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Environmental Sustainability in Practice

Data Analyses and Results

Spatial analysis may be defined as “examining the characteristics or features of spatial data” or “how features spatially relate to one another” (Shellito, 2016, p. 177). Analysis may be considered spatial if the results depend on the locations of the features (i.e., objects, entities) being analyzed. For example, if you were to move these feature(s), the output results would change in a spatial analysis. Some common examples of spatial analysis questions may include:

How many objects or features are within a certain distance of a particular location?
- For example, how many bird feeding platforms are located within 3 km of an inland wetland area?
What are the most suitable locations based on a set of criteria?
- For example, where is the best location to build a new residential waste and recycling drop-off site within the Niagara Region?

One of the main goals of spatial analysis is to extract useful information from a geospatial dataset that can be used in the decision-making process. That is to say, it is beyond simply mapping (a focus of cartography) and includes adding value (i.e., useful information). The extraction of useful information is achieved through the transformation, manipulation, and analysis of geospatial data. In this section we will discuss some common examples of spatial data analyses used in the field of environmental sustainability, including image classification, site suitability analysis, spatial autocorrelation, and spatial interpolation, as well as common errors that occur in spatial data analyses.

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