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.

GIS implementations in the late-1960s to mid-1980s required the use of exotic peripheral devices to encode and display geospatial information. Data encoding was normally performed in one of two modes: automated raster scanning and manual (vector) coordinate recording. Raster scanning systems in this era were extremely expensive, operated in batch mode, and were located at a limited number of centralized facilities, such as federal mapping agencies. Coordinate digitizers were more widely distributed and were often configured with dedicated minicomputers to handle editing and formatting tasks. Data display devices produced hardcopy and softcopy output. Two commonly encountered hardcopy devices were line printers and pen plotters. Softcopy display consisted of cathode ray tube devices that operated using frame buffer and storage tube technologies. Each device was driven by specialized software provided by device manufacturers, leading to widespread hardware-software incompatibly. This problem led to the emergence of device independence to promote increased levels of interoperability among disparate input and output devices.

This entry introduces the concept of a social network (SN), its components, and how to weight those components. It also describes some spatial properties of SNs, and how to embed SNs into GIS. SNs are graph structures that consists of nodes and edges that traditionally exist in Sociology and are newer to GIScience. Nodes typically represent individual entities such as people or institutions, and edges represent interpersonal relationships, connections or ties. Many different mathematical metrics exist to characterize nodes, edges and the larger network. When geolocated, SNs are part of a class of spatial networks, more specifically, geographic networks (i.e. road networks, hydrological networks), that require special treatment because edges are non-planar, that is, they do not follow infrastructure or form a vector on the earth’s surface. Future research in this area is likely to take advantage of 21st Century datasets sourced from social media, GPS, wireless signals, and online interactions that each evidence geolocated personal relationships.

The scientific and engineering advancements in the 21st century pose grand computing challenges in managing big data, using complex algorithms to extract information and knowledge from big data, and simulating complex and dynamic physical and social phenomena. Cloud computing emerged as new computing model with the potential to address these computing challenges. This entry first introduces the concept, features and service models of cloud computing. Next, the ideas of generalized architecture and service models of spatial cloud computing are then elaborated to identify the characteristics, components, development and applications of spatial cloud computing for geospatial sciences.