Goal: To find the best location to start a restricted-type business in Los Angeles County, California.

Parameters: The business needs to be located:

1. Greater than 0.5 aerial miles from any K 12 th school.

2. Greater than 0.5 aerial miles from any church or place of religious worship.

3. Within a municipality (i.e. zipcode) that allows the business type.

4. Within 200 feet of streets that allow commercial businesses (CLASS 2 streets).

5. In a building that has greater than 1000 square feet and less than 2800 square feet of area.

6. In a building that has the lowest monthly cost ((Avg_Cost_sqft * area of building) + (Annual Permit Fee /12)).

1. Load the roads shapefile into a Python selectable layer.

2. Select the class-2 roads in the roads layer.

3. Load the zip codes shapefile Python selectable into a layer.

4. Select the zip codes that are legal for the business type.

5. Load the structures shapefile into a Python selectable layer.

6. Select the structures that are buildings.

7. Select the subset of buildings matching the required square footage limits.

8. Further narrow the building selection to those within 200 feet of a class 2 road.

9. Further narrow the building selection to those that are not within 0.5 miles of a school or church.

10. Create a new layer which is the geographic intersection of the selected buildings with the selected zip codes.

11. Add a "Cost" field to the new intersection layer and set its value to the zip code average cost multiplied by the building area, plus the monthly permit fee.

12. Save the intersection layer in a CSV file called "Final_buildings.csv". These are all the buildings satisfying all constraints, and includes monthly cost data.

13. Use a SearchCursor to iterate through Final_buildings.shp table to find the lowest cost building. Report the building ID, monthly cost, square footage, and city of that lowest cost building.

14. Wrap this code in a function accepting five input parameters, the names of five shapefiles, for schools, churches, zip codes, roads, and structures; and one output parameter, the name of the final building CSV file.

15. Execute your code in a Jupyter notebook.