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GIS Fundamentals
CAPP 30239
Today
Establish a baseline understanding of GIS data.
(Not focused on Data Viz)
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Browser Mapping Libraries
GIS Fundamentals
GIS - Geographic Information System - Specialized set of tools for dealing with geospatial data.
What's special about GIS?
(source: NASA.gov)
Quantity of data: coastline paradox
(source: Coastline Paradox on Wikipedia)
GIS Data Caveats
Resolution
We have to decide what resolution is important. Even at a fairly low fidelity, a shape is likely to have tens of thousands of points.
Projection
The earth is not flat, but we typically work in (x, y, [altitude]). This requires projection.
What's wrong with this picture?
Mercator Projection, Wikipedia
Africa is 14x larger
https://mortenjonassen.dk/maps/greenland-vs-africa-size-comparison
Spatial Data Types
| Raster | Vector | |
|---|---|---|
| Image Format | JPEG/PNG | SVG/PDF |
| GIS File Types | GeoTIFF/NetCDF | Shapefile/GeoJSON/Geopackage/KML |
| Example Data | Satellite imagery | - Points: cities - Lines: roads - Polygons: land parcels |
| Typical Tool | Camera | GPS |
| Raster | Vector | |
|---|---|---|
| Pros | Efficient for continuous surfaces (can sample any point) | Arbitrary precision; Ease of calculation; Network efficiency |
| Cons | Limited by image resolution (sensor & storage) | Harder to create as specific relationships must be calculated and defined |
GIS Vector Data Types
- Point -
(lat, lon)or(lat, lon, altitude)- example: address, tree, bus stop - Line aka Polyline - series of ordered, connected points - examples: roads, rivers
- Polygon - enclosed areas - lakes, land parcels, county
Container Types:
- MultiPoint - example: fleet location, fire hydrants, GPS pings
- MultiLineString - example: branching transit line
- MultiPolygon - example: islands
This allows us to think about a group of related polygons as one: e.g. does this storm path (Polyline) intersect with Hawaii (MultiPolygon)?
Coordinate Reference Systems
These systems define what the x and y values actually mean in a given file.
Two kinds:
- Geographic Coordinate Systems - attempt to map a lat, lon to every point on Earth
- Projected Coordinate Systems - attempt to project earth's curved surface onto a plane (as needed for drawing)
Two kinds, infinite variations.
Why are there multiple of each?
GCS - regional or local variations in earth's curvature, and different estimates as to exact size of earth. PCS - no perfect mapping of sphere to x,y plane exists, as seen with Mercator projection, accuracy in one area of globe means distortion in others
WGS84 is used in GPS and therefore is by far the most common GCS.
PCS is not so uniform, but most common is the UTM is the Transverse Mercator projection, which minimizes distortion within a region via a set of guarantees.
Localities will often publish in a more local projections:
SPCS - State Plane Coordinate System, actually 125 zones with their own system across US. They are fine-tuned to fit the shape of each state to maximize accuracy. (Some use transverse Mercator, some use a Lambert conformal conic projection, all with different parameters.)
(ce: conservation.ca.gov)
Correct Projection is Essential
In practice, it is essential that you know what CRS your data is in and what CRS you are displaying it in.
Failure leads to incorrect results, heavily distorted visualizations, and real-world trouble.
Features
Another property of most GIS filetypes is that they have features. You can think of features as a database or spreadsheet of attributes associated with a given shape. It might take the form:
| County Name | Population (2020) | Area (sq mi) | County Seat | Georeference |
|---|---|---|---|---|
| Cook | 5,275,541 | 1,635 | Chicago | {...} |
| DuPage | 932,877 | 334 | Wheaton | {...} |
| Lake | 714,342 | 448 | Waukegan | {...} |
| Will | 690,743 | 837 | Joliet | {...} |
| Kane | 516,522 | 524 | Geneva | {...} |
Georeference column is either the geometry definition itself, or a foreign key (primary ID) for the given datum.
Formats
-
shapefile - actually a bunch of files that need to live in the same directory
- .shp - geometries
- .shx - indexes for searching
- .dbf - csv-like features
- .prj - projection (CRS)
- .sbn/.sbx/.shp.xml/.cpg - additional optional files that provide more metadata and features
-
GeoJSON/TopoJSON: JSON representation of features and geometries
-
KML: Google XML-based format, mostly out of favor but still commonly found on GIS sites
-
Geopackage: Still uncommon, sqlite based successor to shapefile.
Spatial Queries
These form the common spatial queries, which mostly take the form:
operation(geometry1, geometry2, optional_tolerance) -> geometry_result
Example operations: nearest, contains, adjacent_to
Let's convert all of these to GIS terms:
- Where is the nearest Waffle House?
- nearest(my_location, waffle_house_multipoint)
- What congressional district am I in?
- contains(congressional_district_multipoly, my_location)
- What cool attractions are close to my drive?
- nearest(my_route, roadside_attractions_multipoint, tolerance=10miles)
Spatial Overlays
-
Union: combine two or more geometries into one
-
Intersection: identify overlap (IL & fresh water)
-
Difference: Cook County - Chicago
-
What part of the US is either national park or state park?
union(national_parks_mp, state_parks_mp)
-
How much of Illinois is fresh water?
intersection(il_boundaries, fresh_water_mp)
-
What parts of Cook County are not within Chicago?
difference(cook_co, chicago)
Buffering & Joins
Some other common functionality:
Buffering is taking an existing shape and pushing the edge out by some distance. This turns a point into a circle, or a line into a polygon, or expands polygons.
An example buffering operation:
current_location.buffer(1000).contains(waffle_houses)
Turn a point into a circle with radius 1km, and see if that circle contains any waffle houses.
Geospatial Joins combine two or more datasets on a condition (like overlaps or contains). This technique is used to discover overlaps between datasets, for example, to find what counties are in which cities:
joined_data = [(county, city) for county in counties for city in cities if county.geometry.intersects(city.geometry)]
Geospatial Software
(skip in lecture)
OGC Simple Features - Defines common interface (types and operations) for GIS.
Most geospatial software implements some version of this.
The most common of these is GEOS a C++ implementation of the specification that provides the common types and operations. ( Geometry Engine, Open Source )
A related library is GDAL which handles loading file formats:
https://gdal.org/index.html for list of formats
PostGIS
GIS primitives as first-class types in database. Allows querying directly from database, saving conversion and minimizing memory footprint. (Like most things Postgres, one of the most solid pieces of software ever built.)
GeoPandas
Wraps Shapely and provides a pandas like interface.
NOT EFFICIENT fine for simple use, but quickly baloons to unusably large memory footprints without polygon simplification. (Reducing detail by eliding points.)
ArcGIS
Commercial/proprietary service. Developed by ESRI, the key commercial player in GIS for decades.
Offers cloud/server/desktop based tools.
Favored in government, large corporations, full suite is most feature complete offering around. Under increasing competition from OSS competitors which are often more lean, cutting lesser-used features and antiquated formats.
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Mapping Frameworks