Foundations of Geospatial Analysis

Professor Adam Dennett - @adam_dennett

Bartlett Centre for Advanced Spatial Analysis, University College London

November 21, 2023

About Me

  • Professor of Urban Analytics @ Bartlett Centre for Advanced Spatial Analysis (CASA), UCL

  • Geographer by background - ex-Secondary School Teacher - back in HE for 16+ years

  • Taught GIS / Spatial Data Science at postgrad level for last 11 years

About this session

  • Whistle-stop tour of some of the key concepts relating to spatial data

  • An illustrative example analysing some spatial data in London - demonstrating the “spatial is special” idiom and how we might account for spatial factors in our analysis

  • All slides and examples are produced in RMarkdown using Quarto and R so everything can be forked and reproduced in your own time later - just go to the Github Repo link below

  • By the end I hope you’ll all leave with a better introductory understanding of why and how we should pay attention to the influence of space in any analysis

Key Geospatial Concepts

  • Where? (absolute)
  • Where? (relative)
  • Storing where - spatial data
  • How near or distant?
  • What scale?
  • What shape?

Where? (absolute)

  • Everything happens somewhere

    • We’re here: Wallspace, 22 Duke’s Road, Camden, London, England, *Europe, Northern Hemisphere, Earth

Where? (absolute)

  • How do we know exactly where?

XKCD - No, The Other One

https://xkcd.com/2480/

Where? Coordinate Reference Systems

  • More reliable than names (that are rarely unique or reference fuzzy locations), are coordinates

  • The earth is roughly spherical and points anywhere on its surface can be described using the World Geodetic System (WGS) - a geographic (spherical) coordinate system

  • Points can be referenced according to their position on a grid of latitudes (degrees north or south of the equator) and longitudes (degrees east or west of the Prime - Greenwich - meridian)

  • The last major revision of the World Geodetic System was in 1984 and WGS84 is still used today as the standard system for references places on the globe.

https://www.earthdatascience.org/courses/use-data-open-source-python/intro-vector-data-python/spatial-data-vector-shapefiles/geographic-vs-projected-coordinate-reference-systems-python/

Where? Coordinate Reference Systems

  • Projected Coordinate Reference Systems convert the 3D globe to a 2D plane and can do so in a huge variety of different ways

  • Most national mapping agencies have their own projected coordinate systems - in Britain the Ordnance Survey maintain the British National Grid which locates places according to 6-digit Easting and Northing coordinates

  • Every coordinate system can be referenced by its EPSG code, e.g. WGS84 = 4326 or British National Grid = 27700 with mathematical transformations to convert between them

Where? Describing and Locating Things with Coordinates

  • Once we have a coordinate reference system we can locate objects accurately in space

  • Most objects that spatial data scientists are concerned with (apart from gridded representations, which we will ignore for now!) can be simplified to either a point, a line or a polygon in that space

  • Polygons and lines are just multiple point coordinates joined together!

  • The examples on the right store geometries in the ‘well-known-text’ (WKT) format for representing vector (point, line, polygon) geometries

Storing where - managing spatial data

  • Impossible to talk about spatial data without mentioning the shapefile

  • Developed in the 1980s by ESRI and has become, pretty much, the de facto standard for storing and sharing spatial data - even though it’s a terrible format!

  • Shapefiles store geometries (shapes) and attributes (information about those shapes)

  • Not a single file, actually a collection of files

    • .shp - geometries

    • .shx - index

    • .dbf - attributes

    • +some others!

  • Superseded by LOTS of alternative formats - geojson (web), GeoPackage (everything) which do the same thing in better ways for different applications

Storing where - Simple Features

  • Simple Features - OGC (Open Geospatial Consortium) standard that specifies a common storage and access model for 2D geometries

  • 2 part standard:

    • Part 1 - Common Architecture defining geometries, attributes etc. via WKT

    • Part 2 - supports storage, retrieval, query and update of simple geospatial feature collections via SQL (structured query language – been around since the 1970s)

  • Simple Features implemented in most spatially enabled database management systems (e.g. PostGIS extension for PostgreSQL, Oracle Spatial etc.)

  • sf package in R enables storage of spatial data and attributes in a single data frame object

Where? Relative - Tobler’s First Law of Geography

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

  • This observation underpins much of what spatial data scientists do

  • Being able to locate something in space, relative to something else, allows us to:

    • explain why something may be occurring where it is

    • make better predictions about nearby or further away things

  • Underpins the whole Geodeomographics (customer segmentation) industry!!

Where? Relative - John Snow’s Cholera Map

Where? Relative - Defining ‘near’ and ‘distant’

  • Near and distant can mean different things in different contexts

    • the furthest one would travel to buy a pint of milk is somewhat different to furthest one might be willing to commute for a job
  • In spatial data science one way of separating near from distant can simply be to define their topological relationship - Dimensionally Extended 9-Intersection Model (DE-9IM) is the standard topological model used in GIS

  • Touching or overlapping objects = ‘near’

Where? Relative - Exploring Near and Distant

  • Near and distant in London
  • Map shows 2011 Census Wards in London, within Borough Boundaries
  • The Greater London Authority produced the London Ward Atlas - https://data.london.gov.uk/dataset/ward-profiles-and-atlas - which collates a range of demographic and economic indicators for each of these zones in the city