All Topics

FC-40 - Neighborhoods

Neighborhoods mean different things in varied contexts like computational geometry, administration and planning, as well as urban geography and other fields. Among the multiple contexts, computational geometry takes the most abstract and data-oriented approach: polygon neighborhoods refer to polygons sharing a boundary or a point, and point neighborhoods are defined by connected Thiessen polygons or other more complicated algorithms. Neighborhoods in some regions can be a practical and clearly delineated administration or planning units. In urban geography and some related social sciences, the terms neighborhood and community have been used interchangeably on many occasions, and neighborhoods can be a fuzzy and general concept with no clear boundaries such that they cannot be easily or consensually defined. Neighborhood effects have a series of unique meanings and several delineation methods are commonly used to define social and environmental effects in health applications.

FC-19 - Networks Defined

A network is a widely used term with different definitions and methodologies depending on the applications. In GIS, a network refers to an arrangement of elements (i.e., nodes, links) and information on their connections and interactions. There are two types of networks: physical and logical. While a physical network has tangible objects (e.g., road segments), a logical network represents logical connections among nodes and links. A network can be represented with a mathematical notion called graph theory. Different network components are utilized to describe characteristics of a network including loops, walks, paths, circuits, and parallel edges. Network data are commonly organized in a vector format with network topology, specifically connectivity among nodes and links, whereas raster data can be also utilized for a least-cost problem over continuous space. Network data is utilized in a wide range of network analyses, including the classic shortest path problem.

DM-67 - NoSQL Databases

NoSQL databases are open-source, schema-less, horizontally scalable and high-performance databases. These characteristics make them very different from relational databases, the traditional choice for spatial data. The four types of data stores in NoSQL databases (key-value store, document store, column store, and graph store) contribute to significant flexibility for a range of applications. NoSQL databases are well suited to handle typical challenges of big data, including volume, variety, and velocity. For these reasons, they are increasingly adopted by private industries and used in research. They have gained tremendous popularity in the last decade due to their ability to manage unstructured data (e.g. social media data).

CP-16 - On the Origins of Computing and GIS&T: Part I, A Computer Systems Perspective

This paper describes the evolutionary path of hardware systems and the hardware-software interfaces that were used for GIS&T development during its “childhood”, the era from approximately the late 1960s to the mid-1980s.  The article is structured using a conceptualization that developments occurred during this period in three overlapping epochs that have distinctive modes of interactivity and user control: mainframes, minicomputers and workstations.  The earliest GIS&T applications were developed using expensive mainframe computer systems, usually manufactured by IBM. These mainframes typically had memory measured in kilobytes and operated in batch mode with jobs submitted using punched cards as input.  Many such systems used an obscure job control language with a rigid syntax. FORTRAN was the predominant language used for GIS&T software development. Technological developments, and associated cost reductions, led to the diffusion of minicomputers and a shift away from IBM. Further developments led to the widespread adoption of single user workstations that initially used commodity processors and later switched to reduced instruction set chips. Many minicomputers and workstations ran some variant of the UNIX operating system, which substantially improved user interactivity.

CP-32 - On the Origins of Computing and GIST: Part 2, A Perspective on the Role of Peripheral Devices

GIS implementations in the late-1960s to mid-1980s required the use of exotic peripheral devices to encode and display geospatial information. Data encoding was normally performed in one of two modes: automated raster scanning and manual (vector) coordinate recording. Raster scanning systems in this era were extremely expensive, operated in batch mode, and were located at a limited number of centralized facilities, such as federal mapping agencies. Coordinate digitizers were more widely distributed and were often configured with dedicated minicomputers to handle editing and formatting tasks. Data display devices produced hardcopy and softcopy output. Two commonly encountered hardcopy devices were line printers and pen plotters. Softcopy display consisted of cathode ray tube devices that operated using frame buffer and storage tube technologies. Each device was driven by specialized software provided by device manufacturers, leading to widespread hardware-software incompatibly. This problem led to the emergence of device independence to promote increased levels of interoperability among disparate input and output devices.

DM-80 - Ontology for Geospatial Semantic Interoperability

It is difficult to share and reuse geospatial data and retrieve geospatial information because of geospatial data heterogeneity problems. Lack of semantic interoperability is one of the major problems facing GIS (Geographic Information Science/System) systems and applications today. To solve geospatial data heterogeneity problems and support geospatial information retrieval and semantic interoperability over the Web, the use of an ontology is proposed because it is a formal explicit description of concepts or meanings of words in a well-defined and unambiguous manner. Geospatial ontologies represent geospatial concepts and properties for use over the Web. OWL (Ontology Web Language) is an emerging language for defining and instantiating ontologies. OWL builds on RDF (Resource Description Framework) but adds more vocabulary for describing properties and classes. The downside of representing structured geospatial data in OWL and RDF languages is that it can result in inefficient data access. SPARQL (Simple Protocol and RDF Query Language) is recommended for general RDF query while the GeoSPARQL (Geographic Simple Protocol and RDF Query Language) protocol is proposed as an extension of SPARQL for querying geospatial data. However, the runtime cost of GeoSPARQL queries can be high due to the fine-grained nature of RDF data models. There are several challenges to using ontologies for geospatial semantic interoperability but these can be overcome through collaboration.

FC-35 - Openness

The philosophy of Openness and its use in diverse areas is attracting increasing attention from users, developers, businesses, governments, educators, and researchers around the world. The technological, socio-cultural, economic, legal, institutional, and philosophical issues related to its principles, applications, benefits, and barriers for its use are growing areas of research. The word “Open” is commonly used to denote adherence to the principles of Openness. Several fields are incorporating the use of Openness in their activities, some of them are of particular relevance to GIS&T (Geographic Information Science and Technology) such as: Open Data, Free and Open Source Software; and Open Standards for geospatial data, information, and technologies. This entry presents a definition of Openness, its importance in the area of GISc&T is introduced through a list of its benefits in the fields of Open Data, Open Source Software, and Open Standards. Then some of the barriers, myths, or inhibitors to Openness are presented using the case of Free and Open Source Software (FOSS) and FOSS for Geospatial Applications (FOSS4G).

KE-33 - Organizational Models for GIS Management

Organizational structures and management practices for GIS programs are numerous and complex. This topic begins with an explanation of organizational and management concepts and context that are particularly relevant to GIS program and project management, including strategic planning and stakeholders. Specific types of organizations that typically use GIS technology are described and organizational structure types are explained. For GIS Program management, organizational placement, organizational components, and management control and policies are covered in depth. Multi-organizational GIS Programs are also discussed. Additional topics include management roles and technology trends that affect organizational structure. It concludes with a general description of GIS Project management. 

FC-03 - Philosophical Perspectives

This entry follows in the footsteps of Anselin’s famous 1989 NCGIA working paper entitled “What is special about spatial?” (a report that is very timely again in an age when non-spatial data scientists are ignorant of the special characteristics of spatial data), where he outlines three unrelated but fundamental characteristics of spatial data. In a similar vein, I am going to discuss some philosophical perspectives that are internally unrelated to each other and could warrant individual entries in this Body of Knowledge. The first one is the notions of space and time and how they have evolved in philosophical discourse over the past three millennia. Related to these are aspects of absolute versus relative conceptions of these two fundamental constructs. The second is a brief introduction to key philosophical approaches and how they impact geospatial science and technology use today. The third is a discussion of which of the promises of the Quantitative Revolution in Geography and neighboring disciplines have been fulfilled by GIScience (and what is still missing). The fourth and final one is an introduction to the role that GIScience may play in what has recently been formalized as theory-guided data science.

DM-36 - Physical Data Models

Constructs within a particular implementation of database management software guide the development of a physical data model, which is a product of a physical database design process. A physical data model documents how data are to be stored and accessed on storage media of computer hardware.  A physical data model is dependent on specific data types and indexing mechanisms used within database management system software.  Data types such as integers, reals, character strings, plus many others can lead to different storage structures. Indexing mechanisms such as region-trees and hash functions and others lead to differences in access performance.  Physical data modeling choices about data types and indexing mechanisms related to storage structures refine details of a physical database design. Data types associated with field, record and file storage structures together with the access mechanisms to those structures foster (constrain) performance of a database design. Since all software runs using an operating system, field, record, and file storage structures must be translated into operating system constructs to be implemented.  As such, all storage structures are contingent on the operating system and particular hardware that host data management software.