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Introduction of Spatial Analysis

7. Introduction of Spatial Analysis#

7.1. What’s spatial analysis#

  1. Transformation, manipulation and application of analytical methods to spatial (geographic) data (Goodchild)

  2. It’s about geospatial kdd (Knowledge discovery from data)

    \( data \to information \to knowledge \to wisdom \)

7.1.1. What’s “geospatial”#

  • Location + value (attribute)

    • When location changes, the information content of data changes

  • non-spatial: location does not matter \(\to\) spatial invariance

7.1.2. Components (workflow) of spatial data analysis#

  1. Mapping and geo-visualization

    • Showing interesting patterns

  2. Exploratory spatial data analysis

    • Discovering interesting patterns

  3. Spatial modeling

    • Explaining interesting patterns

    • Optimization, Simulation, Prediction

7.2. What’s Spatial Questions#

  1. Where do things happen

    • Pattern, clusters, hot spots, disparities

  2. Why do they happen

    • Location decisions

    • Spatial regression, correlation analysis

  3. How does where things happen affect other things

    • (context, environment)

    • Spatial autocorrelation

  4. How does context affect what happens

    • Interaction

  5. Where should things be located

    • optimization

7.3. Spatial data types#

Remember match between the scale of process you study and the scale of measurement

7.3.1. Point#

  1. Characteristics

    • Location of events

  2. Research question

    • Randomly in space or clustered?

    • Point pattern analysis

      1. KDE(Heat Map)

7.3.2. Surface#

  1. Characteristics

    • Continuous spatial field

      • Air quality, noise, price

    • Research question

      1. Given discrete measures, what’s an air quality surface for a region

      2. Spatial interpolation

        1. Kriging

7.3.3. Discrete spatial data - lattice data#

  1. Characteristics

    • Area units

      • Census tracts, counties, countries

    • Research question

      • Where are hot spots of income inequality in the city

        1. Cluster detection

        2. Location similarity + Attribute similarity

7.3.4. Networks#

7.4. Modifiable Areal Unit Problem (MAUP)#

  1. What’s the proper spatial scale of analysis?

  2. Spatial heterogeneity - different processes at different locations/scales

  3. Both size and spatial arrangement will affect the result

    image.png

7.5. Change of Support Problem#

  1. Variables measured at different spatial scales

    • Nested, hierarchical structures (county / states)

    • Non-nested, overlapping (school district / census tract)

  2. Solution

    • Aggregate up to a common scale

    • Interpolate/impute - Bayesian approach

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