Section 4.4 A note of caution: MAUP
Now that we’ve shown you how to do a lot of spatial crime analysis, we wanted to close with some words of caution. Remember that everything you’ve learned here are just tools that you will be applying to data you are working with, but it’s up to you, the researcher, the analyst, the domain expert, to apply and use these with careful consideration and cautions. This discussion is very much part of spatial crime analysis, and an important field of thought.
We borrow here from George Rengert and Brian Lockwood:
When spatial analysis of crime is conducted, the analyst should not ignore the spatial units that data are aggregated into and the impact of this choice on the interpretation of findings. Just as several independent variables are considered to determine whether they have statistical significance, a consideration of multiple spatial units of analysis should be made as well, in order to determine whether the choice of aggregation level used in a spatial analysis can result in biased findings ([Rengert and Lockwood, 2009]).
They highlight four main issues inherent in most studies of space:
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issues associated with politically bounded units of aggregation,
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edge effects of bounded space,
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the modifiable areal unit problem (MAUP),
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and ways in which the results of statistical analyses can be manipulated by changes in the level of aggregation.
Subsection 4.4.1 Scale
The scale problem involves results that change based on data that are analyzed at higher or lower levels of aggregation (changing the number of units). For example, evaluating data at the state level vs. census tract level.
The scale problem has moved to the forefront of geographical criminology as a result of the recent interest in small-scale geographical units of analysis. It has been suggested that smaller is better since small areas can be directly perceived by individuals and are likely to be more homogenous than larger areas ([Gerell, 2017]).
Subsection 4.4.2 Zone
The zonal problem involves keeping the same scale of research (say, at the state level) but changing the actual shape and size of those areas.
The basic issue with the MAUP is that aggregate units of analysis are often arbitrarily produced by whoever is in charge of creating the aggregate units. A classic example of this problem is known as gerrymandering. Gerrymandering involves shaping and re-shaping voting districts based on the political affiliations of the resident citizenry.
The inherent problem with the MAUP and with situations such as gerrymandering is that units of analysis are not based on geographic principles and instead are based on political and social biases. For researchers and analysts the MAUP has very important implications for research findings because it is possible that as arbitrarily defined units of analysis change shape findings based on these units will change as well.
When spatial data are derived from counting or averaging data within areal units, the form of those areal units affects the data recorded, and any statistical measures derived from the data. Modifying the areal units therefore changes the data. Two effects are involved: a zoning effect arising from the particular choice of areas at a given scale; and an aggregation effect arising from the extent to which data are aggregated over smaller or larger areas. MAUP arises in part from edge effect. If you’re interested, in particular about politics and voting, you can read this interesting piece by [Bycoffe (2018)] to learn more about gerrymandering.
Subsection 4.4.3 Why does MAUP matter?
The practical implications of MAUP are immense for almost all decision-making processes involving mapping technology, since with the availability of aggregated maps, policy could easily focus on issues and problems which might look different if the aggregation scheme used were changed.
All studies based on geographical areas are susceptible to MAUP. The implications of the MAUP affect potentially any area level data, whether direct measures or complex model-based estimates. Here are a few examples of situations where the MAUP is expected to make a difference, taken from Gerell (2017):
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The special case of the ecological fallacy is always present when census area data are used to formulate and evaluate policies that address problems at individual level, such as deprivation. Also, it is recognised that a potential source of error in the analysis of census data is “the arrangement of continuous space into defined regions for purposes of data reporting”.
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The MAUP has an impact on indices derived from areal data, such as measures of segregation, which can change significantly as a result of using different geographical levels of analysis to derive composite measures.
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The choice of boundaries for reporting ratios is not without consequences: when the areas are too small, the values estimated are unstable; while when the areas are too large, the values reported may be over-smoothed, i.e., meaningful variation may be lost.
Subsection 4.4.4 What can we do?
Most often you will just have to remain aware of the MAUP and its possible effects. There are some techniques that can help you address these issues. It is possible to also use an alternative, zone-free approach to mapping these crime patterns, perhaps by using kernel density estimation. Here we model the relative density of the points as a density surface — essentially a function of location (x,y) representing the relative likelihood of occurrence of an event at that point. We cover KDE in Chapter 7 of this book.
For now, it’s enough that you know of, and understand the MAUP and its implications. Always be smart when choosing your appropriate spatial unit of analysis, and when you use binning of any form, make sure you consider how and if your conclusions might change compared to another possible approach.
