 We are now going to make a very simple example of a model for flood risk so we're going to consider the area around IBM's TJ Watson Institute here and we're going to consider elevation and the soil water holding capacity, okay? So let's create a new query now for the layers we're interested in The water holding capacity and you know this comes in different depths So let's just pick zero to 50 centimeters as well as 50 to a hundred So so we have up to one meter depth now for the area you're going to specify a Rectangle around TJ Watson so you can see what's in down here So we simply select a rectangle such as this But we're gonna filter this area we add a filter based on elevation data for the US, okay? So we add a filter now we have to choose what value so basically we want that the Aggregation doesn't actually stay a row for elevation here, but let's say it should be less Than a hundred meters in altitude. So the idea of course is that Low areas are more likely to be flooded. Okay? So once we've added this filter Simply select next We use the same interval for the data Okay, so the soil water holding capacity of course doesn't change a lot over time There's no aggregation needed. Therefore, we can give this a name Once we've done this we can submit this and while this query is running Let's take a look at a previously paired identical query, which we have here Now that we click on this You can see here in the map is TJ Watson, but you see now basically Only areas of elevation less than a hundred meter have been selected Okay, if we go to the standard map and see that not surprisingly These are areas close to the actual shores of you know There's reservoirs in the area and you can now see in the water holding capacity You know different values here. Okay, usually I guess you would say that areas of low water holding capacity are higher risk for flooding