{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Business Understanding\n", "The amount of particulate matter (PM) in the air is an indicator of air quality which is of interest to ecologists, the European commission, different environmental agencies, and most of all, to the citizens, who are affected on many levels by the extensive amounts of particulate matter and the bad air quality. The question has gathered head in Sofia, as the city has fallen into the category of “dangerous places to breathe.” A study from 2013 finds that Bulgaria’s air is the dirtiest in Europe (*The New York Times, 2013*). And to top it all off, very few people care about environmental issues, which aggravates the problem even further.\n", "\n", "The purpose of this study is to find a robust method for prediction of the amount of PM in the next 24 hours in Sofia, based on the citizen science air quality measurements." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Understanding\n", "\n", "The data from citizens are supplied by AirBG.info. They consists of two datasets – ```data_bg_2017.csv``` and ```data_bg_2018.csv``` and they are not officially submitted to the European Comission. The measurements are carried out by volunteer citizens with their own equipment which could lead to imperfect measurements, instrumental biases and so on. We were not able to find metadata description with explanation for every columns i.e. what it measures and what unit is being used for every column.\n", "\n", "The official data are collected by the European Environmental Agency It is a subject to EU guidelines on air quality monitoring. That is why the data are mostly free of instrumental biases and less error-prone, thus very reliable. It consists of 28 datasets and metadata.\n", "\n", "There are also meteorological and topography measurements, which are standardized and reliable.\n", "\n", "The data exploration process showed that there is quite a difference between the way the data are provided in the official and the citizen datasets. The official ones include useful information about the units used but lack additional information like temperature, humidity and pressure which are present in the citizens’. During the data preparation process we’d need to unify the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preliminary Analysis (Week 1)\n", "At this stage of the process, we need to get familiar with the data, identify missing observations, fix the inconsistencies, if we find any, and prepare our data for further modelling in R." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importing the datasets\n", "As a first step, we load the libraries in R, which we are going to use later in our analysis. These are ```tidyverse```, ```geohash```, ```geoR```, ```ggmap```, ```DT```, ```knitr```, ```rgdal```, ```lubridate```, and ```maps```. Then, we import the first three datasets: the two datasets on citizen science air quality measurements from 2017 and 2018, and the topography data for Sofia. We are going to concentrate on them during the first stage of the project, and we are going to utilize the unused available information later on." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "install.packages(\"rgeos\",repos = \"http://ftp.uni-sofia.bg/CRAN/\")" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "library(tidyverse)\n", "library(geohash)\n", "library(geoR)\n", "library(ggmap)\n", "library(DT)\n", "library(knitr)\n", "library(rgdal)\n", "library(lubridate)\n", "library(maps)\n", "library(corrplot)\n", "library(fBasics)\n", "library(geosphere)\n", "library(rgeos)\n", "library(sp)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "#Import citizen data for 2017\n", "cit17 = read.csv(\"G:\\\\Misc\\\\documents\\\\stopansko\\\\masters\\\\1_semester\\\\Monthly Challenge\\\\Air Tube\\\\data_bg_2017.csv\", na.strings=c(\"\",\".\",\"NA\",\" \"), stringsAsFactors = FALSE)\n", "cit18 = read.csv(\"G:\\\\Misc\\\\documents\\\\stopansko\\\\masters\\\\1_semester\\\\Monthly Challenge\\\\Air Tube\\\\data_bg_2018.csv\", na.strings=c(\"\",\".\",\"NA\",\" \"), stringsAsFactors = FALSE)\n", "topo = read.csv(\"G:\\\\Misc\\\\documents\\\\stopansko\\\\masters\\\\1_semester\\\\Monthly Challenge\\\\TOPO-DATA\\\\sofia_topo.csv\", na.strings=c(\"\",\".\",\"NA\",\" \"), stringsAsFactors = FALSE)\n", "colnames(topo)[1:2] = c(\"lat\", \"long\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspect the data structure \n", "\n", "After we make sure that there is nothing alarming in the way the program has interpreted our variables, and string variables are stored as character, and numeric variables – as numeric, we change the class of the time variable to ‘POSIXct’ and ‘POSIXt’, so that we can work easily with it." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "VarClass17 = data.frame(names(cit17))\n", "VarClass17[,2] = rapply(cit17, class)\n", "VarClass18 = data.frame(names(cit18))\n", "VarClass18[,2] = rapply(cit18, class)\n", "VarClassTopo = data.frame(names(topo))\n", "VarClassTopo[,2] = rapply(topo, class)\n", "\n", "cit17$time = ymd_hms(cit17$time) #Changes the class to POSIXct and POSIXt\n", "cit18$time = ymd_hms(cit18$time) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Identify unique stations and merge the two datasets on citizen science air quality measurements\n", "\n", "After we find out that there are observations on 383 unique stations in 2017, and on 1254 in 2018, we bind the rows of the two datasets, as we leave the stations, which were observed only in 2017 out. It makes little sense to make predictions for stations with no observations from 2018. We do not have any information on whether these stations still exist; their observations do not capture important recent changes in the climate and the air quality, and that is why, we choose to concentrate our analysis on stations, which were observed in 2018." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "383" ], "text/latex": [ "383" ], "text/markdown": [ "383" ], "text/plain": [ "[1] 383" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "1254" ], "text/latex": [ "1254" ], "text/markdown": [ "1254" ], "text/plain": [ "[1] 1254" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "11" ], "text/latex": [ "11" ], "text/markdown": [ "11" ], "text/plain": [ "[1] 11" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "length(unique(cit17[,2])) #383 unique stations in 2017\n", "length(unique(cit18[,2])) #1254 unique geoh in 2018\n", "\n", "diff = setdiff(cit17$geohash, cit18$geohash) #finds geohash from '17 not in '18\n", "length(unique(diff)) #11 values\n", "cit17 = cit17[!cit17$geohash %in% diff,] #removes all obs from cit17 w/ geohash in diff\n", "rm(diff)\n", "\n", "cit_merged = bind_rows(cit17, cit18) #merge the two datasets together\n", "rm(cit17, cit18)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summarize availability of merged data by stations\n", "\n", "We produce a summary table of our observations, grouped by geohash. It shows the earliest and the latest observations, the number of observations, as well as the number of days between the first and the last observation. We then add this information to our new dataset and identify the locations, where fewer than 100 observations were made, or their observation window is shorter than 7 days. These locations’ value of information is not high enough, and therefore, we drop them." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "4" ], "text/latex": [ "4" ], "text/markdown": [ "4" ], "text/plain": [ "[1] 4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sum(is.na(cit_merged$geohash)) #4 missings geohash\n", "cit_merged = cit_merged[!is.na(cit_merged$geohash),] #remove missings\n", "\n", "Summary_cit_merged = cit_merged %>% group_by(geohash) %>% summarise(n(),min(time), max(time), max(time)-min(time))\n", "colnames(Summary_cit_merged) = c(\"geohash\", \"Freq\",\"tmin\", \"tmax\",\"days\" ) \n", "\n", "cit_merged = merge(cit_merged, Summary_cit_merged, by = 'geohash')\n", "rm(Summary_cit_merged)\n", "cit_merged$tmin = NULL\n", "cit_merged$tmax = NULL\n", "\n", "cit_merged = cit_merged[(cit_merged$days > 7 & cit_merged$Freq >100),] #we delete all obs. w/ fewer than 7 day long obs. window\n", "cit_merged = cit_merged[order(cit_merged$Freq, cit_merged$time, decreasing = TRUE),]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summarize on the map\n", "\n", "Now, we want to see where our stations are located in Bulgaria, as we need the observations to be only in Sofia." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "cit_merged = transform(cit_merged, latlng = gh_decode(cit_merged$geohash))\n", "colnames(cit_merged)[10:13] = c(\"lat\", \"long\", \"lat_err\", \"lon_err\") \n", "\n", "Bulgaria = map_data(map = \"world\",region=\"Bulgaria\")\n", "BulgariaMap = ggplot() + geom_polygon(data = Bulgaria, aes(x=long, y = lat, group = group)) + coord_fixed(1.3)\n", "\n", "#get unique stations' coordinates\n", "StationsCoords = data.frame(unique(cit_merged$geohash), stringsAsFactors = FALSE)\n", "colnames(StationsCoords) = \"geohash\"\n", "StationsCoords = transform(StationsCoords, latlng = gh_decode(StationsCoords$geohash))\n", "colnames(StationsCoords)[2:5] = c(\"lat\", \"long\", \"lat_err\", \"lng_err\") \n", "\n", "StationsMapBG = BulgariaMap + \n", " geom_point(data = StationsCoords, aes(x = long, y = lat), color = \"white\", size = 2) +\n", " geom_point(data = StationsCoords, aes(x = long, y = lat), color = \"yellow\", size = 1)\n", "SofiaMapBG = StationsMapBG +\n", " geom_point(data = topo, aes(x = long, y = lat), color = \"blue\", size = 2) +\n", " geom_point(data = topo, aes(x = long, y = lat), color = \"red\", size = 1)\n", "\n", "SofiaMap =ggplot(data = topo) + \n", " geom_point(aes(x = long, y = lat), color = \"black\", size = 6) +\n", " geom_point(aes(x = long, y = lat), color = \"green\", size = 4) +\n", " coord_fixed(1.3)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "image/png": 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Vq+FHyX3UO5tUghT7q1yMEBs4ynSuJDXZF6NGlfpNkHkkIXJD5NG84n46mS+FBZ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\nAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(StationsMapBG)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": 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o98VwoB0uWyGm2nANs0leRnij3dhKkcnVSD5N94AyS/lQDJ3xR7OkB6IgiQXmmK\nPR0gPREESK80xZ4OkJ4IAqRXmmJPB0hPBAUHiWftYpt41s42nQ4SryPFN/E6km2KAVL79WWu\nbAhg4soG23Q+SNVo1TZcaxfAxLV2tikASO+jLfe5jMe2tmkkiau/bRNXf9umOCA9yCzAMI0k\n8X4k28T7kWxTBJCq2ToKMEwjSbxD1jbxDlnbFAOkH81t9mwTFaxNPSBNJblMF8N06+iopPUh\neDfe88imO2jLFASk22zdBeyZRpK6QJpLcpiuS3yKkO2xH9kMBa1NYUBqaNL2lQIEVhDtCJBS\nBQisINoRIKUKEFhBtCNAShUgsIJoR4CUKkBgBdGOAClVgMAKoh0BUqoAgRVEOwKkVAECK4h2\nBEipAgRWEO0IkFIFCKwg2hEgpQoQWEG0I0BKFSCwgmhHgJQqQGAF0Y4AKVWAwAqiHQFSqgCB\nFUQ7AqRUAQIriHYESKkCBFYQ7QiQUgUIrCDaESClChBYQbSjQZCuP//xj25f+PXn6+MXASlv\nQv6ADCD9ZOV6I+rhz9dP/39+87kqf4DACqIdDYF0LYB0boDACqIdjYB0feDl+ulfFUeAlDch\nf0BSkN7/0+j+n0j/epP5P4WQqkyQHh/B3f7964uPD/cKHxB5nIkPiLQ9O8cc4QMiP7Fy3fsi\nH1mc2HTr6Kik9SF4N948Zq+VngTpen/89vkR3hhIy9Lax/5JMlbCytQD0kySz3QxTB8dHZa0\nOgT3xlvH7LaS1+tID8R8gNT90G6p1VEAt3V5oakcnVRmTEOe7WN2XMntBdn136tnwK3JuNFY\nWFOZMY0kbYL05W40dnuG7vrwl/LwbwOk+1wbNyDc/ZXscddCE6SZJLfpYpjK0UllxjTk2Txm\nz5XOv9bu82DcjDmgqRydVGZMQ57m84JeK8UA6XKpRtstwDSNJPWANJrkN10MU5kxjSStQHJv\nvAWS20qng1QTXv1g2Hls2zZNJfmZYk83YSpHJ5UZ05Bn4/vId6UQIF0uq9F2CrBNU0l+ptjT\nTZjK0Uk1SP6NN0DyWwmQ/E2xpwOkJ4IA6ZWm2NMB0hNBgPRKU+zpAOmJIEB6pSn2dID0RFBw\nkHjWLraJZ+1s0+kg8TpSfBOvI9mmGCC1X1/myoYAJq5ssE3ng1SNVm3DtXYBTFxrZ5sCgPQ+\n2nKfy3hsa5tGkrj62zZx9bdtigPSg8wCDNNIEu9Hsk28H8k2RQCpmq2jAMM0ksQ7ZG0T75C1\nTTFA+tHcZs82UcHa1APSVJLLdDFMt46OSlofgnfjPY9suoO2TEFAus3WXcCeaSSpC6S5JIfp\nusSnCNke+z7MoekAAAwHSURBVJHNUNDaFAakhiZtXylAYAXRjgApVYDACqIdAVKqAIEVRDsC\npFQBAiuIdgRIqQIEVhDtCJBSBQisINoRIKUKEFhBtCNAShUgsIJoR4CUKkBgBdGOAClVgMAK\noh0BUqoAgRVEOwKkVAECK4h2BEipAgRWEO0IkFIFCKwg2hEgpQoQWEG0I0BKFSCwgmhHgJQq\nQGAF0Y4AKVWAwAqiHQFSqgCBFUQ7AqRUAQIriHYESKkCBFYQ7QiQUgUIrCDaESClChBYQbSj\nU0Dic+2mTXyune3ZOWapz7Vb7qpmmTGNJHWBNJPkMl0M062jo5LWh+DdePOYvVaKAdKytPax\nf5KMlbAy9YA0k+QzXQzTR0eHJa0Owb3x1jG7rRQBpKVWRwHcjeKFpnJ0UpkxDXm2j9lxpTgg\ncX+ksKYyYxpJ2gSJ+yMNgnSfa+O+abu/kj1utmaCNJPkNl0MUzk6qcyYhjybx+y50vkgfR6M\ne8gGNJWjk8qMacjTfF7Qa6UYIF0u1Wi7BZimkaQekEaT/KaLYSozppGkFUjujbdAclvpdJBq\nwqsfDDuPbdumqSQ/U+zpJkzl6KQyYxrybHwf+a4UAqTLZTXaTgG2aSrJzxR7uglTOTqpBsm/\n8QZIfisBkr8p9nSA9EQQIL3SFHs6QHoiCJBeaYo9HSA9EQRIrzTFng6QnggKDhLP2sU28ayd\nbTodJF5Him/idSTbFAOk9uvLXNkQwMSVDbbpfJCq0aptuNYugIlr7WxTAJDeR1vucxmPbW3T\nSBJXf9smrv62TXFAepBZgGEaSeL9SLaJ9yPZpgggVbN1FGCYRpJ4h6xt4h2ytikGSD+a2+zZ\nJipYm3pAmkpymS6G6dbRUUnrQ/BuvOeRTXfQlikISLfZugvYM40kdYE0l+QwXZf4FCHbYz+y\nGQpam8KA1NCk7SsFCKwg2hEgpQoQWEG0I0BKFSCwgmhHgJQqQGAF0Y4AKVWAwAqiHQFSqgCB\nFUQ7AqRUAQIriHYESKkCBFYQ7QiQUgUIrCDaESClChBYQbQjQEoVILCCaEeAlCpAYAXRjgAp\nVYDACqIdAVKqAIEVRDsCpFQBAiuIdgRIqQIEVhDtCJBSBQisINoRIKUKEFhBtCNAShUgsIJo\nR4CUKkBgBdGOAClVgMAKoh0BUqoAgRVEOwKkVAECK4h2BEipAgRWEO3oFJD4gMhpEx8QaXt2\njlnqAyKXu6pZZkwjSV0gzSS5TBfDdOvoqKT1IXg33jxmr5VigLQsrX3snyRjJaxMPSDNJPlM\nF8P00dFhSatDcG+8dcxuK0UAaanVUQC3dXmhqRydVGZMQ57tY3ZcKQ5I3GgsrKnMmEaSNkHi\nRmODIN3n2rgB4e6vZI+7FpogzSS5TRfDVI5OKjOmIc/mMXuudD5InwfjZswBTeXopDJjGvI0\nnxf0WikGSJdLNdpuAaZpJKkHpNEkv+limMqMaSRpBZJ74y2Q3FY6HaSa8OoHw85j27ZpKsnP\nFHu6CVM5OqnMmIY8G99HviuFAOlyWY22U4BtmkryM8WebsJUjk6qQfJvvAGS30qA5G+KPR0g\nPREESK80xZ4OkJ4IAqRXmmJPB0hPBAHSK02xpwOkJ4KCg8SzdrFNPGtnm04HideR4pt4Hck2\nxQCp/foyVzYEMHFlg206H6RqtGobrrULYOJaO9sUAKT30Zb7XMZjW9s0ksTV37aJq79tUxyQ\nHmQWYJhGkng/km3i/Ui2KQJI1WwdBRimkSTeIWubeIesbYoB0o/mNnu2iQrWph6QppJcpoth\nunV0VNL6ELwb73lk0x20ZQoC0m227gL2TCNJXSDNJTlM1yU+Rcj22I9shoLWpjAgNTRp+0oB\nAiuIdgRIqQIEVhDtCJBSBQisINoRIKUKEFhBtCNAShUgsIJoR4CUKkBgBdGOAClVgMAKoh0B\nUqoAgRVEOwKkVAECK4h2dAxICH1Z8RspQYDACqIdAVKqAIEVRDsCpFQBAiuIdgRIqQIEVhDt\nCJBSBQisINoRIKUKEFhBtCNAShUgsIJoR4CUKkBgBdGOAClVgMAKoh0BUqoAgRVEOwKkVAEC\nK4h2BEipAgRWEO0IkFIFCKwg2tEpIPG5dtMmPtfO9uwcs9Tn2i13VbPMmEaSukCaSXKZLobp\n1tFRSetD8G68ecxeK8UAaVla+9g/ScZKWJl6QJpJ8pkuhumjo8OSVofg3njrmN1WigDSUquj\nAO5G8UJTOTqpzJiGPNvH7LhSHJC4P1JYU5kxjSRtgsT9kQZBus+1cd+03V/JHjdbM0GaSXKb\nLoapHJ1UZkxDns1j9lzpfJA+D8Y9ZAOaytFJZcY05Gk+L+i1UgyQLpdqtN0CTNNIUg9Io0l+\n08UwlRnTSNIKJPfGWyC5rXQ6SDXh1Q+Gnce2bdNUkp8p9nQTpnJ0UpkxDXk2vo98VwoB0uWy\nGm2nANs0leRnij3dhKkcnVSD5N94AyS/lQDJ3xR7OkB6IgiQXmmKPR0gPREESK80xZ4OkJ4I\nAqRXmmJPB0hPBAUHiWftYpt41s42nQ4SryPFN/E6km2KAVL79WWubAhg4soG23Q+SNVo1TZc\naxfAxLV2tikASO+jLfe5jMe2tmkkiau/bRNXf9umOCA9yCzAMI0k8X4k28T7kWxTBJCq2ToK\nMEwjSbxD1jbxDlnbFAOkH81t9mwTFaxNPSBNJblMF8N06+iopPUheDfe88imO2jLFASk22zd\nBeyZRpK6QJpLcpiuS3yKkO2xH9kMBa1NYUBqaNL2lQIEVhDtCJBSBQisINoRIKUKEFhBtCNA\nShUgsIJoR4CUKkBgBdGOAClVgMAKoh0BUqoAgRVEOwKkVAECK4h2BEipAgRWEO0IkFIFCKwg\n2hEgpQoQWEG0I0BKFSCwgmhHgJQqQGAF0Y4AKVWAwAqiHQFSqgCBFUQ7AqRUAQIriHYESKkC\nBFYQ7QiQUgUIrCDaESClChBYQbSjY0AKqn+dPcDzyr9C/g2MFQApg/KvkH8DQJI/wwzKvwEg\nyZ9hBuXfAJAQOl6AhJCDAAkhBwESQg4CJIQcBEgIOUgQpOs/evx3/bVryxhH1gpnzdUv0UN4\n++vG14oiSNdf/7je/vLw5wTnV/ZXePxaXIkewtufrmXzEAApoAApgLYLv5YvA9JPXat///pz\njiN8194KOfYQPIRrAaSfJeR4dP5TuiAlPoSvBtL7juv/SEz3Xfi4wrX18Dyo8m+wWuFaviRI\n5dOyW18LrM1xU/1Gam2w+lpcVSt8/q+8LwDStfNPgbU9biaQmhusvhZW9QrX66+HpV8EpOrH\nR/NrgbUeN9ezdgobNL5nvs5vpOrc6q/lOcLM34bGBvEX2P4+Kl8IpI9fv/dXpje+FltbK+S6\nsiH/BtsrfPzrK1zZgNAJAiSEHARICDkIkBByECAh5CBAQshBgISQgwAJIQcBEkIOAqSMWji2\naOJEMgqQwokTyShACidOJKN+gvT992X5/fvPv33/93L94+3/8P3b8ttfYHaC6Dyj3lD5+7r8\no+vfb3/7+cc/Pr4GSCeIzjPqDZU/lm+lfHvDZ1m+/V3+XK6l/Oefr/39DZBOEJ1n1Bsqvy3/\nPKz7vvz286Hd569xqK8XnWfUGyrvuLT+hF4sOs8oQAonOs+o+qFd9TUO9fWi84yqn2yovsah\nvl50nlH109+fvwZIJ4jOM6p+Qfb+tW/Lb/8FpBNE53paEnzYlZwASUnL8r+3/1D6/ew5vqAA\nSUl/vP8n0vez5/iCAiQp/fnbr/9uQi8WICHkIEBCyEGAhJCDAAkhBwESQg4CJIQcBEgIOQiQ\nEHIQICHkIEBCyEGAhJCDAAkhBwESQg4CJIQcBEgIOQiQEHIQICHkIEBCyEGAhJCDAAkhBwES\nQg4CJIQcBEgIOQiQEHIQICHkIEBCyEGAhJCDAAkhBwESQg4CJIQcBEgIOQiQEHIQICHkIEBC\nyEGAhJCDAAkhBwESQg4CJIQcBEgIOQiQEHIQICHkIEBCyEGAhJCDAAkhBwESQg4CJIQcBEgI\nOQiQEHIQICHkIEBCyEGAhJCDAAkhBwESQg4CJIQcBEgIOQiQEHIQICHkIEBCyEGAhJCDAAkh\nBwESQg4CJIQcBEgIOej/ATYRSaIwpD4sAAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(SofiaMap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is visible that the dots form an area with an almost perfect square shape, which means that if we use the maximum and minimum longitude and latitude of these dots, we are going to have a good idea which the stations located in Sofia are. We create two different datasets; one contains the observations from the stations which are located exactly in the area, isolated by the above map, and the other has a margin, which allows for some stations, which are located a little further, to enter the dataset:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "SofiaStationsCoords = StationsCoords[((StationsCoords$lat+StationsCoords$lat_err) > min(topo$lat) & \n", " (StationsCoords$lat-StationsCoords$lat_err) < max(topo$lat) &\n", " (StationsCoords$long+StationsCoords$lng_err) > min(topo$long) &\n", " (StationsCoords$long-StationsCoords$lng_err) < max(topo$long)), ]\n", "\n", "SofiaStationsMap = SofiaMap +\n", " geom_point(data = SofiaStationsCoords, aes(x = long, y = lat), color = \"#666666\", size = 5.5) +\n", " geom_point(data = SofiaStationsCoords, aes(x = long, y = lat), color = \"#FFFF66\", size = 3.5)\n", "\n", "citSofia = cit_merged[cit_merged$geohash %in% SofiaStationsCoords$geohash,]" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "image/png": 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J8zgKQ/Y3aFZ+3o9ApX6IYQH6XZfd8b1nFMGklphU5Ld1DpUrnH20QGlMhuQA/P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Uj0PYCSH6SsOmj2/ISsChKxRikKUiC8VpGEA1NoHQdB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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(SofiaStationsMap)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "image/png": 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nFV3EfUi8j1CxauXf9gnCq/W/9gw71r1AGXXja74d41v1v/oG3fQUZTl1cNQ3u9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"text/plain": [ "Plot with title \"\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Creating a dataset with the observations within some margin around Sofia\n", "SofiaStationsCoordsMargin = StationsCoords[((StationsCoords$lat+StationsCoords$lat_err) > (min(topo$lat)-0.025) & \n", " (StationsCoords$lat-StationsCoords$lat_err) < (max(topo$lat)+0.025) &\n", " (StationsCoords$long+StationsCoords$lng_err) > (min(topo$long)-0.025) &\n", " (StationsCoords$long-StationsCoords$lng_err) < (max(topo$long)+0.025)), ]\n", "\n", "SofiaStationsMapMargin = SofiaMap +\n", " geom_point(data = SofiaStationsCoordsMargin, aes(x = long, y = lat), color = \"#666666\", size = 5.5) +\n", " geom_point(data = SofiaStationsCoordsMargin, aes(x = long, y = lat), color = \"#FFFF66\", size = 3.5)\n", "\n", "citSofiaMargin = cit_merged[cit_merged$geohash %in% SofiaStationsCoordsMargin$geohash,]\n", "\n", "corr = cor(citSofia[,c(3:7)])\n", "corrplot(corr,method = \"square\", type = \"upper\", addCoef.col = \"black\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Week 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find the closest point from the topography dataset for Sofia to the stations\n", "Since the topography dataset contains information on the elevation of the 196 points in Sofia, and we believe elevation might turn out to be a key factor when predicting the particulate matter in the air, we find each station’s closest point and assume that the station’s elevation would not differ too much from the point’s one. As we are doing this, we make clusters of stations which share the same closest point, and we use these clusters in order to proceed with our analysis. " ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "sp.SofiaStationsCoords = SofiaStationsCoords\n", "coordinates(sp.SofiaStationsCoords) = ~long+lat\n", "for(i in 1:nrow(topo)){\n", " topo$pnt[i]=i\n", "}\n", "sp.topo = topo\n", "coordinates(sp.topo) = ~long+lat\n", "Distance = gDistance(spgeom1 = sp.SofiaStationsCoords, spgeom2 = sp.topo, byid=T)\n", "minDistance = apply(Distance, 2, function(x) order(x, decreasing=F)[1] )\n", "SofiaStationsCoordsElev = cbind(SofiaStationsCoords, topo[minDistance,], apply(Distance,2, function(x) sort(x, decreasing = F)[1] )) \n", "colnames(SofiaStationsCoordsElev) = c(colnames(SofiaStationsCoords), 'n_lat', 'n_long', 'Elev', 'pnt', 'distance') \n", "SofiaStationsCoordsElev$lat_err = NULL\n", "SofiaStationsCoordsElev$lng_err = NULL\n", "colnames(topo)[1:2] = c('n_lat', 'n_long')\n", "\n", "SofiaStationsCoordsElev$distance = NULL \n", "citSofia_elev = merge(citSofia, SofiaStationsCoordsElev, by=c(\"geohash\",\"lat\",\"long\")) \n", "citSofia_elev$lat_err = NULL\n", "citSofia_elev$lon_err = NULL\n", "citSofia_elev = citSofia_elev[,c(1, 15, 14, 2:13)]\n", "citSofia_elev = citSofia_elev[order(citSofia_elev$n_lat, citSofia_elev$n_long, citSofia_elev$geohash),]\n", "colnames(citSofia_elev)[2] = 'Clust.ID'\n", "\n", "clusters = SofiaMap +\n", " geom_point(data = SofiaStationsCoordsElev, aes(x = long, y = lat), color = \"white\", size = 5.5) +\n", " geom_point(data = SofiaStationsCoordsElev, aes(x = long, y = lat), color = SofiaStationsCoordsElev$pnt, size = 3.5)\n", "\n", "\n", "rm(cit_merged, citSofia, Distance, SofiaStationsCoords, sp.SofiaStationsCoords, clusters, sp.topo, topo, i, minDistance)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "image/png": 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SIcl3sqRedC7mvcvlCVJI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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(SofiaMap +\n", " geom_point(data = SofiaStationsCoordsElev, aes(x = long, y = lat), color = \"white\", size = 5.5) +\n", " geom_point(data = SofiaStationsCoordsElev, aes(x = long, y = lat), color = SofiaStationsCoordsElev$pnt, size = 3.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summarize the information by clusters\n", "\n", "Checking out whether we have any odd values, or even whole clusters with odd observations is very important at such an early stage. That is why, we make a summary table which shows key statistics for the key variables, such as min, max, first quartile, third quartile, etc. From the table, it is visible that we face problem of outliers in almost every band, be it the values of the minimum or maximum temperature, pressure, or humidity. In the next step, we are going to take care of those outliers." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "Summary_clust = citSofia_elev %>% group_by(Clust.ID) %>% summarise(obs = n(), Elev = mean(Elev), stations = length(unique(geohash)),\n", " min.p1 = min(P1), first.quart.p1 = quantile(P1,.25), third.quart.p1 = quantile(P1,.75), max.p1 = max(P1), \n", " min.p2 = min(P2),first.quart.p2 = quantile(P2,.25), third.quart.p2 = quantile(P2,.75), max.p2 = max(P2), \n", " first.obs = min(time), last.obs = max(time), days = max(time)-min(time), \n", " min.temp = min(temperature),first.quart.temp = quantile(temperature,.25), third.quart.temp = quantile(temperature,.75), max.temp = max(temperature), \n", " min.hum = min(humidity),first.quart.hum = quantile(humidity,.25), third.quart.hum = quantile(humidity,.75), max.hum = max(humidity), \n", " min.press = min(pressure),first.quart.press = quantile(pressure,.25), third.quart.press = quantile(pressure,.75), max.press = max(pressure))\n" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Clust.ID | obs | Elev | stations | min.p1 | first.quart.p1 | third.quart.p1 | max.p1 | min.p2 | first.quart.p2 | ... | third.quart.temp | max.temp | min.hum | first.quart.hum | third.quart.hum | max.hum | min.press | first.quart.press | third.quart.press | max.press |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 3964 | 1184 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 16 | 25 | 33 | 68.00 | 88 | 96 | 0 | 0.00 | 0.00 | 0 |
8 | 3708 | 822 | 2 | 0 | 0 | 8 | 453 | 0 | 0 | ... | 22 | 39 | 0 | 48.00 | 80 | 100 | 0 | 91944.75 | 92547.00 | 93323 |
9 | 20603 | 771 | 6 | 1 | 9 | 25 | 575 | 1 | 6 | ... | 19 | 35 | 0 | 0.00 | 74 | 100 | 0 | 0.00 | 93188.50 | 122204 |
10 | 3410 | 733 | 2 | 1 | 10 | 27 | 413 | 1 | 7 | ... | 19 | 32 | 0 | 59.25 | 100 | 100 | 0 | 92524.25 | 93334.00 | 120303 |
11 | 5319 | 686 | 2 | 0 | 7 | 16 | 243 | 0 | 5 | ... | 24 | 39 | 13 | 44.00 | 73 | 100 | 92502 | 93675.00 | 94266.00 | 120379 |
12 | 4674 | 672 | 2 | 0 | 8 | 18 | 236 | 0 | 5 | ... | 22 | 47 | 0 | 48.00 | 82 | 100 | 0 | 93612.25 | 94327.75 | 119021 |
13 | 3256 | 640 | 1 | 2 | 8 | 14 | 64 | 1 | 4 | ... | 25 | 39 | 16 | 40.00 | 68 | 96 | 92750 | 93883.00 | 94413.00 | 95181 |
21 | 14408 | 811 | 4 | 0 | 7 | 22 | 752 | 0 | 4 | ... | 19 | 49 | 0 | 55.00 | 75 | 100 | 0 | 91942.00 | 92767.00 | 94076 |
22 | 7653 | 752 | 2 | 0 | 9 | 28 | 449 | 0 | 5 | ... | 14 | 39 | 0 | 0.00 | 69 | 100 | 0 | 0.00 | 93319.00 | 117496 |
24 | 2649 | 670 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 20 | 29 | 27 | 59.00 | 83 | 99 | 0 | 0.00 | 0.00 | 0 |