District 9 - Bishop#


How does funding for local agencies differ district to district? Using E-76 obligation data, we can gain insight to how agencies in District 9 use federal program funds, and help identify DLA‘s core customers.

Quick Stats

There are 8 Unique Agencies
Out of 313 obligations, 15 have transit-related project types
Tehachapi has the highest transit obligations
There are 1 agencies have over 79.90 obligations (95th percentile) since 2013
There are 1 agencies have less than 7.10 obligations (5th percentile) since 2013

Number of Unique Prefix Codes by Agency

Agency Number Of Unqiue Prefix Codes
Ridgecrest 8
Inyo County 7
Tehachapi 6
California City 5
Kern County (District 9) 4

Number of Unique Agencies by Prefix Codes

Prefix Number Of Unqiue Agencies
RPSTPL 6
CML 4
HSIPL 4
STPL 4
ACSTP 2

Top 5 Types of Work

Type Of Work
Pavement Rehabilitation
Road Rehabilitation
Emergency Opening
Class Ii Bicycle Lanes (tc)
Pave Dirt Road

Obligations#


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.

Metrics:

  • 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#


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.

Metrics:

  • 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#

Funding Distributions#


With each E-76, three types of funding amounts are included in the obligations:

  • Total Requested (total_requested)

  • Advance Construction Requested (ac_requested)

  • Federal Requested (fed_requested)

Using this information, we can determine how much on average an organization recieves with these funds, and the distribution of the funds.

Metrics:

  • 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)#

Work Categories#


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:

  • Active Transportation

  • Transit

  • Bridge

  • Street

  • Freeway

  • Infrastructure/Resiliency/Emergency Relief

  • Congestion Relief

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.

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