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GS-24 - Citizen Science with GIS&T

Figure 1. Participant in a BioBlitz records bird observation (Source: Jo Somerfield)

 

Citizen Science is defined as the participation of non-professional volunteers in scientific projects (Dickson et al, 2010) and has experienced rapid growth over the past decade. The projects that are emerging in this area range from contributory projects, co-created projects, collegiate projects, which are initiated and run by a group of people with shared interest, without any involvement of professional scientists.  

In many citizen science projects, GIS&T is enabling the collection, analysis, and visualisation of spatial data to affect decision-making. Some examples may include:

  • Recording the location of invasive species or participating in a BioBlitz to record local biodiversity (Figure 1).
  • Measuring air quality or noise over a large area and over time to monitor local conditions and address them
  • Using tools to educate on and increase access to local resources,  improving community resilience

Such projects have the opportunity to empower or disempower members of the public, depending upon access to and understanding of technology. Citizen Science projects using GIS&T may help communities influence decision makers and support the gathering of large-scale scientific evidence on a range of issues. This may also renew people’s interests in the sciences and foster continued and lifelong learning. 

 

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.

CV-42 - Collaborative Cartography

Collaborative cartography is a newly emerging approach for engaging community-centered processes of map production to represent harm caused by oppressive systems and pathways for healed futures. While mapping has a long history of engagement in activist movements, community involvement is often segmented to considerations determining the topic of the map and the subsequent supporting data-collection/validation processes. Collaborative cartography, however, ensures that communities are also central to discussions around and implementation of the design of the map. While the cartographic processes may differ from those of a professional cartographer, the term cartography and cartographer are used (rather than mapping or mapmaker) to indicate the close attention to design this technique facilitates. A collaborative cartographer commits to work that supports community control, embraces multiple forms of knowledge, and engages in non-linear and iterative process. These three key elements work together to support the production of a map whose standards of effectiveness are defined specifically by the needs, desires, and goals of those who produced it. This may lead to the creation of maps that fall outside of traditional expectations of cartographic design, aesthetic, and function. However, such creative ruptures are considered a necessary aspect in the pursuit of community empowerment and liberation.

CV-09 - Color Theory

Color is the result of the visual perception of an energy source. It is described by its physical characteristics, mainly as a tridimensional variable modeled into a color space. Online tools exist to facilitate the use of color schemes to design a color palette, for artists, web designers, statisticians, etc. Colors in maps and visualizations must be combined to promote the visual hierarchy and harmony, balancing legibility, perceptual processing, and aesthetics. Color is a powerful visual variable and requires understanding the perception of color relationships. Existing color schemes are very useful to select a suitable color palette. As color is not experienced similarly across all map readers, issues about real-world connotations, conventions, specific color contrasts, and adaptation to color visual deficiencies and devices, are also to be taken into account when designing a color palette. This entry describes the main guidelines regarding color theory and related design practices as applied to map or geovisualization design.

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.

CV-11 - Common Thematic Map Types

Thematic maps cover a wide variety of mapping solutions, and include choropleth, proportional symbol, isoline, dot density, dasymetric, and flow maps as well as cartograms, among others. Each thematic map type requires a different data processing method and employs different visual variables, resulting in representations that are either continuous or discrete and smooth or abrupt. As a result, each solution highlights different aspects of the mapped phenomena and shapes the message for the map readers differently. Thematic maps are tools for understanding spatial patterns, and the choice of thematic map type should support this understanding. Therefore, the main consideration when selecting a thematic map type is the purpose of the map and the nature of the underlying spatial patterns.

This entry reviews the common types of thematic maps, describes the visual variables that are applied in them, and provides design considerations for each thematic map type, including their legends. It also provides an overview of the relative strengths and limitations of each thematic map type.

KE-32 - Competence in GIS&T Knowledge Work

“Competence” is a word that rolls off the tongues of instructional designers, education administrators, and HR people. Others find it hard to swallow. For some GIS&T educators, competence connotes an emphasis on vocational instruction that’s unworthy of the academy. This entry challenges skeptical educators to rethink competence not just as readiness for an occupation, but first and foremost as the readiness to live life to the fullest, and to contribute to a sustainable future. The entry considers the OECD’s “Key Competencies for a Successful Life and Well-Functioning Society,” as well as the specialized GIS&T competencies specified in the U.S. Department of Labor’s Geospatial Technology Competency Model. It presents findings of a survey in which 226 self-selected members of Esri’s Young Professionals Network observe that competencies related to the GTCM’s Software and App Development Segment were under-developed in their university studies. Looking ahead, in the context of an uncertain future in which, some say, many workers are at risk of “technological unemployment,” the entry considers which GIS&T competencies are likely to be of lasting value.

DA-46 - Computational Geography

Computational Geography emerged in the 1980s in response to the reductionist limitations of early GIS software, which inhibited deep analyses of rich geographic data. Today, Computational Geography continues to integrate a wide range of domains to facilitate spatial analyses that require computational resources or ontological paradigms beyond that made available in traditional GIS software packages. These include novel approaches for the mass creation of geospatial data, large-scale database design for the effective storage and querying of spatial identifiers (i.e., distributed spatial databases), and methodologies which enable simulations and/or analysis in the context of large-scale, frequently near-real-time, spatially-explicit sources of information. The topics studied within Computational Geography directly enable many of the world’s largest public databases, including Google Maps and Open Street Map (OSM), as well as many modern analytic pipelines designed to study human behavior with the integration of large volumes of location information (e.g., mobile phone data) with other geospatial sources (e.g., satellite imagery).

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.

DM-34 - Conceptual Data Models

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. 

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