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GRASS/R interface workshop and demonstration

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and I think there are indeed we they them so we have now about them in a break and then that may change in the program in the graph half of of all all came out the they want do OK all K that have been so then we can validate the input graph adding made which and then on the OK and the I'm going to good and then
in the in the in the end you have to be a lot of and and and thing and the of 1 thank you and and the for the the have the and the I you I but for those of you who would prefer a break in the battery opportunity to leave for those of you who don't don't need this at the moment that the reason that doing this is a demonstration of timekeeping very short it because when I do my paper tomorrow I'll be demonstrating it and talking to someone the organizing committee I was told that it might be an idea to demonstrate it as well rather than talk about it but there will be people here who don't need it already know the people already use our use grass don't need to know about finding those that what actually asked with opportunities to meet people who could give me that feedback on what should do because I don't know what should do I have the impression of what I find useful whether what I find useful and this is mostly the teaching what other people would find useful research I simply don't know whether be very great people again the back by the American people people asking questions and that that's great but I think a lot of people here have interesting ideas and I'd be very grateful for your ideas of the main purpose of doing this is to get feedback from you not necessarily straight away now but possibly also afterward at the way in which extracted the demonstration to set up a number of demonstrations scripts which I will run through so that I can show you things on the screen rather than that they demonstrate how to install interfaces someone wants me to show them how to install the interface and I can do that separately some other occasion what's the microphone OK but OK that the the fact that things that of the 1st assumption which I have made is that they're using our are in graph using our grass so you have to start grass 1st and then start our from inside this is not necessarily a sensible an assumption that is the assumption that I made the 1st thing I'll do is to start France I'm running on the table released from running on current stable release and ah is also the current stable release of that are going to use of the 1st part of the demonstration using Spearfish the standard dataset again downloaded from the the the the the the the the site and I will make my demonstration script available as well that if somebody wants to run through the same thing or to criticize improving it but 1 now in graphs and allocation is the is the the in dataset and what I'm going to do is run as a shell script which will then call are when running the show scripts that up my location because if I had been using a different window at different resolutions and maybe I wanted to get back to where I was because of our get back to where I was before by running a simple shell script which which you will want to you to see what what what he does when we're going to we're going to what is happening now is that I my region interest and my resolution and what I'm going to be looking at is the rest the interface restaurants with which does most of the sites interface is simple and could just as easily be done by writing the data reading in yourself but it's the restaurant interface where where most of the actions taken place but we continue by starting our and I has been restored lot of previous work like that somebody got a red pointer what I put in the in the in in
the workplace is very small a function which has re recorded just in case I forget the name of the file that I need to use a lot of demonstrations will be run like this what then is is doing is simulating an interactive session what I'm trying to do is enter all this was what I'm doing is simulating sections that instruction is yeah but I could probably also this which would be the 1st instruction and loading the library this assumes that you have installed I running grass you need to have installed on you need to install the interface given that that's the case if you look at the library of loaded the library you need to get the metadata metadata makes 1 assumption which is not necessarily a sensible assumption that if the assumption that I made is that when you are running within grasp you stay within 1 went over region and a 1 resolution you don't make those because in general my assumption has been if you're doing data analysis with rest is then you want them to be in exactly the same resolution for exactly the same window otherwise you be drawing conclusions from what you know which called apples and pears you comparing things which are not similar but in general you would want to have things which is similar to that I make sure that you do that because you can't do it any other way there arguments for doing it in different ways that what you what is returned by this function genetic progressed metafunction is an object that which here has given the name G and which has a class of geometric that the gene that the object and it has a number of things inside which can be retrieved by the function summary Summary this gives you the same thing that you would get by running she dropped region minus the but what we know more or less where we are what we're doing now is retrieving the soil pH the rest from Spearfish understands ph rats that is in fact um categorical rest because it's recoded from the soils uh it's not what you think he did not the measurements of soil pH values taken from the area at the reclassification of sort that that I transferred it in 2 different ways the 2 modes of transport rest of data between graphs and our our grass 1 is numeric the others categorical in our categorical variables called factors the called factor here is called a factor and if you then do this this is the function get rest we give it the the the same names but they would have been in the process if they're not then it will come back and tell you you tried to select the rest of their which doesn't exist as you see these are these are the same name so what about have the same layout becomes the point which is perhaps not the other thing to do what I told him to do in this argument is the last time I wanted to be transferred just as a numerical variable In the 2nd time I want to transfer within category labels category labels were then used as the labels of the fact that and when I then look at the structure of this is a small function which looks at the structure of the object which are retrieved it and tells me that it's a list of 2 objects 1 of which is a numerical vector and the other of which is a factor which I'll stick level and it then shows you that the the 1st values which are retrieved which are not available it was important that there are grass interface to be able to use proper not available or not the number data that you couldn't use 0 this is not a number and not available at the top of if we then take a summary of the 1st of these lists components the numerical 1 we get a numerical summary and a numerical summary is the that the minimum value the maximum value of the upper and lower quartiles the median and the mean and the number of not available observations which were present if you on the other hand you a summary of the fact that you get something which looks like this which shows you the camera just simply does count of the number of instances in each of the of the category labels will proceed from from that there they're on that is it it is visible or not visible and it does that I can't change anything in become change then I think it is am they did that if if if I tried it report a given that the precise but it will be complicated and as I said the scripts will be downloadable so that you can you can you can you can do it yourself the both and you get the candidate it would take
minutes or half a minute that said that this the
summary for a factory is exactly the same as making a frequency table of the but you were making a frequency table which gives us exactly the same way that the same result yeah what we might on the other hand like to to do is to
write this short function and this is 1 of the is it is it
any better when that last line yeah OK so that we have a solution I it is that someone there with with the but I I think that would that would that be yeah the by writing this small function we've written a small function here are also small function here too I can that the that cannot be the a cell can interact you find exactly the same in this chapter about it's just that it's not written the function and is the subject because the kind of thing that that you might simply do something like this and leave it in the directory and loaded at the beginning that you have the function available so that to do these these these kinds of things that this would be the same as doing in our report units 3 quotes actors rather than unity that we we can then deal what Instagram there's a story of the bunch if it had it had been a continuous variable might calling it is the grammar been correct this is bar chart because it's actually a factor so now the thing about plot all of this of these variables this is this is what we've done here we can now see which of the which of the classes with which we which what what the area yeah the hectares for the class is the class as 1 of the other exercises which is done in the books of season referring so that the grass for that is to do a a boxplot all the use of the altitude with not and the number of things in jump rather than this is this this is question looking at soil but we retrieve yeah we retrieve he had a number of other layers we retrieved 2 layers 1 of which is the elevation and the 2nd of which is the the soil categories so the for the innovation it doesn't have a categories for all but the so that category so that it will be a rover and enough factor and add in the in in the book which is another source of information at the sort of thing that is in about to the United States the summary function to patient data but chopping it up by the so soil types I will present the results in this in this object here but then they're looking to find what the areas after the different soil types and with constructing a new version of our data here by summarizing it misogyny output which has come from our 1st 1st attempt in order to make it as humanly readable the previous 1 was not very human readable and I think this does quite well in terms of readability also compared to the box plots in the book I tried to build boxplots on the screen but I thought that it would be even less legible than this 1 that for a given for a given for a given a soil some you can you can hear it extract the minimum the altitude the lower lower quartile median mean altitude the upper quartile the maximum value and you can
also see what the area in hectares of that social classes the another display which I've also looked at 1 of the advantages and perhaps the main advantage from the air interface things spatial data with R is the richness of the graphical presentation so what's which is available and kind of data that they plot which is available is called adult plot the adoption of the here constructed at a slightly different version of top jobs but here are change the order in which the it in which the soil types are displayed but on the table which you could see here they were displayed in by alphabetical order of some of class but yeah I ordered them from the top from the 1 which is the largest cities over 4 thousand hectares down to the the least important 1 which is invited but I've ordered them in a different way to way the ordering is done is here and like this the quite need that you can do it display than ordinary order in a different way and you'll find that if you read it cover they're all Cleveland that quite a lot of the attractive results of data visualization achieved with very simple means particularly reordering the way in which the data being displayed which gives you a handle which allows you to to the how that was what was going on in the bigger ones of the higher ones of along which are driving the the data generation process that here all that has been done is to order the data frame of the ordering is stored in small vector you know and then when when the results of presented there will always be ordered by reordering the vector in the order of the the the size of that particular sample class in I was but using some of the graphical toolbox some of the reordering things to get hold of a hold of this data the data in the table here is not very large reordering it is not a problem if you're reordering 300 thousand arrested cells and it may take a little longer than that for a table like this which is already a result of analyzing the the the the the the rest of this is this is much of an issue I've never liked that to zoom in
and look at another graphical tool but what I'm what I'm doing here is changing the error area of interest to the southwest of the patient changing the resolution their so the resolution of the books they that books like stated that there were recorded the damage by the mountain pine beetle that in certain areas and most of it is concentrated in the south west of what I want to do now to look at whether at the data the dataset contains contains information which would help me to to 1
evaluate the demands and find because we come out of our change in that region and the resolution and come back into what we don't want to confuse yeah but the
2nd demonstration that we will out again we're reading in the metadata again pressure recurrent repression metadata and we see that we have many fewer many fewer that we're now in in in 60 columns
and 60 rows rather than than many more as we were before and printing out the the yen and all the rest the cells which
are available to us and 1 which which I'm going to be particularly interested in is if I think it's side will see when we get to it and they all wanted to look at elevation I want to look obviously at it vegetation cover because if the mountain pine beetles perhaps you would expect to find that the mountain pine eating finding coniferous areas and not in others and that was the this is not always the case that the that that that could be a matter of classification it could be that there is some In the 1st phase of clients in the deciduous woods as well but that said wanted also to look at slope and aspect that I'm not a lot of biology so that if anybody really knows about mountains i'm because please bear with me what I'm doing is in in this little row here is this instruction here is important but like to In the elevation vegetation cover the aspect the slope and because although these rosters have different resolutions in the underlying database but because I'm using the current window all of the data that imported there at the same time so that you see that if you look at the structure of my bob subject which are imported that all of the all of the variables have the same length so that important books like to which is the fact that the elevation vegetation cover which is also a factor the aspects the slot and when I tabulate them I find that I have a no no data for the bug although it was a category in grassland to get rid of it there's no no data for the vegetation cover the market area irrigated agriculture but there are a number of other categories which represented rangeland coniferous forest deciduous forest mixed forest and so I have to do something to get rid of me they're get rid of the theory and the level fluorosis small function which drops the empty level because I will need them later on I'm dropping the energy levels creating a new variable vegetation factor we dropped into levels and doing the same thing for the box here that I only have to levels investment 1 2 3 4 5 levels vegetation and if I do a table of the 1 factor than the other I get something like this there are in the very very few the observations is all there very very few very low so what was the 1 with a little
bit further the there are very few perhaps happily rosters where mountain pine beetle damage is recorded at all but this is the text about the mountain pine beetle where should occur and that's what it does and 1 of the relatively uh pleasant graphical tools which can be used for data analysis is is a mistake what we try a mosaic plots this 1 and here we using 1 of the structures which could talked about before which is a formulas that I'm saying that I believe that in the by mountain pine but damage is related to vegetation and show me what it looks like the and they're both categorical variables like the city to the table or is there some kind of log-linear models but this is a mosaic plots the mosaic plot it what we want to invite the for him it it was a lot of people and expressed in in the area of the whole what happens you have what was is the going be rangeland very small the it would have been easier to choose a dependent variable where there was a larger number but at least you get some impression of what can be done we look again dividing by but damage we look at the variation that doesn't seem to be anything and anything better if you look is that this this very very little difference between the 2 but it does seem to perhaps being slow and the dust perhaps seem to be an aspect that he could be because the coniferous woods they also grow in the same kinds of places that what you will find here the splitting by so this is sort divided into 3 different for different categories needed the highest slope it does appear that has the highest slope coniferous woods other ones with but with the book but whether that simply because more coniferous on those or not it is it is it is another question and this is then the final mosaic plots that showing the they're showing slope by coniferous which indicates that there is more can rests on the steeper
slopes than the than otherwise so that's a very short so but some of the some of the features there are 1 or 2 other points which I I could take up if someone felt like staying so have a 2nd demonstration which covers the the period must take led to the last as well OK so now I have to leave I have to leave grass because I'm changing location but this time I want to take the master database which is also available from the website that can be downloaded that has an empty location because the data is essentially all in the R-package and here I'm going to do yeah OK must is that the classical teaching tool just statistics it's the dataset the central dataset in the that borrow and McDonald book the principles of geographic information systems and covers that pollution along the banks of the river Maas just north of mastery so that we're just not the Maastricht on the border between the Netherlands and and and Belgium in in the previously heavily industrialized area and the students and researchers from the University of Utrecht have done a lot of field work in the area of trying to examine sentiments sediments from pollution along the banks of the river in relation to flooding but they assume that flooding will carry the sediment onto onto the the the the the the land areas around the the dataset is included what a version of the dataset is included in g star angiostatin another of the prime teaching tools that for these these kinds of these kinds of things get that and the the
the reason for this is that it would be possible to write a shell script setting up a location from our that is that I think that the but then you would to go backwards from where I started I said that you start grasp then you start are now if you're going to happen if you're going to initiate a location from inside out and you can't have grass running when you start because then it will be running multiple sessions but that you have yourself to initiate that that the the the location simply by unpacking the top island in the data directory of your and you have uh emulsification to run on unless that they can't do it and this is also the reason why I can grow come around the examples from the package within quote and quote phonics is an excellent testing system because it assumes that you're running graph in the master location in order to run the examples but that given that we should be OK but the data is included in the interface package with the grass location is not if it's it's a matter of layers where you start it explained in the book as well the it's explained in the book it's all there you have it it it included a whole that we we do if people want to what and we can we can take it down afterward and I can show you what happens to you you download the the the the location tower you unpack it in your graph data directory and the what we have here is is the is is the is the data for the location we're starting our again but that's what I'm not going to do is you show that that they begin to think about the only interface you could use the other the other possibilities and and I'd like to also to to give them appropriate exposure so we doing the same thing as before they're getting the metadata so we now have the metadata which is you see is exactly the same as the metadata which we got from running energy region minus B so that it it's it's exactly the same as today but what I've done here is displayed can and then the registered that not registered in UTM rather than being registered in the original Dutch recording that comes from the the that right that 1 of 1 of the students it was yeah it so that there there are possible errors that because it was also scanned in
addition to being rejected yeah but that this is like like like like so that what what we've done here in graphs is it's coming out and we put x is on using using the site to put on the the exported projected coordinates from the grasp this type of data which are included in the R package because that they're just the tabular data that they're not problem at 1 of the questions which certainly that markets after white wine in the examples of the package doing is strange classification for the but the the thing tank how do I know what levels of pollution is serious or not so that that tried to answer this question I checked with Swedish in canadian values for what users can be a given to land which is polluted with missing link is necessary to that below a certain level is bad and over a certain level is bad and between those levels is good but if you plot the you'd contact and people you want to have a set amount when you have too much it indicates that it probably industrial pollution or some of the pollution that the values which are now established of that background saying can be anything between 0 and 100 million that agricultural recreational uses OK but 100 200 and residential industrial 200 390 and so on now the the the idea has been to use this relatively it heavily used it's being used as a gravel pits and it's it's so it's industrial uses as a kind of idea lots of industry around it to reclaim disparate recreational uses the idea would be to restore it they're going to restore it so to these kinds of these kinds of levels that when we look at a classification of the of the data which a collective we find that at some of the point these are the ones which which in here it tend to be OK but there are quite a lot especially along the river bank which are
not that here I'm stepping aside and using a different interface this is the interface for they're bringing in PPN you can bring ppm this possible pixmaps portable any map into our by using in place yeah what has happened is that is that I have to use have system command the right a possible and from graph and then reading the same file into our as possible and this
allows me to the transfer of the the actual color values of the the the color values here and are essentially and essentially the same values and I'm now going to use like grass interfaces with the that the ah grass functions to over plot this would be categorized pollution data of there's this is using the the
ending which is which is based this diagram this is not the end of the possibilities because relatively recently in heat released as an interface between or an experimental interface between R dial but now using of he GDL interface and G. doubt compiled with the graph optional grant element that's actually the same the the same scanned picture which markets and
uh as a Christmas present now it it takes a little bit longer but this is because I forgot something but when I did distracted OK budget go I didn't realize that the G interface doesn't look at the window it just goes straight to the whole the the original data set and there's give me the a lot like it it looked at the original resolution of the data and give you a whole lot although I wasn't that wasn't what I thought I wanted I thought I want to the yeah what I understood as my region resolution but I generate narrow a new 1 and I'm going to do it again and now it's it's doing what it what I wanted to do but resampling has lost me some of the the red color inside the inside the bubble but now leading to the G double indicating that the number words as well you know what I'm suggesting is not the only 1 and it's possibly not even go the it's not even believe that it is not the 1 most people will want most of the time it's a particular view no that the the standard analysis in in borrowing and McDonald goes through a series of steps where they look at the flood plain frequent that the flooding frequencies at all different all of the different sites where they recorded the values and they said they tabulate these and this is an example of how you replicate the data in the borrowed and borrowed tunnel and this is teaching use ma borrower McDonnell chapters on in my teaching and so I needed to be able to indicate that the students how given the data which tabulated the book you would use tools so get hold of it now it's possible to do averages and things within and within graph but perhaps there are other ways of doing it which a more flexible and turned using using this kind of and interface perhaps was more flexible the 1st thing that that borrower McDonald do is tabulate mean values but this flood frequency less than it is more than 5 years between 2 and 5 years and and flooding it annual so that there would be the here the standard deviation however you never commented on in the book about should have been standard deviation as you can see here for the annual flooding is very much larger than the standard deviation to the other but they don't notice they don't even look at that even though it account remember that they look for such the focus on the main this evening is very different and they then do the same with the log-transformation because they that they point out that the distribution of the distinct pollution that's really this is far from being being normal they do the task of the difference between the more than 5 years 2 and 5 years classes that thing really division between the annual flooding in the modern annual flooding and they do some analysis of variance again finding that there are they're quite
clear differences whether you look at the raw data of the log of the raw data between between these these these these values the the frequency plot they have these histograms because this is a continuous variable in both cases is the log of the zinc BPM using and here we've overplotted the means of the flooding frequency classes but this is this is what is this program doing the the R code that we're using here is starting to look at that little bit more complicated but it's because I wanted some eigenvectors but I wanted to say to choose my colors rather than using the default ones doing things like that there's more code there than you need the but it could have been done in in in in in a more compressed way the 1 of them might need things and and as we've seen in in in the presentation and use of density diagrams this is important but their approach the one-dimensional two-dimensional the routines also enough you can get a feel for what you're dataset doing and it does look really in fact is that we're dealing with the bi-modal distribution something here which is indicating that the distribution of the annual flooding data is very different from the distribution of the of the there more than on you the but input data with respect to the to the things that doesn't value that this is a year the empirical cumulative density function which also find in touched on in in in the book which is a very useful way of looking at the distribution and I simply color coded this year to show that the flood classes with the Doppler ones at the annual ones and that you'd expect this for the raw data and this is the log the raw data and the log you see that most of the obviously most of the annual ones the the high end of the day to scale the but there the high end of the day to day but there at that at least 1 down here which looks very strange yeah I'm not saying that this was something other than it but I thought it would be useful to to go back to the map and see where is this is this white diamond here it's an annual flood but it it has a very very low it's among the lowest 3 or 4 pollution values that there's something to be talked about this 1 it doesn't it doesn't seem to me that it it sensible to think about this is an old channel but I don't know whether it was sensible to classify as annually flooded and if you look at the diagram in the book this area is not coated manually flooded hold it is flooded less frequently than every 5 years or more
than every 5 years so that the something in the data which is which is which is perhaps a little little concerning and if you then do a strip chart or box plots of the distributions you see that the distributions are very different it's not that that they have the same variance but to replace different places along the scale of this but there are actually different distributions that doesn't mean that we should drop the rest of the they're borrowing McDonald chapter of geostatistics but at least it raises the question about how appropriate it is to model the way they carry on to do and that and actually be interesting drafted at at what he thinks about the data that that that that that using the and depends mostly on
culture of of g that but where the interface is at the moment it it makes an assumption and the assumption is that is not justified by anything other than my teaching practice that that you choose your grass grass location that the region and resolution you start grass around are from within grass and you stay within the same region resolution the whole time I don't know whether that's relevant assumption but that that's the the way it works the moment beside but doesn't that doesn't really affected so all that will happen if you try and read insights which have coordinates outside your current region is that it will ask you do you think this is sensible but it doesn't do anything more than that and so far there isn't a vector of talking about tomorrow are there any direct questions so of of the of the around the please asked me if things talk to to me and then give me the direction of the wealthiest which recognition this which function there but how and is it possible to the as quick as part the world try to use it for you to get started at the how well understood that the tutorials but that
you want to say to each article quot that the court that each function has to be documented but if you want to use the geometric function you write question mark gene at leveled loaded the library of will have a did that was a hell of whatever was asking for the 3rd month of page for it and it is 1 of the nice things about In this is typically trying to write something in work that that that now but then gives you the help page but and and in in in in ah everything is a question mark name function or if it's a data set an example dataset in exactly the same thing in this case it was the question what you see which is the medium and I n the this this this is this is where you download the this is where you download the empty location from but if you try and start an example that doesn't work it even tells you what to to of the it yes I believe that there is a there is that the besides if the the people the program perhaps we
should put the you this kind this kind of thing as well yes but what we can do that uh this how begin water land and
in all of our I haven't yet theories In based starts about 15 hundred and so that the that the typical the typical tools I don't want to lose all when you get it is strange that lot of the underneath if I you what would you like to search for and and
and and this would depend on on what the package is you can depend on which packages you've installed it depends on your local machine there the that there was a little there are some very grounds that but that's that's 1 way that look yes it is if you have a feeling that could be involved you open that web page and look for the name uh among the authors of the of the packages they're all on on the web page there's also a site search for that and tends to bring you back a large monthly chunks of mailing lists but that the help such function is quite good but a the ML
repays because phrase are plotted very useful it without followed demonstration and they and
barrier it to be patient so if that that that that that that but that's an an alternative way of looking at it that you but I I I existed before they did this so I I don't use I should use it that so this is this is then the and the list the functions which are documented for grass interval but which should be revised but those are the ones which are very small so that's the alternative work getting of so if you want to run an example you just write it sounded and then in the environment of you for the name of the function and then found in it at there should we do not of course not for every interval although I where we anywhere in masks and but there were not supposed to 1 of the examples that are going on in the what happens at the hint that can effectively learn what were at the time were then put people are interested in and then I can manage at and some other time but all you write example you but this and then run
Betriebsmittelverwaltung
Rückkopplung
Web Site
Subtraktion
Stabilitätstheorie <Logik>
Nabel <Mathematik>
Momentenproblem
Gruppenoperation
Besprechung/Interview
Zahlenbereich
Ungerichteter Graph
Bildschirmfenster
Skript <Programm>
Kontrollstruktur
Vorlesung/Konferenz
Installation <Informatik>
Zeiger <Informatik>
Optimierung
Touchscreen
Schnittstelle
Bildauflösung
Graph
Strömungsrichtung
Ein-Ausgabe
Mereologie
GRASS <Programm>
Tabelle <Informatik>
Lesen <Datenverarbeitung>
Punkt
Prozess <Physik>
Kategorizität
Extrempunkt
Datenanalyse
Mathematisierung
Zahlenbereich
Ungerichteter Graph
Zählen
Übergang
Metadaten
Variable
Bildschirmfenster
Programmbibliothek
Luenberger-Beobachter
Skript <Programm>
Vorlesung/Konferenz
Zusammenhängender Graph
Datenstruktur
Einflussgröße
Bildauflösung
Schnittstelle
Lineares Funktional
Parametersystem
Kategorie <Mathematik>
Mailing-Liste
Vektorraum
Elektronische Publikation
Medianwert
Teilbarkeit
Quick-Sort
Objekt <Kategorie>
Arithmetisches Mittel
Flächeninhalt
GRASS <Programm>
Garbentheorie
Geometrie
Instantiierung
Resultante
Subtraktion
Quader
Extrempunkt
Klasse <Mathematik>
Versionsverwaltung
Formale Grammatik
Zellularer Automat
Zahlenbereich
Term
Eins
Variable
Wechselsprung
Einheit <Mathematik>
Datentyp
Vorlesung/Konferenz
Gerade
Touchscreen
Funktion <Mathematik>
Lineares Funktional
Kategorie <Mathematik>
Plot <Graphische Darstellung>
Häufigkeitsverteilung
Quellcode
Medianwert
Teilbarkeit
Quick-Sort
Arithmetisches Mittel
Objekt <Kategorie>
Flächeninhalt
GRASS <Programm>
Faktor <Algebra>
Information
Ordnung <Mathematik>
Verzeichnisdienst
Verkehrsinformation
Resultante
Objektklasse
Subtraktion
Prozess <Physik>
Rahmenproblem
Datensichtgerät
Klasse <Mathematik>
Versionsverwaltung
Zellularer Automat
Kombinatorische Gruppentheorie
Computeranimation
Eins
Prozess <Informatik>
Datentyp
Stichprobenumfang
Visualisierung
Vorlesung/Konferenz
Bildauflösung
Kraft
Plot <Graphische Darstellung>
Vektorraum
Arithmetisches Mittel
Generator <Informatik>
Flächeninhalt
Benutzerführung
Information
Ordnung <Mathematik>
Fehlermeldung
Tabelle <Informatik>
Metadaten
Datensatz
Druckverlauf
Mathematisierung
Zellularer Automat
Vorlesung/Konferenz
Lesen <Datenverarbeitung>
Differenzengleichung
Subtraktion
Quader
Datenanalyse
Zahlenbereich
Physikalische Theorie
Computeranimation
Eins
Ausdruck <Logik>
Übergang
Überlagerung <Mathematik>
Informationsmodellierung
Datensatz
Client
Variable
Bildschirmfenster
Mixed Reality
Luenberger-Beobachter
Datenstruktur
Phasenumwandlung
Bildauflösung
Lineares Funktional
Dicke
Wald <Graphentheorie>
Kategorie <Mathematik>
Datenhaltung
Plot <Graphische Darstellung>
Quick-Sort
Teilbarkeit
Programmfehler
Flächeninhalt
Heegaard-Zerlegung
Benutzerführung
Tabelle <Informatik>
DoS-Attacke
Web Site
Statistik
Punkt
Datenhaltung
Relativitätstheorie
Klassische Physik
Versionsverwaltung
t-Test
Primideal
Frequenz
Computeranimation
Datenfeld
Flächeninhalt
Surjektivität
GRASS <Programm>
URL
Web Site
Punkt
Nabel <Mathematik>
t-Test
Ungerichteter Graph
Computeranimation
Eins
Übergang
Metadaten
Datensatz
Datentyp
Skript <Programm>
Turm <Mathematik>
Schnittstelle
Softwaretest
Addition
Graph
Physikalisches System
Binder <Informatik>
Energiedichte
Menge
GRASS <Programm>
URL
Ordnung <Mathematik>
Verzeichnisdienst
Fehlermeldung
Mapping <Computergraphik>
Lineares Funktional
Graph
Rechter Winkel
Wärmeübergang
GRASS <Programm>
Plot <Graphische Darstellung>
Kantenfärbung
Physikalisches System
Elektronische Publikation
Computeranimation
Schnittstelle
Distributionstheorie
Web Site
Subtraktion
Bit
Klasse <Mathematik>
t-Test
Zahlenbereich
Element <Mathematik>
Division
Computeranimation
Task
Logarithmus
Mittelwert
Verweildauer
Bildschirmfenster
Analysis
Bildauflösung
Schnittstelle
DoS-Attacke
Sichtenkonzept
Graph
Reihe
Fokalpunkt
Frequenz
Dialekt
Konfiguration <Informatik>
Arithmetisches Mittel
Diagramm
Menge
Wort <Informatik>
Varianzanalyse
Kantenfärbung
Standardabweichung
Tabelle <Informatik>
Distributionstheorie
Bit
Subtraktion
Quader
Klasse <Mathematik>
Kumulante
Kombinatorische Gruppentheorie
Doppler-Effekt
Code
Computeranimation
Eins
Dichtefunktional
Geostatistik
Eigenwert
Koroutine
Optimierung
Default
Varianz
DoS-Attacke
Zentrische Streckung
Plot <Graphische Darstellung>
Ein-Ausgabe
Frequenz
Dichte <Physik>
Arithmetisches Mittel
Rhombus <Mathematik>
Diagramm
Histogramm
Flächeninhalt
Kantenfärbung
Momentenproblem
Mereologie
Vorlesung/Konferenz
GRASS <Programm>
Vektorraum
URL
Mustererkennung
Richtung
Bildauflösung
Schnittstelle
Lineares Funktional
Besprechung/Interview
Programmbibliothek
Schreiben <Datenverarbeitung>
Optimierung
Drei
Hilfesystem
Computeranimation
Homepage
Wasserdampftafel
Besprechung/Interview
Physikalische Theorie
Autorisierung
Lineares Funktional
Virtuelle Maschine
Web Site
Vorlesung/Konferenz
Mailing-Liste
Web-Seite
E-Mail
Hilfesystem
Lineares Funktional
Besprechung/Interview
Äußere Algebra eines Moduls
Vorlesung/Konferenz
Mailing-Liste
GRASS <Programm>
Programmierumgebung
Feuchteleitung
Eins

Metadaten

Formale Metadaten

Titel GRASS/R interface workshop and demonstration
Serientitel Open source GIS - GRASS user conference 2002
Anzahl der Teile 45
Autor Bivand, Roger
Lizenz CC-Namensnennung - keine Bearbeitung 3.0 Deutschland:
Sie dürfen das Werk in unveränderter Form zu jedem legalen Zweck nutzen, vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/21757
Herausgeber University of Trento
Erscheinungsjahr 2002
Sprache Englisch

Inhaltliche Metadaten

Fachgebiet Informatik

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