Why is geography important

1
Why is geography important?

• UBC Geography 471

2
The fundamental issue

• "The problem of pattern and scale is the central
  problem in ecology, unifying population biology
  and ecosystems science, and marrying basic and
  applied ecology. Applied challenges ... require
  the interfacing of phenomena that occur on very
  different scales of space, time, and ecological
  organization. Furthermore, there is no single
  natural scale at which ecological phenomena
  should be studied systems generally show
  characteristic variability on a range of spatial,
  temporal, and organizational scales." (Levin
  1992 italics added)
• This quote equally applies to health studies,
  crime analysis, etc., and emphasizes the fact
  that geography is a fundamental element of any
  and all analyses.

3
A working solution

• None-the-less, many have argued that ecological
  phenomena tend to have characteristic spatial and
  temporal scales, or spatiotemporal domains (e.g.,
  Delcourt et al. 1983, Urban et al. 1987). A
  central tenet of landscape ecology is that
  particular phenomena should be addressed at their
  characteristic scales. Likewise, if one changes
  the scale of reference, the phenomena of interest
  change.

4
Multiscale analyses are therefore required if one
truly wants to develop a full understanding of a
place in space. This is the study design of the
BOREAS study.
5
To address the issue of multiple scales that
characterize the African Monsoon a multiscale
approach is being taken.
Source
6
Why is geography important?

• Issues such as
• the scale, grain and extent of a study area,
• the modifiable areal unit problem,
• the nature of the boundaries of a study area, and
• spatial dependence / heterogeneity
• are implicit in any spatial analysis

7
Why geography is important.

• Given the above, landscape ecologists,
  epidemiologists, health geographers, and crime
  analysts all must carefully consider the
  'geography' of their problem, and what effects
  that geography alone may have on their analyses
  (e.g., do more crimes occur in area A than area B
  simply because more people live in area A, or are
  there more crimes because there are higher levels
  of drug use in the area?).
• Simple putare the results dependent upon the
  spatial nature of the data, or do they reflect
  the results of a process? (Most likely, a
  combination of both.)

8
Scale terminology

• Grain
• The minimum resolution of the data (defined by
  scale, the "length of the ruler"). In raster
  lattice data, the cell size in field sample
  data, the quadrat size in imagery, the pixel
  size in vector GIS data, the minimum mapping
  unit.
• Extent
• The scope or domain of the data (defined as the
  size of the study area, typically)

9
Scale

• "Scale" is not the same as "level of
  organization." Scale refers to the spatial domain
  of the study, while level of organization depends
  on the criteria used to define the system.
• For example, population-level studies are
  concerned with interactions among conspecific
  individuals, while ecosystem-level studies are
  concerned with interactions among biotic and
  abiotic components of some process such as
  nutrient cycling.
• One could conduct either a small- or large-scale
  study of either population- or ecosystem-level
  phenomena.

Conspecific Of or belonging to the same species
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Scale

• As one increases scale in a study of a system
• Fine-scale processes or constraints average away
  and become constants. For example, at the scale
  of a quadrat (say, 10 x 10 m) in a forest, it is
  reasonable to ignore larger-scale variability in
  soil parent material the trees within the
  quadrat all see the same soil type. Likewise, at
  the time-scale of years to decades, long-term
  climate trends are not apparent (although
  fluctuations in weather might be).
• Reciprocally, as we increase the extent of our
  analysis parameters that were constant now become
  variable and must be accounted. If we were to
  extend the forest sampling to cover a large
  watershed or basin, soil types would indeed vary
  and we would need to address this variability.
  Likewise, microclimate as it varies with
  elevation and topographic position would become a
  real source of variability affecting forest
  pattern at this larger scale.

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Scale

• Finally, new interactions may arise as one
  increases the extent of inquiry. At the scale of
  a landscape mosaic, interactions among forest
  stands, such as via dispersal of plant or animal
  species, emerge as new phenomena for study.
  (Emergent processes)
• The magnitude or sign of correlations may change
  with spatial extent. At the scale of a single
  habitat patch, abundances of different species
  might be negatively correlated due to
  interspecific interactions but if one considers
  a set of these habitat patches in a heterogeneous
  landscape, any species inhabiting similar habitat
  types will be positively correlated.
• Thus explanatory models are scale-dependent.

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Spatial autocorrelation scale
-ve correlation within each stand, ve
correlation between stands
13
Spatial autocorrelation

• Cliff and Ord (1973) define spatial
  autocorrelation as If the presence of some
  quantity in a sampling unit (county) makes its
  presence in neighbouring sampling units
  (counties) more or less likely, we say that the
  phenomenon exhibits spatial autocorrelation.
• It may be classified as either positive or
  negative. In a positive case similar values
  appear together, while a negative spatial
  autocorrelation has dissimilar values appearing
  in close association.

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Spatial autocorrelation

• The distribution of organisms over the earths
  surface means that most ecological problems have
  a spatial dimension. Biological variables are
  spatially autocorrelated for two reasons
• inherent forces such as limited dispersal, gene
  flow or clonal growth tend to make neighbours
  resemble each other
• organisms may be restricted by, or may actively
  respond to environmental factors such as
  temperature or habitat type, which themselves are
  spatially autocorrelated (Sokal Thomson 1987).

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Spatial autocorrelation
Moran's I is a weighted product-moment
correlation coefficient, where the weights
reflect geographic proximity. Values of I larger
than 0 indicate positive spatial autocorrelation
values smaller than 0 indicate negative spatial
autocorrelation.
16
MAUP

• The modifiable areal unit problem is endemic to
  all spatially aggregated data. It consists of two
  interrelated parts.
• First, there is uncertainty about what
  constitutes the objects of spatial
  study--identified as the scale and aggregation
  problem.
• Second, there are the implications this holds for
  the methods of analysis commonly applied to zonal
  data and for the continued use of a normal
  science paradigm which can neither cope nor admit
  to its existence.

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MAUP
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Scale

• The scale effect is the tendency, within a system
  of modifiable areal units, for different
  statistical results to be obtained from the same
  set of data when the information is grouped at
  different levels of spatial resolution (e.g.,
  enumeration areas, census tracts, cities,
  regions).
• This definition infers that as one changes the
  scale of the study there is a corresponding
  change in grain.

19
Aggregation

• The aggregation or zoning effect is the
  variability in statistical results obtained
  within a set of modifiable units as a function of
  the various ways these units can be grouped at a
  given scale, and not as a result of the variation
  in the size of those areas.
• A figure illustrating the issues

20
Aggregation

• The problem with aggregated data comes not (only)
  with the data themselves or any conclusions drawn
  from them, but from attempts to extend the
  conclusions to another level of spatial
  resolution (usually finer, like to individual
  households or people). Attempting to do this is
  called ecological fallacy.
• All the statistics and model parameters differ
  between the two levels of resolution, and we have
  no way to predict what they are at the finer
  level given the values at the coarser level.

21
MAUP

• The second component of MAUP follows from the
  uncertainty in choosing zonal units. Different
  areal arrangements of the same data produce
  different results, so we cannot claim that the
  results of spatial studies are independent of the
  units being used, and the task of obtaining valid
  generalizations or of comparable results becomes
  extraordinarily difficult.
• MAUP therefore consists of two problems--one
  statistical and the other geographical /
  philosphophical, and it is difficult to isolate
  the effects of one from the other.

22
Given that many policy decisions are made on the
basis of statistical associations obtained from
the analysis of spatial data (e.g., funding for
multicultural activities to neighbourhoods on
the basis of the percentage ethnic population
living there), much more attention needs to be
paid to the problem.
23
Using the MAU effect we can create zonings with
particular statistical aims in mind.
Another example
24
MAUP in action
Redrawing the balanced electoral districts in
this example creates a guaranteed 3-to-1
advantage in representation for the blue voters.
Here, 14 red voters are packed into the yellow
district and the remaining 18 are cracked across
the 3 blue districts. This is known as
gerrymandering.
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Gerrymandering

• The MAUP is a very real issue for politicians.
• One of the requirements of Civil Rights era
  legislation is that states that had a history of
  racial discrimination (generally, the states that
  constituted the Confederacy, including Texas)
  must obtain "pre-clearance" of all redistricting
  plans from the U.S. Department of Justice. This
  is because of the tendency of those states to
  engage in so-called "racial gerrymandering"
  configuring districts in order to minimize
  minority representation. This can be done either
  by concentrating minorities in as few districts
  as possible (minority vote concentration), or
  distributing them across many districts (minority
  vote dilution). Source.

26
Carved out with the aid of a computer, this
congressional district was the product of
California's incumbent gerrymandering.
27
Spatial units

• We should identify that two distinct types of
  spatial units are commonly used in geographic
  analysis--artificial and natural units. Census
  data collected for individuals, but aggregated
  and represented as artificial areas, present a
  major problem in interpretation to social
  geographers, and cannot be treated in the same
  way as 'natural' areal data, such as soil type,
  that is collected and represented as areal data.
• However, even natural units are not without
  their problems (e.g., fuzzy / fractal boundaries)

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Spatial units
29
Simpsons paradox

• However, there are other elements which may
  impact any study using aggregated data.
• Simpson's Paradox is commonly encountered.
• If the values of the variables vary in
  correlation with another (e.g., areas with high
  unemployment rates are often associated with
  areas that also exhibit high rates of other
  social-economic characteristics), then it may be
  impossible to obtain a reliable estimate of the
  true correlation between two variables.

Example
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Simpsons paradox

• The paradox in the example arises because we
  assume that race is the independent variable
  while unemployment is the dependent variable. In
  fact, location is the independent variable (and
  unavailable for examination when we only examine
  the totals) and unemployment and race are the
  dependent variables.
• An example of a common-response relation. (?)

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Why does the MAUP exist?

• Geographical areas are made up not of random
  groupings of individuals / households, but of
  individuals / households that tend to be more
  alike within the area than to those outside of
  the area. Three main classes of models have been
  identified
• Grouping
• Group-dependent
• Feedback

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Neighbourhood models

• Grouping models, in which similar individuals /
  households choose, or are constrained, to locate
  in the same area / group, either when those
  groups are formed or through migrations.
• That is to say, some process has operated and /
  or continues to operate such that individuals /
  households are not randomly assigned to areas.
  (Chinatown, Sikh neighbourhoods in Surrey)
• A tendency for plants with similar ecological
  requirements to be located in 'communities'.

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Neighbourhood models

• Group-dependent models, in which individuals /
  households in the same area / group are subject
  to similar external influences.
• For example, there may be some 'contextual'
  variable affecting all individuals in the area.
  Alternatively, some common influence may have
  operated in the past, the effects of which are
  still felt. Simply said-the effects of other
  characteristics of the area, which may or may not
  be available for analysis (e.g., the restrictive
  covenants that used to be in place in the British
  properties in West Vancouver).
• The rain shadow effects felt in the Okanagan
  Valley, and the dryland communities that result.

34
Neighbourhood models

• Feedback models, in which individuals /
  households interact with each other and influence
  each other, and the frequency / strength of such
  interaction is likely to be greater between
  individuals in the same area / group than between
  individuals in different areas. Simply said-a
  tendency for people living nearby to interact and
  as a result to develop common characteristics.
• A bog community, wherein the acid conditions are
  maintained by the decomposition of the plants
  found therein.

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Neighbourhood models
36
Neighbourhood models

• These models could all be operating, and be
  operating at different scales (block,
  neighbourhood, city, province).
• Therefore, attempting to achieve a perfect
  understanding of the reasons why MAUP occurs may
  be impossible. These models describe different
  ways in which spatial (auto)correlation may be
  acting on the variables of interest.

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Conclusion

• So, as you can see, developing an understanding
  of the role that geography alone can play in an
  analysis is vital--before one can search for
  meaningful biological, environmental or
  sociological explanations for an observation, one
  should first eliminate the geographic
  explanation.
• Ultimately, neighbourhoods are composed of unique
  combinations of biological (behavioral, social,
  political, economic) and physical environments
  (all of which might change over time), and no
  combination of statistical manipulations may be
  able to unpack such a complex set of 'actors.'