##           group 1           group 2           group 3         group 4
## 1    Myoung, Sein       Ong, Alyssa     Moore, Allana Batson, Anthony
## 2                 Wolfenstein, Luci       Qin, Celine    Barga, Jolie
## 3 Bell, Mary Rose    Knowles, Genny Mahoney, Brigette  Devir, Lindsey
## 4   Leahy, Olivia   Kang, Christine       Pham, Canon            <NA>
##           group 5
## 1   Pacheco, Alex
## 2    Mendoza, Ava
## 3     Smith, Reid
## 4 Randall, Javion

• What spatial units are shown in the following maps?
• What spatial units are theoretically important?
• Are all of the maps “spatial”?

##           group 1           group 2           group 3         group 4
## 1    Myoung, Sein       Ong, Alyssa     Moore, Allana Batson, Anthony
## 2                 Wolfenstein, Luci       Qin, Celine    Barga, Jolie
## 3 Bell, Mary Rose    Knowles, Genny Mahoney, Brigette  Devir, Lindsey
## 4   Leahy, Olivia   Kang, Christine       Pham, Canon            <NA>
##           group 5
## 1   Pacheco, Alex
## 2    Mendoza, Ava
## 3     Smith, Reid
## 4 Randall, Javion

• What spatial units are shown in the following maps?
• What spatial units are theoretically important?
• Are all of the maps “spatial”?

• Warm-up: spatial data
• Why Spatial Data?
• The Logic of Simple Features
• Activity: Mapping Points to Polygons

• Using tidycensus to gather spatial information
• Using tigris to gather boundaries
• Joining Points and Polygons
• Interactive Maps (if time)

• Office Hours: Wednesday 1:30pm-3:00pm (Tyler) or Friday 1:30-3:30pm by appointment
• Tuesdays, 10:30am-12:00pm (Yao)

• Slides published on syllabus page
• PSet rubrics information moving forward
• Use warning = FALSE in chunk headers to remove warnings
• PSets: conceptual discussion encouraged, work should be individual
• Final Project: Collaboration encouraged, track individual contributions (e.g. through GitHub)

• Spatial data corresponds to specific geophysical locations (e.g., latitude/longitude, boundaries)
• Spatial data preserves geographic relationships between objects (e.g. distances)
• Some data can be “spatial” or “aspatial”

• In pairs: which of the following are spatial data?
• A list of 50 US states
• Longitude/Latitude of post box locations
• Residential addresses
• Neighborhoods in San Francisco
• Boundaries to a river
• Directions from your apartment to class

• In pairs: which of the following are spatial data?
• A list of 50 US states (definitely aspatial)
• Longitude/Latitude of post box locations (definitely spatial)
• Residential addresses (could be either)
• Neighborhoods in San Francisco (aspatial, unless boundaries are included)
• Boundaries to a river (spatial)
• Directions from your apartment to class (could be either)

• Spatial data corresponds to specific geophysical locations (e.g., latitude/longitude, boundaries)
• Spatial data preserves geographic relationships between objects (e.g. distances)
• Some data can be represented in a “spatial” or “aspatial” manner

• To map curved objects on a flat surface, a “coordinate reference system” is used

• Which is preferred?
• Generally vector (more detailed is always preferred)

• Environmental hazards often require spatial analysis

• In 1854, London suffered a cholera outbreak
• Unknown origin/cause
• Snow’s spatial data analysis (and visualization) pointed to a water pump (Broad St Pump)
• Further analysis showed different rates of infection for those living in areas serviced by different water providers
• Interactive version here

• These designations matter today!
• Neighborhoods with lower HOLC designations are more polluted today

Lane et al., 2022

Using the data handouts:

• What can you tell me about differences in exposure to air quality across neighborhoods?

• The MAUP: the boundaries we choose could influence our findings!
• Example: segregation
• What is another example where this problem may matter?

• The MAUP: the boundaries we choose could influence our findings!
• How would we solve the MAUP?

• We can’t always solve the MAUP! But we can produce better analyses by:
• Drawing meaningful units (i.e. ones that matter theoretically)
• Using as spatially detailed data as possible

• What is a simple feature?
• Two elements:
• Geometry noting its location in space
• Attributes noting all other properties
• Advantages: flexible, reproducible, join and analyze data all in one place

• A dataframe with any kind of information

• There were 3 types of geometries in the activity. What were they?

• Points!
• Lines!
• Polygons!

• Spatial data corresponds to geophysical locations, preserves geographic relationships
• E.g. GIS, raster or vector data
• The modifiable areal unit problem (MAUP):
• The way we draw boundaries matters for statistical aggregation!
• Three types of geometries:
• Points, lines, and polygons

• Using tidycensus to gather spatial information
• Using tigris to gather boundaries
• Joining Points and Polygons
• Interactive Maps (if time)

• Office Hours: Wednesday 1:30pm-3:00pm (Tyler) or Friday 1:30-3:30pm by appointment
• Tuesdays, 10:30am-12:00pm (Yao)

• Slides published on syllabus page
• PSet rubrics information moving forward
• Use warning = FALSE in chunk headers to remove warnings
• PSets: conceptual discussion encouraged, work should be individual
• Final Project: Collaboration encouraged, track individual contributions (e.g. through GitHub)

• PSets: conceptual discussion encouraged, work should be individual
• Final Project: Collaboration encouraged, track individual contributions (e.g. through GitHub)
• Coming: final project feedback, midway course feedback, possible guest speaker

• For Whiteboards: work in pairs/small groups!
• From the air pollution exercise:
• What steps would you take to assess differences in neighborhood pollution levels?
• What data would you need?

• Let’s get started!

library(sf)

# load a simple features object
nc <- st_read(system.file("shape/nc.shp", package="sf"))

## Reading layer `nc' from data source 
##   `/Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library/sf/shape/nc.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 100 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## Geodetic CRS:  NAD27

• You might notice “CRS” in sf objects
• What’s this? Coordinate Reference System
• Projects points on global (or North American) maps

# look at nc
head(nc)

## Simple feature collection with 6 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -81.74107 ymin: 36.07282 xmax: -75.77316 ymax: 36.58965
## Geodetic CRS:  NAD27
##    AREA PERIMETER CNTY_ CNTY_ID        NAME  FIPS FIPSNO CRESS_ID BIR74 SID74
## 1 0.114     1.442  1825    1825        Ashe 37009  37009        5  1091     1
## 2 0.061     1.231  1827    1827   Alleghany 37005  37005        3   487     0
## 3 0.143     1.630  1828    1828       Surry 37171  37171       86  3188     5
## 4 0.070     2.968  1831    1831   Currituck 37053  37053       27   508     1
## 5 0.153     2.206  1832    1832 Northampton 37131  37131       66  1421     9
## 6 0.097     1.670  1833    1833    Hertford 37091  37091       46  1452     7
##   NWBIR74 BIR79 SID79 NWBIR79                       geometry
## 1      10  1364     0      19 MULTIPOLYGON (((-81.47276 3...
## 2      10   542     3      12 MULTIPOLYGON (((-81.23989 3...
## 3     208  3616     6     260 MULTIPOLYGON (((-80.45634 3...
## 4     123   830     2     145 MULTIPOLYGON (((-76.00897 3...
## 5    1066  1606     3    1197 MULTIPOLYGON (((-77.21767 3...
## 6     954  1838     5    1237 MULTIPOLYGON (((-76.74506 3...

• Take a look!

View(nc)

• Is our object a dataframe?

• Is our object a dataframe?

class(nc)

## [1] "sf"         "data.frame"

• Try plotting!
• Whiteboards: One variable is not like the others … which is it?

# plot all vars
plot(nc)

# plot area
plot(nc$AREA)