Lecture 10 - Spatial Data Analysis Flashcards
What is the objective of spatial analysis?
To transform data into useful info to satisfy requirements/objectives of decision-makers at all levels of detail
What does spatial analysis help us to do?
- distinguishes GIS from other info systems
- identify trends in data
- create new relationships from the data
- view complex relationships b/w data sets
- make better decisions
Why do analyses?
Analysis functions use spatial and non-spatial attributes in database to answer questions about real world
Why do we do spatial analysis of massive data sets?
- search for patterns, anomalies, and trends
- semi-automated b/c of data volumes
- widely used in practice in business (unusual credit card usage), epidemiology (detection of disease outbreak), and climate change (data patterns lead to discovering ozone hole)
What are descriptive summaries?
- attempt to summarize useful properties of data sets in one or two statistics
- mean/average widely used b/c measures central tendency
How do you find a spatial mean?
Partial (2D) equivalent of the mean would be a centre. Several ways of defining centres:
- centroid
- dispersion
What is a centroid?
The 2D equivalent of the mean.
- for a set of points, the centroid is found by taking weighted average of x and y coordinates
- balance point: the point about which the 2D pattern would balance if it were transferred to a weightless, rigid plane and suspended
- ex. population centroids in US
What is dispersion?
A measure of spread of points around a centre (useful for determining positional error)
- w/o knowing about how data is dispersed, measure of central tendency may be misleading
- measures of dispersion include: range, average deviation, variance and standard deviation
What are spatial patterns? How can we identify spatial patterns in the data?
Can be clustered, dispersed, or random.
Techniques for identifying:
- measures only looking at physical location and geometric patterns of features (ex. unlabelled points)
- measures looking at geographic patterns in attribute data of features (ex. labelled points)
What is Tobler’s first law of geography?
Everything is related to everything else, but near things are more related than distant things
- fundamental concept for understanding/analyzing spatial phenomena
What is spatial autocorrelation?
Correlation of a variable with itself through space
- measures and analyzes the degree of spatial dependency by comparing values of a sample with values of their neighbours
- if there is any systematic pattern in the spatial distribution of a variable, it is said to be spatially autocorrelated
What is positive spatial autocorrelation?
All similar values are located close together
- may violate assumptions about independence of residuals (diff b/w observed and predicted value)
What is negative spatial autocorrelation?
Dissimilar values appear in close association (neighbouring areas unalike)
What is random patterns in spatial autocorrelation?
Exhibits no spatial autocorrelation
Why is spatial autocorrelation important?
- most stats based on assumption that values of observations in each sample independent of one another
- most GIS provide tools to measure the level of spatial autocorrelation
Goals:
- to measure strength of spatial autocorrelation in a map
- to test the assumption of independence or randomness
What are the main indices of spatial autocorrelation?
- Moran’s I
- Geary’s C
- Ripley’s K
- Join Count Analysis
What do indices of spatial autocorrelation do?
Measures level of interdependence b/w variables and nature and strength of that interdependence
- tests whether the observed value of a variable at one locality is independent of the values of the variable at neighbouring localities
How does Moran’s I work?
Based on p-values.
Ex. Moran’s I = 0.66, then p-value is not statistically significant and you cannot reject null hypothesis
Ex. 2 Moran’s I = 0.015, then p-value is statistically significant, so you can reject null hypothesis
What are spatial modes?
Attempt to represent variation over the earth’s surface (GIS provides suitable platform)
- manipulate geographic info in multiple stages (representing either a single point in time or predictions about future points in time)
What are the benefits of the modelling approach?
- allows experiments to be conducted on simulated systems rather than on the real thing (cheaper and less invasive)
- allows alt scenarios to be evaluated (compare diff policy options and their impacts on the future; planners can experiment “what-if” scenarios)
What are the different types of spatial models?
- static models and indicators
- individual and aggregate models
- cellular models
What are static models and indicators?
- represents a system at a single point in time
- take multiple GIS inputs and compute useful indices
- ex. groundwater protection model
What are individual and aggregate models?
Individual: simulates behaviour of every individual in system (ex. every person in a crowd)
Aggregate: used when too many individual elements to model (ex. impossible to model every molecule of water, grain of sand, etc.)
- ex. simulation of movement of individuals during a parade
What are cellular models?
- model a system based on using raster GIS
- each cell in the raster can be in one of a number of states (ex. change through time is represented by change of cell state)
- ex. urban growth simulation where each cell is either undeveloped or developed
What are multicriteria methods?
- valuable decision-making tool (most widely used analytical facility in GIS)
- many decisions depend on numerous factors
- these methods attempt to reconcile such differences and to reach consensus on what factors should be combined (stakeholder disagreements)
What is decision-making defined as?
The process of choosing between a set of alternatives by analyzing and interpreting geographical info related to alts
What are the decision-making process steps?
- defining decision problem (objective)
- determining set of evaluation criteria to be used
- weighting criteria (b/c multiple criteria have varying importance)
- generating alternative solutions
- apply decision making
- recommend the best solution to the problem
What is multi-criteria evaluation (MCE)? What are the techniques used?
Involves overlaying thematic layers and finding locations that encompass all desired criteria
Techniques:
- Boolean intersection
- weighted linear combination (WLC)
What are Boolean intersection multi-criteria evaluations?
- uses logical operators to find combos of layers
- only locations that are characterized as suitable (value of 1) on all maps will be suitable in the result
What are weighted linear combination multi-criteria evaluation?
- assesses the suitability of locations by weighting and combining maps
- multiply thematic layer by factor weight and add across layers
- weights are positive or zero and sun equal to 1
- resulting cell values are in the same range as input maps
How do you assign weights for WLC MCE?
- each stakeholder compares each pair of factors (indicating relative importance as a ratio; focus is on inputs rather than alt solutions themselves)
- ratings combined to produce a consensus set of weights along with a measure of the strength of agreement of disagreement
