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(cache = '.')
all_sensors = readRDS('boxes_precomputed.rds') # read precomputed file to save resources summary(all_sensors)## boxes total: 11390
##
## boxes by exposure:
## indoor mobile outdoor unknown
## 2364 590 8417 19
##
## boxes by model:
## custom hackair_home_v2 homeEthernet
## 2800 73 73
## homeEthernetFeinstaub homeV2Ethernet homeV2EthernetFeinstaub
## 55 21 40
## homeV2Lora homeV2Wifi homeV2WifiFeinstaub
## 240 577 743
## 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
## 78 286 3066
## luftdaten_sds011_bmp180 luftdaten_sds011_dht11 luftdaten_sds011_dht22
## 114 135 2552
##
## $last_measurement_within
## 1h 1d 30d 365d never
## 0 0 4151 5909 2062
##
## oldest box: 2016-08-09 19:34:42 (OBS Bohmte UK_02)
## newest box: 2023-02-28 09:47:17 (bitburg)
##
## sensors per box:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 5.000 4.994 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.
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 3298
## $ Temperatur : int 9405
## $ rel. Luftfeuchte : int 8315
## $ PM10 : int 8148
## $ PM2.5 : int 8136
## $ Luftdruck : int 5668
## $ Beleuchtungsstärke : int 1670
## $ UV-Intensität : int 1660
## $ Temperature : int 644
## $ Humidity : int 473
## $ VOC : int 423
## $ Luftfeuchte : int 363
## $ Lufttemperatur : int 357
## $ CO₂ : int 305
## $ Pressure : int 293
## $ Bodenfeuchte : int 283
## $ Luftfeuchtigkeit : int 272
## $ atm. Luftdruck : int 246
## $ Lautstärke : int 240
## $ PM01 : int 206
## $ IAQ : int 162
## $ Kalibrierungswert : int 156
## $ rel. Luftfeuchte SCD30 : int 156
## $ Bodentemperatur : int 154
## $ Temperatur SCD30 : int 154
## $ CO2eq : int 153
## $ Windgeschwindigkeit : int 152
## $ pH-Wert : int 143
## $ Gesamthärte : int 142
## $ Blei : int 140
## $ Eisen : int 140
## $ Gesamthärte 2 : int 140
## $ Kupfer C : int 140
## $ Kupfer D : int 140
## $ Kupfer1 : int 140
## $ Kupfer2 : int 140
## $ Nitrat : int 140
## $ Nitrit : int 140
## $ GesamthaerteLabor : int 120
## $ CO2 : int 113
## $ Feinstaub PM10 : int 98
## $ Windrichtung : int 82
## $ rel. Luftfeuchte (HECA) : int 75
## $ Temperatur (HECA) : int 73
## $ 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
## $ Batterie : int 46
## $ temperature : int 46
## $ 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
## $ EisenLabor : 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
## [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] 9405
##
## $`rel. Luftfeuchte`
## [1] 8315
##
## $PM10
## [1] 8148
##
## $PM2.5
## [1] 8136
##
## $Luftdruck
## [1] 5668
##
## $Beleuchtungsstärke
## [1] 1670
##
## $`UV-Intensität`
## [1] 1660
##
## $Temperature
## [1] 644
##
## $Humidity
## [1] 473
##
## $VOC
## [1] 423
##
## $Luftfeuchte
## [1] 363
##
## $Lufttemperatur
## [1] 357
##
## $`CO₂`
## [1] 305
##
## $Pressure
## [1] 293
##
## $Bodenfeuchte
## [1] 283
##
## $Luftfeuchtigkeit
## [1] 272
##
## $`atm. Luftdruck`
## [1] 246
##
## $Lautstärke
## [1] 240
##
## $PM01
## [1] 206
##
## $IAQ
## [1] 162
##
## $Kalibrierungswert
## [1] 156
##
## $`rel. Luftfeuchte SCD30`
## [1] 156
##
## $Bodentemperatur
## [1] 154
##
## $`Temperatur SCD30`
## [1] 154
##
## $CO2eq
## [1] 153
##
## $Windgeschwindigkeit
## [1] 152
##
## $`pH-Wert`
## [1] 143
##
## $Gesamthärte
## [1] 142
##
## $Blei
## [1] 140
##
## $Eisen
## [1] 140
##
## $`Gesamthärte 2`
## [1] 140
##
## $`Kupfer C`
## [1] 140
##
## $`Kupfer D`
## [1] 140
##
## $Kupfer1
## [1] 140
##
## $Kupfer2
## [1] 140
##
## $Nitrat
## [1] 140
##
## $Nitrit
## [1] 140
##
## $GesamthaerteLabor
## [1] 120
##
## $CO2
## [1] 113
##
## $`Feinstaub PM10`
## [1] 98
##
## $Windrichtung
## [1] 82
##
## $`rel. Luftfeuchte (HECA)`
## [1] 75
##
## $`Temperatur (HECA)`
## [1] 73
##
## $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
##
## $Batterie
## [1] 46
##
## $temperature
## [1] 46
##
## $`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'
)pm25_sensors = readRDS('pm25_sensors.rds') # read precomputed file to save resources
summary(pm25_sensors)## boxes total: 3011
##
## boxes by exposure:
## outdoor
## 3011
##
## boxes by model:
## custom hackair_home_v2 homeEthernetFeinstaub
## 175 8 12
## homeV2EthernetFeinstaub homeV2Lora homeV2Wifi
## 9 22 2
## homeV2WifiFeinstaub homeWifi homeWifiFeinstaub
## 132 3 32
## luftdaten_pms1003 luftdaten_pms1003_bme280 luftdaten_pms5003
## 1 3 3
## luftdaten_pms5003_bme280 luftdaten_pms7003 luftdaten_pms7003_bme280
## 10 2 28
## luftdaten_sds011 luftdaten_sds011_bme280 luftdaten_sds011_bmp180
## 117 1365 60
## luftdaten_sds011_dht11 luftdaten_sds011_dht22
## 44 983
##
## $last_measurement_within
## 1h 1d 30d 365d never
## 0 0 3011 3011 0
##
## oldest box: 2017-03-03 18:20:43 (Witten Heven Dorf)
## newest box: 2023-02-28 08:28:27 (eth0)
##
## sensors per box:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 4.000 5.000 4.854 5.000 26.000plot(pm25_sensors)
Thats still more than 200 measuring stations, we can work with that.
