Title: GGR373
Tags: Week01 TBA

Q: classification: Natural Breaks
A: When natural clusters in data are present
Minimizes variance within classes, max variance between

Q: classification: Equal Interval
A: You tell # of classes
-: distribution of values not spread equally
+: evenly distributed data (on a histogram)
+: comparing data sets

Q: classification: Defined interval
A: You tell the width of classes
-: distribution of values also not spread equally across all classes
+: comparing data sets

Q: classification: Quantile
A: Equal number of observations per class
+: symmetrical (normally distributed) data or mild/moderate skew (on histogram)
+: top/bottom percentile of values
-: doesn't consider natural gaps

Q: classification: Standard deviation
A: divides data, categorizes into intervals of standard deviations above/below mean
+: to highlight how far values deviate from average
-: shows as z-scores
-: hard to understand

Q: classification: Geometric class
A: Multiplicatively vary class widths
when data has a highly skewed distribution

Q: central tendency: mode
A: - categorical data
- highest frequency value

Q: central tendency: Qualitative ordinal data, use
A: Median

Q: central tendency: Mean
A: - "typical" score, best for normally distributed data
- outliers have strong influence
- ex bimodal data /\_......_/\, mean not useful
- ex location, point data only for LA, NYC, mean not useful
- ex calculated value may not be in dataset

Q: central tendency: Weighted mean center
A: pulls mean centre towards higher value weights

Q: central tendency: Median center
A: location representing the shortest total distance to all other features
more robust to outliers

Q: central tendency: Central feature
A: Chooses an *existing feature* in dataset that has shortest total distance to ALL other features

Q: dispersion: normally distributed data
A: ~68% within 1 std deviation from mean,
~95% within 2,
~99% within 3

Q: dispersion: Standard distance
A: average distance of each feature to mean center
then use that distance as a radius centered on mean center
(Spatial equiv of std deviation)

Q: Standard deviational ellipse
A: Like standard distance,
but calculate x and y coordinates to those of mean center

Q: Dispersion
A: How spread out/ compact a dataset is around its location of central tendency

Q: Central tendency
A: Single location to summarize a set of locations, "typical"/ "average"/ most representative

Q: Defining neighbours: Number of neighbours
A: nbh defined by a specified number of features closest to the focal feature
distances can vary depending on density of features

Q: Defining neighbours: Fixed distance
A: all features that fall within specified distance of focal features
num of neighbours depends on density of features in area

Q: Defining neighbours: Network distance
A: Travel routes around focal feature
Fixed distance (no bridge) vs realistic

Q: Contiguity: Raster/Vector polygons vs points
A: Only to polygons, because points have no edges

Q: Delaunay triangulation
A: Contiguity but for points
Generates thiessen polygons on points, then use contiguity edges corners method

Q: Defining neighbours: Contiguity edges (Rook)
A: shared border w/ focal feature considered a neighbour
(directly next to each other)

Q: Defining neighbours: Contiguity edges corners (Queen)
A: border or corner shared feature considered neighbour

Q: Spatial analysis issues
A: - MAUP, neighbourhood definition
- Boundary problem
- Spatial sampling
- Tobler

Q: Cluster analysis
A: finding areas with unexpectedly high values, or finding groups of features with similar characteristics/values/locations, or finding point patterns in the landscape

Q: GIS ML tool types:
A: - prediction
- classification
- clustering

Q: density-based clustering
A: grouping of observations based on feature locations

Q: Tobler's first law of geog
A: Everything is related to everything else,
but near things are more related than distant things

Q: Modifiable Areal Unit Problem (MAUP)
A: Combined effects of scale and aggregation
how things are zoned AND the scale of geographic unit (then aggregated) can change outcomes
explore alternate zoning effects and hierarchical models

Q: Spatial weights matrix
A: File that quantifies spatial relationships (neighbourhood/s) among a set of features

Q: Traditional statistical tests can often be applied to spatial data, BUT...
A: doesn't account for Tobler's first law
and other spatial relationships
also, datasets w/ very different distributions can produce the same summary statistics

Q: central tendency: Median
A: "Middle" value, often better choice for skewed data
- common for socio{demographic,economic} data
- exact centre of distribution

central tendency: What do outliers do?

Q: Dispersion: variance
A: difference between min and max values in a distribution
Heavily impacted by outliers (frequency of values not considered)

Q: Dispersion: Standard deviation
A: sqr(variance)
variance: sum of (observations - mean)^2 all  /  mean

Q: z-scores (Standard score)
A: standard deviations above/below mean
( observation - mean ) / stddev

Q: central tendency: mean centre
A: spatial average (add up)

Q: AI
A: ability of a machine to perform tasks traditionally requiring human intelligence

Q: Machine learning
A: set of tools, algos, and techniques to allow computers to learn patterns in data and acquire info w/o human explicitly programming the process

Q: Deep learning
A: Using trainable algos in the form of artificial neural networks (inspired by how human brain works)

Q: DBSCAN
A: # of features to be considered a cluster, max search distance
- FIXED SEARCH DISTANCE, to find clusters of similar densities
- fastest computationally

Q: HDBSCAN
A: uses series of nested clusters and chooses levels that create stable clusters having as many members as possible
- can find clusters of varying densities
- most data-driven

Q: OPTICS
A: Uses reachability plot for distances between neighbours, peaks = big spatial jump, separates clusters
- ex 2 peaks in a row: noise point
- can adjust sensitivity
- most computationally-intensive

Q: DBSCAN vs HDBSCAN
A: DBSCAN struggles w/ different densities unlike HDBSCAN, where search distances can vary

Q: multivariate clustering
A: GROUPING of observations based on feature attributes

Q: trad vs. spatially constrained multivariate clustering
A: traditional:
- not explicitly spatial, but can be applied to spatial problems/ data
- Features in group more alike, but groups may not be spatially contiguous
SC:
- Explicitly incorporate geography
- AKA spatially contiguous groups but maybe less alike

Q: MC: k-means
A: Finds k groups in data based on feature attributes
- think number of variables, plotted on a n-D space, find clusters from there
- boxplot + map (but not inherently spatial)

Q: SC-MvC: Minimum spanning tree
A: features laid out in data / 'spatial' space, connected based on how far they are based on location and attribute values
links are THEN broken in ways that keep clusters as distinct as possible

Q: complete spatial randomness
A: reference spatial distribution, simulates random pattern
you can compare observations to CSR

Q: negative spatial autocorrelation
A: similar values scattered across space, things closer together likely to have diff values
underlying process causes REGULAR DISPERSION

Q: random pattern
A: mix of clustering and dispersion

Q: positive spatial autocorrelation
A: similar values clustered in space
things closer together likely to have similar values
underlying process leads to clustering

Q: p-value
A: probability that the pattern seen is the result of a random process
(low is high certainty that it's not)

Q: Spatial autocorrelation: TEST TYPES
A: - Global: whole study area, generalizes as summary statistic, NO MAP
- Local: global on a subset of the study area
- Scan Statistics: search multiple subsets, return where clustering/dispersal

Q: Moran's I
A: Clustering, dispersion, both?
Clustered +ve
Dispersal -ve
z-score: high/low suggests pattern unlikely to be random

Q: Getis-Ord G
A: Clustering?
G: Spatial density, high values indicate high value clustering, low ...

Q: Getis-Ord Local Gi* (Scan statistic)
A: ID Hot and cold spots (clusters) relative to mean ACROSS study area at different confidence levels (p-value)
is local nbh average (z-score) significantly different from global average?
could have low features included in hot spot

Q: Anselin Local Moran's I (LISA) (Scan statistic)
A: Clusters: high-high, low-low
Outliers: high-low, low-high (a feature doesn't match w/ rest of nbh)
Is nbh average significantly different from global average, and
is feature value significantly different from nbh average?

Q: Bivariate Moran's I
A: Clusters across 2 variables
high-high, low-low, high-low, low-high clusters
better than comparing visually

Q: linear mean: orientation vs direction
A: orientation: angle only
direction: average length and angle

Q: Spatiotemporal pattern mining
A: Finding patterns in space and through time by analyzing snapshots of data

Q: Problem w/ thematic/ hotspot map for each period of time
A: Effectiveness of visual analysis decreases as volume of data increases
- difficult to visually quantify trends/change
- assumes each time period is completely independent (Jan doesn't affect Feb)

Q: Time-series analysis
A: looking at how spatial patterns have changed over time

Q: Space-time cube
A: 3D cube
x,y: location grid or polygon aggregation
z: time

Q: Space-time cube: Bin
A: individual unit: unique spatiotemporal extent

Q: Space-time cube: location
A: column of bins: same location, different temporal extents

Q: Space-time cube: Time Slice
A: row of bins that share the same temporal extent (imagine off the top: most recent)
standalone analysis: 1 slice

Q: Space-time cube: Aggregation
A: Polygons: probably should standardize
Bins (same size: no need to standardize):
- fishnet / square grid: quicker than hexagon
- hexagon: more edges = more neighbours, closest approximation to circle that fits nicely

Q: Space-time cube: Modifiable temporal unit problem
A: Similar to MAUP but over time: How we aggregate time can affect our analysis
Data aggregated to months results != years
Consider: seasonality of data, but also more broad patterns
Also: Feb (less days = less car crashes)
Ensure aggregation divides evenly among data or chop off the oldest bit

Q: Emerging hot spot analysis
A: Getis-Ord Gi*: Spatial + temporal (+-1) neighbours, compares to whole study area for hot/cold spots
Difference: z-score, Significance: p-value
Results:
3D: clustering/significance of clustering at each bin over time for entire space-time cube
2D: Top is summarized
17 categories (8+8+1): consider combining/removing categories depending on audience
can also examine a category (why is there oscillation?)

Q: Local Outlier Analysis
A: Extension of Anselin Local Moran's I: IDs clusters and local outliers in space + time (not as detailed as emerging hotspot analysis)
3D entire output: bin value -- neighbourhood value
2D summary output: no indication of changes to significance of clusters/outliers
- "only": has only ever been that
- multiple types, never significant