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AM-80 - Capturing Spatiotemporal Dynamics in Computational Modeling

We live in a dynamic world that includes various types of changes at different locations over time in natural environments as well as in human societies. Modern sensing technology, location-aware technology and mobile technology have made it feasible to collect spatiotemporal tracking data at a high spatial and temporal granularity and at affordable costs. Coupled with powerful information and communication technologies, we now have much better data and computing platforms to pursue computational modeling of spatiotemporal dynamics. Researchers have attempted to better understand various kinds of spatiotemporal dynamics in order to predict, or even control, future changes of certain phenomena. A simple approach to representing spatiotemporal dynamics is by adding time (t) to the spatial dimensions (x,y,z) of each feature. However, spatiotemporal dynamics in the real world are more complex than a simple representation of (x,y,z,t) that describes the location of a feature at a given time. This article presents selected concepts, computational modeling approaches, and sample applications that provide a foundation to computational modeling of spatiotemporal dynamics. We also indicate why the research of spatiotemporal dynamics is important to geographic information systems (GIS) and geographic information science (GIScience), especially from a temporal GIS perspective.

CV-32 - Cartograms

Cartograms are used for thematic mapping. They are a particular class of map type where some aspect of the geometry of the map is modified to accommodate the problem caused by perceptually different geographies. Standard thematic maps, such as the choropleth, have inherent biases simply due to the fact that areas will likely be very different in size from one another. The tendency to see larger areas as more important, regardless of the variable being mapped, can cause confusion. Cartograms tackle this by modifying the geography, effectively normalizing it to create a map where each area takes on a new shape and/or size based on the variable being mapped. Cartograms therefore depict geographical space diagrammatically as they lose their relationship with true coordinate system geometry. There are four main types of cartogram which each represent the mapped variable differently – non-contiguous, contiguous, graphical and gridded.  

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.

CV-27 - Cartography and Art

The intersections between art and cartography go far beyond the notions of design and illustration, since mapmaking invariably has multiple cultural, social, and political dimensions. Considering this broader perspective, this entry provides a review of these different contemporary intersections, by exploring three main types of relationships: 1) cartography influenced by artistic practices; 2) map art or maps embedded in artistic practices; and 3) cartography at the interface between art and places. These will be discussed in detail following a brief overview of the main historical markers from which these types of relationships between art and cartography have emerged.

CV-26 - Cartography and Power

Over twenty five years ago, Brian Harley (1989, p. 2) implored cartographers to “search for the social forces that have structured cartography and to locate the presence of power – and its effects – in all map knowledge.” In the intervening years, while Harley has become a bit of a touchstone for citational practices acknowledging critical cartography (Edney, 2015), both theoretical understandings of power as well as the tools and technologies that go into cartographic production have changed drastically. This entry charts some of the many ways that power may be understood to manifest within and through maps and mapmaking practices. To do so, after briefly situating work on cartography and power historically, it presents six critiques of cartography and power in the form of dialectics. First, building from Harley’s earlier work, it defines a deconstructivist approach to mapping and places it in contrast to hermeneutic phenomenological approaches. Second, it places state-sanctioned practices of mapping against participatory and counter-mapping ones. Third, epistemological understanding of maps and their affects are explored through the dialectic of the map as a static object versus more processual, ontogenetic understandings of maps. Finally, the chapter concludes by suggesting the incomplete, heuristic nature of both the approaches and ideas explored here as well as the practices of critical cartography itself. Additional resources for cartographers and GIScientists seeking to further explore critical approaches to maps are provided.

CV-01 - Cartography and Science

"Science" is used both to describe a general, systematic approach to understanding the world and to refer to that approach as it is applied to a specific phenomenon of interest, for example, "geographic information science." The scientific method is used to develop theories that explain phenomena and processes. It consists of an iterative cycle of several steps: proposing a hypothesis, devising a way to make empirical observations that test that hypothesis, and finally, refining the hypothesis based on the empirical observations. "Scientific cartography" became a dominant mode of cartographic research and inquiry after World War II, when there was increased focus on the efficacy of particular design decisions and how particular maps were understood by end users. This entry begins with a brief history of the development of scientific cartographic approaches, including how they are deployed in map design research today. Next it discusses how maps have been used by scientists to support scientific thinking. Finally, it concludes with a discussion of how maps are used to communicate the results of scientific thinking.

AM-69 - Cellular Automata

Cellular automata (CA) are simple models that can simulate complex processes in both space and time. A CA consists of six defining components: a framework, cells, a neighborhood, rules, initial conditions, and an update sequence. CA models are simple, nominally deterministic yet capable of showing phase changes and emergence, map easily onto the data structures used in geographic information systems, and are easy to implement and understand. This has contributed to their popularity for applications such as measuring land use changes and monitoring disease spread, among many others.

DC-42 - Changes in Geospatial Data Capture Over Time: Part 2, Implications and Case Studies

Advances in technological approaches and tools to capture geospatial data have contributed to a vast collection of applications and enabled capacity for new programs, functions, products, workflows, and whole national-level spatial data infrastructure. In this entry, such outcomes and implications are described, focusing on developmental changes in specific application areas such as land use & land cover inventory, land parcel administration, and business, as well as examples from federal agencies, including the US Geological Survey, the Census Bureau, US Fish and Wildlife Service, and the US Department of Agriculture. These examples illustrate the diverse ways that the dramatic changes in geospatial data capture methods and approaches have affected workflows within agencies and have spatially empowered millions of users and the general public. For additional information on specific technical changes, see Part 1: 

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.

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