Uncertain geographic context problem

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The uncertain geographic context problem or UGCoP is a source of

statistical bias that can significantly impact the results of spatial analysis when dealing with aggregate data.[1][2][3] The UGCoP is very closely related to the Modifiable areal unit problem (MAUP), and like the MAUP, arises from how we divide the land into areal units.[4][5] It is caused by the difficulty, or impossibility, of understanding how phenomena under investigation (such as people within a census tract) in different enumeration units interact between enumeration units, and outside of a study area over time.[1][6] It is particularly important to consider the UGCoP within the discipline of time geography, where phenomena under investigation can move between spatial enumeration units during the study period.[2] Examples of research that needs to consider the UGCoP include food access and human mobility.[7][8]

Schematic and example of a space-time prism using transit network data: On the right is a schematic diagram of a space-time prism, and on the left is a map of the potential path area for two different time budgets.[9]

The uncertain geographic context problem, or UGCoP, was first coined by Dr. Mei-Po Kwan in 2012.[1][2] The problem is highly related to the ecological fallacy, edge effect, and Modifiable areal unit problem (MAUP) in that, it relates to aggregate units as they apply to individuals.[5] The crux of the problem is that the boundaries we use for aggregation are arbitrary and may not represent the actual neighborhood of the individuals within them.[4][5] While a particular enumeration unit, such as a census tract, contains a person's location, they may cross its boundaries to work, go to school, and shop in completely different areas.[10][11] Thus, the geographic phenomena under investigation extends beyond the delineated boundary .[6][12][13] Different individuals, or groups may have completely different activity spaces, making an enumeration unit that is relevant for one person meaningless to another.[7][14] For example, a map that aggregates people by school districts will be more meaningful when studying a population of students than the general population.[15] Traditional spatial analysis, by necessity, treats each discrete areal unit as a self-contained neighborhood and does not consider the daily activity of crossing the boundaries.[1][2]

Implications

The UGCoP has further implications when considering the area outside of a study area. Tobler's second law of geography states, "the phenomenon external to a geographic area of interest affects what goes on inside."[16][12] As a study area is often a subset of the planet, data on the edges of the study area will be excluded.[17] If the boundary demarcating the study area is permeable to travel, then the phenomena under investigation within it may extend beyond, and be impacted by, forces excluded from the analysis.[6][18] This uncertainty contributes to the UGCoP.[1][2]

All maps are wrong, and a cartographer must ensure that their maps' limitations are well documented to avoid misleading the users.[19] With modern technology, there is an emphasis on individual-level data and understanding how individuals interact with their environment.[5][8] When making maps with this individual-level data, the UGCoP is one source of bias that can impact the results of an analysis.[1] When these results inform policy, they can have real world ramifications.[19]

The UGCoP is particularly important when understanding food access and human mobility.[6][7]

Suggested solutions

Geographic information systems, along with technologies that can monitor the position of individuals in real time, are possible methods for addressing the UGCoP.[2] These technologies allow scientists to analyze and visualize the 3D space-time path of people moving through a study area, and better understand their actual activity space.[2] Web GIS has also been employed to address the UGCoP by allowing researchers to better contextualize subjects' real and perceived activity space.[2][15] These technologies have helped to address the problem by moving away from aggregate data and introducing a temporal component to the modeling of subject activity.[2][15]

See also

References

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  18. doi:10.3390/su15031762
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