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FC-12 - Structured Query Language (SQL) and attribute queries

The structured query language (SQL) for database interrogation is presented and illustrated with a few examples using attribute tables one might find in a common GIS database.  A short background is presented on the history and goals that the creators of the SQL language hoped to achieve, followed by a review of SQL utility for data query, editing, and definition.  While the SQL language is rich in content and breadth, this article attempts to build on a simple SQL and then iteratively add additional complexity to highlight the power that SQL affords to the GIS professional who has limited programming capabilities.  The reader is asked to consider how minor modifications to SQL syntax can add complexity and even create more dynamic mathematical models with simple English-like command statements.  Finally, the reader is challenged to consider how terse SQL statements may be used to replace relatively long and laborious command sequences required by a GIS GUI approach.

KE-12 - GIS&T Project Planning and Management

GIS&T project planning and management falls under the broader category of project management (PM) in general and information technology (IT) PM in particular, providing a rich background and guidelines that are stewarded by associations and their certifications. The lifecycle of a project or its component phases involves a number of process groups involving a series of actions leading to a result that are sequenced in the following manner: initiating, planning, executing and controlling, and closing. Effective project planning and management requires understanding of its knowledge areas in the project management body of knowledge (PM BoK), which include integration, scope, time, cost, quality, human resource, communications, risk, procurement, and stakeholder management. Numerous tools and techniques are available to assist the project manager in planning, executing, and controlling these efforts, some of which are specific to GIS&T projects. The distinctiveness of GIS&T project planning and management lies in an understanding of the uniqueness, overlap and connections that exist between the PM BoK and the GIS&T BoK, both of which have achieved new levels of maturity in recent decades. 

CP-04 - Artificial Intelligence Tools and Platforms for GIS

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

PD-05 - Design, Development, Testing, and Deployment of GIS Applications

A systems development life cycle (SDLC) denes and guides the activities and milestones in the design, development, testing, and de ployment of software applications & information systems. Various choices of SDLC are available for different types of software applications & information systems and compositions of development teams and stakeholders. While the choice of an SDLC for building geographic information system (GIS) applications is similar to that of other types of software applications, critical decisions in each phase of the GIS development life cycle (GiSDLC) should take into account essential questions concern ing the storage, access, and analysis of (geo)spatial data for the target application. This article aims to introduce various considerations in the GiSDLC, from the perspectives of handling (geo)spatial data. The article rst introduces several (geo)spatial processes and types as well as various modalities of GIS applications. Then the article gives a brief introduction to an SDLC, including explaining the role of (geo)spatial data in the SDLC. Finally, the article uses two existing real-world applications as an example to highlight critical considerations in the GiSDLC.

FC-31 - Academic Developments of GIS&T in English-speaking Countries: a Partial History

The constellation of science and technology that is now considered a unit (Geographic Information Science and Technology – GIS&T) has emerged from many source disciplines through many divergent and convergent pasts in different times and places. This narrative limits itself to the perspective of the English-speaking community, leaving other regions for a separate chapter As in the case of many technical developments in the second half of the twentieth century, academic institutions played a key (though far from exclusive) role in innovation and risk-taking. In a number of locations, academic innovators tried out new technology for handling geographic information, beginning as early as the 1960s. Three institutions (University of Washington, Laboratory for Computer Graphics – Harvard University, and Experimental Cartography Unit – Royal College of Art (UK)) deserve particular treatment as examples of the early innovation process. Their innovations may look crude by current standards, but they laid some groundwork for later developments. Academic institutions played a key role in innovation over the past decades, but the positioning of that role has shifted as first government, then commercial sectors have taken the lead in certain aspects of GIS&T. Current pressures on the academic sector may act to reduce this role.

DM-79 - U.S. National Spatial Data Infrastructure

Spatial data infrastructures may be thought of as socio-technical frameworks for coordinating the development, management, sharing and use of geospatial data across multiple organizational jurisdictions and varying geographic extents. The United States was an early adopter of the SDI concept and the U.S. National Spatial Data Infrastructure (NSDI) is an example of a country-wide SDI implementation facilitated by coordination at the federal-government level. At the time of its establishment in the early 1990s, a unique characteristic of the NSDI was a mandate for federal agencies to establish partnerships with state- and local-level government. This entry summarizes the origins of the NSDI’s establishment, its original core components and how they’ve evolved over the last 25 years, the role of the Federal Geographic Data Committee (FGDC), and the anticipated impact of passage of the Geospatial Data Act of 2018. For broader technical information about SDIs, readers are referred to GIST BoK Entry DM-60: Spatial Data Infrastructures (Hu and Li 2017). For additional details on the history of the NSDI, readers are referred to Rhind (1999). For the latest information on recent and emerging NSDI initiatives, please visit the FGDC web site (  

AM-107 - Spatial Data Uncertainty

Although spatial data users may not be aware of the inherent uncertainty in all the datasets they use, it is critical to evaluate data quality in order to understand the validity and limitations of any conclusions based on spatial data. Spatial data uncertainty is inevitable as all representations of the real world are imperfect. This topic presents the importance of understanding spatial data uncertainty and discusses major methods and models to communicate, represent, and quantify positional and attribute uncertainty in spatial data, including both analytical and simulation approaches. Geo-semantic uncertainty that involves vague geographic concepts and classes is also addressed from the perspectives of fuzzy-set approaches and cognitive experiments. Potential methods that can be implemented to assess the quality of large volumes of crowd-sourced geographic data are also discussed. Finally, this topic ends with future directions to further research on spatial data quality and uncertainty.

KE-25 - GIS&T Education and Training

GIS education and training have their roots both in formal educational settings and in professional development.  Methods and approaches for teaching and learning about and with geospatial technologies have evolved in tight connection with the advances in the internet and personal computers.  The adoption and integration of GIS and related geospatial technologies into dozens of academic disciplines has led to a high demand for instruction that is targeted and timely, a combination that is challenging to meet consistently with diverse audiences and in diverse settings. Academic degrees, concentrations, minors, certificates, and numerous other programs abound within formal and informal education.

AM-97 - An Introduction to Spatial Data Mining

The goal of spatial data mining is to discover potentially useful, interesting, and non-trivial patterns from spatial data-sets (e.g., GPS trajectory of smartphones). Spatial data mining is societally important having applications in public health, public safety, climate science, etc. For example, in epidemiology, spatial data mining helps to nd areas with a high concentration of disease incidents to manage disease outbreaks. Computational methods are needed to discover spatial patterns since the volume and velocity of spatial data exceed the ability of human experts to analyze it. Spatial data has unique characteristics like spatial autocorrelation and spatial heterogeneity which violate the i.i.d (Independent and Identically Distributed) assumption of traditional statistic and data mining methods. Therefore, using traditional methods may miss patterns or may yield spurious patterns, which are costly in societal applications. Further, there are additional challenges such as MAUP (Modiable Areal Unit Problem) as illustrated by a recent court case debating gerrymandering in elections. In this article, we discuss tools and computational methods of spatial data mining, focusing on the primary spatial pattern families: hotspot detection, collocation detection, spatial prediction, and spatial outlier detection. Hotspot detection methods use domain information to accurately model more active and high-density areas. Collocation detection methods find objects whose instances are in proximity to each other in a location. Spatial prediction approaches explicitly model the neighborhood relationship of locations to predict target variables from input features. Finally, spatial outlier detection methods find data that differ from their neighbors. Lastly, we describe future research and trends in spatial data mining.

FC-42 - Distance Operations

Distance is a central concept in geography, and consequently, there are various types of operations that leverage the concept of distance. This short article introduces common distance measures, the purpose of distance operations, different types of operations and considerations, as well as sample applications in the physical and social domains. Distance operations can be performed on both vector or raster data, but the operations and results may differ. While performing distance operations, it is important to remember how distance is conceptualized while performing the operation.