Vacant Lots Phase 1: Task 3 -- Intermediate Calculations

[korelasidata]

DFW Vacant Lots summary report project: click here

Task: Answer each of the 34 pre-specified questions.

Here are the questions:

1. How many vacant lots are in each council district?

   2. How long have the vacant lots in each council district been vacant? <-- Compute the min, median, mean, and max

   3. How many vacant lots in each council district are zoned single family residential, commercial and multifamily? <-- rows are council districts; columns are counts within each of the 3 zoning types

2. How many vacant structures are in each council district?

   5. How long have the vacant structures in each council district been vacant? (see question 2)

   6. How many vacant structures in each council district are zoned single family residential, commercial and multifamily? (see question 3)

3. How many vacant lots are in each zip code?

   8. How long have the vacant lots in each zip code been vacant? <-- We don't have this data yet, but we can work on this when it becomes available

   9. How many vacant lots in each zip code are zoned single family residential, commercial and multifamily? <-- rows are zip codes; columns are counts within each of the 3 zoning types

4. How many vacant structures are in each zip code?

   11. How long have the vacant structures in each zip code been vacant? (see question 8)

   12. How many vacant structures in each zip code are zoned single family residential, commercial and multifamily? (see question 9)

       In the following questions, total = sum and average = mean. Compute with NAs ignored.

5. What is the total amount of back taxes owed on vacant land in each City Council District?

6. What is the total amount of back taxes owed on vacant land in each zip code?

7. What is the total amount of back taxes owed on vacant structures in each City Council District?

8. What is the total amount of back taxes owed on vacant structures in each zip code?

9. What is the average age of residential properties in each council district?

10. What is the average age of residential properties in each zip code?

11. What is the average size of residential properties in each council district?

12. What is the average size of residential properties in each zip code?

13. What is the average age of commercial properties in each council district?

14. What is the average age of commercial properties in each zip code?

15. What is the total amount of city liens owed on vacant land in each City Council District?

16. What is the total amount of city liens owed on vacant land in each zip code?

17. What is the total amount of city liens owed on vacant structures in each City Council District?

18. How many properties are in the name of heirship in the City of Dallas?

    28. How many properties in the name of heirship in the City of Dallas are vacant in each zip code?

    29. How many properties in the name of heirship in the City of Dallas are vacant in each council district?

19. How many private owners have five or more vacant properties in the City of Dallas?

    31. How many private owners with five or more vacant properties in the City of Dallas are in each zip code?

    32. How many private owners with five or more vacant properties in the City of Dallas are in each council district?

20. How many churches own vacant properties in the City of Dallas?

    34. How many churches owned vacant properties in the City of Dallas are in each zip code?
    35. How many are in each council district

Deliverable(s):

• Create an empty python dictionary called results. For each question, insert the computed values that answer the question.
• The key for each dictionary slot would be the name of the question
• The value for each dictionary slot would be a dictionary with the question number and the value of the computation.

For example, here's how you would populate the results for the first question.

results["How many vacant lots are in each council district?"] = {
    'question_number': 1,
    'value': vacant_lots_counts_by_district 
}

where vacant_lots_by_district is a pandas dataframe that computes counts of the numbers of vacant lots in each council district.

[korelasidata]

@obafemiL

• [x] Set the question labels
• [x] Re-do public owner calc according to specification
• [x] Remove the results = {} before each calculation
• [x] Remove filter for Is Vacant Property since everything in the dataset is vacant
• [x] Compute min, median, mean, and max for questions 5, 8, 11,
• [x] Use specified grouping for single family residential, commercial and multifamily (in multiple questions)
• [x] Remove filter for Property Type when using structure_df or land_df
• [x] Convert zip code from float to int