A systems development life cycle (SDLC) denes and guides the activities and milestones in the design, development, testing, and de ployment of software applications & information systems. Various choices of SDLC are available for different types of software applications & information systems and compositions of development teams and stakeholders. While the choice of an SDLC for building geographic information system (GIS) applications is similar to that of other types of software applications, critical decisions in each phase of the GIS development life cycle (GiSDLC) should take into account essential questions concern ing the storage, access, and analysis of (geo)spatial data for the target application. This article aims to introduce various considerations in the GiSDLC, from the perspectives of handling (geo)spatial data. The article rst introduces several (geo)spatial processes and types as well as various modalities of GIS applications. Then the article gives a brief introduction to an SDLC, including explaining the role of (geo)spatial data in the SDLC. Finally, the article uses two existing real-world applications as an example to highlight critical considerations in the GiSDLC.

Programming is a sought after skill in GIS, but traditional programming (also called serial programming) only uses one processing core. Modern desktop computers, laptops, and even cellphones now have multiple processing cores, which can be used simultaneously to increase processing capabilities for a range of GIS applications. Parallel programming is a type of programming that involves using multiple processing cores simultaneously to solve a problem, which enables GIS applications to leverage more of the processing power on modern computing architectures ranging from desktop computers to supercomputers. Advanced parallel programming can leverage hundreds and thousands of cores on high-performance computing resources to process big spatial datasets or run complex spatial models.

Parallel programming is both a science and an art. While there are methods and principles that apply to parallel programming--when, how, and why certain methods are applied over others in a specific GIS application remains more of an art than a science. The following sections introduce the concept of parallel programming and discuss how to parallelize a spatial problem and measure parallel performance.

Graphics processing units (GPUs) are massively parallel computing environments with applications in graphics and general purpose programming. This entry describes GPU hardware, application domains, and both graphics and general purpose programming languages.

Linear programming is a set of methods for finding optimal solutions to mathematical models composed of a set of linear functions. Many spatial location problems can be structured as linear programs. However, even modest-sized problem instances can be very difficult to solve due to the combinatorial complexity of the problems and the associated computational expense that they incur. Geographic Information Systems software does not typically incorporate formal linear programming functionality, and instead commonly uses heuristic solution procedures to generate near-optimal solutions quickly. There is growing interest in integrating the spatial analytic tools incorporated in Geographic Information Systems with the solution power of linear programming software to generate guaranteed optimal solutions to spatial location problems.