The Learner's Guide to Geospatial Analysis

Understanding Spatial Fallacies


Complex issues in spatial analysis lead to bias, distortion and errors. These issues are often interlinked but various attempts have been made to separate out particular issues from each other. Here is a brief list:

Known Length - Lengths in earth measurement depend directly on the scale at which they are measured and experienced. So while we measure the length of a river, streetet cetera, this length only has meaning in the context of the relevance of the measuring technique to the question under study.

Locational Fallacy - The locational fallacy refers to error due to the particular spatial characterization chosen for the elements of study, in particular choice of placement for the spatial presence of the element. Spatial characterizations may be simplistic or even wrong. Studies of humans often reduce the spatial existence of humans to a single point, for instance their home address. This can easily lead to poor analysis, for example, when considering disease transmission which can happen at work or at school and therefore far from the home. The spatial characterization may implicitly limit the subject of study. For example, the spatial analysis of crime data has recently become popular but these studies can only describe the particular kinds of crime which can be described spatially. This leads to many maps of assault but not to any maps of embezzlement with political consequences in the conceptualization of crime and the design of policies to address the issue.

Atomic Fallacy - This describes errors due to treating elements as separate 'atoms' outside of their spatial context.

Ecological Fallacy - The ecological fallacy describes errors due to performing analyses on aggregate data when trying to reach conclusions on the individual units. It is closely related to the modifiable areal unit problem.

Modifiable areal unit problem - The modifiable areal unit problem (MAUP) is an issue in the analysis of spatial data arranged in zones, where the conclusion depends on the particular shape or size of the zones used in the analysis. Spatial analysis and modeling often involves aggregate spatial units such as census tracts or traffic analysis zones. These units may reflect data collection and/or modeling convenience rather than homogeneous, cohesive regions in the real world. The spatial units are therefore arbitrary or modifiable and contain artifacts related to the degree of spatial aggregation or the placement of boundaries. The problem arises because it is known that results derived from an analysis of these zones depends directly on the zones being studied. It has been shown that the aggregation of point data into zones of different shapes and sizes can lead to opposite conclusions. More detail is available at the modifiable areal unit problem topic entry.