Geography 625

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Geography 625
Intermediate Geographic Information Science
Week2 The pitfalls and potential of spatial data
Instructor Changshan Wu Department of
Geography The University of Wisconsin-Milwaukee Fa
ll 2006
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Outline

• Introduction
• The bad news the pitfalls of spatial data
• The good news the potential of spatial data

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1. Introduction
Why spatial data require spatial analytic
techniques, distinct from standard statistical
analysis that might be applied to any old
ordinary data?
Anything special with the spatial data?
Number of cases of Lyme disease
(Huxhold and Martin)
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1. Introduction
Bad news many of the standard techniques and
methods documented in standard statistics
textbooks have significant problems when we try
to apply them to the analysis of the spatial
distributions. Good news Geospatial referencing
provides us with a number of new ways of looking
at data and the relations among them. (e.g.
distance, adjacency, interaction, and neighbor)
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2. Pitfalls of Spatial Data
Spatial data always violate the fundamental
requirement of conventional statistical analysis

• Spatial autocorrelation
• Modifiable areal unit problem
• Ecology fallacy
• Scale
• Nonuniformity of space
• Edge effect

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2. Pitfalls of Spatial Data
- Spatial autocorrelation
Data from locations near one another in space are
more likely to be similar than data from
locations remote from one another.

• Example
• Housing market
• Elevation change
• Temperature

(African American Population Concentration)
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2. Pitfalls of Spatial Data
- Spatial autocorrelation

• The nonrandom distribution of phenomena in space
  has various consequences for conventional
  statistic analysis.
• Biased parameter estimates
• Data redundancy (affecting the calculation of
  confidence intervals

y
x
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2. Pitfalls of Spatial Data
- Spatial autocorrelation
Three general possibilities
Positive autocorrelation nearby locations are
likely to be similar to one another. Negative
autocorrelation observations from nearby
observations are likely to be different from one
another. Zero autocorrelation no spatial effect
is discernible, and observations seem to vary
randomly through space
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2. Pitfalls of Spatial Data
- Spatial autocorrelation
Positive
Negative
Zero (Random)
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2. Pitfalls of Spatial Data
- Spatial autocorrelation

• Spatial autocorrelation diagnostic measures
• Joins count statistics
• Morans I
• Gearys C
• Variogram cloud

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2. Pitfalls of Spatial Data
- Spatial autocorrelation
Spatial autocorrelation structure spatial
variation across a study area

• First order spatial variation occurs when
  observations across a study region vary from
  space to space due to changes in the underlying
  properties of the local environment.
• Second order due to local interaction effects
  between observations.

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2. Pitfalls of Spatial Data
- Modifiable Areal Unit Problem
Many geographic data are aggregates of data at a
more detailed level

• National census collected at the household level
  but reported for practical and privacy reasons at
  various levels of aggregation (block, block
  group, tract, county, state, etc.)
• Traffic Analysis Zone (TAZ)
• School district

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2. Pitfalls of Spatial Data
- Modifiable Areal Unit Problem
Modifiable Areal Unit Problem the aggregation
units used are arbitrary with respect to the
phenomena under investigation, yet the
aggregation units used will affect statistics
determined on the basis of data reported in this
way. If the spatial units in a particular study
were specified differently, we might observe very
different patterns and relationships.
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2. Pitfalls of Spatial Data
- Modifiable Areal Unit Problem
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2. Pitfalls of Spatial Data
- Modifiable Areal Unit Problem
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2. Pitfalls of Spatial Data
- Modifiable Areal Unit Problem
Openshaw and Taylor (1979) showed that with the
same underlying data it is possible to aggregate
units together in ways that can produce
correlations anywhere between -1.0 to 1.0.
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2. Pitfalls of Spatial Data
- Modifiable Areal Unit Problem
Two issues
Scale issue involves the aggregation of smaller
units into larger ones. Generally speaking, the
larger the spatial units, the stronger the
relationship among variables.
Aggregation (smoothed)
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2. Pitfalls of Spatial Data
- Modifiable Areal Unit Problem
Modifiable Area Units are arbitrary defined and
different organization of the units may create
different analytical results.
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2. Pitfalls of Spatial Data
- Modifiable Areal Unit Problem
Potential problems in almost every field that
utilizes spatial data
E.g. boundaries of electoral districts
In the 2000 U.S. presidential election, Al Gore,
with more of the population vote than George
Bush, but failed to become president.
A different aggregation of U.S. counties into
states could have produced a different outcome
(switch just one northern Florida county to
Georgia or Alabama would have produced a
different outcome)
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2. Pitfalls of Spatial Data
- Modifiable Areal Unit Problem
What are the reasons for this problem?

• Problems of data?
• Problems of spatial units?

What are the solutions for this problem?

• Using the most disaggregated data
• Produce a optimal zoning system
• Others?

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2. Pitfalls of Spatial Data
- Ecological Fallacy
The Ecological Fallacy is a situation that can
occur when a researcher or analyst makes an
inference about an individual based on aggregate
data for a group.
(Reference http//jratcliffe.net/research/ecolfal
lacy.htm)
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2. Pitfalls of Spatial Data
- Ecological Fallacy
Example we might observe a strong relationship
between income and crime at the county level,
with lower-income areas being associated with
higher crime rate.

• Conclusion
• Lower-income persons are more likely to commit
  crime
• Lower-income areas are associated with higher
  crime rates
• Lower-income counties tend to experience higher
  crime rates

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2. Pitfalls of Spatial Data
- Ecological Fallacy

• Issues
• Identifying associations between aggregate
  figures is defective ?
• Inferences drawn about associations between the
  characteristics of an aggregate population and
  the characteristics of sub-units within the
  population are wrong?
• What should we do?
• Be aware of the process of aggregating or
  disaggregating data may conceal the variations
  that are not visible at the larger aggregate level

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2. Pitfalls of Spatial Data
- Ecological Fallacy
Relationship between ecological fallacy and
modifiable areal unit problem?
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2. Pitfalls of Spatial Data
- Scale
The geographical scale at which we examine a
phenomenon can affect the observations we make
and must always be considered prior to spatial
analysis

• Problems of data representation
• Is there an optimal scale?

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2. Pitfalls of Spatial Data
- Nonuniformity of Space
Nonuniformity space is not uniform
Area with high crime rates?
Crime locations
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2. Pitfalls of Spatial Data
- Edge Effects
Edge effects arise where an artificial boundary
is imposed on a study, often just to keep it
manageable.
Spatial interpolation
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3. Potential of Spatial Data
- Introduction

• Potential insight provided by examination of the
  locational attributes of data
• Distance
• Adjacency
• Interaction
• Neighborhood

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3. Potential of Spatial Data
- Distance
Distance between the spatial entities of interest
can be calculated with spatial data
Euclidean distance Network distance Others (e.g.
travel time)
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3. Potential of Spatial Data
- Adjacency
Adjacency can be thought of as the nominal, or
binary, equivalent of distance. Two spatial
entities are either adjacent or not.
Can be defined differently
Example 1 two entities are adjacent if they
share a common boundary (e.g. Illinois and
Wisconsin) Example 2 two entities are adjacent
if they are within a specified distance
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3. Potential of Spatial Data
- Interaction
Interaction may be considered as a combination of
distance and adjacency and rests on the
intuitively obvious idea that nearer things are
more related than distant things, a notion
often referred to as the first law of geography.
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3. Potential of Spatial Data
- Neighborhood
Different definitions Example 1 a particular
spatial entity as the set of all other entities
adjacent to the entity we are interested
in. Example 2 a region of space associated
with that entity and defined by distance from it.
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3. Potential of Spatial Data
- Neighborhood
Adjacency
Distance
Neighborhood
Interaction
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3. Potential of Spatial Data
- Matrix representation
Distance matrix
A B C D E F
A B C D E F
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3. Potential of Spatial Data
- Matrix representation
Adjacency dlt 50
A B C D E F
A B C D E F
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3. Potential of Spatial Data
- Proximity Polygons
The proximity polygon of any entity is that
region of the space which is closer to the entity
than it is to any other.
Applications Service area delineation (e.g.
schools, hospital, supermarket, etc.)
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3. Potential of Spatial Data
- Proximity Polygons

• Delaunay triangulation
• Potential applications
• TIN model
• Others