District 2 - Redding
District 2 - Redding¶
How does funding for local agencies differ district to district? Using E-76 obligation data, we can gain insight to how agencies in District 2 use federal program funds, and help identify DLA‘s core customers.
Number of Unique Prefix Codes by Agency
|Agency||Number Of Unqiue Prefix Codes|
Number of Unique Agencies by Prefix Codes
|Prefix||Number Of Unqiue Agencies|
Top 5 Types of Work
|Type Of Work|
|Bridge Replacement (tc)|
|Bridge Preventive Maintenance|
|Roadway Rehabilitation (tc)|
Obligations indicate a unique entry in the E-76 dataset. By counting the obligations for each year, district, and organization, we can see what the volume each as well which organizations are the most and last frequent customers.
Obligations by Year
Number of Unique Agencies by District
Agencies With The Most Obligations
Number of Obligations by Year¶
Number of Unique Agencies by District¶
Agencies With The Most Obligations¶
Prefix Codes refer to the program an obligation is in. Similar to the number of obligations, calcuating the unique prefix codes provides insight to how many progams DLA is involved in each year as well as workload at the district and organization level.
Number of Unique Prefix Codes by Districts
Most Used Prefix Codes
Agencies With The Most Unique Prefix Codes
Number of Unique Prefix Codes by District¶
Most Used Prefix Codes¶
Agencies With The Most Unique Prefix Codes¶
With each E-76, three types of funding amounts are included in the obligations:
Total Requested (
Advance Construction Requested (
Federal Requested (
Using this information, we can determine how much on average an organization recieves with these funds, and the distribution of the funds.
Average Total Requested Funds by Agency
Lowest Average Total Funds by Agency
Average Total Requested Funds by Prefix
Average Total Requested Funds by Agency ($2021)¶
Lowest Average Total Funds by Agency ($2021)¶
Average Total Requested Funds by Prefix ($2021)¶
While the data includes a description column, organizations have the option to manually input the descriptions. Using the organizations descriptions of the obligattion type, we can categorize the obligations in terms of types of work. We used the following type of work categories:
With these categories, we can determine which organizations have the most obligations in that category and what percent of the category that organization accounts for.
/opt/conda/lib/python3.9/site-packages/altair/utils/core.py:228: UserWarning: I don't know how to infer vegalite type from 'empty'. Defaulting to nominal. warnings.warn(