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: 1845
##
## boxes by exposure:
## indoor mobile outdoor unknown
## 295 57 1473 20
##
## boxes by model:
## custom homeEthernet homeEthernetFeinstaub
## 344 91 49
## homeV2Wifi homeV2WifiFeinstaub homeWifi
## 1 2 194
## homeWifiFeinstaub luftdaten_pms1003 luftdaten_pms1003_bme280
## 146 1 1
## luftdaten_pms3003_bme280 luftdaten_pms5003_bme280 luftdaten_pms7003_bme280
## 1 11 3
## luftdaten_sds011 luftdaten_sds011_bme280 luftdaten_sds011_bmp180
## 59 209 19
## luftdaten_sds011_dht11 luftdaten_sds011_dht22
## 46 668
##
## $last_measurement_within
## 1h 1d 30d 365d never
## 928 960 1122 1474 245
##
## oldest box: 2014-05-28 15:36:14 (CALIMERO)
## newest box: 2018-06-11 19:49:27 (NewSenseBoxAdKatzwang)
##
## sensors per box:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 4.000 4.605 5.000 33.000This 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 463
## $ Temperatur : int 1661
## $ rel. Luftfeuchte : int 1475
## $ PM10 : int 1256
## $ PM2.5 : int 1254
## $ Luftdruck : int 848
## $ Beleuchtungsstärke : int 483
## $ UV-Intensität : int 474
## $ Luftfeuchtigkeit : int 84
## $ Temperature : int 56
## $ Humidity : int 46
## $ Helligkeit : int 25
## $ Lautstärke : int 21
## $ Pressure : int 21
## $ Schall : int 20
## $ UV : int 20
## $ Licht : int 18
## $ Luftfeuchte : int 14
## $ PM01 : int 14
## $ Umgebungslautstärke : int 14
## $ Lämpötila : int 13
## $ Ilmanpaine : int 12
## $ Signal : int 12
## $ Feinstaub PM10 : int 10
## $ Feinstaub PM2.5 : int 9
## $ Kosteus : int 8
## $ Temperatur DHT22 : int 8
## $ Valonmäärä : int 8
## $ temperature : int 8
## $ UV-säteily : int 7
## $ Niederschlag : int 6
## $ UV-Strahlung : int 6
## $ Wind speed : int 6
## $ Windgeschwindigkeit : int 6
## $ humidity : int 6
## $ Ilmankosteus : int 5
## $ Wassertemperatur : int 5
## $ Windrichtung : int 5
## $ rel. Luftfeuchtigkeit : int 5
## $ Druck : int 4
## $ Light : int 4
## $ Temperature 1 : int 4
## $ UV Index : int 4
## $ UV-Säteily : int 4
## $ lautstärke : int 4
## $ rel. Luftfeuchte 1 : int 4
## $ rel. Luftfeuchte DHT22 : int 4
## $ relative Luftfeuchtigkeit : int 4
## $ Air Pressure : int 3
## $ Air pressure : int 3
## $ Batterie : int 3
## $ Battery : int 3
## $ DS18B20_Probe01 : int 3
## $ DS18B20_Probe02 : int 3
## $ DS18B20_Probe03 : int 3
## $ DS18B20_Probe04 : int 3
## $ DS18B20_Probe05 : int 3
## $ Illuminance : int 3
## $ Licht (digital) : int 3
## $ Luftdruck (BME280) : int 3
## $ PM 10 : int 3
## $ PM 2.5 : int 3
## $ Radioaktivität : int 3
## $ Relative Humidity : int 3
## $ Temp : int 3
## $ Temperatur (BME280) : int 3
## $ Temperatur HDC1008 : int 3
## $ Temperatura : int 3
## $ Temperature 2 : int 3
## $ UV-Index : int 3
## $ Valoisuus : int 3
## $ Wind Direction : int 3
## $ Wind Gust : int 3
## $ Wind Speed : int 3
## $ pressure : int 3
## $ 1 : int 2
## $ 10 : int 2
## $ 2 : int 2
## $ 3 : int 2
## $ 4 : int 2
## $ 5 : int 2
## $ 6 : int 2
## $ 7 : int 2
## $ 8 : int 2
## $ 9 : int 2
## $ Anderer : int 2
## $ Battery voltage : int 2
## $ CO2 : int 2
## $ Feuchte : int 2
## $ Intensity : int 2
## $ Leitfähigkeit : int 2
## $ Lichtintensität : int 2
## $ Luftdruck BMP180 : int 2
## $ Luftfeuchte (BME280) : int 2
## $ Luftqualität : int 2
## $ Lufttemperatur : int 2
## $ PM25 : int 2
## $ Radioactivity : int 2
## $ Regen : int 2
## $ Sound : int 2
## [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] 1661
##
## $`rel. Luftfeuchte`
## [1] 1475
##
## $PM10
## [1] 1256
##
## $PM2.5
## [1] 1254
##
## $Luftdruck
## [1] 848
##
## $Beleuchtungsstärke
## [1] 483
##
## $`UV-Intensität`
## [1] 474
##
## $Luftfeuchtigkeit
## [1] 84
##
## $Temperature
## [1] 56
##
## $Humidity
## [1] 46
##
## $Helligkeit
## [1] 25
##
## $Lautstärke
## [1] 21
##
## $Pressure
## [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: 799
##
## boxes by exposure:
## outdoor
## 799
##
## boxes by model:
## custom homeEthernetFeinstaub homeWifi
## 31 36 8
## homeWifiFeinstaub luftdaten_pms5003_bme280 luftdaten_pms7003_bme280
## 55 5 1
## luftdaten_sds011 luftdaten_sds011_bme280 luftdaten_sds011_bmp180
## 31 137 14
## luftdaten_sds011_dht11 luftdaten_sds011_dht22
## 27 454
##
## $last_measurement_within
## 1h 1d 30d 365d never
## 775 785 792 797 2
##
## oldest box: 2016-06-02 12:09:47 (BalkonBox Mindener Str.)
## newest box: 2018-06-11 19:49:27 (NewSenseBoxAdKatzwang)
##
## sensors per box:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 4.000 4.000 4.615 5.000 10.000plot(pm25_sensors)
Thats still more than 200 measuring stations, we can work with that.
