The GIS Integration Challenge
ABSTRACT: GIS integrates a fleet of rapidly evolving skills and expertise not entirely dissimilar to other highly-applicable, highly-technological fields. The GIS integration challenge refers to the lack of professional focus on this grey portion of the GIS-expertise spectrum. These obstacles will 1) improve the ability to provide a supremely evolved spatial-decision making calculus, and 2) degrade the ability of the individual to provide an all-encompassing set of data to decision-makers. How can we as a community of interest help grapple with the diversity of booming challenges in and adjacent to our fields of expertise?
Many in the spatial sciences fields can remark on a litany of Geographic Information Systems (GIS) challenges. “Which GIS challenge?” is probably a better lead-off tag-line, but GIS fields are not exceptional in this context. If you have been working in the GIS or Remote Sensing industry for any amount of time, you are likely to have been exposed to information or knowledge you had not considered, applied, or perhaps did not know existed. Access to unmanageable quantities of resources and knowledge is not a challenge unique to GIS fields. This is a challenge shared by many in-depth fields like engineering and medicine. The challenge with GIS is the broad array of evolutions concurrently impacting every niche aspect of GIS.
GIS integrates a fleet of rapidly evolving skills and expertise. This is not entirely dissimilar to other highly-applicable, highly-technological fields. A disparity exists between two very unique ends of the GIS expertise spectrum. A GIS generalist is often not quite proficient enough for some more in-depth aspects of GIS. A GIS specialist is often not well-informed enough to fully understand or implement adjacent applications, practices and technology. A remote sensing automating expert who understands how to convert raster to relevant vector data, for example, would often not possess the legal background to determine public policy risks to a process. So what is the GIS integration challenge?
The GIS integration challenge refers to the lack of professional focus on this grey portion of the aforementioned GIS spectrum. It is important to identify what the GIS integration challenge is not. Access to information may be challenging at times, but that is true in any major industry. The challenge is not related to the development and evolution of specific fields that apply to GIS. The challenge is bridging the gap between discrete expertise and generalists. The challenge is a general absence of emphasis on the importance of the integrator, and associated certification, experience and rare aptitudes.
Many developing aspects of GIS are well-documented. It would be unreasonable to fully detail them all here as this is not exhaustive research. Some of the applications are so closely affiliated with GIS, it is hard to imagine GIS experts not tracking or keeping abreast of their advancements.
Global Positioning Systems (GPS) and navigation are growing fields of expertise. Each day the world becomes more dependent on GPS data and technology. GPS served via GIS is the solitary user interface for navigation with few exceptions. Navigation enhancements are integrating more and more GIS service layers and data sources to include open source, traffic conditions, fuel consumption, CO2 emissions and known speed traps. GIS is integrating GPS data and serves as the primary interface for users around the world to navigate, incorporating more and more crowd-sourced information.
Big data is now a significant integration challenge for GIS. Big data typically refers to data Volume, Variety, Velocity, Veracity and Value, but in this case the two aspects presenting the most challenging integration problem is High-Velocity, High-Volume (HVHV) data. GIS data dependencies on HVHV data are increasing. This requires an understanding of the databases and architecture that supports the consumption, analysis and value proposition of integrating this data. Customers are seeking to increase the fusion of a wider variety of HVHV data into a single decision-aid, product or visualization. This is not easy, and it is complicated by the increase in demand for near-real time analytics. Quality and veracity of HVHV data can be difficult to assess in comparison to more traditional, more static data sets. Thus better methodologies are needed to sift, measure, normalize and throughput usable HVHV data that demonstrates veracity. These challenges are compounded by the desire to handle all of this this in near-real time.
Not all data integrity challenges are related to HVHV data. Crowd-sourced data is an important, new component of data outsourcing. It provides a voluntary element in situations where alternatives limit the ability to support decision makers. Crowd-sourced data results from an ever-evolving “sport,” often driving participation by a point-based system. Obtaining data this way raises concerns regarding questionable validity, accuracy and precision which can limit the value of integrating this data. Failing to incorporate crowd-sourced data is almost negligent in an advanced society. Harnessing crowd-sourced information, particularly spatial data, provides too much value to ignore. Where do GIS practitioners draw the line and how is that determination made? How can data science help, or rather can it?
The Data Science trade is a relatively nascent trade despite its established applications in statistics and computer programming. While Data Science is not directly aligned with GIS, its applications are nearly one-for-one in GIS data expertise. Data Science applications, approaches, methods, and technologies can help GIS practitioners with data valuation, refinement, and even the development of previously unexplored research topics. But, can data science tell us more about the value of X, Y and Z (coordinates) than we can already extract within a GIS? And, what aspects of spatial science might enhance Data Science as a trade? Certainly, the application of Data Science is demonstrated by some more recent phenomenologies in Remote Sensing disciplines.
Remote Sensing, and Terrestrial Imaging are not new fields, nor are they strangers to GIS integration. Perhaps Remote Sensing was the original GIS Challenge, merging raster, matrix and vector data types. The growth of these applications in modern society, coupled with accessibility and the growing use don’t simplify this age-old GIS integration challenge. New micro-satellites, and airborne sensor’s novel phenomenology applications (e.g. LiDAR, Hyper-Spectral Imagery, RADAR) are increasing in commercial, public-sector and research industries. Terrestrial Imaging (to include video) is on the rise in an era of ever-increasing state, corporate and personal surveillance. Integrating into GIS has only just begun, but separating the signal from the noise is the challenge in a Petabyte per Day environment. Perhaps there can be an advancement in technology that will help us comb, fuse and warehouse this information more effectively.
At the end of the day, these challenges are not just GIS challenges. They are obstacles that will 1) improve the ability to provide a supremely evolved spatial-decision making calculus, and 2) degrade the ability of the individual to provide an all-encompassing set of data to decision-makers. With the advancement of adjacent fields come opportunities and pitfalls. These must be considered because Artificial Intelligence may provide solutions, just as informed legal advice ensures GIS practitioners’ work remains relevant, sound and legal. Addressing the technical aspect is only half the story because policies, from local to international policies, affect the way we do business. This is all to say that collaboration and education are critical.
But what education exists for the GIS integrator? There are many one-off certificates and courses that can fill-in the expertise to support these applications. There are not applied GIS integration degrees nor are there spatial integration professional certifications. For many of us, this is the draw to conferences and symposiums — a chance to collaborate with other experts in our field of expertise, and to be exposed to novel concepts being developed in adjacent fields. Assembling teams with these sorts of expertise can be equally challenging. GIS Professional (GISP) certifications and Geospatial or Geographic Sciences degrees are important, but they do not address the growing need for multifaceted expertise seeking to connect and leverage the power of many of the technologies mentioned herein. So why are spatial sciences and GIS not viewed in the same way as engineering and medicine? How can we as a community of interest help grapple with the diversity of booming challenges in and adjacent to our fields of expertise?