Spatial DBs

• spatial DBs: definition, characteristics, need, creation..
• spatial datatypes
• spatial operators
• spatial indices
• implementations
• miscellany

"A spatial database is a database that is optimized to store and query data related to objects in space, including points, lines and polygons."

In other words, it includes objects that have a SPATIAL location (and extent). A chief category of spatial data is geospatial data - derived from the geography of our earth.

• has location
• has size
• is auto-correlated ["...everything is related to everything else, but near things are more related than distant things."]
• scale dependent
• might be temporally dependent too

Geographic data is NOT 'business as usual'!

In spatial data analysis, we distinguish between two conceptions of space:

• entity view: space as an area filled with a set of discrete objects
• field view: space as an area covered with essentially continuous surfaces

For our purposes, we will adopt the 'entity' view, where space is populated by discrete objects (roads, buildings, rivers..).

So a spatial DB is a collection of the following, specifically built to handle spatial data:

• types
• operators
• indices

Soon, we will explore what types, operators and indices mean.

CAD data:

Agricultural data:

3D data:

• crime data
• spread of disease, risk of disease [look at this too]
• drug overdoses - over time
• census data
• income distribution, home prices
• locations of Starbucks (!)
• (real-time) traffic
• agricultural land use, deforestation

Various government agencies routinely coordinate spatial data collection and use, operating in effect, a national spatial data infrastructure (NSDI) - these include federal, state and local agencies. At the federal level, participating agencies include:

• Department of Commerce
  • Bureau of the Census
  • NIST
  • NOAA
• Department of Defense
  • Army Corps of Engineers
  • Defense Mapping Agency
• Department of the Interior
  • Bureau of Land Management
  • Fish and Wildlife Service
  • U.S Geological Survey
• Department of Agriculture
  • Agricultural Stabilization and Conservation Service
  • Economic Research Service
  • Forest Service
  • National Agriculture Statistical Service
  • Soil Conservation Service
• Department of Transportation
  • Federal Highway Administration
• Environmental Protection Agency
• NASA

As you can see, spatial data is a SERIOUS resource, vital to national interests.

Spatial data is created in a variety of ways:

• CAD: user creation
• CAD: reverse engineering
• maps: cartography (surveying, plotting)
• maps: satellite imagery
• maps: 'copter, drone imagery
• maps: driving around
• maps: walking around

All spatial data can be described via the following entities/types:

• points/vertices/nodes
• polylines/arcs/linestrings
• polygons/regions
• pixels/raster

Once we have spatial data (points, lines, polygons), we can:

• 'model' features such as lakes, soil type, highways, buildings etc, using the geometric primitives as underlying types
• add 'extra', non-spatial attributes/features to the underlying spatial data

Look at this map, overlaid with scary data..

In 1D (and higher), spatial relationships can be expressed using 'intersects', 'crosses', 'within', 'touches' (these are T/F predicates).

Here is a sampling of spatial relationships in 2D:

Another diagram showing the [binary] operations:

Minimum Bounding Rectangles (MBRs) are what are used to compute the results of operations shown above:

We can perform the following, on spatial data:

• spatial measurements: find the distance between points, find polygon area..
• spatial functions: find nearest neighbors..
• spatial predicates: test for proximity, containment..

As (more so than) with non-spatial data, the creation and use of spatial indexes VASTLY speed up processing!

R trees use MBRs to create a hierarchy of bounds.

Variations, FYI: R+ tree, R* tree, Buddy trees, Packed R trees..

k-d tree

Alternate: K-D-B tree:

Each node is either a leaf node, with indexed points or null, or an internal (non-leaf) node that has exactly 4 children. The hierarchy of such nodes forms the quadtree.

A variety of non-spatial attrs can be mapped on to spatial data, providing an intuitive grasp of patterns, trends and abnormalities. Following are some examples.

Dot map:

Here's another one.

Proportional symbol map:

Diagram map:

Another diagram map:

Also possible to plot multivariate data this way.

Choropleth maps (plotting of a variable of interest, to cover an entire region of a map):

Everyone!

Thanks to SQL's facility for custom datatype ('UDT') and function creation ('functional extension'), "spatial" has been implemented for every major DB out there:

• Oracle: Locator, Spatial, SDO
• Postgres: PostGIS
• DB2: Spatial Datablade
• Informix: Geodetic Datablade
• SQL Server: Geometric and Geodetic Geography types
• MySQL: spatial library comes 'built in'
• SQLite: SpatiaLite
• ..

Google's KML format is used to encode spatial data for Google Earth, etc. Here is a page on importing other geospatial dataset formats into Google Earth.

OpenLayers is an open GIS platform.

ESRI is the home of the powerful, flexible family of ArcGIS products - and they are local!

Here is a presentation from 2017, that highlights ArcGIS.

There is a variety of inexpensive/open source mapping platforms, competing with more pricey commercial offerings (from ESRI etc). Here are several:
• QGIS
• MapBox
• Carto
• Boundless
• GIS Cloud