Karl Ho
Data Generation datageneration.io
Methods of Data Collection and Production
Karl Ho
School of Economic, Political and Policy Sciences
University of Texas at Dallas
Spatial Data
What is Spatial Data?
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[data structures] Information about the locations and shapes of geographic features and the relationships between them, usually stored as coordinates and topology.
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[data models] Any data that can be mapped.
-ESRI GIS Dictionary
What is Spatial Data?
The two major types of GIS file formats
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Raster - grids of cells or pixels. Raster formats are useful for storing GIS data that vary, such as elevation (as seen on this gorgeous Martian landscape map).
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Vector - polygons that use points (called nodes) and lines. Vector formats are useful for storing GIS data with firm borders, such as school districts or streets.
- National Geographic Society
What is Spatial Data?
Raster Model
area is covered by grid with (usually) equal-sized, square cells attributes are recorded by assigning each cell a single value based on the majority feature (attribute) in the cell, such as land use type.
Image data is a special case of raster data in which the “attribute” is a reflectance value from the geomagnetic spectrum cells in image data often called pixels (picture elements).
What is Spatial Data?
Vector Model
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Points or dots (nodes): trees, poles, fire plugs, airports, cities
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Lines (arcs): streams, streets, sewers,
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Areas (polygons): land parcels, cities, counties, forest, rock type
What is GIS?
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GIS is ‘. . . a powerful set of tools for collecting, storing, retrieving at will, transforming, and displaying spatial data from the real world for a particular set of purposes’.
- Burrough and McDonnell (1998, p. 11)
What is GIS?
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Another definition (from same authors) is "checking, manipulating, and analysing data, which are spatially referenced to the Earth".
What is GIS?
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GI
- Geographic/Geospatial -
I
-Information -
S
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Systems: the technology
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Science: the concepts and theory
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Studies: the societal context
GIS and Spatial data
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Global Positioning System (GPS)
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Satellites provide data of locations on the earth’s surface
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Remote Sensing
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Use of satellites or aircraft to capture information about the earth’s surface
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GIS database
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Storage of multiple layers of spatial data
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Why using spatial data?
Applications:
Does the spatial patterning of disease incidences give rise to the conclusion that they are clustered, and if so, are the clusters found related to factors such as age, relative poverty, or pollution sources?
Why using spatial data?
Applications:
Given a number of observed soil samples, which part of a study area is polluted?
Given scattered air quality measurements, how many people are exposed to high levels of black smoke or particulate matter (e.g. PM10),1 and where do they live?
Why using spatial data?
Applications:
Do governments tend to compare their policies with those of their neighbours, or do they behave independently?
Spatial data and Attribute data
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Spatial data specify location stored in a shape file, geodatabase or similar geographic file
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Attribute (descriptive) data specify characteristics at that location (what, how much, when)
GIS systems (e.g. ArcGIS) traditionally maintain spatial and attribute data separately, then “join” them for display or analysis.
Spatial data: Geocoding
The process of identifying locations to be placed on a map is called geocoding, that is coding the location of an object, a place, an event, a building, or an address where something of interest took place.
Spatial data: Geocoding
At its most basic, something can be geocoded by knowing its latitude and longitude; for example, the capital of Texas, Austin, is located at 30.274694°N latitude and -97.74036°W longitude.
Census data
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Integrated Public Use Microdata Series (IPUMS)
Spatial data layers
source: https://saylordotorg.github.io/text_essentials-of-geographic-information-systems/s11-02-multiple-layer-analysis.html
Workshop: Spatial data in R
Packages:
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ggmap
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maps
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tigris
Map of the United States
Map of the United States
library(tidyverse)
library(ggmap)
us=map_data("state")
# Create US map data frame
USmapDF <- data.frame(state.name, stringsAsFactors=F)
USmapDF$state=tolower(USmapDF$state.name)
# Create Black and White US map
library(mapproj)
us=map_data("state")
# Create US map data frame
USmapDF <- data.frame(state.name, stringsAsFactors=F)
USmapDF$state=tolower(USmapDF$state.name)
# Create US map
map.us=ggplot(USmapDF,aes(map_id=state))
map.us=map.us +
geom_map(map=us,fill="light yellow", color="black")
map.us=map.us +
expand_limits(x=us$long,y=us$lat)
map.us=map.us +
coord_map() +
ggtitle("Basic Map of Continental USA")
map.us=map.us +
theme_bw()
map.us=map.us +
theme(plot.title = element_text(hjust = 0.5)) # Does order matter?
map.usMap of the United States
Map of the United States
library(tidycensus)
library(tigris) # Load Census TIGER/Line Shapefiles
options(tigris_use_cache = TRUE)
# Get a list of American Community Survey (ACS) 2019 variables
acs19 = tidycensus::load_variables(2019, "acs5", cache = TRUE)
acs19_Profile = load_variables(2019 , "acs5/profile", cache = TRUE)
us_median_age19 <- get_acs(
geography = "state",
variables = "B01002_001",
year = 2019,
survey = "acs1",
geometry = TRUE,
resolution = "20m"
) %>%
shift_geometry()
plot(us_median_age$geometry)
ggplot(data = us_median_age19, aes(fill = estimate)) +
geom_sf(col="white") + # Why color is white?
theme_bw() +
scale_fill_distiller(palette = "PuBuGn", # Try other palette?
direction = 1) +
labs(title = " Median Age by State, 2019",
caption = "Data source: 2019 1-year ACS, US Census Bureau",
fill = "", family="Palatino") +
theme(legend.position=c(.08,.6), legend.direction="vertical") +
theme(text = element_text(family = "Palatino"), plot.title = element_text(hjust = 0.5))
Further reading
Bivand, R.S., Pebesma, E.J., Gomez-Rubio, V. and Pebesma, E.J., 2008. Applied spatial data analysis with R (Vol. 747248717). New York: Springer.
Data Methods: Spatial Data
By Karl Ho
