FS-Security

Section 1

TO-DO:

[x] HIGH - Get the data that is supposed to be in the map (URLs, keys, etc.) [x] Make the data that is being extracted for the map to update progressively as the years come and take the data from the years before (check if there is a better method to do this than having to save the information to a csv file because it will take a lot of data as the years come) * One method is explained in the last two responses of ChatGPT [5].

    * Make sure to check that the data for that year is available and THEN update the map according to that
        - This might be able to be done with the following (provided by ChatGPT [5]):
            ```response = requests.get(base_url, params=params)
                    if response.status_code == 200:
                        return response.json()
                    else:
                        raise Exception(f"Error {response.status_code}: {response.text}")```

    * Make sure that for the step of updating according to the release date is scheduled according to the following website: https://www.census.gov/programs-surveys/acs/news/data-releases/2021/release-schedule.html

    * Create a function that calls the createMap function according to the new updated data

[x] Make the map interactive so that it updates the map according to the data extracted from the last step [ ] HIGH - Pass the maps to Wordpress. More information about this on reference link [6] [ ] AFTER - Fix README.md to be readable in GitHub [x] LOW - Verify if .venv can be activated when the user runs the program from GitHub so that it can run smoothly [x] HIGH - Change the buttons of the Dash app to show the years of the map on the button-dropdown [x] HIGH - Change this to make the csv according to the year (e.g. '2023perPerSubCou') [x] Create collapse of data from total_calories [8] [ ] Personalize the toml files from Ouslam's branch [ ] DUMP - Remove the verifyFile() , cleaningData() from total_calories.py since a module for the same function was created [x] Use Render.com to deploy my website

Recommended by Ouslam:

• Read: 158 a 172 lines of https://github.com/ouslan/mov/blob/main/src/data/data_pull.py
  • Note: The key in line 172 is not neccesary

Priority of tasks:

• LOW - Not required, but a preference
• HIGH - Must be done and it takes a lot of effort to complete
• AFTER - Task to be done after finishing the project
• DUMP - To reduce space

In progress:

Personal notes: API User Guide [1] - Guidelines - Page 22 is to guide on use of UCGID - Page 16 includes instructions of the last step on how to handle the URL of the API

• Page 35-36 [2] contains information of all the common ACS API Variables
• Total population group name: P1_001N
• Reference [7] gave a lot of details about how to work with Dash when the image does not show.
• Other ways to host an app, other than Render.com:
  • https://www.reddit.com/r/datascience/comments/rwrfmk/deploy_dash_app_for_free/
  • https://www.pythonanywhere.com/
  • Simply search up "how to host dash app for free"
• How to refresh the requirements.txt whenever a library is installed $ pip freeze > requirements.txt

Requirements

• Have python 3.12.5+
• Install geopandas, mapclassify, requests, pandas, pathlib, plotly, shapely, plotly-geo*, pyshp*, dash, json, openpyxl, gunicorn, datetime, git, re
  • Libraries with asterik were only installed for the Plotly map, which currently won't be used

How to install dependencies (libraries)

1. Use the following command in a new environment (e.g. .venv) to install all the dependencies listed in the file: $ pip install -r requirements.txt

Command to activate .venv - for a smoother run experience

$ .venv/Scripts/activate

Requirements for Windows when activating .venv

• Run $ Set-ExecutionPolicy Unrestricted -Scope Process

Note: To run this code, it is recommended to install and activate .venv for a smoother run of the program

References

[1] https://www.census.gov/content/dam/Census/data/developers/api-user-guide/api-user-guide.pdf [2] https://www2.census.gov/about/training-workshops/2020/2020-07-22-cedsci-presentation.pdf [3] https://portal.nifa.usda.gov/web/crisprojectpages/1029124-food-security-and-agricultural-research-data-center.html [4] https://data.census.gov/table/ACSDP5Y2023.DP05?q=DP05:%20ACS%20Demographic%20and%20Housing%20Estimates&g=040XX00US72$0600000 [5] https://chatgpt.com/share/675f46fe-9334-8011-aba7-b02d3ffad84a (Chat named: Sorting GeoDataFrame Column) [6] https://chatgpt.com/share/675f4648-ca64-8011-8120-ee3d45e2ef63 (Chat named: Display Map from GitHub) [7] https://chatgpt.com/share/67630cf7-12b8-8011-a192-5ba70e7abe4c [8] https://outlook.office.com/mail/id/AAQkADg3ODZlNWM3LTM3ZTItNGUyOS05MDI1LTJmYTU4NGVlZTY4ZQAQAL81162xZvlEiasxtrIs%2F1w%3D [9] https://chatgpt.com/share/67708be0-61e0-8011-b2b8-771151339a7e (Chat named: Correccion de progreso)

Section 2

Section 2.1

Variable names that are being used for the population:

1. B01001_003E Estimate!!Total:!!Male:!!Under 5 years
2. B01001_004E Estimate!!Total:!!Male:!!5 to 9 years
3. B01001_005E Estimate!!Total:!!Male:!!10 to 14 years
4. B01001_006E Estimate!!Total:!!Male:!!15 to 17 years
5. B01001_007E Estimate!!Total:!!Male:!!18 and 19 years
6. B01001_008E Estimate!!Total:!!Male:!!20 years
7. B01001_009E Estimate!!Total:!!Male:!!21 years
8. B01001_010E Estimate!!Total:!!Male:!!22 to 24 years
9. B01001_011E Estimate!!Total:!!Male:!!25 to 29 years
10. B01001_012E Estimate!!Total:!!Male:!!30 to 34 years
11. B01001_013E Estimate!!Total:!!Male:!!35 to 39 years
12. B01001_014E Estimate!!Total:!!Male:!!40 to 44 years
13. B01001_015E Estimate!!Total:!!Male:!!45 to 49 years
14. B01001_016E Estimate!!Total:!!Male:!!50 to 54 years
15. B01001_017E Estimate!!Total:!!Male:!!55 to 59 years
16. B01001_018E Estimate!!Total:!!Male:!!60 and 61 years
17. B01001_019E Estimate!!Total:!!Male:!!62 to 64 years
18. B01001_020E Estimate!!Total:!!Male:!!65 and 66 years
19. B01001_021E Estimate!!Total:!!Male:!!67 to 69 years
20. B01001_022E Estimate!!Total:!!Male:!!70 to 74 years
21. B01001_023E Estimate!!Total:!!Male:!!75 to 79 years
22. B01001_024E Estimate!!Total:!!Male:!!80 to 84 years
23. B01001_025E Estimate!!Total:!!Male:!!85 years and over

---

24. B01001_027E Estimate!!Total:!!Female:!!Under 5 years
25. B01001_028E Estimate!!Total:!!Female:!!5 to 9 years
26. B01001_029E Estimate!!Total:!!Female:!!10 to 14 years
27. B01001_030E Estimate!!Total:!!Female:!!15 to 17 years
28. B01001_031E Estimate!!Total:!!Female:!!18 and 19 years
29. B01001_032E Estimate!!Total:!!Female:!!20 years
30. B01001_033E Estimate!!Total:!!Female:!!21 years
31. B01001_034E Estimate!!Total:!!Female:!!22 to 24 years
32. B01001_035E Estimate!!Total:!!Female:!!25 to 29 years
33. B01001_036E Estimate!!Total:!!Female:!!30 to 34 years
34. B01001_037E Estimate!!Total:!!Female:!!35 to 39 years
35. B01001_038E Estimate!!Total:!!Female:!!40 to 44 years
36. B01001_039E Estimate!!Total:!!Female:!!45 to 49 years
37. B01001_040E Estimate!!Total:!!Female:!!50 to 54 years
38. B01001_041E Estimate!!Total:!!Female:!!55 to 59 years
39. B01001_042E Estimate!!Total:!!Female:!!60 and 61 years
40. B01001_043E Estimate!!Total:!!Female:!!62 to 64 years
41. B01001_044E Estimate!!Total:!!Female:!!65 and 66 years
42. B01001_045E Estimate!!Total:!!Female:!!67 to 69 years
43. B01001_046E Estimate!!Total:!!Female:!!70 to 74 years
44. B01001_047E Estimate!!Total:!!Female:!!75 to 79 years
45. B01001_048E Estimate!!Total:!!Female:!!80 to 84 years
46. B01001_049E Estimate!!Total:!!Female:!!85 years and over

---

47. DP05_0002PE Percent!!SEX AND AGE!!Total population!!Male
48. DP05_0003PE Percent!!SEX AND AGE!!Total population!!Female

Section 2.2

1. DP03_0051E Estimate!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households
2. DP03_0051PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households
3. DP03_0052PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households!!Less than $10,000
4. DP03_0053PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households!!$10,000 to $14,999
5. DP03_0054PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households!!$15,000 to $24,999
6. DP03_0055PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households!!$25,000 to $34,999
7. DP03_0056PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households!!$35,000 to $49,999
8. DP03_0057PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households!!$50,000 to $74,999
9. DP03_0058PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households!!$75,000 to $99,999
10. DP03_0059PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households!!$100,000 to $149,999
11. DP03_0060PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households!!$150,000 to $199,999
12. DP03_0061PE Percent!!INCOME AND BENEFITS (IN 2023 INFLATION-ADJUSTED DOLLARS)!!Total households!!$200,000 or more

Information on syntaxes:

Geopandas

.columns = returns df's columns .rows = returns df's rows

Python

• if name == "main": This is used to prevent a code block from being executed when importing that code block's module to another module.

Creator notes:

• This program creates the maps assuming that the data for the input year exists. If the CENSUS Beaureu does not create the data for that year, this program is sensitive to that, and it is a fault that has to be fixed.