Setting up the data

1. The CHASS data was downloaded in a dbf format so we joined the tables to a boundary layer using their CTUID.

2. After joining the dbf files into shapefiles, we can display the data from symbology. 

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Hotspot Analyses

1. Took 5 layers: Public_transit, Apartment, %65+, Income, Health_care. Conducted a hot spot analysis for each of them.

2. Selected by attribute to get GI_bin greater than 0 to get hot spots. Selected GI Bin less than 0 to get cold spots. Exported as a layer for each hot spot analysis

3. Joined all the Hotspot layers (public transit, apartment, %65+, and percentage of healthcare workers)

4. Added GiZscores from every layer to create a new column (hotspots_all)

5. Selected groups of highest and lowest combined values to get hotspots and cold spots.

Buffers

1. Added 10 km buffers to Hospital layer 

2. Added 200 m buffer to Residential Care facilities

3. For Greenspaces, we selected by attribute for parks with an area over 30,000 sqm and created a layer

4. Added a 1 km buffer to the selected Greenspaces

5. We then selected by attribute for parks with an area below 30,000 sqm and created a layer

6. Added 500 m buffer to the selected Greenspaces

Grouping Analysis

1. Joined tables (percent over 65, journey to work, dwelling type, income, jobtype, industry) to Metro_Van_CT layer. Normalized job type and industry by total population. 

   • ​Do this by going to the attribute table and clicking Calculate. Divide health_care, public_transit, and whatever else is population dependent by total_population. Need to make it so it doesn’t matter that there are more people in one CT versus another...

2. Exported Metro_Van_CT into the gdb

3. Ran grouping analysis with the following variables: Income, Apartment, Apartment >5, Health_care, Health, %65+, public_transit

   • *you can put other ones in here or scrap some if you find it necessary!

   • **use Metro_Van_CT to run the analysis. If you want to join another table, join it to metro_van. Then export metro_van as a feature (right-click → data → export → feature). Save in the .gdb as whatever you want, make sure the variables are normalized if necessary, then run the analysis!