layer geoid topic period stratification
160 City or town 5589150 FVDESVYF 2020-2024 NA
161 City or town 5564675 FVDESVYF 2020-2024 NA
162 City or town 5555750 FVDESVYF 2020-2024 NA
163 City or town 5532790 FVDESVYF 2020-2024 NA
164 City or town 5577400 FVDESVYF 2020-2024 NA
165 City or town 5544950 FVDESVYF 2020-2024 NA
166 City or town 5511500 FVDESVYF 2020-2024 NA
167 City or town 5509725 FVDESVYF 2020-2024 NA
168 City or town 5526982 FVDESVYF 2020-2024 NA
169 City or town 5556925 FVDESVYF 2020-2024 NA
missing index_range index_value
160 <NA> <NA> 57.94899
161 <NA> <NA> 57.45878
162 <NA> <NA> 57.14393
163 EKW (Walkability Index) is Missing 52.9 - 69.6 57.09900
164 <NA> <NA> 56.89611
165 <NA> <NA> 56.68403
166 <NA> <NA> 55.93024
167 <NA> <NA> 55.81588
168 EKW (Walkability Index) is Missing 51.1 - 67.8 55.28361
169 <NA> <NA> 55.06802
FVDEX Reliable Transportation 2020-2024 Findings
Current Progress
Over the last 2.5 weeks, I have…
Downloaded and implemented new 2020-24 Reliable Transportation data to construct an updated index
Tested two methods to address missing values (Custom Index Formulas and Imputation)
Created geospatial visuals of the new indices grouped by local region
Executed basic data analysis comparing 2020-24 indices to 2022-23 results
Assembled a preliminary csv of 2020-24 Lifelong Learning indices (not done yet)
Reliable Transportation Indexes
I reproduced the code and index formula used for the 2022-23 data to create the updated indexes. However, four observations contained missing values, so I tried using custom index formulas and imputation methods for each. The simplified index formulas relied solely on the averages of the index metrics available to us, while imputation pushed observations closer to the the mean of the index rankings. Imputation was used for the final csv below.
Geospatial Index Map - Census Tracts
Comparisons to Previous Data - Census Tracts
:::: {.rows}
Warning in geom_text(aes(x = mean_val1, y = 8, label = paste("Mean:", round(mean_val1, : All aesthetics have length 1, but the data has 103 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
Warning in geom_text(aes(x = mean_val2, y = 6, label = paste("Mean:", round(mean_val2, : All aesthetics have length 1, but the data has 103 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
::::
Next Steps
As I continue, I will work on…
- Polishing and building on the Lifelong Learning indices
- Reading more about indexing by national percentile
- Implementing percentile break points to update the Reliable Transportation and Lifelong Learning indices