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DM-36 - Physical Data Models

Constructs within a particular implementation of database management software guide the development of a physical data model, which is a product of a physical database design process. A physical data model documents how data are to be stored and accessed on storage media of computer hardware.  A physical data model is dependent on specific data types and indexing mechanisms used within database management system software.  Data types such as integers, reals, character strings, plus many others can lead to different storage structures. Indexing mechanisms such as region-trees and hash functions and others lead to differences in access performance.  Physical data modeling choices about data types and indexing mechanisms related to storage structures refine details of a physical database design. Data types associated with field, record and file storage structures together with the access mechanisms to those structures foster (constrain) performance of a database design. Since all software runs using an operating system, field, record, and file storage structures must be translated into operating system constructs to be implemented.  As such, all storage structures are contingent on the operating system and particular hardware that host data management software. 

FC-26 - Problems of Scale and Zoning

Spatial data are often encoded within a set of spatial units that exhaustively partition a region, where individual level data are aggregated, or continuous data are summarized, over a set of spatial units. Such is the case with census data aggregated to enumeration units for public dissemination. Partitioning schemes can vary by scale, where one partitioning scheme spatially nests within another, or by zoning, where two partitioning schemes have the same number of units but the unit shapes and boundaries differ. The Modifiable Areal Unit Problem (MAUP) refers to the fact the nature of spatial partitioning can affect the interpretation and results of visualization and statistical analysis. Generally, coarser scales of data aggregation tend to have stronger observed statistical associations among variables. The ecological fallacy refers to the assumption that an individual has the same attributes as the aggregate group to which it belongs. Combining spatial data with different partitioning schemes to facilitate analysis is often problematic. Areal interpolation may be used to estimate data over small areas or ecological inference may be used to infer individual behaviors from aggregate data. Researchers may also perform analyses at multiple scales as a point of comparison.

GS-11 - Professional and Practical Ethics of GIS&T

Geospatial technologies are often and rightly described as “powerful.” With power comes the ability to cause harm – intentionally or unintentionally - as well as to do good. In the context of GIS&T, Practical Ethics is the set of knowledge, skills and abilities needed to make reasoned decisions in light of the risks posed by geospatial technologies and methods in a wide variety of use cases. Ethics have been considered from different viewpoints in the GIS&T field. A practitioner's perspective may be based on a combination of "ordinary morality," institutional ethics policies, and professional ethics codes. By contrast, an academic scholar's perspective may be grounded in social or critical theory. What these perspectives have in common is reliance on reason to respond with integrity to ethical challenges. This entry focuses on the special obligations of GIS professionals, and on a method that educators can use to help students develop moral reasoning skills that GIS professionals need. The important related issues of Critical GIS and Spatial Law and Policy are to be considered elsewhere.  

KE-31 - Professional Certification

Professional Certification has been a part of the GIS enterprise for over two decades. There are several different certification programs and related activities now in operation within GIS, though there has been much debate over its merits, how it should be done and by whom. 

DC-01 - Professional Land Surveying

Professional Land Surveyors are the only profession that create the legal description of land parcels, which are then officially recorded to show ownership and rights pertaining to each and every land parcel within a jurisdiction. The Surveyor is skilled at undertaking the physical measurements that are needed to locate accurately land parcels on the ground and to write the unambiguous legal description of the land to create legal title in real estate. These land ownership records are critical for the transfer of ownership in the real estate market. The legal land description provided by Surveyors forms the foundation, and the real estate market provides the mechanism, for real estate to become the largest store of tangible wealth in any free market economy.

PD-11 - Python for GIS

Figure 1. PySAL within QGIS Processing Toolbox: Hot-spot analysis of Homicide Rates in Southern US Counties.

 

Python is a popular language for geospatial programming and application development. This entry provides an overview of the different development modes that can be adopted for GIS programming with Python and discusses the history of Python adoption in the GIS community. The different layers of the geospatial development stack in Python are examined giving the reader an understanding of the breadth that Python offers to the GIS developer. Future developments and broader issues related to interoperability and programming ecosystems are identified.

PD-15 - R for Geospatial Analysis and Mapping

R is a programming language as well as a computing environment to perform a wide variety of data analysis, statistics, and visualization. One of the reasons for the popularity of R is that it embraces open, transparent scholarship and reproducible research. It is possible to combine content and code in one document, so data, analysis, and graphs are tied together into one narrative, which can be shared with others to recreate analyses and reevaluate interpretations. Different from tools like ArcGIS or QGIS that are specifically built for spatial data, GIS functionality is just one of many things R offers. And while users of dedicated GIS tools typically interact with the software via a point-and-click graphical interface, R requires command-line scripting. Many R users today rely on RStudio, an integrated development environment (IDE) that facilitates the writing of R code and comes with a series of convenient features, like integrated help, data viewer, code completion, and syntax coloring. By using R Markdown, a particular flavor of the Markdown language, RStudio also makes it particularly easy to create documents that embed and execute R code snippets within a text and to render both, static documents (like PDF), as well as interactive html pages, a feature particularly useful for exploratory GIS work and mapping.

DC-26 - Remote Sensing Platforms

Remote sensing means acquiring and measuring information about an object or phenomenon via a device that is not in physical or direct contact with what is being studied (Colwell, 1983).To collect remotely sensed data, a platform – an instrument that carries a remote sensing sensor – is deployed. From the mid 1800’s to the early 1900’s, various platforms such as balloons, kites, and pigeons carried mounted cameras to collect visual data of the world below. Today, aircraft (both manned and unmanned) and satellites collect the majority of remotely sensed data. The sensors typically deployed on these platforms include film and digital cameras, light-detection and ranging (LiDAR) systems, synthetic aperture radar (SAR) systems, and multi-spectral and hyper-spectral scanners. Many of these instruments can be mounted on land-based platforms, such as vans, trucks, tractors, and tanks. In this chapter, we will explore the different types of platforms and their resulting remote sensing applications.

CV-18 - Representing Uncertainty

Using geospatial data involves numerous uncertainties stemming from various sources such as inaccurate or erroneous measurements, inherent ambiguity of the described phenomena, or subjectivity of human interpretation. If the uncertain nature of the data is not represented, ill-informed interpretations and decisions can be the consequence. Accordingly, there has been significant research activity describing and visualizing uncertainty in data rather than ignoring it. Multiple typologies have been proposed to identify and quantify relevant types of uncertainty and a multitude of techniques to visualize uncertainty have been developed. However, the use of such techniques in practice is still rare because standardized methods and guidelines are few and largely untested. This contribution provides an introduction to the conceptualization and representation of uncertainty in geospatial data, focusing on strategies for the selection of suitable representation and visualization techniques.

FC-21 - Resolution

Resolution in the spatial domain refers to the size of the smallest measurement unit observed or recorded for an object, such as pixels in a remote sensing image or line segments used to record a curve. Resolution, also called the measurement scale, is considered one of the four major dimensions of scale, along with the operational scale, observational scale, and cartographic scale. Like the broader concept of scale, resolution is a fundamental consideration in GIScience because it affects the reliability of a study and contributes to the uncertainties of the findings and conclusions. While resolution effects may never be eliminated, techniques such as fractals could be used to reveal the multi-resolution property of a phenomenon and help guide the selection of resolution level for a study.

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