[70pts] For this question you are going to look at Sea Surface Temperatures (SST) around Iceland over a range of times, conduct an Emerging Hot Spot Analysis to determine how SSTs are changing over time in this area, create a plot (i.e. chart) demonstrating two different types of trends observed in this area, and finally create a print map using the Map Layout functionality (map adhering to the Map Evaluation Guidelines). For all analysis settings you can use the same ones as used in this tutorial.
Steps for completion:
download the SST data. These data come within a zip file. Download it to your system (where you know where it is) and extract the files so they can be loaded into ArcGIS Pro. Note that this is a .nc file but is NOT a space-time cube - it is a multidimensional raster
load the SST data into ArcGIS Pro
zoom into the SST data so that Iceland is in the center of the view but you can still see SE Greenland and Northern Scotland
you now want to clip the SST data by the current extent. Just go to the "imagery" tab and click the scissors icon. After the clip is completed you can now remove the full SST dataset from your map (keeping only the clipped portion displayed). This smaller raster will make analyses go much quicker.
convert the clipped SST data into a space-time cube. Save this space-time cube to your computer.
Conduct an Emerging Hot Spot Analysis on this space-time cube. Pay special attention to "intensifying cold spots" and "diminishing cold spots". Here are definitions if you need a refresher. Create a Temporal Profile chart that contains two time series - (i) "intensifying cold spots" and (ii) "diminishing cold spots". Feel free to choose any two areas on your map that satisfy these conditions by drawing two polygons - one to identify each. Properly label the chart and export it as an image that you can then paste into your Google Doc.
Create a Map Layout (adhering to the Map Evaluation Guidelines) featuring the results from your Emerging Hot Spot Analysis. Export this map layout as an image that you can paste into your Google Doc.
Finally - write 6 or 7 sentences explaining your results. Include discussion of both the chart you created as well as the hot spot analysis that you completed. Do the results from the hostspot analysis agree with what you are seeing in your chart? How so? Take a stab and explaining why you think you are seeing what you see.
[40pts] You are being asked to help in determining which existing assisted living facilities in Chicago are eligible for additional investment and refurbishment. Your task is to create a web map showing the existing assisted living facilities in Chicago that meet the following criteria:
the average heat index of the area should be less than 5.5
it should be within a 5 minute drive from a hospital (5 min or closer)
it should be at least a 5 minute drive from any existing assisted living facilities (no closer than a 5 minute drive)
it should have at least one existing assisted living facility within a 10 minute drive (don't let it be too isolated)
Create a shareable link for this map and paste it in your Google Doc
[40pts] Conduct an unsupervised classification anywhere in the world using whatever program your like (must be a different place than what your turned in for your tutorial). Tweak the parameters until you are happy with the product. Provide simple screenshots of (i) a true color satellite image of your ROI and (ii) the results of your classification. Paste these 2 images into your Google Doc. Provide a 6 to 7 sentence critique of how good of a job the algorithm did in classifying your image and ideas for improving your classification.
[up to +20EC] Create a plot of precipitation over at least a 3 year period for any point in the world. Try to pick a point where you will see some variation (land - not ocean, no perennial ice cover, not a desert). Remember that TerraClimate has precipitation data. Do your results broadly agree with what your expectations were? Why or why not? Paste your plot into your Google Doc with your colab link and explanation.