Geography

• What is your favourite sport?
• Do you speak Spanish?
• Do you know who Fofão is?
• How many kisses on the cheek do you greet someone with?
• If you are on your own in a taxi do you sit in the front or back?
• Do you think government policy should allow free migration?
• Where do you live?

Geography

• Knowledge and communication depend on where we live

• Social norms and customs depend on where we live

• Political preferences depend on where we live

Geography

Tobler’s First Law of Geography:

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

Geography

• What does ‘near’ mean?

• Concepts of distance:
  • Euclidean
  • Great Circle
  • Manhattan
  • Levensthein
  • Mahalanobis
  • Driving
  • Network
  • Minimum-cost
  • Genetics

Geography

• What does ‘related’ mean?
  • Correlated
  • More similar
  • More different (ex. dialing codes to avoid typing errors)

• ‘Related’ does not mean one person ‘causes’ a similar effect on another
  • It may just be a common response to a similar environment
  • But interactions and spillovers are common

Geography

• Locations of ‘Events’ could be ‘near’ to each other

Geography

• Or characteristics of locations could be ‘near’ to each other

Geography

• Multiple characteristics could also be ‘near’ to each other

Geography

Geography

Geography

Geography

• But isn’t the world getting smaller?
  • ‘The death of distance’
  • Everything is ‘near’ on the internet
• Relevant distances may be changing
  • Cost of flights instead of kilometres or hours
  • Language and social network instead of proximity to radio tower
• Spatial relationships take place at multiple scales
  • I am Welsh, British, European etc.
  • The similarities between rural China and rural Russia are greater than the differences

Geography

• Lots of interesting questions are really non-spatial
  • We can draw maps of them
  • But the conclusion does not depend on the locations of the units

Spatial Political Science Questions

• Politics is about “who gets what, when and how” (Laswell 1936)
  • And “where”
• The ‘outcomes’ of politics are spatial
  • Northern Brazil is poorer than the South
  • A child born just inside the border of North Korea has fewer rights
• The ‘causes’ of politics are also spatial
  • 78% of China’s GDP located in its cities
  • Social networks are spatially concentrated
• Political decision-making is arranged by space

Spatial Political Science Questions

• Types of spatial political analysis
  1. Identifying clustering
     • Eg. Seabrook (2009)

Spatial Political Science Questions

• Types of spatial political analysis
  2. Correlating spatial relationships
     • Eg. Acharya, Blackwell and Sen (2016)

Spatial Political Science Questions

• Types of spatial political analysis
  3. Measuring how one country affects its neighbours
     • Eg. Oberdabernig et al (2017)

Spatial Political Science Questions

• Types of spatial political analysis
  4. How natural geography affects politics
     • Eg. Bleakley et al 2010

Spatial Political Science Questions

• Types of spatial political analysis
  5. How political borders affect modern politics
     • Eg. Dell (2010)

Spatial Political Science Questions

• Types of spatial political analysis
  5. How political borders affect modern politics

Source: The Economist

Merits of Spatial Analysis

Merits of Spatial Analysis

Map Literacy

• Maps are clear and convincing
  • Patterns may only be visible when arranged spatially
  • If you have spatial data, why put it in a table or a chart?

Map Literacy

• Eg. The population of Acre state by municipality:

Map Literacy

Map Literacy

Map Literacy

• But maps still require careful interpretation
  • Scale
  • Direction
  • Indicator
  • Mapping values to colours

Map Literacy

• Scale
  • Can I walk from The Art Institute of Chicago to Union Station in 10 minutes?

Map Literacy

• Scale

Map Literacy

• Scale

Map Literacy

• Compass
  • What’s the best place to view the sunset in the Wirral (UK)?

Map Literacy

• Compass
  • What’s the best place to view the sunset in the Wirral (UK)?

Map Literacy

• Choosing the Indicator
  • The most important!
  • What precisley do we want to measure?

Map Literacy

• Choosing the Indicator
  • The most important!
  • What precisley do we want to measure?

Map Literacy

• Choosing the Indicator
  • The most important!
  • What precisley do we want to measure?

Map Literacy

• Mapping values to colours
• Can be manipulated to convey relevant (or misleading!) conclusions

Map Literacy

• Mapping values to colours
  • What type of colour scale matches your data?

Map Literacy

• Mapping values to colours
  • What type of colour scale matches your data?

Map Literacy

• Mapping values to colours
  • What type of colour scale matches your data?

Map Literacy

• Mapping values to colours
  • What type of colour scale matches your data?

Map Literacy

• Mapping values to colours
  • Hard: Chosing break points between categories

Map Literacy

• Mapping values to colours
  • Hard: Chosing break points between categories

Map Literacy

• Mapping values to colours
  • Hard: Chosing break points between categories

Map Literacy

• Understand your data!

538 Nigeria Kidnappings

Geographic Information Systems

1. Convert the real world into a digital model
   • Necessarily simplified

2. Compare multiple spatial layers

3. Create new spatial data

4. Create measures and statistics to describe spatial relationships

Vector vs. Raster Data

Vector vs. Raster Data

Types of Vector Data

• The choice may depend on scale
  • What type of vector data is a river?

Types of Vector Data

• The attributes we assign to vector objects also vary

Locations in Space

• How do we describe the location of an object in space?

• The real world is 3-dimensional
  • Mostly we deal with points on the earth’s surface
  • This is not a problem for computers that can create ‘virtual earths’

• Geographic Coordinate Systems
  • ‘Perfect’ representations of earth in the computer
  • Longitude and Latitude define any point on earth
  • Distance is ‘Great Circle’ Distance

Locations in Space

Locations in Space

• Longitude
  • Lines of longitude are perpendicular to the equator (North-South)
  • They measure the angle from Greenwich, London, East-West

• Latitude
  • Lines of latitude are parallel to the equator (East-West)
  • They measure the angle from the equator, North-South

Locations in Space

• Longitude & Latitude can be measured in different units
  • DMS: 49°30’00″N, 123°30’00″W
  • DM: 49°30.0′, -123°30.0’
  • Decimal Degrees: 49.5000°,-123.5000°
• But all of these use the same Geographic Coordinate System
  • And we ‘always’ use the same one
  • WGS-84

Locations in Space

• What shape is the earth?
  • An ‘oblate spheroid’

• This oblate spheroid is estimated by a ‘datum’ so we get the location correct
  • No need to worry about this, WGS-84 includes its own datum

Locations in Space

• But we view maps on flat surfaces: paper or screens
  • Try peeling an orange

• To produce flat maps we need a Projected Coordinate Reference System
  • Translating 3-D locations to 2-D locations
  • There are many different ways to do this, just as there are many ways to peel an orange

Locations in Space

• Projections can preserve shape, area or distance, but not all three!

Locations in Space

• Projections are less distorted if they are localized to one part of the earth
  • So we choose a projection based on the extent of our analysis/map

• Eg. UTM (Universal Transverse Mercator) Zone

• Use http://epsg.io/ to find appropriate local projections

Locations in Space

Locations in Space

• Coordinate Reference Systems have useful shortcut EPSG codes
  • In R, this is all you need

Locations in Space

• Which Coordinate Reference System (CRS) should I use?
  • Important: You don’t choose - your data sources already come with a specific CRS
  • Important: ALL data in the analysis must use the same CRS
  • That means sometimes transforming from one coordinate system to another

• For projections, do you want to convey shape, area or distance accurately?

• For distance, what units do you want to use?
  • Geographic: Degrees
  • Projected: Meters (usually)

Spatial Datasets

• Non-spatial datasets are just tables

• Spatial datasets just add location data to the table

Spatial Datasets

Spatial Datasets

• Vector Spatial Datasets
  • Coordinates for every object
  • Multiple coordinates for lines, polygons

Spatial Datasets

• Vector Spatial Datasets
  • Coordinates for every object
  • Multiple coordinates for lines, polygons

Spatial Datasets

• One single ‘Multipolygon’ can be complicated
  • Comprised of many distinct polygons
  • Polygons can have ‘holes’ in them

Spatial Datasets

• Raster Spatial Datasets
  • Coordinates for every data point

Spatial Datasets

• Historically, vector data has been stored as shapefiles
  • Shapefiles separate out the tables, location data, projection into separate files

Spatial Datasets

• Raster data is typically stored as .tiff files
  • The same as you get from a camera or scanner
  • But with location and projection data so that we know ‘where’ the image corresponds to
  • ‘GeoTiff’ files

Georeferencing

• Computers understand locations such as -23.562778, -46.725261
• But what if we have a street address?
  • Ex. Av. Paulista, 1578, Sao Paulo
  • We can use georeferencing databases to return the coordinates
  • Ex. https://nominatim.openstreetmap.org

Georeferencing

• We can also take an image and georeference it to a map
• We need to ‘pin’ the map to at least two points
  • And ‘wrap’ it around the earth’s surface
  • Ex. https://www.georeferencer.com/

Georeferencing

Non-Spatial Joins

• Most of our data is non-spatial, but could be made spatial
  • Election results
  • Death rates
  • Welfare payments
  • Conflict
• We can make this data spatial if we link it to existing spatial (location) data
  • Using common identifiers in both datasets
  • Non-spatial joins

Non-Spatial Joins

• Governments publish school performance data
  • We know which schools are ‘best’
  • But what is the spatial pattern of school performance?
  • Better in the city centre or in the suburbs?
• We need a source for the location of the schools
  • Perhaps from a separate geographical survey
  • Or by georeferencing their addresses
• How do we combine the school performance and location datasets?
  • By code
  • By name?

Non-Spatial Joins