Source: https://drive.google.com/file/d/1ueNGxlTiVnVweovyJJdLpbFit-ddNY2h/view
Geospatial Analysis
Why Geospatial Analysis?
“Everything is related to everything else, but near things are more related than distant things.” - Tobler 1979
”…the purpose of geographic inquiry is to examine relationshiops between geographic features collectively and to use the relationships to describe the real-world phenomena that map features represent” - Clarke 2001
Map Projections
Never all 4 at once
- True directions
- True distances
- True areas
- True shapes
When looking at a map always ask
- What is the purpose for this map and who made it?
- What facets of data have been included/excluded?
- Where did the data come from? Is there a source?
- How has this map been modified and what principles were applied and for what purpose?
Visualizing Geospatial Data
Chloropleth maps
Chloropleth maps are useful for visualizing clear regional patterns in the data
Use light colors for low values, dark colors for high values
Shine when displaying a single variable
Excel at displaying the big picture, but not subtle differences
Should display relative differences, not absolute numbers
Can be misleading - geographic maps can just be population maps
Consider using the smallest unit possible
Sometimes summarizing at the state level is ok
Cartograms
Cartograms should be considered when displaying how many people were affected (vs. area affected in chloropleths)
If geographic space and population were correlated, this wouldn’t be an issue
Tradeoffs should be carefully considered
| Comparison of Cartogram Types | Contiguous Cartograms | Grid/Tile Cartograms | Dorling Cartograms | Geographical Map |
|---|---|---|---|---|
| Easy recognition by position? | 2/3 | 1/3 | 0/3 | 3/3 |
| Easy recognition by size? | 0/3 | 0/3 | 0/3 | 3/3 |
| Easy recognition by shape? | 1/3 | 1/3 | 0/3 | 3/3 |
| Easy to compare the shape sizes? | 1/3 | 2/3 | 3/3 | |
| Accurate? | 2/3 | 2/3 | 3/3 |
Isarithmic Maps
Isarithmic maps demonstrate smooth, continuous phenomena (temperature, elevation, rainfall, etc.)
Spatial Statistics
The statistical techniques we’ve discussed so far won’t work well when visualizing spatial distributions…
…Which means we have a chance to look at data and the relationship between the data in new and interesting ways (distance, adjacency, interaction, and neighbor)
Spatial data violate conventional statistics
Violations:
- Spatial autocorrelation
- Modifiable areal unit problem (MAUP)
- Edge effects (Boundary problem)
- Ecology fallacy
- Nonuniformity of space
Spatial Autocorrelation
Data from locations near one another in space are more likely to be similar than data from locations remote from one another
- Housing market
- Elevation change
- Temperature
Modifiable Areal Unit Problem (MAUP)
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.
MAUP: Units are arbitrarily defined and different organization of the units may create different analytical results (i.e. gerrymandering)
Edge Effects (The Boundary Problem)
Analyzing A vs. B ignores similarities between the two based on their shared boundary
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.
Issues:
Inferences drawn about associations between the characteristics of an aggregate population and the characteristics of sub-units within the population are wrong. That is: results from aggregated data (e.g. counties) cannot be applied to individual people
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
Nonuniformity