed New York’s subways. Crime dropped by 69% over the next five years. Putting that in perspective, two of every three victims were spared. Unfortunately, 629 people were still murdered in New York City, but it was a drastic departure from the peak of 1,946 just five years before, meaning 1,317 men, women and children did not suffer a violent crime that year or any other year thereafter.
The values of crime are most often represented as a 1:100,000 scale ration; however, this chart shows three different categories, each represented by a different order of magnitude. (Data from Disastercenter.com)
Rudy Giuliani, then mayor of New York, recognized the value of what Maple had developed. Maple called his maps the Charts of the Future. His colleagues called it wallpaper. The mayor called it amazing and gave Jack Maple his full support, praising him by saying, “One of the truly great innovators in law enforcement, who helped make New York City the safest large city in America.” Maple was promoted to Deputy Police Commissioner of Crime Control Strategies.
Maple founded CompStat, Computerized Statistics, calling it his electronic pin maps to support his four precepts: accurate and timely intelligence, rapid deployment of forces, effective tactics, and relentless follow-through.
New York’s CompStat program for the NYPD.
CompStat changed policing to a data-driven business. GIS professionals will recognize CompStat as a geographic information system, and Jack as a self-trained geospatial developer and analyst. Geospatial science was still a very niche technology at the time.
Jack Maple’s success continued to grow. Two men, William Bratton and John Timoney, both police commissioners and senior to Maple in the police hierarchy, became evangelists of Maple’s CompStat, spreading it to other cities throughout the world, and through those two men, predictive policing and crime mapping evolved.
Maple, Bratton and Timoney became independent consultants helping cities worldwide establish their own CompStat programs.
His success did not end there. Based on his experiences fighting criminals on the streets and fighting change in the antiquated police system, he wrote the book, The Crime Fighter: How You Can Make Your Community Crime Free. The book is an excellent read and readily available online. He also co-wrote the TV series The District, based on his exploits in the book.
If you haven’t seen the series, the show is worth watching. Season 1, Episode 3, shows a 1990s projector screen with a large GIS display and the city’s police chiefs being held to account for telling their district’s crime stories in accordance with the map.
Jack Maple was a modern-day rags to riches story and a pioneer of the GIS profession. When he passed away in 2001, he had become a beloved character in New York. When he died, each of the major New York City publications covered the story of his life crediting him for reducing crime and giving the citizens back their city. The CompStat room at 1 Police Plaza CompStat, New York, was renamed after him in tribute. Craig Horowitz, writer for New York Magazine, penned a worthy tribute.
CompStat would be further developed with more advanced crime mapping and crime analysis methods, predictive analytics, environmental criminology and geographic profiling. Kim Rossmo coined the term geographic profiling, based on his patented Rossmo Formula, which is a form of predictive analytics that takes location, time, social behavior and the psychology of criminals into account and turns it into a mathematical equation that can be fed into a GIS. This narrows down the probable location of a suspect, allowing investigators and police to better focus their resources.
Geographic profiling was used during the D.C. sniper case. The Rossmo Formula was featured on the TV series Numb3rs. I hope to write a future article on Dr. Rossmo complete with interviews.
The trend in crime has continued decreasing ever since the peak in 1991. Crime in New York City has now dropped back to 1940s levels as of 2017 and continues to decline.
The power to change the world lies with those fervent, intrepid souls — the unrelenting dreamers, who seek a better world and through innovation, creativity and courage, and manifest it into reality.
It is a great time to be in the geospatial profession. The United States leads the world in geospatial science. Take heart, because opportunities abound in this industry. I hope you become a hero in the field, and someday I have the opportunity to write about you.
Geotab has released an interactive infographic depicting the evolution of America’s iconic Interstate Highway System.
The interactive timeline allows users to watch the network expand over the years, providing a detailed look at the development of the infrastructure that has supported transportation and trade across the America for several decades.
Today, the Interstate Highway System accounts for 25 percent of all highway traffic in the United States. As the system nears a major milestone, with the total network approaching 50,000 miles in length, Geotab created the interactive infographic to highlight the development of the highway system in the U.S. throughout the years.
Since its inception in 1956, the Interstate Highway System has been regarded as the backbone of U.S. commerce and infrastructure, playing a vital role in America’s economic growth. The map highlights major developments over the last 60 years, such as the 1974 completion of the I-5 that now connects Mexico and Canada with a singular route and the opening of the I-80, the country’s first coast-to-coast highway.
Geotab’s “Evolution of the Interstate” infographic provides the public with the ability to watch the Interstate Highway System expand over the years, enabling them to engage with particular dates and sections of the extensive network. The interactive map also includes relevant details about the city each highway serves, the length of that specific highway, and provides the total mileage covered by the entire network in any given year.
“As America’s Interstate Highway System approaches a major milestone, we wanted to pay tribute to this intricate and expansive network,” said Maria Sotra, vice president of marketing at Geotab. “Connecting people, enabling business and providing a straightforward path across one of the largest countries in the world, the nearly 50,000 miles of highway that makes up the Interstate Highway System has played an undeniable role in transportation and trade in the United States.”
Most image analysis tasks that required ENVI or Erdas Imagine software are now available online with EOS Platform, a new cloud service launched by Earth Observing System (EOS). It provides GIS professionals with a one-stop solution for search, analysis, storing and visualization of large amounts of geospatial data.
EOS Platform is an ecosystem of four mutually integrated EOS products, which together provide a powerful toolset for geospatial analysts, according to the company. Image data obtained from LandViewer or uploaded from a user’s computer is stored in cloud-based EOS Storage and is instantly available for remote sensing analysis or image processing.
EOS Processing offers 16 processing workflows that run online, including raster tools (merge, reprojection, pansharpening), remote sensing analytics, photogrammetry and proprietary feature extraction algorithms designed by EOS engineers and data scientists to address the main challenges of agriculture, forestry, oil, gas, retail, city planning, defense and other industries. Such pre-processing tasks as cloud detection or radiometric calibration refine raw data for further analysis. Images can be corrected for atmospheric effects to obtain the real ground radiance or reflectance values.
Users can also use the cartographic features of EOS Vision for vector data visualization and analysis (analysis coming soon). Other features in upcoming updates include lidar analysis and 3D modeling.
Data agnostic platform
Users can work with a variety of satellite and airborne raster datasets in EOS Processing, EOS Storage and LandViewer, which enables quick and intuitive search of images within collections of Sentinel-1 and 2, Landsat 8 and 7, MODIS, NAIP, CBERS-4, Landsat 4 and 5. Besides downloading images from public datasets, users can also upload their own GeoTiff, JPEG, JPEG 2000 files and apply GIS data-processing algorithms via API or from the web interface. EOS Vision is a tool for vector data operations with multiple format support (ESRI Shapefile, GeoJSON, KML, KMZ).
Object detection, change detection and classification
The convolutional neural networks, pre-trained by EOS to extract features from imagery, allow users to apply state-of-art methods to detect objects and track changes from space.
Having only a set of multi-temporal images and change detection workflow, users can track how illegal deforestation progresses over time.
Edge detection can show the exact boundaries of agricultural lands down to the last pixel.
It is possible to estimate the parking lot traffic of the largest shopping centers with a car detection algorithm.
Products within EOS Platform support almost all remote sensor types. Users can choose from numerous spectral indices to calculate on the fly.
Aside from the complete set of vegetation indices (Normalized Difference Vegetation Index, NDVI; Red-Edge Chlorophyll Index, ReCI; etc.), there are also indices to outline landscape features (Normalized Difference Water Index, NDWI; Normalized Difference Snow Index, NDSI) and burned areas (Normalized Burn Ratio, NBR).
One of the most useful features is the ability to experiment with spectral bands: users can create custom band combinations and indexes on top of the default ones.
The user-friendly interface of EOS Processing makes it easy to manage processing workflows depending on the user’s business needs. Users can set the parameters for processing and repeatedly use customized workflows to automate high-frequency analytical tasks. Coming updates will add an ability to create custom algorithms from the available data-processing operations.
Agriculture, forestry, oil and gas and more industries
A tandem of EOS products form a comprehensive toolbox both for general use and for industry-specific cases, the company said. With vegetation indices and crop classification feature, agronomists can continuously monitor crop conditions to detect plant diseases, pests and droughts. Forestry specialists can classify forest types, assess fire damage, monitor forest health, and track and enforce logging restrictions.
EOS Platform can also be used for regional and urban planning. It helps users identify land cover classes to generate a vegetation map and can also make a complete list of urban features such as buildings, roads or other major features in the region.
The platform can tackle disaster management by measuring flood extent and finding fire boundaries. When it comes to oil and gas, it is capable of identifying oil rigs and assessing the environmental impact.
According to Esri, ArcGIS Indoors applies the latest location technology to allow users to see and share where assets, rooms, departure gates and offices are located. Click to enlarge. Photo: Esri
Esri has debuted ArcGIS Indoors, which is designed to enable interactive indoor mapping of corporate facilities, retail and commercial locations, airports, hospitals, event venues, universities and more.
According to Esri, the solution applies the latest location technology to allow users to see and share where assets, rooms, departure gates and offices are located.
ArcGIS Indoors uses data streams, real-time processing and location intelligence tools to help businesses and other organizations understand how to better coordinate space and other resources with their facilities and campuses. Insights from sensor networks deliver real-time information to managers and executives through interactive dashboards, while visitors and employees can find useful information about the buildings they occupy, the company said.
The solutions also allows users to quickly access and explore critical business information, such as the location and status of fire extinguishers and their last inspection dates.
“ArcGIS Indoors brings the interior building space into the future by placing data about employees, schedules, meetings, customers and events into a geographic context,” said Nitin Bajaj, product manager at Esri. “Having spatial awareness gives executives, managers and employees better insight so they can operate more efficiently and competitively.”
According to Esri, ArcGIS Indoors will be available for widespread use by the end of 2018. In addition, a beta version of the product will be released at this year’s Esri User Conference, taking place July 9-13 at the San Diego Convention Center in San Diego, California.
East View Geospatial (EVG) is offering a new version of MapVault, a streaming service that brings together maps from around the world.
According to the company, MapVault provides access to more than 500,000 geo-referenced map sheets from more than 1,000 authoritative map series, which can save organizations the costs of procuring, storing and digitizing physical maps.
MapVault users have access to a diverse collection of topographic, aeronautical, nautical and geological map series sourced from international mapping agencies. Each series has been mosaicked for easy use and quick navigation. Robust metadata along with series index maps and individual sheet-level metadata are included.
New map series are added to MapVault on a regular basis, and subscriptions are customizable. Users can choose to subscribe to the series that cover their exact areas of interest or select from multiple regional package options.
East View Geospatial also provides custom series solutions and encourages users to contact the company about adding their own mapping resources to the MapVault platform.
MapVault was designed for a wide variety of users, both GIS and non-GIS specialists, and data is easily integrated into GIS software, the company said. The MapVault catalog can be accessed over the internet or through any WMTS (web mapping tile services) connection. Layer files formatted specifically for ArcGIS Desktop, QGIS, Global Mapper or other open-source GIS packages can be downloaded.
“What makes MapVault unique is the many advantages it brings to users,” said Kent Lee, president and CEO of East View Geospatial. “We’ve taken the time and cost out of tiling entire map series, giving users consistent, reliable data served up in a straightforward, easy-to-use streaming service. Whether you are interested in global or country-wide mapping coverage, or even county- or city-level mapping, MapVault gives users of all experience levels a simple and accessible environment in which to discover and utilize maps.”
Esri has published its latest book, “GIS for Surface Water: Using the National Hydrography Dataset,” by Jeff Simley, which details how to use geographic information system (GIS) technology to visualize and analyze data sets. Simley is an award-winning cartographer and the former lead of the Hydrography Program at the United States Geological Survey (USGS).
The book examines the complexities of surface water systems and shows readers how to use the Esri ArcGIS software, the USGS’s National Hydrography Dataset (NHD) and the Watershed Boundary Dataset (WBD), and the U.S. Environmental Protection Agency’s NHDPlus dataset to better study and manage the United States’ vast water system.
According to Esri, the book thoroughly examines the representation of water features and their attributes in a GIS and then turns its attention on how that data is structured in the NHD, WBD and NHDPlus datasets. In addition, after seeing how surface water hydrography can be modeled in a GIS, readers can then learn how to use these tools to solve real-world problems, such as protecting and restoring the fisheries habitat in Washington.
The book also offers instructions to guide readers to create surface water flow-volume maps that show how much water flows through any given river system.
“This book is unique in that it is the most comprehensive, authoritative source for the NHD,” said hydrologist David Maidment in the book’s foreword. “But it is more than that: It is a monument to the intellectual craft and dedicated effort of a generation of digital mapmakers who devoted their professional careers to the completion of this enormous task.”
(From left) Francois Lombard and Dirk Hoke, Airbus, sign agreement with Will Marshall, Planet.
Airbus and Planet have entered into a partnership to facilitate access to each other’s data and the co-development of new geospatial solutions.
The companies are establishing a framework agreement to explore opportunities for joint cooperation in new and existing markets, product offerings, sales and marketing efforts.
Both companies aim to provide a comprehensive suite of global satellite data at multiple temporal and spatial resolutions, and develop new analytic products for a wide range of applications to benefit their customers.
Benefitting from both companies’ constellations, customers will have access to the entire Earth’s landmass every day at 3m resolution with PlanetScope satellites, as well as to intra-daily sub-meter resolution imagery with Pléiades and SkySat constellations.
In addition, they will also have the capability to order images with resolutions of 1.5m (SPOT 6/7), 5m (Rapideye) and 22m (DMC Constellation).
Lastly, TerraSAR-X, TanDEM-X and PAZ radar satellites will allow the acquisition of images regardless of weather and daylight conditions, ensuring access to any place on Earth independent of cloud coverage.
“By combining our strengths, we will provide a key capability to address all market needs, both in terms of data and value-added products, and to best serve our clients, whatever their industry and their requirements,” said François Lombard, director of the Intelligence Business at Airbus Defence and Space.
“Airbus and Planet are truly complementary partners. Airbus brings long-standing success in serving reliable, high resolution remote sensing, and Planet brings its unique global coverage and temporal cadence, as well as agile aerospace iteration to get sensors quickly to space,” said Will Marshall, CEO and co-founder of Planet. “Together we will be able to deliver sophisticated offerings to fit customer needs across international markets.”
A little more than a decade ago, the IT world began to buzz about the next big thing, a concept called service-oriented architecture (SOA). SOA promised a better way to build enterprise applications, delivering efficiency, business agility and fluid communication — a near revolution in business workflows. Such was its promise that business executives — not just CIOs — began to ask, How do I get an SOA?
In the fog of excitement, few executives asked the more appropriate question: What exactly is an SOA? Is it an off-the-shelf product, an IT methodology, a business philosophy? And where does it belong in my organization — do I need a strategy to drive business value from it?
Today, artificial intelligence (AI) triggers similar levels of excitement, with a chaser of fear. In a recent survey by New Vantage Partners, C-level executives crowned AI the most disruptive technology — far outranking cloud computing and blockchain. And nearly 80 percent of those executives fear competitors will harness AI to outflank their business.
An Executive Checklist for AI in the Enterprise
Create a strategy. AI is already making an impact in the enterprise — via chatbots, virtual assistants, and other point solutions. Experts advise executives to establish a framework for how AI will be incorporated into business strategy and processes, and to define measurable goals.
Apply executive support. Assign a C-level executive to oversee the company’s strategy. “When companies are looking to do fundamental digital transformations and reinvention of the business, there is incredible value in having top-down guidance drive much of that activity,” says Microsoft’s Joseph Sirosh.
Mind the data. “Predictions will be accurate only if the training data used to teach the AI prediction model is truly representative of the target cases being classified or predicted,” explains Esri’s Sud Menon. “AI is a data-driven game, hands down.”
Incorporate robust datasets, including location information. In nearly all its forms, business data can become more valuable when coupled with information about its location. This form of geoenrichment is especially useful for AI models, which can discover insight that humans might overlook. (See “A Business Case” in the article.)
From an executive’s perspective, now is the time to answer critical questions: What is AI, what can it do for my business, and who should be responsible for its development and strategic alignment?
AI in the Enterprise
Although 93 percent of businesses are investing in artificial intelligence, not all are using it in the same way or toward the same end, says Sud Menon, director of software product development at Esri. “AI is a very broad term, and businesses are adopting different aspects of it at different rates,” Menon notes.
Sud Menon, Esri
When envisioning how AI can deliver value to their enterprises, business executives should think of three primary processes, according to Menon and Joseph Sirosh, corporate vice president of artificial intelligence and research at Microsoft: internal business operations, customer interactions, and business planning. Interestingly, a survey by Tata Consultancy Services found that high-performing companies are more likely to focus their AI efforts on internal operations, while AI followers tend to concentrate on customer interactions.
Regardless, each process is being transformed with help from cloud computing, data, and intelligent algorithms that power AI. Here are a few examples of how:
Internal Operations. AI is improving companies’ internal operations in several ways. In some workplaces, AI-based facial recognition systems regulate employee access to secure areas. Predictive maintenance systems run by AI help determine the optimal service schedule for fleets of delivery vans. And AI-infused bots are performing HR tasks that once required human intervention, such as guiding employees through the steps of changing their last name, or adjusting the allocation of their 401k plan. The bots connect to systems of record like ERP and HR software, analyze pertinent data, and lead employees through an intuitive workflow.
Customer Interactions. AI is adding intelligence to some customer-facing tasks. For example, AI powers many of the recommendation systems that suggest a relevant product or a message to a website visitor who lives in a particular location. It anchors security systems that recognize a fraudster’s voice signature or suspicious online activity in real time and deny the person access to an online account. And it supports the chatbots that interact with millions of consumers online each day.
Business Planning. For executives and decision-makers looking for strategic guidance, AI can predict shifts in supply and demand and how businesses might react. To plan next quarter’s operations, the technology can sift through customer purchasing habits and factors such as planned competitor stores to predict sales, product mix, and staffing levels. Business decisions that were once governed primarily by an executive’s intuition — like where to invest and when — are now being strengthened by data-driven AI. (See the section titled “A Business Case” for an example.)
AI Accuracy: Machine Learning Keeps on Learning
Much has been made of AI’s abilities — to see, to understand human speech, to predict outcomes. But some wonder whether the technology has evolved enough to form the foundation of business decisions. For instance, a recent WIRED story reported that an AI-based image detection program was 91 percent sure that a photo of two skiers was a dog. It turns out that like any computer program, AI will need debugging before it is put into production.
AI systems today are statistical learning systems that drink in data. If the data used to teach AI systems is flawed, either because it’s wrong, statistically unsound, or does not cover the use cases the AI system was designed for, the outcomes can be erroneous.
As companies increasingly turn to AI and machine learning to inform business decisions, experts advise a meticulous approach to data. “While AI models have increased greatly in sophistication, including the ability to learn from ever larger datasets of known cases, businesses need to understand that the approach is still empirical,” Menon says. “Predictions will be accurate only if the data used to train the prediction model truly represent the target cases being classified or predicted.”
For example, an AI model schooled to predict the health outcomes of a certain diet might overstate results if the data used in training the model is tied to a specific subgroup of the population. In such a case, the model would have no way of taking into account the genetic and lifestyle variations in other groups that could modulate the effect of diet on health, and its results could be flawed if applied broadly.
Joseph Sirosh, Microsoft
The good news, Sirosh says, is that AI systems can be tested in scientific ways — with new data — and validated. Especially in the case of AI designed for mission-critical operations, it may be important to have controlled statistical testing, similar in spirit to clinical trials in medicine.
“It is up to a business to gather the right data for the problem at hand and apply prediction results appropriately depending on the type of problem being solved and the decisions being made,” Menon says. Executive-level support can set these ground rules for AI, helping ensure accurate decision support throughout the enterprise.
With the right data, the business case for applying AI widely is growing stronger by the week — across many forms of AI. A Danish company, for example, claims that the AI behind its pricing technology can improve gas stations’ margins by as much as 5 percent. Meanwhile, the insurance company Lemonade recently claimed a world record, saying the company’s AI bot settled a client’s claim in three seconds (including sending wiring instructions for the payout and notifying the client of the settlement).
In all these instances, businesses are either offloading decisions to AI or strengthening them with AI’s help — and creating new experiences for customers, new business models, and new ways of working.
Trend Spotting: Adding Location Data to AI
(Image: Esri)
“All this decision-making feeds on data,” Menon says. “The more data you have that is relevant to the problem, the better the decision-making process is.”
One type of data driving AI in new directions is location, Sirosh says. “Geographic information systems [GIS], which can correlate and analyze location in time and space and integrate it with many other types of information — and then serve it up for higher-order AI to be applied on it — are particularly interesting,” he told WhereNext.
“GIS and geography provide organizations with additional contextual information that enriches observations, leading to better predictions,” Menon explains. That might be the quarterly sales at stores in a particular market. Or the rate of home ownership in the area where a bank is considering building a new branch. It could even be data on physical phenomena such as weather, vegetation, or urban density. The more data elements that GIS catalogs, the more oxygen AI has, and the better its predictions will be.
“Most things are located in the world and related to or influenced by nearby things,” Menon says. That simple statement underscores the value of using location data to strengthen AI-based decision making.
The Pillars of Artificial Intelligence
Unlike technologies that are well known but struggling for widespread business adoption — among them, virtual reality and blockchain — artificial intelligence is already being put to work in organizations worldwide.
The coming-out party for AI is due to three factors, according to Joseph Sirosh, corporate vice president of artificial intelligence and research at Microsoft. The first is the massive compute power now available in the cloud or on premises, which allows data to be processed into insight. The second is the data unleashed by digital transformation, including sensors that relay information via the Internet of Things (IoT), GPS and mobile devices that report accurate locations, and innumerable other sources. Sirosh calls data the oxygen of artificial intelligence.
The third pillar of AI is the algorithms that fuel its intelligence. Recent innovations have provided AI with “the ability for computers to learn from every type of data, make predictions, and act without being programmed explicitly,” Sirosh says.
Together, those forces help AI mimic — and in some cases, outperform — humans’ abilities to see, analyze, communicate with, and make predictions about the world around them.
A Business Case: AI Powered by Location Intelligence
Just as search engines revolutionized the speed of information discovery and knowledge sharing, AI and location data are accelerating business activities by performing some tasks faster than humans can, with more data. The benefit isn’t simply faster decisions, Sirosh and Menon say. It’s smarter decisions.
A new breed of AI-based sales analysis is a case in point. A sales executive at a national retailer has identified young parents as a core customer segment and wants to learn more about them. But manually gleaning insight from thousands of customers and hundreds of thousands of transactions is an impossible task. The company turns to a machine learning model in the hope of discovering more insight.
The goal is to find patterns in the data that will help the company understand this core customer segment — insight that will improve the company’s marketing messages, store assortments, and the events it sponsors in its communities. The project team tutors an AI model using data from multiple stores, including customer addresses and a record of purchases attributed to each address.
The AI model sifts through these records looking for insight. It homes in on diaper purchases as a signal for young parents and discovers a curious correlation: many diaper purchases are accompanied by purchases of pill organizers, denture cream, and senior vitamins.
To refine the analysis, the team enriches the AI model with location-based demographic data pulled from GIS. To each customer address, the AI model adds hundreds of data points about the demographic characteristics of the surrounding neighborhood — average household income, family composition, marital status, hobbies, languages spoken, and recreational preferences.
Combing through that location-enriched big data, the AI algorithm reveals something executives hadn’t expected. At many of the company’s stores, young parents from the surrounding area live in multigenerational homes. And, as it turns out, the grandparents are doing most of the shopping.
The AI model helped executives adjust plans for marketing, merchandizing, and community outreach before they spent millions targeting the wrong demographic. And it did so by using the three traits that make AI a valuable tool for augmenting the human workforce, according to the consultants at PwC:
Automating complex business processes
Spotting patterns in historical data that lead to business value
Providing insight that strengthens human decisions
Business Strategy: Who Oversees AI — CXOs or LOB Managers?
Considering AI’s expected business impacts and the fact that 93 percent of organizations are already investing in the technology, it’s worth asking where artificial intelligence should live in the organization, and who should be responsible for it. There may be no simple answer, but those with a ringside seat for AI’s emergence have some suggestions.
“When it involves the data that a company uses and the way that decisions are made, AI requires top-down vision and investment,” Menon says.
Sirosh agrees. “Where we have found dramatic wins related to AI, the CEO had a vision of how to transform the organization toward creative work and away from old-economy and labor-intensive processes, or to create new customer experiences and business models. That vision was much more cohesive and integrative than what would have bubbled up” from the lines of business, he says.
AI Need Not Apply — Business Processes Untouched by AI
Despite the sense that AI is sweeping through every function of business, some remain AI free, according to Joseph Sirosh, corporate vice president of artificial intelligence and research at Microsoft. “For example, engineering and physics are incredibly well-developed mathematical sciences, and we are going to make tremendous progress in those areas. That will include breakthroughs in quantum computing and other disciplines. Those are all areas that are just core scientific and engineering work. AI doesn’t encompass all of that, although it may help amplify some of this work.”
Using AI to move companies away from labor-intensive processes will likely have profound effects on the workforce. McKinsey researchers assert that 45 percent of activities in today’s workforce could be automated — whether through AI or other means. And when natural-language processing — a form of AI — reaches the median level of human capability, another 13 percent of jobs could be on the block.
C-level executives will need to find an effective balance. Writing about the C-level challenges of AI, McKinsey senior partners Jacques Bughin and Eric Hazan note that measurable ROI typically comes only when AI is laced into a business’s culture and workflows. That in itself is a sizable feat, the partners say, possible only with the guidance of company leaders.
“When companies are looking to do fundamental digital transformations and reinvention of the business,” Sirosh says, “there is incredible value in having top-down guidance drive much of that activity.”
Workforce shifts and workflow transformation aside, Sirosh and Menon advise concerned executives to focus on the foundation of AI. The goal of such a sophisticated technology, they say, is rather simplistic.
AI, informed by location data, helps organizations reason and interact with the increasingly sophisticated world around us,” Sirosh says.
“If I had to put it in one term,” Menon adds, “AI is basically about decision-making — smarter decision making.”
(Listen to a podcast featuring Joseph Sirosh to explore this concept in more depth, including a look at how AI is changing business models.)
Marianna Kantor joined Esri as chief marketing officer in 2015. Prior to Esri, Marianna was the VP of Marketing at PTC, where she built the worldwide services marketing and field-enablement organization, helping drive sustained revenue growth in dynamic and changing markets. Marianna has held technology and marketing leadership positions throughout her career in leading organizations such as AT&T, Akamai, and Los Alamos National Labs. At Esri, Marianna is exposing and amplifying the transformational capabilities of geospatial technology as an indispensable tool for problem solving and decision making in business and government. Marianna holds two engineering degrees from Columbia University and University of Pennsylvania, and an Executive MBA from MIT.  As Esri’s chief technology officer, Jay Theodore guides the long-term vision for the ArcGIS platform. Jay is passionate about harnessing innovative ideas to increase the value companies gain from location intelligence, geoscience, computer science, and technology. He takes great pride in working with outstanding software developers, architects, and product engineers. Jay earned a master’s degree in computer science from Florida Institute of Technology, where his research focused on finite element analysis and modeling (FEA/FEM), computer graphics, and composite structure design for Space Station Freedom. He also holds a bachelor’s degree in computer engineering.
A little more than a decade ago, the IT world began to buzz about the next big thing, a concept called service-oriented architecture (SOA). SOA promised a better way to build enterprise applications, delivering efficiency, business agility and fluid communication — a near revolution in business workflows. Such was its promise that business executives — not just CIOs — began to ask, How do I get an SOA?
In the fog of excitement, few executives asked the more appropriate question: What exactly is an SOA? Is it an off-the-shelf product, an IT methodology, a business philosophy? And where does it belong in my organization — do I need a strategy to drive business value from it?
Today, artificial intelligence (AI) triggers similar levels of excitement, with a chaser of fear. In a recent survey by New Vantage Partners, C-level executives crowned AI the most disruptive technology — far outranking cloud computing and blockchain. And nearly 80 percent of those executives fear competitors will harness AI to outflank their business.
An Executive Checklist for AI in the Enterprise
Create a strategy. AI is already making an impact in the enterprise — via chatbots, virtual assistants, and other point solutions. Experts advise executives to establish a framework for how AI will be incorporated into business strategy and processes, and to define measurable goals.
Apply executive support. Assign a C-level executive to oversee the company’s strategy. “When companies are looking to do fundamental digital transformations and reinvention of the business, there is incredible value in having top-down guidance drive much of that activity,” says Microsoft’s Joseph Sirosh.
Mind the data. “Predictions will be accurate only if the training data used to teach the AI prediction model is truly representative of the target cases being classified or predicted,” explains Esri’s Sud Menon. “AI is a data-driven game, hands down.”
Incorporate robust datasets, including location information. In nearly all its forms, business data can become more valuable when coupled with information about its location. This form of geoenrichment is especially useful for AI models, which can discover insight that humans might overlook. (See “A Business Case” in the article.)
From an executive’s perspective, now is the time to answer critical questions: What is AI, what can it do for my business, and who should be responsible for its development and strategic alignment?
AI in the Enterprise
Although 93 percent of businesses are investing in artificial intelligence, not all are using it in the same way or toward the same end, says Sud Menon, director of software product development at Esri. “AI is a very broad term, and businesses are adopting different aspects of it at different rates,” Menon notes.
Sud Menon, Esri
When envisioning how AI can deliver value to their enterprises, business executives should think of three primary processes, according to Menon and Joseph Sirosh, corporate vice president of artificial intelligence and research at Microsoft: internal business operations, customer interactions, and business planning. Interestingly, a survey by Tata Consultancy Services found that high-performing companies are more likely to focus their AI efforts on internal operations, while AI followers tend to concentrate on customer interactions.
Regardless, each process is being transformed with help from cloud computing, data, and intelligent algorithms that power AI. Here are a few examples of how:
Internal Operations. AI is improving companies’ internal operations in several ways. In some workplaces, AI-based facial recognition systems regulate employee access to secure areas. Predictive maintenance systems run by AI help determine the optimal service schedule for fleets of delivery vans. And AI-infused bots are performing HR tasks that once required human intervention, such as guiding employees through the steps of changing their last name, or adjusting the allocation of their 401k plan. The bots connect to systems of record like ERP and HR software, analyze pertinent data, and lead employees through an intuitive workflow.
Customer Interactions. AI is adding intelligence to some customer-facing tasks. For example, AI powers many of the recommendation systems that suggest a relevant product or a message to a website visitor who lives in a particular location. It anchors security systems that recognize a fraudster’s voice signature or suspicious online activity in real time and deny the person access to an online account. And it supports the chatbots that interact with millions of consumers online each day.
Business Planning. For executives and decision-makers looking for strategic guidance, AI can predict shifts in supply and demand and how businesses might react. To plan next quarter’s operations, the technology can sift through customer purchasing habits and factors such as planned competitor stores to predict sales, product mix, and staffing levels. Business decisions that were once governed primarily by an executive’s intuition — like where to invest and when — are now being strengthened by data-driven AI. (See the section titled “A Business Case” for an example.)
AI Accuracy: Machine Learning Keeps on Learning
Much has been made of AI’s abilities — to see, to understand human speech, to predict outcomes. But some wonder whether the technology has evolved enough to form the foundation of business decisions. For instance, a recent WIRED story reported that an AI-based image detection program was 91 percent sure that a photo of two skiers was a dog. It turns out that like any computer program, AI will need debugging before it is put into production.
AI systems today are statistical learning systems that drink in data. If the data used to teach AI systems is flawed, either because it’s wrong, statistically unsound, or does not cover the use cases the AI system was designed for, the outcomes can be erroneous.
As companies increasingly turn to AI and machine learning to inform business decisions, experts advise a meticulous approach to data. “While AI models have increased greatly in sophistication, including the ability to learn from ever larger datasets of known cases, businesses need to understand that the approach is still empirical,” Menon says. “Predictions will be accurate only if the data used to train the prediction model truly represent the target cases being classified or predicted.”
For example, an AI model schooled to predict the health outcomes of a certain diet might overstate results if the data used in training the model is tied to a specific subgroup of the population. In such a case, the model would have no way of taking into account the genetic and lifestyle variations in other groups that could modulate the effect of diet on health, and its results could be flawed if applied broadly.
Joseph Sirosh, Microsoft
The good news, Sirosh says, is that AI systems can be tested in scientific ways — with new data — and validated. Especially in the case of AI designed for mission-critical operations, it may be important to have controlled statistical testing, similar in spirit to clinical trials in medicine.
“It is up to a business to gather the right data for the problem at hand and apply prediction results appropriately depending on the type of problem being solved and the decisions being made,” Menon says. Executive-level support can set these ground rules for AI, helping ensure accurate decision support throughout the enterprise.
With the right data, the business case for applying AI widely is growing stronger by the week — across many forms of AI. A Danish company, for example, claims that the AI behind its pricing technology can improve gas stations’ margins by as much as 5 percent. Meanwhile, the insurance company Lemonade recently claimed a world record, saying the company’s AI bot settled a client’s claim in three seconds (including sending wiring instructions for the payout and notifying the client of the settlement).
In all these instances, businesses are either offloading decisions to AI or strengthening them with AI’s help — and creating new experiences for customers, new business models, and new ways of working.
Trend Spotting: Adding Location Data to AI
(Image: Esri)
“All this decision-making feeds on data,” Menon says. “The more data you have that is relevant to the problem, the better the decision-making process is.”
One type of data driving AI in new directions is location, Sirosh says. “Geographic information systems [GIS], which can correlate and analyze location in time and space and integrate it with many other types of information — and then serve it up for higher-order AI to be applied on it — are particularly interesting,” he told WhereNext.
“GIS and geography provide organizations with additional contextual information that enriches observations, leading to better predictions,” Menon explains. That might be the quarterly sales at stores in a particular market. Or the rate of home ownership in the area where a bank is considering building a new branch. It could even be data on physical phenomena such as weather, vegetation, or urban density. The more data elements that GIS catalogs, the more oxygen AI has, and the better its predictions will be.
“Most things are located in the world and related to or influenced by nearby things,” Menon says. That simple statement underscores the value of using location data to strengthen AI-based decision making.
The Pillars of Artificial Intelligence
Unlike technologies that are well known but struggling for widespread business adoption — among them, virtual reality and blockchain — artificial intelligence is already being put to work in organizations worldwide.
The coming-out party for AI is due to three factors, according to Joseph Sirosh, corporate vice president of artificial intelligence and research at Microsoft. The first is the massive compute power now available in the cloud or on premises, which allows data to be processed into insight. The second is the data unleashed by digital transformation, including sensors that relay information via the Internet of Things (IoT), GPS and mobile devices that report accurate locations, and innumerable other sources. Sirosh calls data the oxygen of artificial intelligence.
The third pillar of AI is the algorithms that fuel its intelligence. Recent innovations have provided AI with “the ability for computers to learn from every type of data, make predictions, and act without being programmed explicitly,” Sirosh says.
Together, those forces help AI mimic — and in some cases, outperform — humans’ abilities to see, analyze, communicate with, and make predictions about the world around them.
A Business Case: AI Powered by Location Intelligence
Just as search engines revolutionized the speed of information discovery and knowledge sharing, AI and location data are accelerating business activities by performing some tasks faster than humans can, with more data. The benefit isn’t simply faster decisions, Sirosh and Menon say. It’s smarter decisions.
A new breed of AI-based sales analysis is a case in point. A sales executive at a national retailer has identified young parents as a core customer segment and wants to learn more about them. But manually gleaning insight from thousands of customers and hundreds of thousands of transactions is an impossible task. The company turns to a machine learning model in the hope of discovering more insight.
The goal is to find patterns in the data that will help the company understand this core customer segment — insight that will improve the company’s marketing messages, store assortments, and the events it sponsors in its communities. The project team tutors an AI model using data from multiple stores, including customer addresses and a record of purchases attributed to each address.
The AI model sifts through these records looking for insight. It homes in on diaper purchases as a signal for young parents and discovers a curious correlation: many diaper purchases are accompanied by purchases of pill organizers, denture cream, and senior vitamins.
To refine the analysis, the team enriches the AI model with location-based demographic data pulled from GIS. To each customer address, the AI model adds hundreds of data points about the demographic characteristics of the surrounding neighborhood — average household income, family composition, marital status, hobbies, languages spoken, and recreational preferences.
Combing through that location-enriched big data, the AI algorithm reveals something executives hadn’t expected. At many of the company’s stores, young parents from the surrounding area live in multigenerational homes. And, as it turns out, the grandparents are doing most of the shopping.
The AI model helped executives adjust plans for marketing, merchandizing, and community outreach before they spent millions targeting the wrong demographic. And it did so by using the three traits that make AI a valuable tool for augmenting the human workforce, according to the consultants at PwC:
Automating complex business processes
Spotting patterns in historical data that lead to business value
Providing insight that strengthens human decisions
Business Strategy: Who Oversees AI — CXOs or LOB Managers?
Considering AI’s expected business impacts and the fact that 93 percent of organizations are already investing in the technology, it’s worth asking where artificial intelligence should live in the organization, and who should be responsible for it. There may be no simple answer, but those with a ringside seat for AI’s emergence have some suggestions.
“When it involves the data that a company uses and the way that decisions are made, AI requires top-down vision and investment,” Menon says.
Sirosh agrees. “Where we have found dramatic wins related to AI, the CEO had a vision of how to transform the organization toward creative work and away from old-economy and labor-intensive processes, or to create new customer experiences and business models. That vision was much more cohesive and integrative than what would have bubbled up” from the lines of business, he says.
AI Need Not Apply — Business Processes Untouched by AI
Despite the sense that AI is sweeping through every function of business, some remain AI free, according to Joseph Sirosh, corporate vice president of artificial intelligence and research at Microsoft. “For example, engineering and physics are incredibly well-developed mathematical sciences, and we are going to make tremendous progress in those areas. That will include breakthroughs in quantum computing and other disciplines. Those are all areas that are just core scientific and engineering work. AI doesn’t encompass all of that, although it may help amplify some of this work.”
Using AI to move companies away from labor-intensive processes will likely have profound effects on the workforce. McKinsey researchers assert that 45 percent of activities in today’s workforce could be automated — whether through AI or other means. And when natural-language processing — a form of AI — reaches the median level of human capability, another 13 percent of jobs could be on the block.
C-level executives will need to find an effective balance. Writing about the C-level challenges of AI, McKinsey senior partners Jacques Bughin and Eric Hazan note that measurable ROI typically comes only when AI is laced into a business’s culture and workflows. That in itself is a sizable feat, the partners say, possible only with the guidance of company leaders.
“When companies are looking to do fundamental digital transformations and reinvention of the business,” Sirosh says, “there is incredible value in having top-down guidance drive much of that activity.”
Workforce shifts and workflow transformation aside, Sirosh and Menon advise concerned executives to focus on the foundation of AI. The goal of such a sophisticated technology, they say, is rather simplistic.
AI, informed by location data, helps organizations reason and interact with the increasingly sophisticated world around us,” Sirosh says.
“If I had to put it in one term,” Menon adds, “AI is basically about decision-making — smarter decision making.”
(Listen to a podcast featuring Joseph Sirosh to explore this concept in more depth, including a look at how AI is changing business models.)
Marianna Kantor joined Esri as chief marketing officer in 2015. Prior to Esri, Marianna was the VP of Marketing at PTC, where she built the worldwide services marketing and field-enablement organization, helping drive sustained revenue growth in dynamic and changing markets. Marianna has held technology and marketing leadership positions throughout her career in leading organizations such as AT&T, Akamai, and Los Alamos National Labs. At Esri, Marianna is exposing and amplifying the transformational capabilities of geospatial technology as an indispensable tool for problem solving and decision making in business and government. Marianna holds two engineering degrees from Columbia University and University of Pennsylvania, and an Executive MBA from MIT.  As Esri’s chief technology officer, Jay Theodore guides the long-term vision for the ArcGIS platform. Jay is passionate about harnessing innovative ideas to increase the value companies gain from location intelligence, geoscience, computer science, and technology. He takes great pride in working with outstanding software developers, architects, and product engineers. Jay earned a master’s degree in computer science from Florida Institute of Technology, where his research focused on finite element analysis and modeling (FEA/FEM), computer graphics, and composite structure design for Space Station Freedom. He also holds a bachelor’s degree in computer engineering.
Hexagon AB has launched the Leica RTC360, a laser scanner equipped with edge computing technology to enable fast and accurate creation of 3D models in the field. The Leica RTC360 is one of many innovations showcased at HxGN Live 2018, the company’s annual digital technology conference.
According to Hexagon, the Leica RTC360 combines high-performance laser scanning, edge computing and mobile app technologies to pre-register captured scans quickly and accurately. With the push of a button, two million points per second of high dynamic range imagery can be captured to create a full-dome scan in under two minutes, Hexagon added.
In addition, the laser scanner features a visual inertial system that automatically tracks movements between setup positions. The scans captured by the Leica RTC360 can be combined and pre-registered on a mobile device, where they can be viewed and augmented with information tags.
“We designed the Leica RTC360 for maximum productivity. For construction professionals, plant operators, public safety officials and other professionals who face complex projects with tight constraints, it provides a better way to digitally capture the reality of their sites — and process and visualize that data for faster, immediate decision making,” said Ola Rollén, Hexagon president and CEO. “What these professionals do on site every day is challenging, and we aim to continue to make their work quicker, easier and more accurate.”
Hexagon AB provides digital solutions that create autonomous connected ecosystems, a state where data is connected seamlessly through the convergence of the physical world with the digital, and intelligence is built-in to all processes.
His success did not end there. Based on his experiences fighting criminals on the streets and fighting change in the antiquated police system, he wrote the book, 
MapVault users have access to a diverse collection of topographic, aeronautical, nautical and geological map series sourced from international mapping agencies. Each series has been mosaicked for easy use and quick navigation. Robust metadata along with series index maps and individual sheet-level metadata are included.
Marianna Kantor joined Esri as chief marketing officer in 2015. Prior to Esri, Marianna was the VP of Marketing at PTC, where she built the worldwide services marketing and field-enablement organization, helping drive sustained revenue growth in dynamic and changing markets. Marianna has held technology and marketing leadership positions throughout her career in leading organizations such as AT&T, Akamai, and Los Alamos National Labs. At Esri, Marianna is exposing and amplifying the transformational capabilities of geospatial technology as an indispensable tool for problem solving and decision making in business and government. Marianna holds two engineering degrees from Columbia University and University of Pennsylvania, and an Executive MBA from MIT.
As Esri’s chief technology officer, Jay Theodore guides the long-term vision for the ArcGIS platform. Jay is passionate about harnessing innovative ideas to increase the value companies gain from location intelligence, geoscience, computer science, and technology. He takes great pride in working with outstanding software developers, architects, and product engineers. Jay earned a master’s degree in computer science from Florida Institute of Technology, where his research focused on finite element analysis and modeling (FEA/FEM), computer graphics, and composite structure design for Space Station Freedom. He also holds a bachelor’s degree in computer engineering.