A Geographic Information System (GIS), also known as a geospatial information system, facilitates the recording of a base map using a geospatial referencing system like latitude or longitude. This system then enables you to superimpose more layers of distinct geographic information on this base map in order to perform spatial analysis.
GIS software has various statistical and analytical tools that can be utilized to study the relationships between different spatial features. This allows the user to map out patterns and trends across a landscape.
For example, epidemiologists can use GIS analysis to model and predict the spread of a disease. Urban planners can use GIS in order to understand how new developments will affect traffic flow and demand on local services. Geologists can use geospatial analysis to predict the location of resources such as oil and gas based on geographic features and soils.
The Uses of GIS
Summarized below are some of the more common and basic uses of GIS analysis.
The most basic function of any GIS is to simply map out where things are. An oft cited statistic is that “80% of all data has a geographic component“. Whether or not that’s true, we do know that a lot of the data we can compile can be mapped out.
GIS provides a way to stored this data, georeference the data, and then map it out. Often mapping out data can provide the first clues about any potential spatial relationships.
Basic geospatial analysis
Once the data has geographic coordinates, some elementary spatial analysis can be performed to tease out potential correlations and relationships between geographic features.
Here are some common basic GIS analysis.
A proximity analysis is an analytical technique that is used to define the relationship between a specific location and other locations or points that are linked in some way.
This analysis can be used to make sure a new service, such as a bus line, has stops near parks, schools, and other places that transit riders want access to. Proximity analysis can also be used by businesses to make sure their new retail location isn’t too close to competitors.
Proximity analysis can be used to answer several types of questions that include:
- How far is it between point a and point b? The simplest type of proximity analysis calculates distances between two vector points.
- On average how far is one point from a set of other points or conditions?
- What is the closest point in terms of time or cost taken to reach that point?
- What is the straight line distance between a single point and other selected points in that layer?
- How far are the points or edges of the nearest polygon?
A GIS technique called buffering is commonly used with proximity analysis to indicate the sphere of influence of a given point.
Buffering involves creating a zone around a given point, line, or polygon (area) of a specified distance. Buffering is useful for creating a zone around a given geographic feature for further analysis using the overlay method.
For example, a 1000′ buffer could be generated around a school to then use overlay analysis to find out how many libraries are within 1000′ of that school.
Using multiple algorithms it is possible to select a group of unrelated points on a theme that match a set of criteria. A cluster could include members where distance between them is less than a specific amount or areas where there is density of points greater than a specific level.
Typically a GIS will require multiple levels of iteration before the correct algorithms are identified.
Typical clustering models include:
- Connectivity models – the simplest that depend upon simple distance based relationships
- Centroid models – where inclusion in a cluster is determined by identifying the mean value of the cluster that is most appropriate to the point being considered
- Distribution models – where inclusion is determined by the application of a statistical distribution theory such as the normal probability
- Density models – using techniques specially identified for GIS work that link areas with specific densities of an event or population such as racial profiles in a given area
- Subspace models – this technique allows the element to be included into a cluster by considering specific attributes of that element
- Group models – those models where an algorithm cannot be established to demonstrate a shared link where they are in effect linked manually
A technique that can be used to measure the distances between a point and the edge of a specific element that has been defined as a polygon using vector points.
Nearest neighbor algorithms have been the subject of intense research since the 1980s and new approaches were defined by academics such as Benezecri and Juan in 1982. The algorithm defined focuses on identifying points that are either maximal, minimal or median members of the data set.
A basic analysis that allows a given area from one later to be overlaid onto data from other themes. A good example would be – what type of soil do we find in the school grounds or what type of industrial uses has this area been put to in the past 50 years.
There are two methods of performing this type of analysis:
- Feature overlay – a simple technique to drop single or multiple points into an area
- Raster overlay – best used when characteristics of multiple themes are required to be examined because each area is referenced and combined on a grid basis
Location analysis is a way to select the best location for a site given a set of geographic parameters.
For example, this analysis is often used in business settings, where it can help companies determine the most advantageous locations for their stores, factories, or distribution centers, considering factors such as customer location, access to transportation, and competitor location.
In a site suitability analysis, multiple factors like proximity to transportation routes, availability of resources, regulatory constraints, environmental impact, and demographics might be taken into account to identify the best location for a new facility. By integrating these data into a GIS, a visual and spatial dimension can be added to the decision-making process, making it easier to understand and interpret the data.
This article was originally written on August 6, 2012 and has since been updated.