All Topics

A B C D E F G H I J K L M N O P R S T U V W
DM-16 - Linear Referencing

Linear referencing is a term that encompasses a family of concepts and techniques for associating features with a spatial location along a network, rather than referencing those locations to a traditional spherical or planar coordinate system. Linear referencing is used when the location on the network, and the relationships to other locations on the network, are more significant than the location in 2D or 3D space. Linear referencing is commonly used in transportation applications, including roads, railways, and pipelines, although any network structure can be used as the basis for linearly referenced features. Several data models for storing linearly referenced data are available, and well-defined sets of procedures can be used to implement linear referencing for a particular application. As network analysis and network based statistical analysis become more prevalent across disciplines, linear referencing is likely to remain an important component of the data used for such analyses.

AM-23 - Local Measures of Spatial Association

Local measures of spatial association are statistics used to detect variations of a variable of interest across space when the spatial relationship of the variable is not constant across the study region, known as spatial non-stationarity or spatial heterogeneity. Unlike global measures that summarize the overall spatial autocorrelation of the study area in one single value, local measures of spatial association identify local clusters (observations nearby have similar attribute values) or spatial outliers (observations nearby have different attribute values). Like global measures, local indicators of spatial association (LISA), including local Moran’s I and local Geary’s C, incorporate both spatial proximity and attribute similarity. Getis-Ord Gi*another popular local statistic, identifies spatial clusters at various significance levels, known as hot spots (unusually high values) and cold spots (unusually low values). This so-called “hot spot analysis” has been extended to examine spatiotemporal trends in data. Bivariate local Moran’s I describes the statistical relationship between one variable at a location and a spatially lagged second variable at neighboring locations, and geographically weighted regression (GWR) allows regression coefficients to vary at each observation location. Visualization of local measures of spatial association is critical, allowing researchers of various disciplines to easily identify local pockets of interest for future examination.

AM-43 - Location and Service Area Problems

Many facilities exist to provide essential services in a city or region. The service area of a facility refers to a geographical area where the intended service of the facility can be received effectively. Service area delineation varies with the particular service a facility provides. This topic examines two types of service areas, one that can be defined based on a predetermined range such as travel distance/time and another based on the nearest facility available. Relevant location models are introduced to identify the best location(s) of one or multiple facilities to maximize service provision or minimize the system-wide cost. The delineation of service areas and structuring of a location model draw extensively on existing functions in a GIS. The topic represents an important area of GIS&T.

GS-04 - Location Privacy

How effective is this fence at keeping people, objects, or sensitive information inside or outside? Location Privacy is concerned with the claim of individuals to determine when, how, and to what extent information about themselves and their location is communicated to others. Privacy implications for spatial data are growing in importance with growing awareness of the value of geo-information and the advent of the Internet of Things, Cloud-Based GIS, and Location Based Services.  

In the rapidly changing landscape of GIS and public domain spatial data, issues of location privacy are more important now than ever before. Technological trailblazing tends to precede legal safeguards. The development of GIS tools and the work of the GIS&T research and user community have typically occurred at a much faster rate than the establishment of legislative frameworks governing the use of spatial data, including privacy concerns. Yet even in a collaborative environment that characterizes the GIS&T community, and despite progress made, the issue of location privacy is a particularly thorny one, occurring as it does at the intersection of geotechnology and society.

AM-46 - Location-allocation modeling

Location-allocation models involve two principal elements: 1) multiple facility location; and 2) the allocation of the services or products provided by those facilities to places of demand. Such models are used in the design of logistic systems like supply chains, especially warehouse and factory location, as well as in the location of public services. Public service location models involve objectives that often maximize access and levels of service, while private sector applications usually attempt to minimize cost. Such models are often hard to solve and involve the use of integer-linear programming software or sophisticated heuristics. Some models can be solved with functionality provided in GIS packages and other models are applied, loosely coupled, with GIS. We provide a short description of formulating two different models as well as discuss how they are solved.

CP-12 - Location-Based Services

Location-Based Services (LBS) are mobile applications that provide information depending on the location of the user. To make LBS work, different system components are needed, i.e., mobile devices, positioning, communication networks, and service and content provider. Almost every LBS application needs several key elements to handle the main tasks of positioning, data modeling, and information communication. With the rapid advances in mobile information technologies, LBS have become ubiquitous in our daily lives with many application fields, such as navigation and routing, social networking, entertainment, and healthcare. Several challenges also exist in the domain of LBS, among which privacy is a primary one. This topic introduces the key components and technologies, modeling, communication, applications, and the challenges of LBS.

DM-35 - Logical Data Models

A logical data model is created for the second of three levels of abstraction, conceptual, logical, and physical. A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct  tags. However, the logical data model formed is independent of a particular database management software product. Nonetheless such a model is often constrained by a class of software language techniques for representation, making implementation with a physical data model easier. Complex entity types of the conceptual data model must be translated into sub-type/super-type hierarchies to clarify data contexts for the entity type, while avoiding duplication of concepts and data. Entities and records should have internal identifiers. Relationships can be used to express the involvement of entity types with activities or associations. A logical schema is formed from the above data organization. A schema diagram depicts the entity, attribute and relationship detail for each application. The resulting logical data models can be synthesized using schema integration to support multi-user database environments, e.g., data warehouses for strategic applications and/or federated databases for tactical/operational business applications.

AM-94 - Machine Learning Approaches

Machine learning approaches are increasingly used across numerous applications in order to learn from data and generate new knowledge discoveries, advance scientific studies and support automated decision making. In this knowledge entry, the fundamentals of Machine Learning (ML) are introduced, focusing on how feature spaces, models and algorithms are being developed and applied in geospatial studies. An example of a ML workflow for supervised/unsupervised learning is also introduced. The main challenges in ML approaches and our vision for future work are discussed at the end.

CV-34 - Map Icon Design

The use of map icons is an efficient way to condense a map object into a concise expression of geospatial data. Like all cartographic design, map icon design merges artistic and scientific elements into symbolic representations intended to be readily legible to map readers. This entry reviews the types of map icons and elements of icon design, including the ways in which the visual variables are used in map icon communication. As communicative devices, icons are imbued with cultural meanings and can oftentimes lead to the preservation of stereotypes. This review concludes with an examination of icons’ perpetuation of – and challenge to – cultural stereotypes.

CV-30 - Map Production and Management

Map production describes the experience of managing the many aspects and details of map creation. Often the map product is created for someone else—a client, supervisor, or instructor. Describing the intention of the map and evaluating available resources ahead of the project can help the cartographer define content requirements, stay on task, and ultimately meet deadlines. The project management life cycle involves clear communication between the cartographer and client, with resolutions to common questions best addressed at the beginning of the project. The process then iteratively cycles through phases that include research and production, followed by quality control, and concludes with file preparation and delivery.

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