Figure 1. Group movement patterns as illustrated in this coordinated escape behavior of a group of mountain goat (Rubicapra rubicapra) evading approaching hikers on the Fuorcla Trupchun near the Italian/Swiss border are at the core of computational movement analysis. Once the trajectories of moving objects are collected and made accessible for computational processing, CMA aims at a better understanding of the characteristics of movement processes of animals, people or things in geographic space.
Computational Movement Analysis (CMA) develops and applies analytical computational tools aiming at a better understanding of movement data. CMA copes with the rapidly growing data streams capturing the mobility of people, animals, and things roaming geographic spaces. CMA studies how movement can be represented, modeled, and analyzed in GIS&T. The CMA toolbox includes a wide variety of approaches, ranging from database research, over computational geometry to data mining and visual analytics.
Within an initial phase of database design, a conceptual data model is created as a technology-independent specification of the data to be stored within a database. This specification often times takes the form of a formalized diagram. The process of conceptual data modeling is meant to foster shared understanding among data modelers and stakeholders when creating the specification. As such, a conceptual data model should be easily readable by people with little or no technical-computer-based expertise because a comprehensive view of information is more important than a detailed view. In a conceptual data model, entity classes are categories of things (person, place, thing, etc.) that have attributes for describing the characteristics of the things. Relationships can exist between the entity classes. Entity-relationship diagrams have been and are likely to continue to be a popular way of characterizing entity classes, attributes and relationships. Various notations for diagrams have been used over the years. The main intent about a conceptual data model and its corresponding entity-relationship diagram is that they should highlight the content and meaning of data within stakeholder information contexts, while postponing the specification of logical structure to the second phase of database design called logical data modeling.
Differentiate between a controlled vocabulary and an ontology
Describe a domain ontology or vocabulary (i.e., land use classification systems, surveyor codes, data dictionaries, place names, or benthic habitat classification system)
Describe how a domain ontology or vocabulary facilitates data sharing
Define “thesaurus” as it pertains to geospatial metadata
Describe the primary focus of the following content standards: FGDC, Dublin Core Metadata Initiative, and ISO 19115
Differentiate between a content standard and a profile
Describe some of the profiles created for the Content Standard for Digital Geospatial Metadata (CSDGM)
Collaborate effectively with colleagues of differing social backgrounds in developing balanced GIS applications
Describe the ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information
Recognize the impact of one’s social background on one’s own geographic worldview and perceptions and how it influences one’s use of GIS
Cyberinfrastructure (sometimes referred to as e-infrastructure and e-science) integrates cutting-edge digital environments to support collaborative research and education for computation- and/or data-intensive problem solving and decision making (Wang 2010).
AM-90 - Computational Movement Analysis
Figure 1. Group movement patterns as illustrated in this coordinated escape behavior of a group of mountain goat (Rubicapra rubicapra) evading approaching hikers on the Fuorcla Trupchun near the Italian/Swiss border are at the core of computational movement analysis. Once the trajectories of moving objects are collected and made accessible for computational processing, CMA aims at a better understanding of the characteristics of movement processes of animals, people or things in geographic space.
Computational Movement Analysis (CMA) develops and applies analytical computational tools aiming at a better understanding of movement data. CMA copes with the rapidly growing data streams capturing the mobility of people, animals, and things roaming geographic spaces. CMA studies how movement can be represented, modeled, and analyzed in GIS&T. The CMA toolbox includes a wide variety of approaches, ranging from database research, over computational geometry to data mining and visual analytics.