Exploring the dataset
Before we look at actual observations, lets get a grasp of the openSenseMap datasets’ structure.
library(magrittr)
library(opensensmapr)
all_sensors = osem_boxes()summary(all_sensors)## boxes total: 11367
##
## boxes by exposure:
## indoor mobile outdoor unknown
## 2344 591 8413 19
##
## boxes by model:
## custom hackair_home_v2 homeEthernet
## 2776 73 73
## homeEthernetFeinstaub homeV2Ethernet homeV2EthernetFeinstaub
## 55 21 40
## homeV2Lora homeV2Wifi homeV2WifiFeinstaub
## 246 578 743
## homeWifi homeWifiFeinstaub luftdaten_pms1003
## 215 222 9
## luftdaten_pms1003_bme280 luftdaten_pms3003 luftdaten_pms3003_bme280
## 10 1 7
## luftdaten_pms5003 luftdaten_pms5003_bme280 luftdaten_pms7003
## 7 60 6
## luftdaten_pms7003_bme280 luftdaten_sds011 luftdaten_sds011_bme280
## 78 285 3060
## luftdaten_sds011_bmp180 luftdaten_sds011_dht11 luftdaten_sds011_dht22
## 114 135 2553
##
## $last_measurement_within
## 1h 1d 30d 365d never
## 3601 3756 4252 5938 2052
##
## oldest box: 2016-08-09 19:34:42 (OBS Bohmte UK_02)
## newest box: 2023-02-23 07:56:59 (Steinbrink 29)
##
## sensors per box:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.981 5.000 76.000This gives a good overview already: As of writing this, there are more than 700 sensor stations, of which ~50% are currently running. Most of them are placed outdoors and have around 5 sensors each. The oldest station is from May 2014, while the latest station was registered a couple of minutes ago.
Another feature of interest is the spatial distribution of the boxes:
plot() can help us out here. This function requires a bunch
of optional dependencies though.
if (!require('maps')) install.packages('maps')
if (!require('maptools')) install.packages('maptools')
if (!require('rgeos')) install.packages('rgeos')
plot(all_sensors)
It seems we have to reduce our area of interest to Germany.
But what do these sensor stations actually measure? Lets find out.
osem_phenomena() gives us a named list of of the counts of
each observed phenomenon for the given set of sensor stations:
phenoms = osem_phenomena(all_sensors)
str(phenoms)## List of 3289
## $ Temperatur : int 9385
## $ rel. Luftfeuchte : int 8317
## $ PM10 : int 8147
## $ PM2.5 : int 8135
## $ Luftdruck : int 5667
## $ Beleuchtungsstärke : int 1674
## $ UV-Intensität : int 1665
## $ Temperature : int 643
## $ Humidity : int 473
## $ VOC : int 422
## $ Luftfeuchte : int 362
## $ Lufttemperatur : int 356
## $ CO₂ : int 304
## $ Pressure : int 293
## $ Bodenfeuchte : int 284
## $ Luftfeuchtigkeit : int 272
## $ atm. Luftdruck : int 245
## $ Lautstärke : int 240
## $ PM01 : int 206
## $ IAQ : int 162
## $ Kalibrierungswert : int 156
## $ rel. Luftfeuchte SCD30 : int 156
## $ Bodentemperatur : int 155
## $ Temperatur SCD30 : int 154
## $ CO2eq : int 153
## $ Windgeschwindigkeit : int 152
## $ pH-Wert : int 123
## $ Gesamthärte : int 122
## $ Blei : int 120
## $ Eisen : int 120
## $ GesamthaerteLabor : int 120
## $ Gesamthärte 2 : int 120
## $ Kupfer C : int 120
## $ Kupfer D : int 120
## $ Kupfer1 : int 120
## $ Kupfer2 : int 120
## $ Nitrat : int 120
## $ Nitrit : int 120
## $ CO2 : int 112
## $ Feinstaub PM10 : int 98
## $ Windrichtung : int 82
## $ rel. Luftfeuchte (HECA) : int 74
## $ Temperatur (HECA) : int 72
## $ Temperatura : int 69
## $ Helligkeit : int 67
## $ Feinstaub PM2.5 : int 65
## $ Taupunkt : int 62
## $ Latitude : int 61
## $ Longtitude : int 58
## $ Durchschnitt Umgebungslautstärke : int 51
## $ Minimum Umgebungslautstärke : int 51
## $ UV-Index : int 49
## $ temperature : int 46
## $ Batterie : int 45
## $ Feinstaub PM1.0 : int 41
## $ Umgebungslautstärke : int 41
## $ UV : int 40
## $ humidity : int 38
## $ Abstand nach links : int 34
## $ Beschleunigung Z-Achse : int 34
## $ Beschleunigung X-Achse : int 33
## $ Beschleunigung Y-Achse : int 33
## $ Geschwindigkeit : int 33
## $ Niederschlag : int 33
## $ Feinstaub PM25 : int 32
## $ PM1 : int 32
## $ Abstand nach rechts : int 31
## $ PM1.0 : int 30
## $ rel. Luftfeuchtigkeit : int 30
## $ Relative Humidity : int 29
## $ Sonnenstrahlung : int 29
## $ Luftdruck relativ : int 28
## $ Luftdruck absolut : int 26
## $ Rain : int 26
## $ Regenrate : int 26
## $ CO2 Konzentration : int 25
## $ RSSI : int 22
## $ gefühlte Temperatur : int 22
## $ PM 2.5 : int 21
## $ Battery : int 20
## $ Ciśnienie : int 20
## $ Air Pressure : int 19
## $ Regen : int 19
## $ Schall : int 19
## $ Signal : int 19
## $ Ilmanpaine : int 18
## $ Lämpötila : int 18
## $ UV Index : int 18
## $ Wind speed : int 18
## $ PM 10 : int 17
## $ PM4 : int 17
## $ Air pressure : int 16
## $ Temperatur DHT22 : int 16
## $ Wind Direction : int 16
## $ Altitude : int 15
## $ Illuminance : int 15
## $ Speed : int 15
## $ Wind Speed : int 15
## $ pressure : int 15
## [list output truncated]Thats quite some noise there, with many phenomena being measured by a single sensor only, or many duplicated phenomena due to slightly different spellings. We should clean that up, but for now let’s just filter out the noise and find those phenomena with high sensor numbers:
phenoms[phenoms > 20]## $Temperatur
## [1] 9385
##
## $`rel. Luftfeuchte`
## [1] 8317
##
## $PM10
## [1] 8147
##
## $PM2.5
## [1] 8135
##
## $Luftdruck
## [1] 5667
##
## $Beleuchtungsstärke
## [1] 1674
##
## $`UV-Intensität`
## [1] 1665
##
## $Temperature
## [1] 643
##
## $Humidity
## [1] 473
##
## $VOC
## [1] 422
##
## $Luftfeuchte
## [1] 362
##
## $Lufttemperatur
## [1] 356
##
## $`CO₂`
## [1] 304
##
## $Pressure
## [1] 293
##
## $Bodenfeuchte
## [1] 284
##
## $Luftfeuchtigkeit
## [1] 272
##
## $`atm. Luftdruck`
## [1] 245
##
## $Lautstärke
## [1] 240
##
## $PM01
## [1] 206
##
## $IAQ
## [1] 162
##
## $Kalibrierungswert
## [1] 156
##
## $`rel. Luftfeuchte SCD30`
## [1] 156
##
## $Bodentemperatur
## [1] 155
##
## $`Temperatur SCD30`
## [1] 154
##
## $CO2eq
## [1] 153
##
## $Windgeschwindigkeit
## [1] 152
##
## $`pH-Wert`
## [1] 123
##
## $Gesamthärte
## [1] 122
##
## $Blei
## [1] 120
##
## $Eisen
## [1] 120
##
## $GesamthaerteLabor
## [1] 120
##
## $`Gesamthärte 2`
## [1] 120
##
## $`Kupfer C`
## [1] 120
##
## $`Kupfer D`
## [1] 120
##
## $Kupfer1
## [1] 120
##
## $Kupfer2
## [1] 120
##
## $Nitrat
## [1] 120
##
## $Nitrit
## [1] 120
##
## $CO2
## [1] 112
##
## $`Feinstaub PM10`
## [1] 98
##
## $Windrichtung
## [1] 82
##
## $`rel. Luftfeuchte (HECA)`
## [1] 74
##
## $`Temperatur (HECA)`
## [1] 72
##
## $Temperatura
## [1] 69
##
## $Helligkeit
## [1] 67
##
## $`Feinstaub PM2.5`
## [1] 65
##
## $Taupunkt
## [1] 62
##
## $Latitude
## [1] 61
##
## $Longtitude
## [1] 58
##
## $`Durchschnitt Umgebungslautstärke`
## [1] 51
##
## $`Minimum Umgebungslautstärke`
## [1] 51
##
## $`UV-Index`
## [1] 49
##
## $temperature
## [1] 46
##
## $Batterie
## [1] 45
##
## $`Feinstaub PM1.0`
## [1] 41
##
## $Umgebungslautstärke
## [1] 41
##
## $UV
## [1] 40
##
## $humidity
## [1] 38
##
## $`Abstand nach links`
## [1] 34
##
## $`Beschleunigung Z-Achse`
## [1] 34
##
## $`Beschleunigung X-Achse`
## [1] 33
##
## $`Beschleunigung Y-Achse`
## [1] 33
##
## $Geschwindigkeit
## [1] 33
##
## $Niederschlag
## [1] 33
##
## $`Feinstaub PM25`
## [1] 32
##
## $PM1
## [1] 32
##
## $`Abstand nach rechts`
## [1] 31
##
## $PM1.0
## [1] 30
##
## $`rel. Luftfeuchtigkeit`
## [1] 30
##
## $`Relative Humidity`
## [1] 29
##
## $Sonnenstrahlung
## [1] 29
##
## $`Luftdruck relativ`
## [1] 28
##
## $`Luftdruck absolut`
## [1] 26
##
## $Rain
## [1] 26
##
## $Regenrate
## [1] 26
##
## $`CO2 Konzentration`
## [1] 25
##
## $RSSI
## [1] 22
##
## $`gefühlte Temperatur`
## [1] 22
##
## $`PM 2.5`
## [1] 21Alright, temperature it is! Fine particulate matter (PM2.5) seems to be more interesting to analyze though. We should check how many sensor stations provide useful data: We want only those boxes with a PM2.5 sensor, that are placed outdoors and are currently submitting measurements:
pm25_sensors = osem_boxes(
exposure = 'outdoor',
date = Sys.time(), # ±4 hours
phenomenon = 'PM2.5'
)summary(pm25_sensors)## boxes total: 3002
##
## boxes by exposure:
## outdoor
## 3002
##
## boxes by model:
## custom hackair_home_v2 homeEthernetFeinstaub
## 174 8 12
## homeV2EthernetFeinstaub homeV2Lora homeV2Wifi
## 10 21 2
## homeV2WifiFeinstaub homeWifi homeWifiFeinstaub
## 126 3 30
## luftdaten_pms1003 luftdaten_pms1003_bme280 luftdaten_pms5003
## 1 2 3
## luftdaten_pms5003_bme280 luftdaten_pms7003 luftdaten_pms7003_bme280
## 11 2 26
## luftdaten_sds011 luftdaten_sds011_bme280 luftdaten_sds011_bmp180
## 115 1365 59
## luftdaten_sds011_dht11 luftdaten_sds011_dht22
## 45 987
##
## $last_measurement_within
## 1h 1d 30d 365d never
## 2977 3002 3002 3002 0
##
## oldest box: 2017-03-03 18:20:43 (Witten Heven Dorf)
## newest box: 2023-02-23 07:56:59 (Steinbrink 29)
##
## sensors per box:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 4.000 5.000 4.838 5.000 26.000plot(pm25_sensors)
Thats still more than 200 measuring stations, we can work with that.
