Summary
Highlights
Overlay analysis in GIS involves superimposing multiple data layers to analyze or identify relationships between different themes. It creates new spatial datasets by merging information from two or more input layers and is a powerful GIS technique for analyzing multiple layers with a common coordinate system. Examples include analyzing cropping patterns, population densities, and physical landforms. Overlay analysis is critical for applications like choosing sites for new housing developments, considering factors such as land cost, proximity to services, and flood frequency.
To perform overlay analysis, one must define the problem, break it into sub-models, determine significant layers, reclassify or transform data within layers, assign weights to input layers, and then combine or add these layers for analysis.
Vector-based overlay combines point, line, and polygon features. Operations rely on geometry and topology. Common operations include point in area (point in polygon), line in area (line in polygon), and area on area (polygon on polygon). These operations generate combined properties, such as determining which points fall within a polygon or identifying road segments within a forest. Tools like 'erase', 'intersect', 'union', 'merge', and 'append' are foundational for vector-based analysis within GIS software.
Raster-based overlay combines pixel-based calculations or map algebra, offering a quick and efficient way to process data. This involves combining values from grid cells of different layers using mathematical operations like addition, subtraction, division, or multiplication to create a new output layer. Applications include creating risk surfaces or assessing sustainability. Raster data processing methods are categorized into local operations (cell by cell analysis using logical or arithmetic operators), neighborhood operations (focal operations that consider adjacent cells using a moving window), and regional operations (zonal operations that extract cell values based on homogeneous regions).
Map algebra uses algebraic functions (addition, subtraction, multiplication, division), statistical calculations (mean, median), relationship operations (greater than, lesser than), and Boolean operations (not, and, or) to compare and analyze values within raster layers. A key application is change detection, identifying alterations in the landscape over time.
Weighted overlay scales input data, weights the input raster, and adds them together. More favorable locations are reclassified to higher values. The weights assigned to input rasters must sum to 100%. This approach assumes that more favorable factors result in higher values in the output, identifying optimal locations.
Weighted sum overlay requires input layers to be reclassified. Unlike weighted overlay, the assigned weights can be any value and do not need to add up to a specific sum. The output values are a direct result of the addition of multiplied values by their weights, maintaining the attribute resolution. Similar to weighted overlay, it identifies optimal locations based on higher values.
Fuzzy overlay analysis is based on set theory, quantifying the membership relationship of phenomena to specified sets. It reclassifies data values to a common scale that defines the possibility of belonging to a specified set (e.g., 0 to 1). Unlike weighted overlay and weighted sum, input rasters are not weighted in the addition and combined steps, and the values represent possibilities of membership rather than preference on a ratio scale.
Overlay operations are extensively used in suitability modeling to find optimal locations for facilities like schools, hospitals, or industrial corridors. It involves selecting criteria, reclassifying data, applying Boolean or map algebra, and extracting suitable sites. Overlay analysis is a general method for analyzing co-occurring geographic phenomena, transforming traditional map overlay into an analytical tool for descriptive, deductive, and inductive analysis. It's crucial for change analysis, spatial data accuracy assessment, and multi-criteria evaluation. suitability modeling, a common application, identifies the best or most preferred locations by applying a common scale of values to diverse inputs.