Visualizing E-scooter Trips by Census Block
Nina Rosenbaum
2/21/2021
data_frame <- read.csv("escooter_trips_open_data.csv")Top Census Blocks for Trip Start
most_common_starts <- data_frame %>% group_by(start_census_block_group) %>% summarize(Count = n())
most_common_starts %>% arrange(desc(Count))## # A tibble: 477 x 2
## start_census_block_group Count
## <fct> <int>
## 1 NULL 358540
## 2 410510106001 53961
## 3 410510106003 52185
## 4 410510050001 47300
## 5 410510051002 32543
## 6 410510011011 29966
## 7 410510023032 23816
## 8 410510051001 18178
## 9 410510049001 17673
## 10 410510021001 17575
## # ... with 467 more rows
2018-2020 data
Top Census Blocks for Trip End
most_common_end <- data_frame %>% group_by(end_census_block_group) %>%summarize(Count = n())
most_common_end %>% arrange(desc(Count))## # A tibble: 531 x 2
## end_census_block_group Count
## <fct> <int>
## 1 NULL 358540
## 2 410510050001 28029
## 3 410510011011 27108
## 4 410510106003 25242
## 5 410510023032 24984
## 6 410510051001 23364
## 7 410510056002 19738
## 8 410510049001 19618
## 9 410510052002 17653
## 10 410510106001 16508
## # ... with 521 more rowswrite.csv(most_common_end,file="end_census_block_group.csv")
write.csv(most_common_starts,file="start_census_block_group.csv") 
2018-2020 trips
Most Common Census Block Start-End Pairs
count_trips <- data_frame %>% group_by(start_census_block_group,end_census_block_group) %>%summarize(Count = n())
count_trips %>% arrange(desc(Count))## # A tibble: 14,141 x 3
## # Groups: start_census_block_group [477]
## start_census_block_group end_census_block_group Count
## <fct> <fct> <int>
## 1 NULL NULL 358540
## 2 410510011011 410510011011 10903
## 3 410510023032 410510023032 10624
## 4 410510106003 410510106003 10279
## 5 410510050001 410510050001 8493
## 6 410510059001 410510059001 5940
## 7 410510051001 410510051001 5713
## 8 410510106001 410510106001 4814
## 9 410510049001 410510049001 4685
## 10 410510050001 410510051001 4671
## # ... with 14,131 more rowsTrimet Stop Density by Census Block
trimet_census_join<-read.csv("trimet_census_join.csv")
trimet_density <- trimet_census_join %>% group_by(geoid) %>% summarize(Count = n())
trimet_density %>% arrange(desc(Count))## # A tibble: 868 x 2
## geoid Count
## <dbl> <int>
## 1 410510043001 79
## 2 410510072021 77
## 3 410510073001 43
## 4 410510046012 39
## 5 410519800001 35
## 6 410510023032 34
## 7 410510102001 33
## 8 410050215001 32
## 9 410510106001 32
## 10 410670301013 32
## # ... with 858 more rows