Determining flood risk for disaster response in Houston TX, with GIS.
First we set a boundary for the city in question by adding a layer from Esri online corresponding to World Urban Areas. After this a flood zones map is added as well which shows us the areas in which floods and disaster may occur.
Basemap basics for flood risk
While this is useful information in itself, what we need to know is how to plan and respond to flood risk so we need further information about evacuation routes and, importantly, the relative proximities of routes to high risk places within the boundary.
Overlaying hurricane evacuation routes will help tell us major identified routes of evacuation in response to natural disaster. As this is the United states, people's ability to evade disasters of this types of affected by whether or not they own a car. Census and demographic data will be helpful for this which, fortunately for urban areas, is typically easy to find and updated frequently. Overlaying census data can overwhelm the map visually so we'll go to the contents pane to uncheck the layer. The data is still there, we just have removed the visual for better viewing and analysis.
Identifying at risk demographics
To analyze the data for Houston it's important to first narrow the filter on the World Urban Areas layer so that only the boundary of Houston is shown. Within the demographics layer we'll now open the table feature to find the census data. The category of "does not own a vehicle" can be chosen. We will retrieve our statistics about proximity here and use it to create our threshold or our "zones".
Using the standard deviation statistically information found in the Census data, add an expression within the layer that shows owner and renters. Running the analysis will take a moment.
Visualizing levels of risk
We'll now add an additional expression that shows spaces where these people live within .25 miles of a flood risk zone, this shows us our highest risk areas. When the data is analyzed it will show up purple which isn't the best visualization on this already purple map. The style can be changed to red for better viewing. Both fill and outline colors can be editing on this polygon.
In the derive new locations pane we add expressions that output different layers. For this map we want to know where the high, medium high, medium and medium low risk areas are for floods. This is determined by the stats related to car ownership we found earlier in the Census Data table. We can go in and rerun each expression with slight edits to the query in order to create layers that show a graduating scale of risk from high to low. One these are placed on the map, the last step is to go through and rework some of the style of the more base layers to optimize visualization. Below is the resulting map.
Working with Tessellations
Tessellations are "boxes" that are of a predetermined size and can be used to represent data on a map. Tessellations are useful to scientists taking specific sample transects and may be particularly useful for random sampling. An immediate example that comes to mind fo useful places for tessellations on a map is for foresters who, when working globally, typically refer to land in terms of hectares.
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