Outline ESDA. Exploratory Spatial Data Analysis ESDA. Luc Anselin

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1 Exploratory Spatial Data Analysis ESDA Luc Anselin University of Illinois, Urbana-Champaign Outline ESDA Exploring Spatial Patterns Global Spatial Autocorrelation Local Spatial Autocorrelation ESDA 1

2 Classes of Spatial Data (Cressie) Point Patterns points on a map Geostatistical Data points as sample locations Lattice/Regional Data polygons or points (centroids) Lattice or Regional Data Spatial Process index set D is fixed collection of countably many points in R d finite, discrete spatial units Data fixed points or discrete locations (regions)» examples: county tax rates, state unemployment Research Question interest focuses on statistical inference patterns, estimation, specification tests WV Housing Values (1990) counties, county centroids, Thiessen polygons 2

3 EDA and Space EDA = Discover Potentially Explicable Patterns (Good) Data Visualization (Buja) Interactive View Manipulation» focusing individual views» linking multiple views» arranging many views No Role for Explicit Treatment of Space Exploratory Spatial Data Analysis EDA + Describe Spatial Distributions spatial trends, spatial means Identify Atypical Observations spatial outliers Discover Patterns of Spatial Association spatial clusters Suggest Spatial Regimes spatial non-stationarity ESDA Functionality Dynamic Graphics linking and brushing statistical plots and map Visualizing Spatial Distributions spatial box map smoothing rates Visualizing Spatial Autocorrelation spatial lag pie charts and bar charts Moran scatterplot and map, LISA maps 3

4 Exploring Spatial Patterns Dynamically Linked Windows Dynamic Graphics different views of data: histogram, box plot, scatterplot, list views dynamically linked: click on one, corresponding points (areas) on others highlighted geographic brushing: map as a view of data 4

5 Global Spatial Autocorrelation 5

6 Spatial Association Null Hypothesis: No Spatial Association values observed at a location do not depend on values observed at neighboring locations observed spatial pattern of values is equally likely as any other spatial pattern the location of values may be altered without affecting the information content of the data Observed (left) and randomized (right) distribution for Columbus Crime Randomization polyid 1 became 14 polyid 2 became 20 polyid 3 became Observed (left) and randomized (right) distribution for Columbus Crime Moran s I = Moran s I =

7 Alternative Hypotheses of SA Positive Spatial Association like values tend to cluster in space neighbors are similar Negative Spatial Association neighbors are dissimilar checkerboard pattern Moran s I Spatial Autocorrelation Statistic Moran s I cross-product statistic I = (N/S 0 ) Σ i Σ j w ij.z i.z j / Σ i z i 2 with z i = x i - µ=and S 0 = Σ i Σ j w ij Inference normal distribution randomization permutation Interpretation of Moran s I Positive Spatial Autocorrelation I > -1/(n-1), or z > 0 spatial clustering of high and/or low values» no distinction between high or low Negative Spatial Autocorrelation I < -1/(n-1), or z < 0 checkerboard pattern, competition 7

8 Spatial Lag Chart Spatial Lag Visualization value at i compared to weighted average of neighbors: x i relative to (Wx) i similar values = positive SA dissimilar values = negative SA Spatial Lag Pie Chart x i and (Wx) i as proportions of pie (x > 0 only) Spatial Lag Bar Chart x i and (Wx) i as bars Ww_hoval Hoval 8

9 Moran Scatterplot Linear Spatial Association linear association between value at i and weighted average of neighbors: Σ j w ij y j vs. y i, or Wy vs y four quadrants» high-high, low-low = spatial clusters» high-low, low-high = spatial outliers Moran s I slope of linear scatterplot smoother I = z Wz / z z 9

10 Use of Moran Scatterplot Classification of Spatial Association Local Nonstationarity outliers high leverage points sensitivity to boundary values Regimes nonlinear association» different slopes in subsets of the data 10

11 Local Spatial Autocorrelation LISA Definition (Anselin 1995) Local Indicators of Spatial Association LISA satisfies two requirements indicate significant spatial clustering for each location sum of LISA proportional to a global indicator of spatial association LISA Forms of Global Statistics local Moran, local Geary, local Gamma Use of LISA Identify Hot Spots significant local clusters in the absence of global association significant local outliers» high surrounded by low and vice versa Indicate Local Instability local deviations from global pattern of spatial association 11

12 Local Moran Local Moran Statistic I i = (z i / m 2 )Σ j w ij.z j Σ i I i = N.I Inference randomization assumption conditional permutation local dependence or heterogeneity? Visualization LISA map and Moran Significance Map 12