Cartographic modeling is an integrated sequence of data processing tasks that organize, combine, analyze and display information to answer a question. Cartographic modeling is effective in GIS environments because they rely heavily upon visualization, making it easy to show input and output layers in map form. In many GIS platforms, the sequence of tasks can be created and modified graphically as well. The modeling is visual, intuitive, and requires some knowledge of GIS commands and data preparation, along with curiosity to answer a particular question about the environment. It does not require programming skill. Cartographic modeling has been used in applications to delineate habitats, to solve network routing problems, to assess risk of storm runoff across digital terrain, and to conserve fragile landscapes. Historical roots emphasize manual and later automated map overlay. Cartographic models can take three forms (descriptive, prescriptive and normative). Stages in cartographic modeling identify criteria that meet an overarching goal; collect data describing each criterion in map form; design a flowchart showing data, GIS operations and parameters; implement the model; and evaluate the solution. A scenario to find a suitable site for biogas energy production walks through each stage in a simple demonstration of mechanics.
This chapter describes Multi-Criteria Evaluation (MCE) from the perspective of spatial decision support methodology adopted for geospatial problems and GIS applications. It highlights that MCE is essentially a systematic way of comparing pros and cons of choice alternatives, often using weighted criteria to generate a measure of a relative strength of each alternative vis-à-vis other alternatives. The chapter emphasizes the everyday use of MCE in geographic information science and technology (GIS&T), and starts from introducing the MCE principles followed by examples of MCE implementation in GIS. Theoretical considerations of geospatial MCE are discussed by focusing on issues of space and scale as well as spatio-temporal representation in MCE. The chapter concludes with an overview of recent trends in geospatial MCE including the adoption of behavioral theories explaining spatial choice preferences, data-driven approaches leveraging large data sets and machine learning techniques to derive MCE model parameters, and development of methods for addressing uncertainty in parameters, with applications in urban land use, renewable energy planning, and geomarketing.
GIS-based computational models are explored. While models vary immensely across disciplines and specialties, the focus is on models that simulate and forecast geographical systems and processes in time and space. The degree and means of integration of the many different models with GIS are covered, and the critical phases of modeling: design, implementation, calibration, sensitivity analysis, validation and error analysis are introduced. The use of models in simulations, an important purpose for implementing models within or outside of GIS, is discussed and the context of scenario-based planning explained. To conclude, a survey of model types is presented, with their application methods and some examples, and the goals of modeling are discussed.