1. Data
1.1. **61 variables**
1.1.1. First steps
1.1.1.1. Unit of Analysis: city
1.1.1.2. Identify Type: all numeric
1.1.1.3. policy related
1.1.1.3.1. Input
1.1.1.3.2. Output
2. Options
2.1. USE ALL THE INFORMATION
2.1.1. Comprehensive
2.1.1.1. HeatMap
2.1.1.1.1. facetting
2.1.2. Averaging
2.1.2.1. insisting with heatmaps
2.1.2.1.1. facetting?
2.1.2.1.2. filtering may be needed
2.1.2.2. using radar plot
2.1.2.2.1. reordered version
2.1.2.3. simpler
2.1.2.3.1. lollipop is back
2.2. REDUCE DIMENSIONALITY
2.2.1. EFA (exploratory factor analysis)
2.2.1.1. CorrMatrix
2.2.1.2. KMO
2.2.1.3. Factor to use
2.2.1.4. loadings
2.2.1.5. rename Factors
2.2.1.6. Get Scores
2.2.1.6.1. plot scores
2.2.2. Multidimensional scaling
2.2.2.1. Compute distance matrix
2.2.2.2. GOF / Stress
2.2.2.3. get coordinates
2.2.2.3.1. plot
2.2.3. Clustering
2.2.3.1. Partition ALL cases into N groups
2.2.3.2. Color MDS by cluster
2.2.3.2.1. here