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.
A common goal in spatial analysis is the identification of regions containing unusually high or low values. These areas may be called hot spots if the values are high and cold spots if the values are low. These hot/cold spots indicate where the effects of spatial heterogeneity are greatest. Point density, heat, and choropleth maps all highlight these areas in one way or another. However, due to the limitations of subjective symbolization, statistical methods of hot spot detection are common. Some, like Moran’s I, simply identify the pattern for the entire study area. Local methods display the location and magnitude of individual high and low clusters. Getis-Ord Gi* analysis is the local method most associated with the term hot spots and it is the focus of the second half of the article. Getis-Ord Gi* combines the logic of a probability map with moving windows, kernels and/or adjacency weights. The result is an output surface showing neighborhoods with means significantly above or below the global mean. A primary concern is the correct parameterization, especially the correct conceptualization of spatial relationships. Spatiotemporal variants, limitations, and future directions of hot spot analysis are briefly discussed.