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DC-36 - Historical Maps in GIS

The use of historical maps in coordination with GIS aids scholars who are approaching a geographical study in which an historical approach is required or is interested in the geographical relationships between different historical representations of the landscape in cartographic document.  Historical maps allow the comparison of spatial relationships of past phenomena and their evolution over time and permit both qualitative and quantitative diachronic analysis. In this chapter, an explanation of the use of historical maps in GIS for the study of landscape and environment is offered. After a short theoretical introduction on the meaning of the term “historical map,” the reader will find the key steps in using historic maps in a GIS, a brief overview on the challenges in interpretation of historical maps, and some example applications.

CP-24 - ArcGIS Online

ArcGIS Online is a hosted geographic information system (GIS) created and hosted by Environmental Systems Research Institute (Esri). In a few short years, it has eclipsed desktop software as the most popular tool for mapping and spatial analysis. ArcGIS Online is more than a traditional GIS software in that it also includes access to a wide range of authoritative datasets. ArcGIS fits into the Web 2.0 model where users of the platform are able to create and share maps.

CP-07 - Spatial MapReduce

MapReduce has become a popular programming paradigm for distributed processing platforms. It exposes an abstraction of two functions, map and reduce, which users can define to implement a myriad of operations. Once the two functions are defined, a MapReduce framework will automatically apply them in parallel to billions of records and over hundreds of machines. Users in different domains are adopting MapReduce as a simple solution for big data processing due to its flexibility and efficiency. This article explains the MapReduce programming paradigm, focusing on its applications in processing big spatial data. First, it gives a background on MapReduce as a programming paradigm and describes how a MapReduce framework executes it efficiently at scale. Then, it details the implementation of two fundamental spatial operations, namely, spatial range query and spatial join. Finally, it gives an overview of spatial indexing in MapReduce systems and how they can be combined with MapReduce processing.

DC-19 - Ground Verification and Accuracy Assessment

Spatial products such as maps of land cover, soil type, wildfire, glaciers, and surface water have become increasingly available and used in science and policy decisions.  These maps are not without error, and it is critical that a description of quality accompany each product.  In the case of a thematic map, one aspect of quality is obtained by conducting a spatially explicit accuracy assessment in which the map class and reference class are compared on a per spatial unit basis (e.g., per 30m x 30m pixel).  The outcome of an accuracy assessment is a description of quality of the end-product map, in contrast to conducting an evaluation of map quality as part of the map production process.  The accuracy results can be used to decide if the map is of adequate quality for an intended application, as input to uncertainty analyses, and as information to improve future map products.

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: 

CP-15 - Mobile Devices

Mobile devices refer to a computing system intended to be used by hand, such as smartphones or tablet computers. Mobile devices more broadly refer to mobile sensors and other hardware that has been made for relatively easy transportability, including wearable fitness trackers. Mobile devices are particularly relevant to Geographic Information Systems and Technology (GIS&T) in that they house multiple locational sensors that were until recently very expensive and only accessible to highly trained professionals. Now, mobile devices serve an important role in computing platform infrastructure and are key tools for collecting information and disseminating information to, from, and among heterogeneous and spatially dispersed audiences and devices. Due to the miniaturization and the decrease in the cost of computing capabilities, there has been widespread social uptake of mobile devices, making them ubiquitous. Mobile devices are embedded in Geographic Information Science (GIScience) meaning GIScience is increasingly permeating lived experiences and influencing social norms through the use of mobile devices. In this entry, locational sensors are described, with computational considerations specifically for mobile computing. Mobile app development is described in terms of key considerations for native versus cross-platform development. Finally, mobile devices are contextualized within computational infrastructure, addressing backend and frontend considerations.

DC-11 - Street-level Imagery

Street-level imagery consists of collections of photographs taken from the perspective of moving pedestrians or vehicles. These collections are often stitched together digitally and georeferenced to create interactive and immersive landscapes that are virtually navigable by users. Such landscapes, sometimes called 360-degree panoramas, or bubbles, are uploaded onto web platforms, and linked with geographical databases, which allows users to search and explore the imagery in various ways. IT companies such as Google have created street-level imagery platforms that rely primarily on paid drivers, although they have begun to rely on contributor submissions to complement and expand their coverage. Recently services such as Mapillary and OpenStreetCam have advanced a model that relies primarily on volunteer contributors, leveraging community interest from projects such as OpenStreetMap. While street-level imagery has become a widespread tool with multiple commercial and non-commercial applications, it is also entangled various legal and public opinion controversies, due to its capabilities for private data collection and surveillance. 

DC-04 - Social Media Platforms

Social media is a group of interactive Web 2.0 Internet-based applications that allow users to create and exchange user-generated content via virtual communities. Social media platforms have a large user population who generate massive amounts of digital footprints, which are valuable data sources for observing and analyzing human activities/behavior. This entry focuses on social media platforms that provide spatial information in different forms for Geographic Information Systems and Technology (GIS&T) research. These social media platforms can be grouped into six categories: microblogging sites, social networking sites, content sharing sites, product and service review sites, collaborative knowledge sharing sites, and others. Four methods are available for capturing data from social media platforms, including Web Application Programming Interfaces (Web APIs), Web scraping, digital participant recruitment, and direct data purchasing. This entry first overviews the history, opportunities, and challenges related to social media platforms. Each category of social media platforms is then introduced in detail, including platform features, well-known platform examples, and data capturing processes.

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.

CP-14 - Web GIS

Web GIS allows the sharing of GIS data, maps, and spatial processing across private and public computer networks. Understanding web GIS requires learning the roles of client and server machines and the standards and protocols around how they communicate to accomplish tasks. Cloud computing models have allowed web-based GIS operations to be scaled out to handle large jobs, while also enabling the marketing of services on a per-transaction basis.

A variety of toolkits allow the development of GIS-related websites and mobile apps. Some web GIS implementations bring together map layers and GIS services from multiple locations. In web environments, performance and security are two concerns that require heightened attention. App users expect speed, achievable through caching, indexing, and other techniques. Security precautions are necessary to ensure sensitive data is only revealed to authorized viewers.

Many organizations have embraced the web as a way to openly share spatial data at a relatively low cost. Also, the web-enabled expansion of spatial data production by nonexperts (sometimes known as “neogeography”) offers a rich field for alternative mappings and critical study of GIS and society.