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AM-12 - Cartographic Modeling

Cartographic modeling is an integrated sequence of data processing tasks that organize, combine, analyze and display information to answer a question. Cartographic modeling is effective in GIS environments because they rely heavily upon visualization, making it easy to show input and output layers in map form. In many GIS platforms, the sequence of tasks can be created and modified graphically as well. The modeling is visual, intuitive, and requires some knowledge of GIS commands and data preparation, along with curiosity to answer a particular question about the environment. It does not require programming skill. Cartographic modeling has been used in applications to delineate habitats, to solve network routing problems, to assess risk of storm runoff across digital terrain, and to conserve fragile landscapes. Historical roots emphasize manual and later automated map overlay. Cartographic models can take three forms (descriptive, prescriptive and normative). Stages in cartographic modeling identify criteria that meet an overarching goal; collect data describing each criterion in map form; design a flowchart showing data, GIS operations and parameters; implement the model; and evaluate the solution. A scenario to find a suitable site for biogas energy production walks through each stage in a simple demonstration of mechanics.

PD-31 - PySAL and Spatial Statistics Libraries

As spatial statistics are essential to the geographical inquiry, accessible and flexible software offering relevant functionalities is highly desired. Python Spatial Analysis Library (PySAL) represents an endeavor towards this end. It is an open-source python library and ecosystem hosting a wide array of spatial statistical and visualization methods. Since its first public release in 2010, PySAL has been applied to address various research questions, used as teaching materials for pedagogical purposes in regular classes and conference workshops serving a wide audience, and integrated into general GIS software such as ArcGIS and QGIS. This entry first gives an overview of the history and new development with PySAL. This is followed by a discussion of PySAL’s new hierarchical structure, and two different modes of accessing PySAL’s functionalities to perform various spatial statistical tasks, including exploratory spatial data analysis, spatial regression, and geovisualization. Next, a discussion is provided on how to find and utilize useful materials for studying and using spatial statistical functions from PySAL and how to get involved with the PySAL community as a user and prospective developer. The entry ends with a brief discussion of future development with PySAL.

CP-27 - GIS and Computational Notebooks

Researchers and practitioners across many disciplines have recently adopted computational notebooks to develop, document, and share their scientific workflows—and the GIS community is no exception. This chapter introduces computational notebooks in the geographical context. It begins by explaining the computational paradigm and philosophy that underlie notebooks. Next it unpacks their architecture to illustrate a notebook user’s typical workflow. Then it discusses the main benefits notebooks offer GIS researchers and practitioners, including better integration with modern software, more natural access to new forms of data, and better alignment with the principles and benefits of open science. In this context, it identifies notebooks as the “glue” that binds together a broader ecosystem of open source packages and transferable platforms for computational geography. The chapter concludes with a brief illustration of using notebooks for a set of basic GIS operations. Compared to traditional desktop GIS, notebooks can make spatial analysis more nimble, extensible, and reproducible and have thus evolved into an important component of the geospatial science toolkit.

KE-19 - Managing GIS&T Operations and Infrastructure

This article discusses the key role of effective management practices to derive expected benefits from the infrastructure and operations of enterprise GIS, including needs assessment, data evaluation and management, and stakeholder involvement. It outlines management factors related to an emerging application of enterprise GIS.  How to configure GIS infrastructure and operations to support enterprise business needs is the focus. When appropriate, additional information is provided for programs, projects, and activities specifically relevant for equity and social justice.

DM-70 - Problems of Large Spatial Databases

Large spatial databases often labeled as geospatial big data exceed the capacity of commonly used computing systems as a result of data volume, variety, velocity, and veracity. Additional problems also labeled with V’s are cited, but the four primary ones are the most problematic and focus of this chapter (Li et al., 2016, Panimalar et al., 2017).  Sources include satellites, aircraft and drone platforms, vehicles, geosocial networking services, mobile devices, and cameras. The problems in processing these data to extract useful information include query, analysis, and visualization. Data mining techniques and machine learning algorithms, such as deep convolutional neural networks, often are used with geospatial big data. The obvious problem is handling the large data volumes, particularly for input and output operations, requiring parallel read and write of the data, as well as high speed computers, disk services, and network transfer speeds. Additional problems of large spatial databases include the variety and heterogeneity of data requiring advanced algorithms to handle different data types and characteristics, and integration with other data. The velocity at which the data are acquired is a challenge, especially using today’s advanced sensors and the Internet of Things that includes millions of devices creating data on short temporal scales of micro seconds to minutes. Finally, the veracity, or truthfulness of large spatial databases is difficult to establish and validate, particularly for all data elements in the database.

GS-04 - Location Privacy

How effective is this fence at keeping people, objects, or sensitive information inside or outside? Location Privacy is concerned with the claim of individuals to determine when, how, and to what extent information about themselves and their location is communicated to others. Privacy implications for spatial data are growing in importance with growing awareness of the value of geo-information and the advent of the Internet of Things, Cloud-Based GIS, and Location Based Services.  

In the rapidly changing landscape of GIS and public domain spatial data, issues of location privacy are more important now than ever before. Technological trailblazing tends to precede legal safeguards. The development of GIS tools and the work of the GIS&T research and user community have typically occurred at a much faster rate than the establishment of legislative frameworks governing the use of spatial data, including privacy concerns. Yet even in a collaborative environment that characterizes the GIS&T community, and despite progress made, the issue of location privacy is a particularly thorny one, occurring as it does at the intersection of geotechnology and society.

DM-51 - Vertical (Geopotential) Datums

The elevation of a point requires a reference surface defining zero elevation. In geodesy, this zero-reference surface has historically been mean sea level (MSL) – a vertical datum. However, the geoid, which is a particular equipotential surface of Earth’s gravity field that would coincide with mean sea level were mean sea level altogether unperturbed and placid, is the ideal datum for physical heights, meaning height associated with the flow of water, like elevations. Tidal, gravimetric, and ellipsoidal are common vertical datums that use different approaches to define the reference surface. Tidal datums average water heights over a period of approximately 19 years, gravimetric datums record gravity across Earth’s surface, and ellipsoidal datums use specific reference ellipsoids to report ellipsoid heights. Increasingly, gravity measurements, positional data from GNSS (Global Navigation Satellite System), and other sophisticated measurement technologies GRACE-FO (Gravity Recovery and Climate Experiment – Follow On) are sourced to accurately model the geoid and its geopotential surface advancing the idea of a geopotential datum. Stemming from these advancements, a new geopotential datum for the United States will be developed: North American-Pacific Geopotential Datum 2022 (NAPGD2022).

DA-45 - GIS&T in Business

Geographic Information Systems and Technology are utilized extensively in the business sector and have become a strategic element for competition and partnering.  Although the traditional digital map layers and tables remain at the core of business GIS, the spatial architecture in firms now includes location analytics, location intelligence, AI, machine learning, imagery, social media linkages.  Cloud-based solutions provide platform flexibility, centralized data, and potential to roll out user-friendly webGIS across large segments of business users and customers. GIS is well suited to the digital transformations that are essential for firms, large and small.  With these advances, GIS has become prominent and its function has moved upwards in companies’ organizational hierarchies, with enterprise GIS even being recognized in the C-suite.  UPS is an example in which GIS is now a critical corporate competitive factor. In spite of these successes, a gap remains in the supply of skilled spatial workforce for companies. Business schools can contribute by changing by school leadership “getting it” about spatial, bringing GIS into the mainstream curricula, developing training for business faculty in teaching, conducting research in location analytics, and populating student body and alumni base with knowledge and enthusiasm for spatial thinking and management.

GS-13 - Epistemological critiques

As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.

DM-35 - Logical Data Models

A logical data model is created for the second of three levels of abstraction, conceptual, logical, and physical. A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct  tags. However, the logical data model formed is independent of a particular database management software product. Nonetheless such a model is often constrained by a class of software language techniques for representation, making implementation with a physical data model easier. Complex entity types of the conceptual data model must be translated into sub-type/super-type hierarchies to clarify data contexts for the entity type, while avoiding duplication of concepts and data. Entities and records should have internal identifiers. Relationships can be used to express the involvement of entity types with activities or associations. A logical schema is formed from the above data organization. A schema diagram depicts the entity, attribute and relationship detail for each application. The resulting logical data models can be synthesized using schema integration to support multi-user database environments, e.g., data warehouses for strategic applications and/or federated databases for tactical/operational business applications.