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Geog418SptStts > Intro > Flashcards

Intro Flashcards

1
Q

What is Spatial Analysis?

A
  • A set of tools (stats, math, software, hardware) to analyze (concepts, theories, techniques, models) spatial processes
  • A subset of analytic techniques whose result depend on the spatial frame, or will change if the frame changes or if objects are repositioned within it.
  • Reveals patterns that are otherwise invisible
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2
Q

Aspatial data

A

Attribute, Pi (z)

- Value/info

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3
Q

Spatial Data

A

Location and Attribute

  • Pi (x,y,z)
  • This is why matrices are often used
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4
Q

What does it mean that Spatial Analysis has no locational invariance?

A
  • Results change when locations of study objects change

- ‘Where’ matters

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5
Q

What are the 4 main components of spatial analysis?

A
  • Data manipulation
  • Exploratory spatial data analysis and visualization
  • Spatial statistical analysis
  • Spatial modelling
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6
Q

Data manipulation

A
  • GIS, databases, processing, projecting
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7
Q

Exploratory spatial data analysis and visualization

A

Showing and identifying interesting patterns

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8
Q

Spatial statistical analysis

A

Investigating data to determine whether or not data can be represented in spatial model

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9
Q

Spatial Modeling

A

Explaining interesting patterns and/or predict spatial outcomes

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10
Q

Spatial Sampling

A
  • Location as an experimental design problem

- Location as a given

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11
Q

Location as an experimental design problem

A
  • Spatial sampling = where to collect data
  • Which villages
  • Where to locate air quality monitoring stations
  • Design sampling approach to fit surface
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12
Q

Location as a given

A
  • Most spatial data analyses have no choice in location
  • No sampling in the usual sense
  • data = attributes augmented with location information
  • ex. census tract boundaries not under control of analyst
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13
Q

Spatial Autocorrelation

A
  • Why is something the way it is?
  • There is an underlying process for why the surface is the way it is (not random as is ‘required’ for stats)
  • Ex. Elevation has underlying trend in topography, tectonics, erosion
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14
Q

What are the 4 major problems in spatial sampling?

A
  • Maup
  • Ecological fallacy
  • Boundary/extent
  • Scale
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15
Q

What is critical when ‘the where’ is introduced?

A
  • Spatial dependence, the relatedness of data in space
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16
Q

MAUP

A
  • Modifiable Areal Unit Problem
  • Problem when data relates to discrete zones (most socioeconomic data)
  • Densities in an area change/vary in space
  • Grouping the data can have infinite possibilities and can greatly affect results (mean, etc.)
  • Data is strongly dependent on groupings (tell different stories)
  • Partly depends on underlying micro data and nature of zoning system
  • Some can be justified (watersheds) but some change over time (neighbourhoods)
  • Paly depends on underlying micro data and nature of zoning system
17
Q

Correlation relationship, R^2

A
  • Does not imply causation
  • Shows how strong the relationship is between the dependent and independent variables
  • Can change/shift based on aggregation of groups
18
Q

Scale Problem

A
  • Scale of spatial process and scale of spatial measurement
  • Up/down scale and results change
  • Use fractals (similar spatial pattern at increasing scales) to understand how to scale up/down
19
Q

Ecological Fallacy

A
  • Inference on individual based on aggregated group data
  • Results from belief that relationships observed fro groups hold for individuals
  • Ex. use aggregated province to infer on municipalities, can greatly differ from provincial mean
  • Ex. Countries with more fat in diet have higher rate of breast cancer, must mean women who eat fatty foods more likely to get cancer
20
Q

Boundary Problem

A
  • Spatial processes are generally unbounded
  • Artificial and arbitrary boundaries are often imposed for analysis purposes (grid points)
  • Edge effects outside of boundary often impossible to control
  • Does surface extend outside study area even though we have no observations?
  • Potential fix: collect data outside of study area to help control edge effects
21
Q

Diffusion

A
  • Who has it, who doesn’t
  • Spreads slowly outwards
  • Requires contact/adjacency
22
Q

Exchange and Transfer

A
  • Commodities and income
  • Adjacency
  • Spill over effects
23
Q

Interaction

A
  • Events at a location affect events at another
24
Q

Spatial organization can be exploited to?

A
  • Design sampling plans
  • Interpolate
  • Fill in missing values in a database
  • Classify
25
Q

Main issues of Spatial Processes?

A

Representation of spatial dependence

26
Q

Spatial Processes

A
  • Diffusion
  • Exchange and Transfer
  • Interaction
27
Q

Spatial Dependence

A
  • Recall 1st law of geography
  • Affects outcome of stat tests
  • If present and not accounted for, the variance of correlation coefficient is underestimated
  • Overlap on graphs
  • Leads to redundancy, greater chance of outliers, chance of accepting null hypothesis when it is wrong
28
Q

1st Law of Geography

A

Everything is related to everything else, but near things are more related than distant things

29
Q

How do you tell if data is spatially dependent?

A
  • Test for it!

- Geary’s C and Moran’s I (Positive, Negative, None)

30
Q

First order spatial autocorrelation

A
  • Spatial variation occurs when observations across a study region vary from place to place due to changes in the underlying properties of the local environment
31
Q

Second order spatial autocorrelation

A

Variation is due to interaction effects between observations

Geog418SptStts (6 decks)

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