The Boolean Suitability Analysis method identifies the best location by assigning True or False values to the data set, then using the intersect operation to create a final layer that combines all the areas with a True value. For example, the distance from a freeway exit layer will have a True value in a 0.5 mile buffer from the exits, and a False value everywhere else in the study area. When overlaying this with multiple datasets, we get a final layer that will return results that match all conditions.

For this analysis, I have already gone thorough and selected parcels that are suitable for the construction of a gas station. To do this, I used the select tool to make sure the parcel was at least 15,000 square feet (requirement for gas stations), the land is zoned as commercial, and the imagery shows the land is vacant.

Next, we will use the Buffer and Erase tool to create a layer where there are no gas stations within 0.5 miles.

Next, we will select block groups that have more than 1,250 people in them.

Finally, we will use the Buffer and Clip tool to create a layer to show a 0.5 mile buffer from freeway offramps.

The Ranked Suitability Analysis uses a weighted linear combination to create a new layer used for suitability analysis. This layer is created by assigning weights to each variable. For example, the developers are more concerned with the gas station being located close to a freeway exit more than how many people live in the block group that the gas station will be located in. To achieve this, the weight given to the gas station's ranking of it's proximity to a freeway exit will be higher (have more impact) on the overall analysis. 

First, we will create a layer to rank the locations based on how far away they are from existing gas stations. We will use the Multiple Ring Buffer tool to create buffers around the existing locations, then use the Union and Clip tools to create a layer that covers the entire study area. Finally, we will use the Add Field and Calculate Field tools to create the rankings. Our rankings will be the following: Greater than 0.5 miles away (1), Between 0.3 and 0.5 miles away (2), Between 0.1 and 0.3 miles away (3), Within 0.1 miles (4). 1 is considered High Suitability while 4 is considered Not Suitable.

Next, we will create a layer to rank the locations based on how far away they are from freeway offramps. We will use the Multiple Ring Buffer tool to create buffers around the freeway offramps, then use the Union and Clip tools to create a layer that covers the entire study area. Finally, we will use the Add Field and Calculate Field tools to create the rankings. Our rankings will be the following: Greater than 0.5 miles away (4), Between 0.3 and 0.5 miles away (3), Between 0.1 and 0.3 miles away (2), Within 0.1 miles (1). 1 is considered High Suitability while 4 is considered Not Suitable.

Next, we will create a layer to rank the locations based on how many people live in each block group. We will use the Add Field and Calculate Field tools to create the rankings. Our rankings will be the following: More than 1,500 (1), Between 1,250 and 1,500 (2), Between 1,000 and 1,250 (3), Less than 1,000 (4). 1 is considered High Suitability while 4 is considered Not Suitable.

The criteria weights will be as follows: 45% proximity to other gas stations, 35% proximity to freeway offramps, 20% population living in the block group.

Results

To create our final Boolean Suitability layer, we will use the Intersect tool to find suitable locations.

To create our final Ranked Suitability layer, we will use the Identity tool to combine the attribute tables of each feature class, then delete all of the fields except for the rank fields. We will then use the Calculate Field tool to assign the final suitability ranking for each of the 8 parcels.