GIS-based models
Geographical information systems (GIS) offer powerful technologies for use in integrated assessments. Their power comes from the fact that they provide a means:
- to link and integrate different data sets from different sources - e.g. between different environmental phenena or between environment and population;
- to explore and analyse spatial patterns and relationships in the data - e.g. to estimate numbers of people potentially exposed because they live close to emission sources;
- for spatial modelling - e.g. to simulate propagation and dispersion of environmental pollutants;
- for mapping and other forms of visualisation of spatial data.
Spatial data types
While, in practice, almost all real-world phenomena comprise volumes (i.e. they have dimensions of height, width and depth), the data that we use to describe them often represent them in different ways. Three fundamental spatial structures can be recognised:
- points - e.g. a sampling location, a point emission source, or a residential address;
- lines - e.g. the centreline of a roadway or stream;
- areas - e.g. an area of woodland or industrial land, a census tract, a country.
Each of these, however, can be represented either as an irregular structure or as a regular one. Most natural phenomena are somewhat irregular: for example, sampling locations are spread unevenly across the landscape, roads are laid out haphazardly, and administrative regions are bounded by irregular boundaries. In GIS, these irregular structures are all represented by vector data.
In many cases, however, it is advantageous to present data in a regular form - as an array of points, a lattice of lines, or a grid of regular cells. In GIS, these are all forms of raster data. Although presenting data in this form may involve some degree of distortion of reality, it has several major advantages. In particular, it makes computation much more efficient (and therefore allows larger data sets to be analysed) and, as a basis for mapping, improves interpretability of the results. For these reasons, many of the most powerful GIS techniques operate in raster form, and in many cases it is helpful to present results as gridded maps.
GIS-based modelling techniques
Because of their great flexibility (and the large range of different modelling tools that they contain) GIS offer a huge range of different modelling techniques. As implied above, many of these are restricted to (and designed to be used with) specific data types. The table below outlines five general approaches that have special utility for exposure modelling in integrated impact assessments. Further information is provided on a number of these via the embedded links and the panel to the left.
| Technique | Data types | Description | Examples of applications |
| Interpolation | Points | Estimates conditions at unsampled (intermediate) locations by fitting a surface through the data points. Range of methods including Kriging, splines and inverse distance weighting. Mainly applied to point data, but can also be used with areas. | To model air pollution surfaces based on data from an air pollution monitoring network |
| Buffering | Points | Creates buffer zones of specified radius around a set of target locations, in order to explore relationships with their surrounding areas. Target locations are often points, but buffering can also be applied to lines and areas. Searches within the buffer zones can be made for point, line or area features. Multiple buffer zones can be generated and analysed. |
1. To generate models of air pollution by relating measured concentrations at monitoring sites to environmental conditions in the surrounding area ('land use regression'). 2. To identify potentially exposed populations living within a specified distance of emission sources. |
| Focal sum | Raster | Calulates the weighted sum of values in surrounding grid cells for a series of focal (target) cells. Kernel files of different shape (e.g. rectangular, circular, elliptical) can be applied to represent the weights for surrounding cells. Weights can be derived from a specified model (e.g. based on distance from the focal cell) or applied uniquely to each cell in the kernel file. Though commonly applied using summation (focal sum), other statistical measures (e.g. average, range) can also be used. |
To model air pollution as the distance-weighted sum of emission or source intensity in surrounding areas. |
| Network analysis | Lines | Models accessibility along a line network by computing the total cost or impedance between two points. Cost can be based on connective distance (i.e. distance along the network) or on other (attached) attributes of the network (e.g. travel time). |
1. To model optimum (minimum cost) route choices through a road network. 2. To assess the potential catchment area and population of a service (e.g. hospital) on a road network. |
| Hydrological models | Raster | Models flow routes through a gridded area (raster) from a start point (seed) to exit point. Preferred routes between adjacent cells are defined on the basis of a simple optimising rule (e.g. maximum downward gradient). Mainly used for modelling hydrological processes, but can also be more generally applied to any diffusion process. | To model surface runoff of rainfall in a catchment. |
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