 This course has tried to make the case, and to help you make the case that GIS matters. Each lesson began with a story about the roles geospatial technology and GIS people play in fighting disease, in helping assess progress toward the United Nations sustainable development goals, in supporting analysis and modeling of everyday tasks and extreme events, and in enabling developers and app builders to lever the power of geography to inform the public and policymakers. Beyond the case studies presented here, there's plenty of evidence that the work we do is valuable. We've seen that the economic impact of so-called geoservices is estimated to be $2 to $300 billion per year worldwide, and Google's definition of geoservices didn't even include key business sectors of the GIS industry, such as local, state, and national governments. We know that geoservices increase efficiency across many industries, that they make business operations greener and cities smarter, and that they even help save lives. Half a million livelihoods or more around the world depend on GIS technology. Thousands of GIS users joined the annual pilgrimage to San Diego for the Esri User Conference. Nearly 10,000 individuals have voluntarily earned certification as GIS professionals, and thousands of students like you seek education and training opportunities every year to help start or advance careers related to GIS. Summing it all up, there's plenty of evidence that GIS matters, but there are also signs that the world is changing in fundamental ways. Will GIS still matter, and will GIS professionals continue to have meaningful and rewarding roles to play in the years to come? First, let's consider whether GIS itself is likely to persist. This guy is Mark Weiser. Mark was a leading computer scientist at Xerox PARC Labs. As you may know, PARC is where Apple got ideas about graphical user interfaces and the mouse, among other things. In 1995, Mark famously wrote in a Scientific American article that the most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it. As an example, Weiser asked us to consider writing. He pointed out that our lives are suffused with literacy technology, written information in printed or digital forms, and in street signs, billboards, shop signs, and even graffiti. These do not require active attention, Weiser wrote, but the information to be conveyed is ready for use at a glance. By contrast, he said, silicon-based information technology is far from having become part of the environment. How things have changed since Weiser's untimely passing in 1999. One big change is the ongoing evolution of the Internet into a ubiquitous network of interconnected objects, an Internet of Things. In the video prelude to this case study, the narrator states that the Internet of Things is changing much about the world we live in, from the way we drive to the way we make purchases, and even how we get energy to our home. Sophisticated chips and sensors are embedded in the physical things that surround us, we're told. In fact, the Organization for Economic Cooperation and Development, and others, project that the IoT will connect 50 billion devices by 2020. What kinds of things? Consider this chart from the OECD's digital economy outlook. As of this writing, the OECD includes 35 developed countries with high-income economies and high human development indices. The chart compares the number and kinds of connected devices in use in typical households in OECD countries in 2012, 2017, and 2022. How does your household compare with the one described here? Gartner, the influential corporate strategy advisors, predict $30 trillion of spending on the Internet of Things by 2020. Connected automobiles and other transportation modes will account for a large share of spending, as will home automation, security, and energy management. Gartner stresses that the IoT is not one thing. It's the integration of several technologies that sense and collect data, that analyze the data, and take action upon the data to accomplish business goals. Make no mistake, the IoT is all about business. Beecham Research, a technology market research consultancy, specializes in what it calls the connected devices market, sometimes referred to as the M2M or machine-to-machine sector, also known as the Internet of Things. Like Gartner, Beecham sells market research reports and advice to businesses that seek opportunity in the IoT. Beecham created this amazing map of the IoT to help its clients identify business opportunities. The map highlights nine market sectors, buildings, energy, consumer and home, healthcare and life science, industrial, transportation, retail, security and public safety, and IT and networks. The map also identifies applications and particular devices within each sector. For example, the security and public safety sector includes surveillance applications, connected equipment including weapons, vehicles, ships and other gear, location tracking of people and assets, connected public infrastructure, such as water treatment facilities and environmental sensors, and connected emergency services personnel and equipment. Consider that every one of those billions of IoT devices has a location and many are location aware. We'll consider an implication of that in a minute. If the IoT makes you nervous, it's probably because of hackers. Many of those billions of IoT connected devices have a tiny bit of computing power. Hackers who can harness millions of tiny computing devices can combine them to create a massive computing capacity they can use to mount large scale attacks on businesses, government agencies and public infrastructure. Cyber security threats are also business opportunities. Beecham research maps these threats and offers advice to businesses that aim to sell security solutions. Gartner says it expects many new IoT security and management vendors to arrive on the scene. Lots of new businesses are focusing on IoT. There's even a Geo IoT World Conference and Geo IoT World Awards for innovative products and services. At the second Geo IoT World Conference in Brussels, four companies were recognized. One is Crowdlock, a low-power location tracking service designed to prevent theft and facilitate recovery of stolen vehicles and other assets. Another award winner is Onyx Beacon, a real-time location service that uses Bluetooth beacons to track the locations of assets like utility vehicles. Wittra is a low-power indoor positioning system that uses radio frequency bands reserved for industrial, scientific and medical purposes. And CIO describes itself as a precise indoor tracking and data analytics platform for the digitization of movement in industry 4.0, retail and sport. Industry 4.0, by the way, refers to what the World Economic Forum calls the fourth industrial revolution. First came an agricultural revolution about 10,000 years ago. Then, beginning in the 18th century, the invention of the steam engine and construction of railroads brought the first industrial revolution. A second industrial revolution began in the 19th century with the advent of mass production. Digital computers heralded a third industrial revolution beginning in the 1960s. And today, the drivers of the fourth industrial revolution include a ubiquitous and mobile internet, smaller, cheaper and more powerful sensors, and artificial intelligence and machine learning. You can think of these diagrams as treasure maps. Use your imagination. How might geospatial technologies, analytics and apps create value from the big data and the big threats produced by the Internet of Things? As Alec Ross writes in The Industries of Tomorrow, If you can imagine an innovation in information technology, chances are somebody somewhere is already working on developing and commercializing it. Tim Forsman is a former United Nations Chief Environmental Scientist and National Manager for the Digital Earth Initiative under Vice President Al Gore. With Operations Research Specialist Ruth Luscombe of Brisbane, Australia, Forsman recently proposed a second law of geography for the fourth industrial age. Things that know where they are can act on their locational knowledge, Forsman and Luscombe assert. Furthermore, spatially-enabled things have an increased financial and functional utility. This increased utility, they argue, creates the basis for a spatially-enabled economy. That is the economy we and our children will inherit. Location is elemental to the spatially-enabled economy and to the IoT. Location analytics is a defining feature of geographic information systems. So, will GIS, as we know it, still be a thing in the Internet of Things? Or will it disappear, like other profound technologies? As we certainly believe that GIS is IoT ready and here to stay, the ArcGIS Enterprise suite introduced in 2017 includes specialized server technologies engineered to ingest, analyze and store millions of sensor events per second. That's fast enough, as reclames, to monitor all the sensors and smart beaters used by major water, oil, gas and electric utilities, and to track and analyze the movement and disposition of large fleets of trucks, ships and aircraft. Meanwhile, leading data management and analytics corporations like Oracle and SAP, cloud vendors such as Google and Salesforce, and established industrial technology providers like General Electric, are expected to offer their own IoT platforms. Will GIS successfully compete with those? Or integrate with them? Or will its key capabilities disappear into them? Paper maps and portable navigation devices have already disappeared, in a sense. They are counted among the 10 things killed by the smartphone. By accelerating the trend toward integration of GIS with mainstream information technology, will the Internet of Things and the Fourth Industrial Revolution kill GIS? On the other hand, recall Mark Weiser's observation that profound technologies disappear. If geographic information systems persist, does that mean they are not a profound technology? Is spatial really not special after all, but just another data type? More important than the fate of GIS technology per se are the prospects for GIS people. Will the education you're investing so much in have lasting value? According to the US Department of Labor, the outlook for geospatial information scientists and technologists is bright. DOL estimates that nearly a quarter of a million people are employed in this occupation, and although predicted growth is just 2-4% through 2024, that's still nearly 38,000 additional GIS jobs in this one GIS-related occupation in the US alone. On the other hand, thought leaders concerned with the impacts of the Fourth Industrial Revolution worry that many of today's occupations may not be sustainable. In a widely-cited research article, economist Carl Benedict Frey and machine-learning researcher Michael Osborn estimate that 47% of US workers are at risk of technological unemployment. Of the 702 occupations Frey and Osborn analyzed, one of the most susceptible was surveying and mapping technicians. Frey and Osborn calculated a 96% probability that workers in that occupation will be displaced by automation in the coming decade or two. Although the Bureau of Labor Statistics predicts only an 8% decline in surveying and mapping technician jobs, it does attribute the decline to advances in technology. Although Frey's and Osborn's research has its critics, their prognosis is generally consistent with a body of research by economists, tech leaders, and forward-looking historians who anticipate fundamental disruption of traditional employment by increasingly capable machines. What does this mean for GIS work? Innovation expert Alec Ross observes that through history our most valued commodities have gone from salt and sugar to chemicals and fuels to data and services. Not just the Internet of Things, but international finance, social media, and other human activities generate an unprecedented and ever-increasing volume, velocity, and variety of data. Google Chairman Eric Schmidt once claimed that we create as much information every two days as was created between the dawn of civilization until 2003. Tim Forsman and Ruth Luskum observe that the magnitude and complexity of data has outpaced the expertise and modeling capacity of scientists and economists. Human analysts and their employers, Ross and others foresee, will rely increasingly on machine learning and artificial intelligence to cope with the data deluge. In 2014, Zhang Zhen and colleagues published an illuminating paper about how the IoT enables planners and engineers to design smart cities. Illustrated by a case study involving noise mapping in Melbourne, Australia, Jin and Tim discussed the data collection, data processing and management, and data interpretation aspects of an IoT-enabled urban information system. GIS plays a role in their framework, specifically for the integration and visualization of geo-referenced data. Considering the massive data throughputs generated by the IoT, Jin and colleagues observe that to make sense of the information and convert it into knowledge, state-of-the-art computational intelligence techniques such as genetic algorithms, evolutionary algorithms, and neural networks are necessary. Machine learning, they conclude, will help achieve automated decision-making and provide useful policy. Think about that for a minute. Automated decision-making. Remember the ethical algorithms discussed earlier in the course in relation to self-driving cars? Can you imagine the ethical algorithms that would be needed for an autonomous urban decision support system? Richard and Daniel Suskind, authors of The Future of the Professions, foresee that in the long run, increasingly capable machines will transform the work of professionals, leaving most to be replaced by less expert people and high-performing systems. Their hope is that practical expertise will become more openly available, freeing many users from obstacles currently imposed by gatekeepers like physicians, lawyers, accountants and, well, surveying and mapping technicians. Predictions like the Suskinds about a coming robo-pocalypse have given rise to what Wired magazine called the Great Tech Panic. Columnist James Surowicki argues that the evidence disagrees that automation will take away our jobs. Neither the increased productivity that should accompany automation nor growing unemployment are evident. Surowicki points out that the U.S. corporate investment in robotics in 2016 was just $11.3 billion, about one-sixth of what Americans spend every year on their pets. And he cites economist James Besson, who found that of the 271 occupations listed in the 1950 census, only one had been rendered obsolete by automation, elevator operators. So if this is what your GIS work feels like, you probably should worry. Surowicki rightly points out that the outsourcing of work to machines is not new. From the cotton gin to the washing machine to the car, jobs have been destroyed, but others have been created. Over and over, he reminds us, we've been terrible at envisioning the new jobs people would end up doing. The Suskinds recognize this and don't predict future occupations that may replace the traditional professions. However, they do suggest 12 future roles that education should help people prepare for. Several of these, and one in particular, are related to the knowledge and skills we've discussed in this class. Remember this diagram of the knowledge and skills that data scientists possessed? For Stephen Colassa, who drew the diagram, data scientists combine competencies in statistics, programming, business, and communication. The Suskinds described data scientists as, masters of the tools and techniques required to capture and analyze large bodies of information with the intent of identifying correlations, trends, and causal insights. This course has provided an overview of the core competencies that workers in the geospatial technology industry exhibit. How many of these do you think may be relevant to the role of spatial data scientist? I believe that if you pursue an education that balances knowledge and skills in data acquisition and wrangling, spatial analysis and modeling, and coding and app building, you'll be pretty well prepared for current and future roles. And of all the competencies needed to navigate an uncertain future, the most valuable may be ongoing, voluntary, and self-motivated pursuit of knowledge on your own or as part of a team. It's that foundational competency learning itself that this course has emphasized most of all.