Grid operations are manipulation and analytical computations performed on raster data. Map Algebra is a language for organizing and implementing grid operations in Geographic Information Systems (GIS) software, and is typically categorized into Local, Focal, and Zonal functions, where each function typically ingests one or more grids and outputs a new grid. The value of a specific grid cell in the output grid for Local functions is determined from the value(s) of the analogous cell position(s) in the input grid(s), for Focal functions from the grid cell values drawn from a neighborhood around the specific output grid cell, and for Zonal functions from a set of grid cells specified in a separate zone grid. Individual functions within a category vary by applying a different arithmetic, statistical, or other type of operator to the function. Map Algebra also includes Global and Block function categories. Grid operations can be categorized as data manipulation procedures or within domain-specific applications, such as terrain analysis or image processing. Grid operations are employed in a variety of GIS-based analyses, but are particularly widely used for suitability modeling and environmental analyses.
Raster data is commonly used by cartographers in concert with vector data. Choice of raster file format is important when using raster data or producing raster output from vector data. Raster formats are designed for specific purposes and have limitations in color representation and data loss. The simplest raster formats are just a single two-dimensional array of pixels, where multi-band raster datasets use additional data values to represent color or other data. The article covers considerations for the intended use of raster formats. Formats and resolutions appropriate for the web may not be appropriate for print or higher resolution devices. Several types of raster sources are available including single band measures, imagery, and existing raster maps or basemaps. The future of raster will evolve as more formats, sources, and computational improvements are made.
Geographic Information Systems (GIS) are fueled by geospatial data. This comprehensive article reviews the evolution of procedures and technologies used to create the data that fostered the explosion of GIS applications. It discusses the need to geographically reference different types of information to establish an integrated computing environment that can address a wide range of questions. This includes the conversion of existing maps and aerial photos into georeferenced digital data. It covers the advancements in manual digitizing procedures and direct digital data capture. This includes the evolution of software tools used to build accurate data bases. It also discusses the role of satellite based multispectral scanners for Earth observation and how LiDAR has changed the way that we measure and represent the terrain and structures. Other sections deal with building GIS data directly from street addresses and the construction of parcels to support land record systems. It highlights the way Global Positioning Systems (GPS) technology coupled with wireless networks and cloud-based applications have spatially empowered millions of users. This combination of technology has dramatically affected the way individuals search and navigate in their daily lives while enabling citizen scientists to be active participants in the capture of spatial data. For further information on changes to data capture, see Part 2: Implications and Case Studies.
As we translate real world phenomena into data structures that we can store in a computer, we must determine the most appropriate spatial representation and how it relates to the characteristics of such a phenomenon. All spatial representations are derivatives of graph theory and should therefore be described in such terms. This then helps to understand the principles of low-level GIS operations. A constraint-driven approach allows the reader to evaluate implementations of the geo-relational principle in terms of the hierarchical level of mathematical space adopted.
Spatial data can be represented in vector or raster form. The vector spatial data model is coordinate-based and represents geographic features as points, lines, and polygons. The raster spatial data model is pixel-based and represents geographic phenomena as an organized matrix of cells. Each model possesses advantages, disadvantages, and tradeoffs in how data can be manipulated, analyzed, and rendered. As a result, GIS professionals often need to work between data models to achieve their analytical goals. Vector-to-raster and raster-to-vector conversions are fundamental spatial data manipulation processes used to transform one model of spatial data representation into the other to extend the utility of a spatial dataset. Vector-to-raster conversion, also known as rasterization, is the process of converting vector points, lines, and polygons into a surface of gridded cells or pixels. Advanced rasterization techniques, such as spatial interpolation and density mapping, can be used to predict raster surfaces at unsampled locations based on known values of nearby vector spatial data inputs. Raster-to-vector conversion, also known as vectorization, is the process of converting gridded cell- or pixel-based data into vector points, lines, and polygons. While powerful, these conversion processes also have implications for geographic accuracy and potential feature loss.