Helsingin kaupunkipyörät: Avoin data telinekohtaisista vapaiden pyörien määristä kausilta 2017, 2018, 2019 ja 2020
Markus Kainu
Päivitetty 2020-06-23
Päivitetty 2020-06-12: kauden 2020 datat julkaistaan kerran viikossa maanantaiaamuisin
Helsingissä on toiminut kaupunkipyöräjärjestelmä vuodesta 2016 alkaen. Järjestelmä tarjoaa avoimen rajapinnan, josta voi hakea reaaliaikaisen vapaiden pyörien määrän kullakin telineellä. Olen kerännyt tätä dataa viiden minuutin välein kaudet 2017, 2018, 2019 ja 2020. Tältä sivulta voit ladata nuo datat vapaasti käyttöösi Nimeä 4.0 Kansainvälinen (CC BY 4.0)-lisenssin ehdoilla.
There has been a public bike sharing scheme in Helsinki since 2016. System offers a public API that provides real-time data on availability of bikes at each station. I have collected that data every five minutes since season 2017. You can download the files for any use from this site according to Attribution 4.0 International (CC BY 4.0)-license.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Tällä projektilla ei ole mitään yhteyksiä HKL;ään, HSL:ään tai CityGikeFinland:iin! This project is not connected with HKL, HSL or CityGikeFinland!
- Lisätietoja/Question :
markus.kainu@kapsi.fi - Seuraa/Follow:
@tellinkibotti
Ladattavat tiedostot / files to download
Data: Vapaat pyörät / available bikes
| tiedosto / file | koko / size | rivejä / rows | sarakkeita / columns |
|---|---|---|---|
http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2017.csv.gz |
32.8 Mt | 7 795 206 | 16 |
http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2018.csv.gz |
61.9 Mt | 15 126 567 | 16 |
http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2019.csv.gz |
124 Mt | 25 966 001 | 16 |
http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2020.csv.gz |
xxx | yyy | 16 |
Ensimmäiset rivit / First rows
id bikesAvailable spacesAvailable allowDropoff isFloatingBike state realTimeData time week yday
<chr> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <chr> <dbl> <dbl>
1 070 4 10 1 NA NA 1 2017… 19 128
2 071 16 12 1 NA NA 1 2017… 19 128
3 072 14 5 1 NA NA 1 2017… 19 128
4 073 8 6 1 NA NA 1 2017… 19 128
5 074 15 5 1 NA NA 1 2017… 19 128
6 075 3 17 1 NA NA 1 2017… 19 128
# … with 6 more variables: day <dbl>, month <dbl>, hour <dbl>, minute <dbl>, yhour <dbl>, year <dbl>Data: Tellingit / Stations
| tiedosto / file | koko / size | rivejä / rows | sarakkeita / columns |
|---|---|---|---|
http://data.markuskainu.fi/opendata/kaupunkipyorat/tellingit_2017-2020.csv.gz |
xxMt | xxxxx | 7 |
Ensimmäiset rivit / First rows
id name x y time yday year
<chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
1 120 Mäkelänkatu 25.0 60.2 2017/05/09 12:00:03 129 2017
2 088 Kiskontie 24.9 60.2 2017/05/09 12:00:03 129 2017
3 121 Vilhonvuorenkatu 25.0 60.2 2017/05/09 12:00:03 129 2017
4 070 Sammonpuistikko 24.9 60.2 2017/05/09 12:00:03 129 2017
5 071 Hietaniemenkatu 24.9 60.2 2017/05/09 12:00:03 129 2017
6 072 Eteläinen Hesperiankatu 24.9 60.2 2017/05/09 12:00:03 129 2017Metadatat / metadatas
Vapaat pyörät / available bikes
varname description
<chr> <chr>
1 id Station id (character)
2 bikesAvailable Number of available bikes (integer)
3 spacesAvailable Number of available spaces (integer)
4 allowDropoff Does the station allow dropoff of a bike: 1 = yes, 2 = no (integer)
5 isFloatingBike isFloatingBike (all values NA) (logical)
6 state state (all values NA) (logical)
7 realTimeData realTimeData (integer)
8 time Timestamp. Format 'yyyy/mm/dd hh:mm:ss' (character)
9 week Week of the year (integer)
10 yday Day of the year (integer)
11 day Day of the month (integer)
12 month Month of the year (integer)
13 hour Hour of the day (integer)
14 minute Minute of the hour (integer)
15 yhour Hour of the year (integer)
16 year Year (integer)
Tellingit / bike stations
varname description
<chr> <chr>
1 id Station id (character)
2 name Station name (character)
3 x longitude (double)
4 y latitude (double)
5 time Timestamp. Format 'yyyy/mm/dd hh:mm:ss' (character)
6 yday Day of the year (integer)
7 year Year (integer)
Sää / weather
varname description
<chr> <chr>
1 ASEMA Weather station name (character)
2 AIKA Time. Format 'mm:hh, d.m.yy' (character)
3 LÄMPÖTILA_C Temperature in Celsius (double)
4 KOSTEUS Relative humidity in percent (double)
5 KASTEPISTE_C Dew point in Celsius (double)
6 ILMANPAINE_HPA Atmospheric pressure in hPA (double)
7 TUULI_MS Wind speed, m/s. (double)
8 PUUSKA_MS Wind gust, m/s. (double)
9 SUUNTA Precipitation intensity, mm/h (double)
10 SAT_RANKKUUS_MMH Wind direction, degrees (integer)
11 NÄKYVYYS_M Visibility, meters (integer)
12 LUMI_CM Snow depth, cm (integer)
13 PILVISYYS Cloud cover, 0-8 (integer)
14 TIME Timestamp. Format 'yyyy/mm/dd hh:mm:ss' (character)
Sääennuste / weather forecast
varname description
<chr> <chr>
1 AJANKOHTA Time for forecast, format 'dd.mm.yyyy hh.mm' (character)
2 OLOSUHDE Condition (logical)
3 LÄMPÖTILA_C Temperature in Celsius (double)
4 KOSTEUS Relative humidity in percent (double)
5 TUULEN_NOPEUS_MS Wind speed, m/s. (double)
6 TUULEN_SUUNTA Wind direction, degrees (integer)
7 PUUSKA_MS Wind gust, m/s. (double)
8 SADE_1H_MM Precipitation intensity, mm/h (double)
9 ILMANPAINE_HPA Atmospheric pressure in hPA (double)
10 PILVISYYS Cloud cover, 0-8 (integer)
11 GLOBAALISÄTEILY_WM Global radiation, W/m2. (double)
12 TIME Timestamp for forecast. Format 'yyyy/mm/dd hh:mm:ss' (character)
Esimerkkejä R-kielellä / Examples using R-language
I Vapaiden pyörien määrät viikolla 25 vuosina 2018-2019 tellingeillä, jotka alkavat sanalla ‘Töölö’
library(tidyverse)
tmpdir <- tempdir()
tmpfly <- paste0(tmpdir,"/tmpdata.csv.gz")
download.file(url = "http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2017.csv.gz",
destfile = tmpfly)
dat17 <- read_csv2(tmpfly) %>% filter(week == 24)
download.file(url = "http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2018.csv.gz",
destfile = tmpfly)
dat18 <- read_csv2(tmpfly) %>% filter(week == 24)
download.file(url = "http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2019.csv.gz",
destfile = tmpfly)
dat19 <- read_csv2(tmpfly) %>% filter(week == 24)
download.file(url = "http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2020.csv.gz",
destfile = tmpfly)
dat20 <- read_csv2(tmpfly) %>% filter(week == 24)
dat <- bind_rows(
dat17,
dat18,
dat19,
dat20
)
stations <- read_csv2("http://data.markuskainu.fi/opendata/kaupunkipyorat/tellingit_2017-2020.csv.gz")
dat25 <- dat %>%
left_join(stations %>%
select(-time)) %>%
filter(grepl("Töölö", name)) %>%
mutate(time = as.POSIXct(time))
ggplot(dat25, aes(x = time, y = bikesAvailable, color = name, group = name)) +
geom_line() +
facet_wrap(~year, scales = "free_x", ncol = 1) +
theme_minimal(base_family = "PT Sans") +
labs(title = "Vapaiden pyörien määrä viikolla 24 vuosina 2017, 2018, 2019 ja 2020",
subtitle = "Mukana `Töölö`-alkuiset telineet",
y = "Vapaiden pyörien määrä", x = NULL, color = "tellinki")## [1] "LC_CTYPE=fi_FI.UTF-8;LC_NUMERIC=C;LC_TIME=fi_FI.UTF-8;LC_COLLATE=fi_FI.UTF-8;LC_MONETARY=fi_FI.UTF-8;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_IDENTIFICATION=C"
II Tellinkien elinvuodet kartalla / Stations lifespan on map
library(tidyverse)
library(sf)
stations <- read_csv2("http://data.markuskainu.fi/opendata/kaupunkipyorat/tellingit_2017-2020.csv.gz")
stations %>%
distinct(id,name,year, .keep_all = TRUE) %>%
group_by(id,name) %>%
count(year) %>%
pivot_wider(names_from = year, values_from = n) %>%
mutate(type = case_when(
`2017` == 1 & `2018` == 1 & `2019` == 1 & `2020` == 1 ~ "2017-2020",
`2017` == 1 & `2018` == 1 & is.na(`2019`) ~ "2017-2018",
`2017` == 1 & is.na(`2018`) & is.na(`2019`) ~ "2017",
is.na(`2017`) & `2018` == 1 & `2019` == 1 ~ "2018-2019",
is.na(`2017`) & `2018` == 1 & `2019` == 1 & `2020` == 1 ~ "2018-2020",
is.na(`2017`) & `2018` == 1 & is.na(`2019`) ~ "2018",
is.na(`2017`) & is.na(`2018`) & `2019` == 1 ~ "2019",
is.na(`2017`) & is.na(`2018`) & `2019` == 1 & `2020` == 1 ~ "2019-2020",
is.na(`2017`) & is.na(`2018`) & is.na(`2018`) & `2020` == 1 ~ "2020",
TRUE ~ "joku muu"
)) %>% ungroup() %>%
select(id,type) -> alive
alive %>%
left_join(distinct(stations, id,name,x,y)) %>%
# poistetaan testit / remove tests
filter(x <= 30, y >= 55) %>%
sf::st_as_sf(coords = c(4,5)) -> spatdat
library(leaflet)
pal <- colorFactor(
palette = "Set2",domain = unique(alive$type))
leaflet(spatdat) %>%
leaflet::addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(color = ~pal(type),
label = ~paste(name,"\n", type),
labelOptions = labelOptions(noHide = F, direction = "auto",
style = list(
"color" = "black",
"box-shadow" = "3px 3px rgba(0,0,0,0.25)",
"font-size" = "10px",
"border-color" = "rgba(0,0,0,0.5)",
"background" = "rgba(235, 235, 235, 0.61)"
))) %>%
addLegend(pal = pal,
values = ~type,
opacity = 0.7,
title = "Tellinki olemassa",
position = "bottomright")III Säätila ja aseman vilkkaus touko-kesäkuun taitteen aamuina Meilahden sairaalan ja Unioninkadun tellingeillä
library(tidyverse)
tmpdir <- tempdir()
tmpfly <- paste0(tmpdir,"/tmpdata.csv.gz")
download.file(url = "http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2017.csv.gz",
destfile = tmpfly)
dat17 <- read_csv2(tmpfly) %>% filter(week %in% 23:24,hour %in% 6:10)
download.file(url = "http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2018.csv.gz",
destfile = tmpfly)
dat18 <- read_csv2(tmpfly) %>% filter(week %in% 23:24,hour %in% 6:10)
download.file(url = "http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2019.csv.gz",
destfile = tmpfly)
dat19 <- read_csv2(tmpfly) %>% filter(week %in% 23:24,hour %in% 6:10)
download.file(url = "http://data.markuskainu.fi/opendata/kaupunkipyorat/data_2020.csv.gz",
destfile = tmpfly)
dat20 <- read_csv2(tmpfly) %>% filter(week %in% 23:24,hour %in% 6:10)
dat <- bind_rows(
dat17,
dat18,
dat19,
dat20
)
stations <- read_csv2("http://data.markuskainu.fi/opendata/kaupunkipyorat/tellingit_2017-2020.csv.gz")
mo <- dat %>%
left_join(stations %>%
select(-time)) %>%
filter(grepl("Meilahden|Unionin", name)) %>%
mutate(time = as.POSIXct(time)) %>%
group_by(id,name) %>%
mutate(freq = abs(bikesAvailable-lag(bikesAvailable))) %>%
ungroup()
mohour <-
mo %>%
group_by(id,name,year,week,month,yday,day,yhour,week,hour) %>%
summarise(freqs = sum(freq, na.rm = TRUE)) %>%
ungroup() %>%
arrange(yhour)
# Data from FMI
## Install fmi2 with remotes::install_github("ropengov/fmi2")
library(fmi2)
dat_date <- mo %>%
distinct(year,yday, .keep_all = TRUE) %>%
mutate(date = as.Date(time)) %>%
select(year,date)
library(glue)
dates <- dat_date$date
fmi_tmp <- list()
for (i in 1:length(dates)){
pvm <- dates[i]
fmi_tmp[[pvm]] <- fmi2::obs_weather_hourly(starttime = glue("{pvm}T06:00:00Z"),
endtime = glue("{pvm}T10:00:00Z"),
fmisid = "100971") %>%
filter(variable %in% c("TA_PT1H_AVG","PRI_PT1H_MAX","WS_PT1H_AVG","RH_PT1H_AVG")) %>%
sf::st_set_geometry(value = NULL) %>%
as_tibble() %>%
mutate(var = case_when(
variable == "TA_PT1H_AVG" ~ "Ilman lämpötila / Air temperature (C)",
variable == "PRI_PT1H_MAX" ~ "Sateen intensiteetti / Maximum precipitation intensity (mm/h)",
variable == "WS_PT1H_AVG" ~ "Tuulen nopeus / Wind speed (m/s)",
variable == "RH_PT1H_AVG" ~ "Ilmankosteus / Relative humidity (%)"),
station = "Kaisaniemi") %>%
select(-variable)
}
fmi <- do.call(bind_rows, fmi_tmp) %>%
mutate(year = lubridate::year(time),
yday = lubridate::yday(time),
hour = lubridate::hour(time),
yhour = (yday - 93) * 24 + hour) %>%
select(-hour,-yday)
df <- left_join(mohour, fmi) %>%
filter(!is.na(var))
ggplot(df, aes(x = value, y = freqs, color = factor(year), group = factor(year))) +
geom_point(shape = 21, alpha = .6) +
facet_grid(name~sub("/", "\n", var), scales = "free") +
geom_smooth(method = "lm", se = FALSE) +
theme_minimal(base_family = "PT Sans") +
labs(y = "Lainauksia tai palautuksia tunnissa / Rentals or return per hour",
x = NULL, color = NULL)