All Topics

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.

FC-02 - Epistemology

Epistemology is the lens through which we view reality. Different epistemologies interpret the earth and patterns on its surface differently. In effect, epistemology is a belief system about the nature of reality that, in turn, structures our interpretation of the world. Common epistemologies in GIScience include (but are not limited by) positivism and realism. However, many researchers are in effect pragmatists in that they choose the filter that best supports their work and a priori hypotheses. Different epistemologies – or ways of knowing and studying geography – result in different ontologies or classification systems. By understanding the role of epistemology, we can better understand different ways of representing the same phenomena.

CP-26 - eScience, the Evolution of Science

Science—and research more broadly—face many challenges as its practitioners struggle to accommodate new challenges around reproducibility and openness.  The current practice of science limits access to knowledge, information and infrastructure, which in turn leads to inefficiencies, frustrations and a lack of rigor.  Many useful research outcomes are never used because they are too difficult to find, or to access, or to understand.

New computational methods and infrastructure provide opportunities to reconceptualize how science is conducted, how it is shared, how it is evaluated and how it is reused.  And new data sources changed what can be known, and how well, and how frequently.  This article describes some of the major themes of eScience/eResearch aimed at improving the process of doing science.

GS-15 - Feminist Critiques of GIS

Feminist interactions with GIS started in the 1990s in the form of strong critiques against GIS inspired by feminist and postpositivist theories. Those critiques mainly highlighted a supposed epistemological dissonance between GIS and feminist scholarship. GIS was accused of being shaped by positivist and masculinist epistemologies, especially due to its emphasis on vision as the principal way of knowing. In addition, feminist critiques claimed that GIS was largely incompatible with positionality and reflexivity, two core concepts of feminist theory. Feminist critiques of GIS also discussed power issues embedded in GIS practices, including the predominance of men in the early days of the GIS industry and the development of GIS practices for the military and surveillance purposes.

At the beginning of the 21st century, feminist geographers reexamined those critiques and argued against an inherent epistemological incompatibility between GIS methods and feminist scholarship. They advocated for a reappropriation of GIS by feminist scholars in the form of critical feminist GIS practices. The critical GIS perspective promotes an unorthodox, reconstructed, and emancipatory set of GIS practices by critiquing dominant approaches of knowledge production, implementing GIS in critically informed progressive social research, and developing postpositivist techniques of GIS. Inspired by those debates, feminist scholars did reclaim GIS and effectively developed feminist GIS practices.

CV-31 - Flow Maps

Flow mapping is a cartographic method of representing movement of phenomena. Maps of this type often depict the vector movement of entities (imports and exports, people, information) between geographic areas, but the general method also encompasses a range of graphics illustrating networks (e.g., transit and communications grids) and dynamic systems (e.g., wind and water currents). Most flow maps typically use line symbols of varying widths, lengths, shapes, colors, or speeds (in the case of animated flow maps) to show the quality, direction, and magnitude of movements. Aesthetic considerations for flow maps are numerous and their production is often done manually without significant automation. Flow maps frequently use distorted underlying geography to accommodate the placement of flow paths, which are often dramatically smoothed/abstracted into visually pleasing curves or simply straight lines. In the extreme, such maps lack a geographic coordinate space and are more diagrammatic, as in Sankey diagrams, alluvial diagrams, slope graphs, and circle migration plots. Whatever their form, good flow maps should effectively visualize the relative magnitude and direction of movement or potential movement between a one or more origins and destinations.

PD-33 - GDAL/OGR and Geospatial Data IO Libraries

Manipulating (e.g., reading, writing, and processing) geospatial data, the first step in geospatial analysis tasks, is a complicated step, especially given the diverse types and formats of geospatial data combined with diverse spatial reference systems. Geospatial data Input/Output (IO) libraries help facilitate this step by handling some technical details of the IO process. GDAL/OGR is the most widely-used, broadly-supported, and constantly-updated free library among existing geospatial data IO libraries. GDAL/OGR provides a single raster abstract data model and a single vector abstract data model for processing and analyzing raster and vector geospatial data, respectively, and it supports most, if not all, commonly-used geospatial data formats. GDAL/OGR can also perform both cartographic projections on large scales and coordinate transformation for most of the spatial reference systems used in practice. This entry provides an overview of GDAL/OGR, including why we need such a geospatial data IO library and how it can be applied to various formats of geospatial data to support geospatial analysis tasks. Alternative geospatial data IO libraries are also introduced briefly. Future directions of development for GDAL/OGR and other geospatial data IO libraries in the age of big data and cloud computing are discussed as an epilogue to this entry.

AM-78 - Genetic Algorithms and Evolutionary Computing

Genetic algorithms (GAs) are a family of search methods that have been shown to be effective in finding optimal or near-optimal solutions to a wide range of optimization problems. A GA maintains a population of solutions to the problem being solved and uses crossover, mutation, and selection operations to iteratively modify them. As the population evolves across generations, better solutions are created and inferior ones are selectively discarded. GAs usually run for a fixed number of iterations (generations) or until further improvements do not obtain. This contribution discusses the fundamental principles of genetic algorithms and uses Python code to illustrate how GAs can be developed for both numerical and spatial optimization problems. Computational experiments are used to demonstrate the effectiveness of GAs and to illustrate some nuances in GA design.

FC-22 - Geometric Primitives and Algorithms

Geometric primitives are the representations used and computations performed in a GIS that concern the spatial aspects of the data, data objects described by coordinates. In vector geometry, we distinguish in zero-, one-, two-, and three-dimensional objects, better known as points, linear features, areal or planar features, and volumetric features. A GIS stores and performs computations on all of these. Often, planar features form a collective known as a (spatial) subdivision. Computations on geometric objects show up in data simplification, neighborhood analysis, spatial clustering, spatial interpolation, automated text placement, segmentation of trajectories, map matching, and many other tasks. They should be contrasted with computations on attributes or networks.

There are various kinds of vector data models for subdivisions. The classical ones are known as spaghetti and pizza models, but nowadays it is recognized that topological data models are the representation of choice. We overview these models briefly.

Computations range from simple to highly complex: deciding whether a point lies in a rectangle needs four comparisons, whereas performing map overlay on two subdivisions requires advanced knowledge of algorithm design. We introduce map overlay, Voronoi diagrams, and Delaunay triangulations and mention algorithmic approaches to compute them.

DC-30 - Georeferencing and Georectification

Georeferencing is the recording of the absolute location of a data point or data points. Georectification refers to the removal of geometric distortions between sets of data points, most often the removal of terrain, platform, and sensor induced distortions from remote sensing imagery. Georeferencing is a requisite task for all spatial data, as spatial data cannot be positioned in space or evaluated with respect to other data that are without being assigned a spatial coordinate within a defined coordinate system. Many data are implicitly georeferenced (i.e., are labeled with spatial reference information), such as points collected from a global navigation satellite system (GNSS). Data that are not labeled with spatial reference information can be georeferenced using a number of approaches, the most commonly applied of which are described in this article. The majority of approaches employ known reference locations (i.e., Ground Control Points) drawn from a reliable source (e.g., GNSS, orthophotography) to calibrate georeferencing models. Regardless of georeferencing approach, positional error is present. The accuracy of georeferencing (i.e., amount of positional error) should be quantified, typically by the root mean squared error between ground control points from a reference source and the georeferenced data product.

DM-71 - Geospatial Data Conflation

Spatial data conflation is the process of combining overlapping spatial datasets to produce a better dataset with higher accuracy or more information. Conflation is needed in many fields, ranging from transportation planning to the analysis of historical datasets, which require the use of multiple data sources. Geospatial data conflation becomes increasingly important with the advancement of GIS and the emergence of new sources of spatial data such as Volunteered Geographic Information.

Conceptually, conflation is a two-step process involving identifying counterpart features that correspond to the same object in reality, and merging the geometry and attributes of counterpart features. In practice, conflation can be performed either manually or with the aid of GIS with varying degrees of automation. Manual conflation is labor-intensive, time consuming and expensive. It is often adopted in practice, nonetheless, due to the lack of reliable automatic conflation methods.

A main challenge of automatic conflation lies in the automatic matching of corresponding features, due to the varying quality and different representations of map data. Many (semi-)automatic feature methods exist. They typically involve measuring the distance between each feature pair and trying to match feature pairs with smaller dissimilarity using a specially designed algorithm or model. Fully automated conflation is still an active research field.