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DA-32 - GIS&T and Natural Resource Management

Geographic Information Systems (GIS) is a geospatial technology that has matured with the help of natural resource management applications. Since its early beginnings as an extension of cartography, GIS has been used to capture, manipulate, store, analyze and manage data. GIS has matured as additional sciences began to adopt and apply it to multidisciplinary problems. In the mid-90s, much of the emphasis moved to desktop GIS making the access and use more mainstream and capable on personal desktop computers. Government agencies with more available and distributed datasets through the internet enabled more applications and use across disciplines because of the access. Soil scientists, wildlife biologists, hydrologists, engineers, planners, and others could now pursue spatial problems efficiently and effectively. More and more advances were being made in the sciences due to the new technology. The following discussion will focus on the use and applications of GIS for natural resource management. Areas covered in this review will be for forestry, watershed analysis, wildlife management, and landscape analysis. First a background of the applications will be introduced followed by a discussion of their applicability and uses.

DA-13 - GIS&T in Criminal Justice and Law Enforcement

Linking crime and place has been the objective of crime mapping since the early nineteenth century. Contemporary scholars have since investigated spatio-temporal crime patterns to explain why crime concentrates in certain places during certain times. Collectively, this body of research has identified various environmental and situational factors that contribute to the formation of crime hot spots and spawned widespread crime prevention and reduction strategies commonly referred to as place-based policing.  Environmental criminology guides the bulk of this crime-and-place research and provides a means for interpreting place and crime. The chapter details theories behind place-based policing, examples of place-based policing strategies that leverage geographic information science and its associated technologies (GIS&T), and relevant data visualization tools used by law enforcement to implement place-based strategies to address crime.

PD-28 - Visual Programming for GIS Applications

Visual programming languages (VPLs) in GIS applications are used to design the automatic processing of spatial data in an easy visual form. The resulted visual workflow is useful when the same processing steps need to be repeated on different spatial data (e.g. other areas, another period). In the case of visual programming languages, simple graphical symbols represent spatial operations implemented in GIS software (tools, geoalgorithms). Users can create a sequence of operation in a simple visual form, like a chain of graphical symbols. Visual programs can be stored and reused. The graphical form is useful to non-programmers who are not familiar with a textual programming language, as is the case with many professionals such as urban planners, facility managers, ecologists and other users of GIS. VPLs are implemented not only in GIS applications but also in remote sensing (RS) applications. Sometimes both types of applications are bundled together in one geospatial application that offers geoalgorithms in a shared VPL environment. Visual programming languages are an integral part of software engineering (SE). Data flow and workflow diagrams are one of the oldest graphical representations in informatics.

CP-23 - Google Earth Engine

Google Earth Engine (GEE) is a cloud-based platform for planetary scale geospatial data analysis and communication.  By placing more than 17 petabytes of earth science data and the tools needed to access, filter, perform, and export analyses in the same easy to use application, users are able to explore and scale up analyses in both space and time without any of the hassles traditionally encountered with big data analysis.  Constant development and refinement have propelled GEE into one of the most advanced and accessible cloud-based geospatial analysis platforms available, and the near real time data ingestion and interface flexibility means users can go from observation to presentation in a single window.

CV-04 - Scale and Generalization

Scale and generalization are two fundamental, related concepts in geospatial data. Scale has multiple meanings depending on context, both within geographic information science and in other disciplines. Typically it refers to relative proportions between objects in the real world and their representations. Generalization is the act of modifying detail, usually reducing it, in geospatial data. It is often driven by a need to represent data at coarsened resolution, being typically a consequence of reducing representation scale. Multiple computations and graphical modication processes can be used to achieve generalization, each introducing increased abstraction to the data, its symbolization, or both.

AM-09 - Classification and Clustering

Classification and clustering are often confused with each other, or used interchangeably. Clustering and classification are distinguished by whether the number and type of classes are known beforehand (classification), or if they are learned from the data (clustering). The overarching goal of classification and clustering is to place observations into groups that share similar characteristics while maximizing the separation of the groups that are dissimilar to each other. Clusters are found in environmental and social applications, and classification is a common way of organizing information. Both are used in many areas of GIS including spatial cluster detection, remote sensing classification, cartography, and spatial analysis. Cartographic classification methods present a simplified way to examine some classification and clustering methods, and these will be explored in more depth with example applications.

DA-05 - GIS&T and Local Government

GIS is an important tool for local governments. It is utilized to provide spatial information, metrics, and visualizations to constituents, businesses, and decision-makers. Internally, a well-managed GIS can be the basis for innovation and process improvement and can be a single source for employees to find a plethora of integrated data. This entry discusses how GIS supports local government, important considerations for maintaining a successful local government GIS, and current trends. This entry is based on the author’s experience in a GIS program at a medium-sized city in the Rocky Mountain Region of the United States. Not everything discussed may apply to other areas of the country or world. Additionally, smaller-sized programs may not have the resources to implement everything discussed. The key purpose of this entry is to provide students and instructors with tangible examples of processes, skills, and organizational structures that make for an effective local government GIS.

PD-12 - Commercialization of GIS Applications

The commercialization of GIS applications refers to the process of bringing a software solution to market. The process involves three broad categories of tasks: identifying a problem or aspect of a problem that a GIS application can solve or address; designing and creating a GIS application to address the problem; and developing and executing a marketing plan to reach those with the problem, the potential users. Ideally these categories would be addressed in this order, but in practice, aspects of each are likely to be addressed and iterated throughout the commercialization process.

Bringing a GIS application to market requires expertise in 1) the target industry or market (e.g., forestry); 2) software development (how to design and build a product); 3) law (licenses, contracts, taxes); and 4) business (how to fund development, guide the process, evaluate success, marketing). A single individual or organization, referred to as the provider in this discussion, may lead or execute all three categories of tasks, or engage third parties when specific expertise is required.

FC-27 - Thematic Accuracy Assessment

Geographic Information System (GIS) applications often involve various analytical techniques and geographic data to produce thematic maps for gaining a better understanding of geospatial situations to support spatial decisions. Accuracy assessment of a thematic map is necessary for evaluating the quality of the research results and ensuring appropriate use of the geographic data. Thematic accuracy deals with evaluating the accuracy of the attributes or labels of mapped features by comparing them to a reference that is assumed to be true. The fundamental practice presents the remote sensing approach to thematic accuracy assessment as a good guidance. For instance, the accuracy of a remote sensing image can be represented as an error matrix when the map and reference classification are conducted based on categories. This entry introduces basic concepts and techniques used in conducting thematic accuracy with an emphasis on land cover classification based on remote sensing images. The entry first introduces concepts of spatial uncertainty and spatial data quality standards and further gives an example of how spatial data quality affects thematic accuracy. Additionally, the entry illustrates the techniques that can be used to access thematic accuracy as well as using spatial autocorrelation in thematic accuracy sampling design.

GS-29 - GIS Participatory Modeling

Participatory research is increasingly used to better understand complex social-environmental problems and design solutions through diverse and inclusive stakeholder engagement. A growing number of approaches are helping to foster co-production of knowledge among diverse stakeholders. However, most methods don’t allow stakeholders to directly interact with the models that often drive environmental decision-making. Geospatial participatory modeling (GPM) is an approach that engages stakeholders in co-development and interpretation of models through dynamic geovisualization and simulations. GPM can be used to represent dynamic landscape processes and spatially explicit management scenarios, such as land use change or climate adaptation, enhancing opportunities for co-learning. GPM can provide multiple benefits over non-spatial approaches for participatory research processes, by (a) personalizing connections to problems and their solutions, (b) resolving abstract notions of connectivity, and (c) clarifying the scales of drivers, data, and decision-making authority. An adaptive, iterative process of model development, sharing, and revision can drive innovation of methods, improve model realism or applicability, and build capacity for stakeholders to leverage new knowledge gained from the process. This co-production of knowledge enables participants to more fully understand problems, evaluate the acceptability of trade-offs, and build buy-in for management actions in the places where they live and work.

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