1. Brainstorming How to Analyze the 3AuCountHand Datasets
Use the interestingness hotspot discovery framework (see next slide for interestingness functions)
Identify Flood Danger Hotspots and ultimately rank them; regions with low average hand values are interesting and the lower their average hand values are the more interesting they are and the higher is their rank (initially use =1 and =50 foot, as parameter for the iHAND interestingness function)
Using the low variance interestingness function, trying to identify homogeneous regions with respect to the Hand Value, and then create a map from the obtained hotspots.
Using the high variance interestingness function, trying to identify regions that have a high variance with respect to hand values
2. Create a complete clustering of whole dataset that minimizes the variance of hand values for each clusteruse the average hand value and the hand variance of each cluster as a summary.
3. Just cluster the Texas Address Points using our spatial clustering algorithm with different density thresholds and report findings on a map; possibly compare with population density in the three Austin counties to identify density discrepancies regions with too few address points considering their population density. Do we have a good population density dataset for the 3 Austin Counties?
4. Use the hand-values and create hand values at grid-intersection points of a grid by using an interpolation function and then apply the backend of our spatial clustering framework to obtain hand contour maps for different hand value thresholds.
5. Same as 4., but we smoothen the hand-values by making them more similar to their close-by neighbors, before following the procedure outlined in 4.
6. Maybe us Gino Lim’s approach, if it is promising---I distributed the paper
Plan: We are currently working on approaches 1a, 1b, and 4

2. Brainstorming on What Aspects of the Data Set to Analyze
Used Interestingness Functions
Low Variance: ivar (p) (H)= −𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒(𝐻,𝑝), 𝑖𝑓 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝐻,𝑝 < 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
High Variance: ivar (p) (H)= 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝐻,𝑝 − 𝑖𝑓 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝐻,𝑝 > 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
High-Hand: iHAND 𝐻 = − av_ℎ𝑎𝑛𝑑 𝐻 ∗∗, 𝑖𝑓 𝑎v_hand(H)< 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

3. Exploratory Data Analysis for the Dataset
Put address points on a map
Create Hand value histograms for the whole dataset and the 3 counties
Create Hand value boxplot for the whole dataset and 3 counties
Can we create some kind of heatmap for the address points
Grid-based Summary of the Dataset
Grid the dataset based on longitude and latitude
Compute the average hand value in each grid cell and associate it with each grid-cell; associate ‘NULL’ with empty grid-calls
Color the cell using the coloring schema on slide 5
Repeat steps a-c for different grid-sizes
Summarize the results in a parallel display
Explore the use of other heatmaps,… for hand value visualization
What else should we do?

4. Challenges Resulting from the Dataset Size (please edit! )
The interestingness framework
The algorithm go create Gabriel graph has O(n**3) complexity
The number of edges in the Graph is between n and 3n; e.g. for the dataset at hand we have 600,000 nodes and slightly more edges
Particularly, step a takes much too long
We try to speedup a by using to a faster approximate algorithm to create a Gabriel graph / using other proximity graphs
We resort to Sampling initially
What else could we / should we do?
Interpolation Contouring Approach
give details

5. Method for Determining Flood Risk: Height Above Nearest Drainage (HAND)
Flooding occurs when Water Depth is greater than HAND
Flood
HAND
Normal

6. Statewide Addresses with HAND
237 counties mapped
16 CSEC counties geocoded
1 non-CSEC county geocoded
Statewide Addresses with HAND