In the last ten years, the rise of efficient computing devices with significant processing power and storage has caused a surge in digital data collection and publication. As more software programs and hardware devices are released, we are not only seeing an increase in available data, but also an increase in available data formats. Cartographers today have access to a wide range of interesting datasets, and online portals for downloading geospatial data now frequently offer that data in several different formats. This chapter provides information useful to modern cartographers working with vector data, including an overview of common vector data formats (e.g. shapefile, GeoJSON, file geodatabase); their relative benefits, idiosyncrasies, and limitations; and a list of popular sources for geospatial vector data (e.g. United States Census Bureau, university data warehouses).
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.