|
|
<!DOCTYPE html>
|
|
|
|
|
|
<html>
|
|
|
|
|
|
<head>
|
|
|
|
|
|
<meta charset="utf-8" />
|
|
|
<meta name="generator" content="pandoc" />
|
|
|
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
|
|
|
|
|
|
<meta name="viewport" content="width=device-width, initial-scale=1" />
|
|
|
|
|
|
<meta name="author" content="Norwin Roosen" />
|
|
|
|
|
|
<meta name="date" content="2023-03-08" />
|
|
|
|
|
|
<title>Exploring the openSenseMap Dataset</title>
|
|
|
|
|
|
<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
|
|
|
// be compatible with the behavior of Pandoc < 2.8).
|
|
|
document.addEventListener('DOMContentLoaded', function(e) {
|
|
|
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
|
|
|
var i, h, a;
|
|
|
for (i = 0; i < hs.length; i++) {
|
|
|
h = hs[i];
|
|
|
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
|
|
|
a = h.attributes;
|
|
|
while (a.length > 0) h.removeAttribute(a[0].name);
|
|
|
}
|
|
|
});
|
|
|
</script>
|
|
|
|
|
|
<style type="text/css">
|
|
|
code{white-space: pre-wrap;}
|
|
|
span.smallcaps{font-variant: small-caps;}
|
|
|
span.underline{text-decoration: underline;}
|
|
|
div.column{display: inline-block; vertical-align: top; width: 50%;}
|
|
|
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
|
|
|
ul.task-list{list-style: none;}
|
|
|
</style>
|
|
|
|
|
|
|
|
|
|
|
|
<style type="text/css">
|
|
|
code {
|
|
|
white-space: pre;
|
|
|
}
|
|
|
.sourceCode {
|
|
|
overflow: visible;
|
|
|
}
|
|
|
</style>
|
|
|
<style type="text/css" data-origin="pandoc">
|
|
|
pre > code.sourceCode { white-space: pre; position: relative; }
|
|
|
pre > code.sourceCode > span { display: inline-block; line-height: 1.25; }
|
|
|
pre > code.sourceCode > span:empty { height: 1.2em; }
|
|
|
.sourceCode { overflow: visible; }
|
|
|
code.sourceCode > span { color: inherit; text-decoration: inherit; }
|
|
|
div.sourceCode { margin: 1em 0; }
|
|
|
pre.sourceCode { margin: 0; }
|
|
|
@media screen {
|
|
|
div.sourceCode { overflow: auto; }
|
|
|
}
|
|
|
@media print {
|
|
|
pre > code.sourceCode { white-space: pre-wrap; }
|
|
|
pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
|
|
|
}
|
|
|
pre.numberSource code
|
|
|
{ counter-reset: source-line 0; }
|
|
|
pre.numberSource code > span
|
|
|
{ position: relative; left: -4em; counter-increment: source-line; }
|
|
|
pre.numberSource code > span > a:first-child::before
|
|
|
{ content: counter(source-line);
|
|
|
position: relative; left: -1em; text-align: right; vertical-align: baseline;
|
|
|
border: none; display: inline-block;
|
|
|
-webkit-touch-callout: none; -webkit-user-select: none;
|
|
|
-khtml-user-select: none; -moz-user-select: none;
|
|
|
-ms-user-select: none; user-select: none;
|
|
|
padding: 0 4px; width: 4em;
|
|
|
color: #aaaaaa;
|
|
|
}
|
|
|
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
|
|
|
div.sourceCode
|
|
|
{ }
|
|
|
@media screen {
|
|
|
pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
|
|
|
}
|
|
|
code span.al { color: #ff0000; font-weight: bold; }
|
|
|
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; }
|
|
|
code span.at { color: #7d9029; }
|
|
|
code span.bn { color: #40a070; }
|
|
|
code span.bu { color: #008000; }
|
|
|
code span.cf { color: #007020; font-weight: bold; }
|
|
|
code span.ch { color: #4070a0; }
|
|
|
code span.cn { color: #880000; }
|
|
|
code span.co { color: #60a0b0; font-style: italic; }
|
|
|
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; }
|
|
|
code span.do { color: #ba2121; font-style: italic; }
|
|
|
code span.dt { color: #902000; }
|
|
|
code span.dv { color: #40a070; }
|
|
|
code span.er { color: #ff0000; font-weight: bold; }
|
|
|
code span.ex { }
|
|
|
code span.fl { color: #40a070; }
|
|
|
code span.fu { color: #06287e; }
|
|
|
code span.im { color: #008000; font-weight: bold; }
|
|
|
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; }
|
|
|
code span.kw { color: #007020; font-weight: bold; }
|
|
|
code span.op { color: #666666; }
|
|
|
code span.ot { color: #007020; }
|
|
|
code span.pp { color: #bc7a00; }
|
|
|
code span.sc { color: #4070a0; }
|
|
|
code span.ss { color: #bb6688; }
|
|
|
code span.st { color: #4070a0; }
|
|
|
code span.va { color: #19177c; }
|
|
|
code span.vs { color: #4070a0; }
|
|
|
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; }
|
|
|
</style>
|
|
|
<script>
|
|
|
// apply pandoc div.sourceCode style to pre.sourceCode instead
|
|
|
(function() {
|
|
|
var sheets = document.styleSheets;
|
|
|
for (var i = 0; i < sheets.length; i++) {
|
|
|
if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
|
|
|
try { var rules = sheets[i].cssRules; } catch (e) { continue; }
|
|
|
var j = 0;
|
|
|
while (j < rules.length) {
|
|
|
var rule = rules[j];
|
|
|
// check if there is a div.sourceCode rule
|
|
|
if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") {
|
|
|
j++;
|
|
|
continue;
|
|
|
}
|
|
|
var style = rule.style.cssText;
|
|
|
// check if color or background-color is set
|
|
|
if (rule.style.color === '' && rule.style.backgroundColor === '') {
|
|
|
j++;
|
|
|
continue;
|
|
|
}
|
|
|
// replace div.sourceCode by a pre.sourceCode rule
|
|
|
sheets[i].deleteRule(j);
|
|
|
sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
|
|
|
}
|
|
|
}
|
|
|
})();
|
|
|
</script>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<style type="text/css">body {
|
|
|
background-color: #fff;
|
|
|
margin: 1em auto;
|
|
|
max-width: 700px;
|
|
|
overflow: visible;
|
|
|
padding-left: 2em;
|
|
|
padding-right: 2em;
|
|
|
font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
|
|
|
font-size: 14px;
|
|
|
line-height: 1.35;
|
|
|
}
|
|
|
#TOC {
|
|
|
clear: both;
|
|
|
margin: 0 0 10px 10px;
|
|
|
padding: 4px;
|
|
|
width: 400px;
|
|
|
border: 1px solid #CCCCCC;
|
|
|
border-radius: 5px;
|
|
|
background-color: #f6f6f6;
|
|
|
font-size: 13px;
|
|
|
line-height: 1.3;
|
|
|
}
|
|
|
#TOC .toctitle {
|
|
|
font-weight: bold;
|
|
|
font-size: 15px;
|
|
|
margin-left: 5px;
|
|
|
}
|
|
|
#TOC ul {
|
|
|
padding-left: 40px;
|
|
|
margin-left: -1.5em;
|
|
|
margin-top: 5px;
|
|
|
margin-bottom: 5px;
|
|
|
}
|
|
|
#TOC ul ul {
|
|
|
margin-left: -2em;
|
|
|
}
|
|
|
#TOC li {
|
|
|
line-height: 16px;
|
|
|
}
|
|
|
table {
|
|
|
margin: 1em auto;
|
|
|
border-width: 1px;
|
|
|
border-color: #DDDDDD;
|
|
|
border-style: outset;
|
|
|
border-collapse: collapse;
|
|
|
}
|
|
|
table th {
|
|
|
border-width: 2px;
|
|
|
padding: 5px;
|
|
|
border-style: inset;
|
|
|
}
|
|
|
table td {
|
|
|
border-width: 1px;
|
|
|
border-style: inset;
|
|
|
line-height: 18px;
|
|
|
padding: 5px 5px;
|
|
|
}
|
|
|
table, table th, table td {
|
|
|
border-left-style: none;
|
|
|
border-right-style: none;
|
|
|
}
|
|
|
table thead, table tr.even {
|
|
|
background-color: #f7f7f7;
|
|
|
}
|
|
|
p {
|
|
|
margin: 0.5em 0;
|
|
|
}
|
|
|
blockquote {
|
|
|
background-color: #f6f6f6;
|
|
|
padding: 0.25em 0.75em;
|
|
|
}
|
|
|
hr {
|
|
|
border-style: solid;
|
|
|
border: none;
|
|
|
border-top: 1px solid #777;
|
|
|
margin: 28px 0;
|
|
|
}
|
|
|
dl {
|
|
|
margin-left: 0;
|
|
|
}
|
|
|
dl dd {
|
|
|
margin-bottom: 13px;
|
|
|
margin-left: 13px;
|
|
|
}
|
|
|
dl dt {
|
|
|
font-weight: bold;
|
|
|
}
|
|
|
ul {
|
|
|
margin-top: 0;
|
|
|
}
|
|
|
ul li {
|
|
|
list-style: circle outside;
|
|
|
}
|
|
|
ul ul {
|
|
|
margin-bottom: 0;
|
|
|
}
|
|
|
pre, code {
|
|
|
background-color: #f7f7f7;
|
|
|
border-radius: 3px;
|
|
|
color: #333;
|
|
|
white-space: pre-wrap;
|
|
|
}
|
|
|
pre {
|
|
|
border-radius: 3px;
|
|
|
margin: 5px 0px 10px 0px;
|
|
|
padding: 10px;
|
|
|
}
|
|
|
pre:not([class]) {
|
|
|
background-color: #f7f7f7;
|
|
|
}
|
|
|
code {
|
|
|
font-family: Consolas, Monaco, 'Courier New', monospace;
|
|
|
font-size: 85%;
|
|
|
}
|
|
|
p > code, li > code {
|
|
|
padding: 2px 0px;
|
|
|
}
|
|
|
div.figure {
|
|
|
text-align: center;
|
|
|
}
|
|
|
img {
|
|
|
background-color: #FFFFFF;
|
|
|
padding: 2px;
|
|
|
border: 1px solid #DDDDDD;
|
|
|
border-radius: 3px;
|
|
|
border: 1px solid #CCCCCC;
|
|
|
margin: 0 5px;
|
|
|
}
|
|
|
h1 {
|
|
|
margin-top: 0;
|
|
|
font-size: 35px;
|
|
|
line-height: 40px;
|
|
|
}
|
|
|
h2 {
|
|
|
border-bottom: 4px solid #f7f7f7;
|
|
|
padding-top: 10px;
|
|
|
padding-bottom: 2px;
|
|
|
font-size: 145%;
|
|
|
}
|
|
|
h3 {
|
|
|
border-bottom: 2px solid #f7f7f7;
|
|
|
padding-top: 10px;
|
|
|
font-size: 120%;
|
|
|
}
|
|
|
h4 {
|
|
|
border-bottom: 1px solid #f7f7f7;
|
|
|
margin-left: 8px;
|
|
|
font-size: 105%;
|
|
|
}
|
|
|
h5, h6 {
|
|
|
border-bottom: 1px solid #ccc;
|
|
|
font-size: 105%;
|
|
|
}
|
|
|
a {
|
|
|
color: #0033dd;
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
a:hover {
|
|
|
color: #6666ff; }
|
|
|
a:visited {
|
|
|
color: #800080; }
|
|
|
a:visited:hover {
|
|
|
color: #BB00BB; }
|
|
|
a[href^="http:"] {
|
|
|
text-decoration: underline; }
|
|
|
a[href^="https:"] {
|
|
|
text-decoration: underline; }
|
|
|
|
|
|
code > span.kw { color: #555; font-weight: bold; }
|
|
|
code > span.dt { color: #902000; }
|
|
|
code > span.dv { color: #40a070; }
|
|
|
code > span.bn { color: #d14; }
|
|
|
code > span.fl { color: #d14; }
|
|
|
code > span.ch { color: #d14; }
|
|
|
code > span.st { color: #d14; }
|
|
|
code > span.co { color: #888888; font-style: italic; }
|
|
|
code > span.ot { color: #007020; }
|
|
|
code > span.al { color: #ff0000; font-weight: bold; }
|
|
|
code > span.fu { color: #900; font-weight: bold; }
|
|
|
code > span.er { color: #a61717; background-color: #e3d2d2; }
|
|
|
</style>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
</head>
|
|
|
|
|
|
<body>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<h1 class="title toc-ignore">Exploring the openSenseMap Dataset</h1>
|
|
|
<h4 class="author">Norwin Roosen</h4>
|
|
|
<h4 class="date">2023-03-08</h4>
|
|
|
|
|
|
|
|
|
|
|
|
<p>This package provides data ingestion functions for almost any data
|
|
|
stored on the open data platform for environmental sensordata <a href="https://opensensemap.org" class="uri">https://opensensemap.org</a>. Its main goals are to provide
|
|
|
means for:</p>
|
|
|
<ul>
|
|
|
<li>big data analysis of the measurements stored on the platform</li>
|
|
|
<li>sensor metadata analysis (sensor counts, spatial distribution,
|
|
|
temporal trends)</li>
|
|
|
</ul>
|
|
|
<div id="exploring-the-dataset" class="section level3">
|
|
|
<h3>Exploring the dataset</h3>
|
|
|
<p>Before we look at actual observations, lets get a grasp of the
|
|
|
openSenseMap datasets’ structure.</p>
|
|
|
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(magrittr)</span>
|
|
|
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(opensensmapr)</span>
|
|
|
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
|
|
|
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="co"># all_sensors = osem_boxes(cache = '.')</span></span>
|
|
|
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>all_sensors <span class="ot">=</span> <span class="fu">readRDS</span>(<span class="st">'boxes_precomputed.rds'</span>) <span class="co"># read precomputed file to save resources </span></span></code></pre></div>
|
|
|
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(all_sensors)</span></code></pre></div>
|
|
|
<pre><code>## 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 76.000</code></pre>
|
|
|
<p>This 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.</p>
|
|
|
<p>Another feature of interest is the spatial distribution of the boxes:
|
|
|
<code>plot()</code> can help us out here. This function requires a bunch
|
|
|
of optional dependencies though.</p>
|
|
|
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(all_sensors)</span></code></pre></div>
|
|
|
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
|
|
<p>It seems we have to reduce our area of interest to Germany.</p>
|
|
|
<p>But what do these sensor stations actually measure? Lets find out.
|
|
|
<code>osem_phenomena()</code> gives us a named list of of the counts of
|
|
|
each observed phenomenon for the given set of sensor stations:</p>
|
|
|
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>phenoms <span class="ot">=</span> <span class="fu">osem_phenomena</span>(all_sensors)</span>
|
|
|
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(phenoms)</span></code></pre></div>
|
|
|
<pre><code>## 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]</code></pre>
|
|
|
<p>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:</p>
|
|
|
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>phenoms[phenoms <span class="sc">></span> <span class="dv">20</span>]</span></code></pre></div>
|
|
|
<pre><code>## $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] 21</code></pre>
|
|
|
<p>Alright, 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:</p>
|
|
|
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>pm25_sensors <span class="ot">=</span> <span class="fu">osem_boxes</span>(</span>
|
|
|
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a> <span class="at">exposure =</span> <span class="st">'outdoor'</span>,</span>
|
|
|
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a> <span class="at">date =</span> <span class="fu">Sys.time</span>(), <span class="co"># ±4 hours</span></span>
|
|
|
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a> <span class="at">phenomenon =</span> <span class="st">'PM2.5'</span></span>
|
|
|
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div>
|
|
|
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>pm25_sensors <span class="ot">=</span> <span class="fu">readRDS</span>(<span class="st">'pm25_sensors.rds'</span>) <span class="co"># read precomputed file to save resources </span></span>
|
|
|
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a></span>
|
|
|
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(pm25_sensors)</span></code></pre></div>
|
|
|
<pre><code>## 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 26.000</code></pre>
|
|
|
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(pm25_sensors)</span></code></pre></div>
|
|
|
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
|
|
<p>Thats still more than 200 measuring stations, we can work with
|
|
|
that.</p>
|
|
|
</div>
|
|
|
<div id="analyzing-sensor-data" class="section level3">
|
|
|
<h3>Analyzing sensor data</h3>
|
|
|
<p>Having analyzed the available data sources, let’s finally get some
|
|
|
measurements. We could call <code>osem_measurements(pm25_sensors)</code>
|
|
|
now, however we are focusing on a restricted area of interest, the city
|
|
|
of Berlin. Luckily we can get the measurements filtered by a bounding
|
|
|
box:</p>
|
|
|
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(sf)</span>
|
|
|
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(units)</span>
|
|
|
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
|
|
|
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span></code></pre></div>
|
|
|
<p>Since the API takes quite long to response measurements, especially
|
|
|
filtered on space and time, we do not run the following chunks for
|
|
|
publication of the package on CRAN.</p>
|
|
|
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co"># construct a bounding box: 12 kilometers around Berlin</span></span>
|
|
|
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>berlin <span class="ot">=</span> <span class="fu">st_point</span>(<span class="fu">c</span>(<span class="fl">13.4034</span>, <span class="fl">52.5120</span>)) <span class="sc">%>%</span></span>
|
|
|
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_sfc</span>(<span class="at">crs =</span> <span class="dv">4326</span>) <span class="sc">%>%</span></span>
|
|
|
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="dv">3857</span>) <span class="sc">%>%</span> <span class="co"># allow setting a buffer in meters</span></span>
|
|
|
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_buffer</span>(<span class="fu">set_units</span>(<span class="dv">12</span>, km)) <span class="sc">%>%</span></span>
|
|
|
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="dv">4326</span>) <span class="sc">%>%</span> <span class="co"># the opensensemap expects WGS 84</span></span>
|
|
|
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_bbox</span>()</span>
|
|
|
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a>pm25 <span class="ot">=</span> <span class="fu">osem_measurements</span>(</span>
|
|
|
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a> berlin,</span>
|
|
|
<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a> <span class="at">phenomenon =</span> <span class="st">'PM2.5'</span>,</span>
|
|
|
<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a> <span class="at">from =</span> <span class="fu">now</span>() <span class="sc">-</span> <span class="fu">days</span>(<span class="dv">3</span>), <span class="co"># defaults to 2 days</span></span>
|
|
|
<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a> <span class="at">to =</span> <span class="fu">now</span>()</span>
|
|
|
<span id="cb14-13"><a href="#cb14-13" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div>
|
|
|
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>pm25 <span class="ot">=</span> <span class="fu">readRDS</span>(<span class="st">'pm25_berlin.rds'</span>) <span class="co"># read precomputed file to save resources </span></span>
|
|
|
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(pm25)</span></code></pre></div>
|
|
|
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
|
|
<p>Now we can get started with actual spatiotemporal data analysis.
|
|
|
First, lets mask the seemingly uncalibrated sensors:</p>
|
|
|
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>outliers <span class="ot">=</span> <span class="fu">filter</span>(pm25, value <span class="sc">></span> <span class="dv">100</span>)<span class="sc">$</span>sensorId</span>
|
|
|
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>bad_sensors <span class="ot">=</span> outliers[, drop <span class="ot">=</span> <span class="cn">TRUE</span>] <span class="sc">%>%</span> <span class="fu">levels</span>()</span>
|
|
|
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a></span>
|
|
|
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>pm25 <span class="ot">=</span> <span class="fu">mutate</span>(pm25, <span class="at">invalid =</span> sensorId <span class="sc">%in%</span> bad_sensors)</span></code></pre></div>
|
|
|
<p>Then plot the measuring locations, flagging the outliers:</p>
|
|
|
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">st_as_sf</span>(pm25) <span class="sc">%>%</span> <span class="fu">st_geometry</span>() <span class="sc">%>%</span> <span class="fu">plot</span>(<span class="at">col =</span> <span class="fu">factor</span>(pm25<span class="sc">$</span>invalid), <span class="at">axes =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
|
|
|
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
|
|
<p>Removing these sensors yields a nicer time series plot:</p>
|
|
|
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>pm25 <span class="sc">%>%</span> <span class="fu">filter</span>(invalid <span class="sc">==</span> <span class="cn">FALSE</span>) <span class="sc">%>%</span> <span class="fu">plot</span>()</span></code></pre></div>
|
|
|
<p><img src="data:image/png;base64,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" /><!-- --></p>
|
|
|
<p>Further analysis: comparison with LANUV data <code>TODO</code></p>
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
<!-- code folding -->
|
|
|
|
|
|
|
|
|
<!-- dynamically load mathjax for compatibility with self-contained -->
|
|
|
<script>
|
|
|
(function () {
|
|
|
var script = document.createElement("script");
|
|
|
script.type = "text/javascript";
|
|
|
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
|
|
|
document.getElementsByTagName("head")[0].appendChild(script);
|
|
|
})();
|
|
|
</script>
|
|
|
|
|
|
</body>
|
|
|
</html>
|