Role of GIS

Location science and geographic information systems (GIS) have evolved almost independently. There are four reasons for this. First, early models in location science were simple and structured as geometric problems (such as those of Weber and/or Fermet). Second, many location science models incorporate elements of operations research (OR). This field entails modeling for decision making, with techniques applicable in both spatial and non-spatial domains. Since the 1940s, the field of OR has evolved, and many of the models discussed in this text are solved using OR-based techniques. Third, the field of GIS developed not to support location science, but rather to support a wide range of uses and services. Geographical information systems(GIS) were created to collect, manage, manipulate, display, and analyze spatial data. Such systems are intended to present spatial data in the form of a map (e.g., a thematic map) and to retrieve data in a format suitable for analysis. As a result, GIS was created to support a wide range of needs, from mapping to spatial queries, and from visualizing a terrain to supplying data to models and statistical tools. That is, the goals of developing GIS go beyond the specific needs of location science because the application domain is much broader. Finally, the number of professionals working in both fields has been relatively small, and work in one has been somewhat independent of work in the other until recently.

Spatial planning problems

Certain issues addressed in location science are actually spatial planning problems that can be solved in GIS without knowledge of operations research or location science. Furthermore, certain issues in location science can be addressed within a theoretical framework that does not involve actual data or specific operations research techniques. Many location problems, from retail store siting to biological reserve site design, involve the need to characterize an application domain complete with spatial data of considerable detail (e.g., road network, census tracts, population estimates, and so on), and rely on a combination of functionality, ranging from GIS to models and algorithms based on operations research.

Location modeling

As problem-solving applications become more sophisticated, the spatial data required in their applications must be supported in some way by GIS. As a result, the role of GIS in location modeling ranges from central to peripheral data support, recognizing the need for complex spatial manipulation, query, and computation. For example, locating cell-phone towers necessitates characterizing the terrain as well as surface clutter, which are elements that reflect, bend, or obscure cell-phone signals (e.g., buildings and vegetation).

Uses of GIS examples

Many GIS packages include terrain modeling, and keeping track of ground cover via data attributes aids in estimating clutter height. As a result, GIS keeps track of the information required to estimate the area coverage of a potential cell-phone site. Simply put, an antenna reception model can be easily integrated with a GIS data model to generate map coverages of potential sites, whereas such a model would necessitate extensive database development and data collection without the use of a general purpose GIS.

What we tried to show is that as these three modeling areas (GIS, OR, and location science) matured, there is a convergence and burgeoning overlap between these fields based on the demand for better and more accurate spatial data, the demand for better models characterizing real landscape problem domains, and the demand for better models characterizing real landscape problem domains, and the need to map and visualize solutions to support decision making at a variety of scales, ranging from the warehouse floor to harvest areas in a large forest plantation to the infrastructure of pipes and pumps, reservoirs, and tanks in a water supply system. Whether it's a water tower or a retail store, future applications are likely to be inextricably linked with GIS, relying on a wealth of spatial data and spatial operations and utilizing models that characterize the problem domain as accurately as possible. This is the future of not only business location decisions, but also location science applications in general. Looking beyond theoretical location constructs and focusing on actual siting problems will result in the development of new models, data structures, algorithms, and theoretical principles.