Boek/Hage

Mapping Statistical Linked DataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat: A Tutorial on Combining Linked Open DataInformatie gepubliceerd als Open Data en dat aan andere data linkt, wordt Linked Open Data (LOD) genoemd. Data wordt nog beter ontsloten, omdat men zoveel mogelijk verwijzingen aanbrengt naar andere kennisbronnen en anderen gemakkelijk naar informatie kunnen verwijzen. and Tabular Data in R

 

Auteur

Willem Robert van Hage (SynerScope)

 

Introduction

This chapter is a tutorial on data analysis. We will demonstrate, step by step, how to analyze a combination of linked data and non-linked data with the R statistical programming language. We will show how to join linked data with tabular statistical dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat. Specifically, we will be working with linked data from the Netherlands’ Cadastre, Land Registry, and Mapping Agency (Kadaster), about addreses and buildings, the Basisregistratie voor Adressen en Gebouwen (BAG); linked data from DBpediaDBpedia is a crowd-sourced community effort to extract structured information from Wikipedia and make this information available on the Web. DBpedia allows you to ask sophisticated queries against Wikipedia, and to link the different data sets on the Web to Wikipedia data. We hope that this work will make it easier for the huge amount of information in Wikipedia to be used in some new interesting ways. Furthermore, it might inspire new mechanisms for navigating, linking, and improving the encyclopedia itself., and non-linked tabular statistical data from the Statistics Netherlands (CBS). We will investigate geographical correlations between building propertiesAls in OWL properties of RDFS properties. and the socio-economical status of their inhabitants. Topics covered in this tutorial are: simple SPARQL endpoint interaction (Section 2), loading tabular CSV data (Section 3), preprocessing the data (Section 4), joining linked data with non-linked data by a shared key (Section 5), basic data exploration and statistics (Section 6), aggregation (Section 7), and geographical visualization (Section 6 and 7).

 

Importing Linked DataLinked Data is een internettechnologie waarmee je gestructureerde gegevens op een uniforme manier kunt beschrijven, modelleren, opslaan, publiceren en onderling kunt koppelen op het internet met behulp van RDF-gebaseerde open standaarden en URI's (bijv. W3C-aanbevelingen en vocabulaires).

To start with this tutorial, you will need a running setup of the R programming language (R, http://r-project.org). We advise you to use the RStudio graphical user interface for R (RStudio, http://rstudio.org), which provides many useful editing features. Inside R, we will make use of two contributed libraries, SPARQL and ggmap, that respectively provide access to SPARQL end-points over the Web, and plotting of data on Google Maps. These libraries can be installed automatically from inside R by typing the following command inside R:

 

> install.packages(c('SPARQLSimple Protocol And RDF Query Language (SPARQL) is een RDF zoektaal (Query Language) - die gebruikt wordt om RDF-gebaseerde data te bevragen middels zoekopdrachten (Query).','ggmap'),dependencies=TRUE)

 

Once these libraries are installed they can be loaded using the library function.

 

> # load the SPARQL and ggmap libraries

> library(SPARQLSimple Protocol And RDF Query Language (SPARQL) is een RDF zoektaal (Query Language) - die gebruikt wordt om RDF-gebaseerde data te bevragen middels zoekopdrachten (Query).)

> library(ggmap)

 

Now that we’ve prepared our system and loaded the necessary libraries we can get started with loading the data from the BAG SPARQL end-point using a SPARQL query. This can be done with the SPARQL function. We need to tell this function a few things: At which URLEen Uniform Resource Locator (afgekort URL) is een gestructureerde naam die verwijst naar een stuk data. Voorbeelden zijn het unieke adres waarmee de locatie van een webpagina op internet wordt aangegeven of een e-mailadres. In de naam is alle informatie opgenomen over de benodigde techniek om de betreffende gegevens te bereiken. De URL is een bijzondere vorm van de URI. to get the dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat, and which query to use to get the dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat. We will define two variables to contain exactly these bits of information, so that we can pass these variables to the SPARQL function.

 

We can get the data from the BAG end-point at Geodan:

 

> endpoint <- "[http://lod.geodan.nl/BAG/sparql http://lod.geodan.nl/BAG/sparql]";

 

The data in the triple store behind the SPARQL store follows a schema defined by Geodan. The specific part of the data we will use in this tutorial is the link between the postal code (postcode), street name (straatnaam), usage (gebruiksdoel), the location (wkt, short forWell-Known Text geometry), and building year (bouwjaar). Per address, these are linked together in little graphs of nodes and edges. We will define a query that looks up occurrences of graph patterns that link them together and we will find one occurrence for each address in the store. To make this tutorial run, we limit ourselves to a subset of the addresses, specifically, those that are in the city of Amsterdam. This can also be specified as part of the SPARQL graph pattern query. In Figure 1 you can see a picture of the graph pattern that we will look up with the SPARQL query listed below.

 

> # define a SPARQL query to load dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat

> q <- 'PREFIX bagv: <[http://lod.geodan.nl/BAG/vocab/resource/ http://lod.geodan.nl/BAG/vocab/resource/]>

+ PREFIX bag: <[http://lod.geodan.nl/BAG/ http://lod.geodan.nl/BAG/]>

+ PREFIX ogc: <[http://lod.geodan.nl/geosparql_vocab_all.rdf# http://lod.geodan.nl/geosparql_vocab_all.rdf#]>

+ SELECT ?postcode ?straatnaam ?gebruiksdoel ?wkt ?bouwjaar

+ WHERE {

+ ?wp bagv:woonplaats_naam "Amsterdam"^^xsd:string .

+ ?or bagv:woonplaats ?wp ;

+ bagv:openbareruimte_naam ?straatnaam .

+ ?na bagv:openbareruimte ?or ;

+ bagv:nummeraanduiding_postcode ?pc .

+ BIND(str(?pc) AS ?postcode)

+ ?vo bagv:nummeraanduiding ?na ;

+ ogc:WKTLiteral ?shape .

+ FILTER(regex(str(?shape),"^[^<]"))

+ BIND(str(?shape) AS ?wkt)

+ ?p bagv:verblijfsobject ?vo ;

+ bagv:pand_bouwjaar ?bouwjaar .

+ ?vo bagv:gebruiksdoel ?gd .

+ ?gd bagv:gebruiksdoel_gebruiksdoel ?gebruiksdoel .

+ }'

 

The query can be any SPARQL statement (SPARQLSimple Protocol And RDF Query Language (SPARQL) is een RDF zoektaal (Query Language) - die gebruikt wordt om RDF-gebaseerde data te bevragen middels zoekopdrachten (Query)., http://www.w3.org/TR/sparql11-query/). Detailed descriptions of SPARQL can be found on the Web. A brief discussion of the query we use in this tutorial follows.

 

 
D9-fig1.jpg

Figure 1: Part of the RDF schema used in the Geodan RDF version of the BAG that we will extract using SPARQLSimple Protocol And RDF Query Language (SPARQL) is een RDF zoektaal (Query Language) - die gebruikt wordt om RDF-gebaseerde data te bevragen middels zoekopdrachten (Query)..

 

The PREFIX statements declare abbreviations we can use in the query to specify the URIs of the graph edges, which represent propertiesAls in OWL properties of RDFS properties. of things. For instance, we can write bagv:woonplaats instead of http://lod.geodan.nl/BAG/vocab/resource/woonplaats. With the SELECT statement, which is analogous to the SQL SELECT statement, we delare which variables (the keywords that start with a questionmark) we want to return as columns of a result table. Each row in the result table will be a distinct match of the graph pattern specified in the WHERE expression. The WHERE expression contains a graph pattern in a concise notation, as well as additional variable bindings and filters. For example, on line four and five of the graph pattern we match a triple consisting of a property bagv:openbareruimte between two variables, ?na and ?or, and a property bagv:nummeraanduiding_postcode between ?na and ?pc. The BIND statement selects the string value of the postal code captured in the variable ?pc, which contains a typed string (e.g. we select ‘1081HV’ from ‘1081HV’^^xsd:string). The FILTER statement three lines below states that we want shapes that do not start with an opening bracket (‘<‘), which in the case of the Geodan version of the BAG separate WGS84 coordinates from coordinates in the Dutch coordinate system Rijksdriehoeksmeting.

 

Now that we have a specification of which variable instantiations we want to gather obeying which pattern in the form of a SPARQL query, we can send the query to the input with the SPARQL function. The table is stored in R as a data frame called bag_res.

 

> # fire the query at the SPARQL end-point

> bag_res <- SPARQLSimple Protocol And RDF Query Language (SPARQL) is een RDF zoektaal (Query Language) - die gebruikt wordt om RDF-gebaseerde data te bevragen middels zoekopdrachten (Query).(url=endpoint,query=q)$results

 

To demonstrate what comes back we use the head function to show the first three lines of the result table.

 

> # show the first lines of the resulting data frame

> head(bag_res,n=3)

 

postcode  straatnaam                     gebruiksdoel

1                     1019GM   Vriesseveem                 overige gebruiksfunctie

2                     1019GW   Jollemanhof                    kantoorfunctie

3                     1019GW   Jollemanhof                    kantoorfunctie

 

wkt              bouwjaar

1                     POINT(4.91891809473891       52.3773045855061)                       2005

2                     POINT(4.91978793944898       52.376984465474)  2005

3                     POINT(4.9198026246807          52.3769845234389)                       2005

 

The first line contains an address with postal code 1019GM in the street Vriesseveem with a aspecific usage (gebruiksdoel), the other two are offices in the street Jollemanhof. All three addresses are in a building built in 2005.

 

Importing Tabular DataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat

In the previous section we demonstrated how to get tabular data from a triple store using the SPARQL package for R. A much simpler way to read and write data frames is character separated files with the read.table and write.table functions in R. We will quickly show how this is done by loading a file, ‘Eindbestand SEC.dat’ from the CBS on social economical classification of addresses (CBS SEC, http://bit.ly/146jcfH). This file contains tab separated values per row delimited by newline characters. Missing data values are indicated with the letter x. We can specify that the first line contains column names and that we would like to treat the missing data values as ‘Not Available’ values in R. These are displayed differently for numerical values (‘<NA>‘) and categorical values (‘NA’).

 

> # load Social Economic figures from the CBS from a CSV table file

> sec <- read.table("Eindbestand SEC.dat",

+ header=TRUE,sep="\t",na.strings="x")

> head(sec,n=3)

pc6 gemeentecode Inkomensontvangers Laaginkomen Hooginkomen

1 1011AB 363 NA <NA> <NA>

2 1011AC 363 10 <NA> <NA>

3 1011AE 363 NA <NA> <NA>

Uitkeringsontvangers Zelfstandigen Fiscaalmaandinkomen

1 NA NA NA

2 NA NA 2300

3 NA NA NA

 

You can see that this table contains a postal code column, pc6, with compatible values to the postcode column in the BAG. In Section 5 we will join these two data frames into a single data frame with all the values on this shared column.

 

Caching SPARQL results as Tabular DataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat

You can use the table reading and writing functions to store SPARQL result tables in a character separated file as follows.

 

> # write results to a cache file

> write.table(bag_res,file="amsterdam_bag_res.tsv",sep="\t")

> # read from a cached result file

> bag_res <- read.table("amsterdam_bag_res.tsv",+ header=TRUE,sep="\t")

This saves you the latency of Web lookups and the parsing of SPARQL result XML files. Also, it allows you to keep working when Web access is unavailable, and it reduces the stress on the SPARQL server.

 

Data Preprocessing

Quite often when loading data from third parties, you will run into data representations that are different from how they are needed for analysis. In this case, we would like to plot points on a Google map using the ggmap package, which requires vectors of latitude and longitude coordinates. Our data is supplied in WKT shapes. We we use a quick and dirty way to get the coordinates out of the WKT strings. We simply useregular expression substitusion to get the coordinates out of the WKT strings. A more robust, but slower, way would be to use a WKT parser, like the parser in the rgeos package.

 

> bag_res$long <-

+ as.numeric(gsub("POINT\\((\\S+) (\\S+)\\).*","\\1",bag_res$wkt))

> bag_res$lat <-

+ as.numeric(gsub("POINT\\((\\S+) (\\S+)\\).*","\\2",bag_res$wkt))

 

In the CBS data the letter x was used for missing values. In the BAG we see that the number 9999 is used to indicate unknown building years. Also we noticed that the building years below 1850 are suddenly very sparse and that the number 1005 is used for unknown early dates. To make visualization easier (the few old buildings upset the coloring if we want a color gradient over time) we restrict the range of viable building years to everything since 1850. We treat every year below that and the year 9999 as ‘Not Available’ by replacing these years by NA.

 

> # interpret the building date 9999 as "Not Available"

> bag_res[which(bag_res$bouwjaar == 9999 |

+ bag_res$bouwjaar < 1850),]$bouwjaar = NA

 

Joining Data Sets on a Shared Key

In this section we will join the BAG data with the CBS socio economical classification data by postal code. This enables us to combine, for example, the average monthly income from the CBS (smoothed numbers for anonymization) with the geocoordinates and building years from the BAG. We can join two data frames in R with the merge function. We simply call it on the two data frames and specify which column from each of them to match the rows by. This means, if a value in the CBS SEC pc6 column literally matches a value in the BAG postcode column, the two rows are concatenated.

 

> # perform an inner join between the CBS and BAG data on postal code

> sec_bag <- merge(sec,bag_res,by.x="pc6",by.y="postcode")

> head(sec_bag,n=2)

 

[table, alles rechts uitlijnen]

pc6               gemeentecode              Inkomensontvangers                     Laaginkomen                Hooginkomen

1                     1011AB     363               NA                <NA>          <NA>

2                     1011AB     363               NA                <NA>          <NA>

Uitkeringsontvangers                    Zelfstandigen                Fiscaalmaandinkomen                   straatnaam

1                     NA                NA                NA                De Ruijterkade

2                     NA                NA                NA                De Ruijterkade

gebruiksdoel                 wkt              bouwjaar long

1                     woonfunctie                  POINT(4.90560744056207       52.3777814734737)                       1916            4.905607

2                     kantoorfunctie            POINT(4.90578386133317       52.3777642154535)                       1916            4.905784

lat

1                     52.37778

2                     52.37776

 

In this case the match is a literal equality match, but it is quite possible to use merge in combination with subset and a custom similarity function to do approximate matching. This is shown in detail in a tutorial on the Web by Tony Hirst (5R-bloggers, http://bit.ly/15gnjoQ).

 

Data Analysis

In this section we will explore the CBS and BAG dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat. We will start by plotting the building year distribution from the BAG and the average monthly income from the CBS data to get an overview. Then we will exploit the join over the postal code to see whether or not there is a correlation between the two. This will answer the question: ‘Do rich people live in new buildings in Amsterdam?’.

 

Visualizing Distributions

In R you can make a histogram of values with the hist function. You can specify the bin size, i.e. the aggregation interval or the width of the bars, with the breaks parameter.

 

> # overview of building years

> hist(sec_bag$bouwjaar,

+ xlab="Building year",

+ main="Distribution of building year of buildings",

+ breaks=seq(1850,2015,by=5))

 

As you can see in Figure 2, big urbanization started at the end of the 19th century and

there are clear dips in the second world war and the crisis of the 1970s.

 

D9-fig2.jpg

 

Figure 2: Distribution of building years.

 

Now we will do the same with average monthly income. When bin sizes are not

supplied, R decides for itself how to bin the dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat.

 

> # overview of income per month

> hist(sec_bag$Fiscaalmaandinkomen,

+ xlab="Fiscal income per month",

+ main="Distribution of income")

 

In Figure 3 notice social security at the bottom of the distribution and the cap set at 10000 euro per month.

 

Statistical Correlation

If rich people live in new buildings then rows with a high income also have a high building year. By joining the tables on postal code we can simply select the column containing monthly incomes and the column with building years, because these will match up according to postal code. To compute a Pearson correlation (rR is een taal en omgeving voor statistisch computergebruik en grafische afbeeldingen.) in R, you can simply use the cor function on these two vectors. A slight complication is that some values, both in the building year column and in the income column, are not available.

 

These are not necessarily in the same rows. To do a fair correlation test, we leave out all rows with unavailable data in either column. This does introduce a non-response bias, but there is no simple way around this methodological problem. A proper treatment of missing data falls outside of the scope of this tutorial.

 

> # compute correlation between monthly income and building date,

> # ignoring incomplete dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat

> cor(sec_bag$Fiscaalmaandinkomen,sec_bag$bouwjaar,use="complete.obs")

 

D9-fig3.jpg

 

Figure 3: Monthly income distribution.

 

As you can see, the correlation is slightly negative. We can not conclude there is any correlation. To illustrate this, let’s plot income and building year on the map to see where the rich people live and where the new buildings are.

 

Mapping the DataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat

To plot values on a map, you can use the qmap function from the ggmap package. This requires you to specify which coordinates to put on the x and y axis of the map plot. qmap uses Google maps, which in turn use WGS84 coordinates, so the x and y coordinates have to use latitude and longitude as well. The ggmap package uses the Grammar of Graphics (GOG) pradigm to combine visualization type, data specification, and styling in a modular way. The implementation of GOG in R overloads the + operator to “add” up these different aspects of a graphical visualization.

 

In Figure 4 you can see that the rich people prefer to live near to the water, but also in the south of the central part of Amsterdam, “Amsterdam zuid”, specifically in the area south of the Vondel park. We transformed the income logarithmically, to get more definition in the coloring of the higher incomes. If we do the same visualization, but with a square root transform to get a better definition of the coloring of the newer buildings.

 

> qmap(location='Amsterdam', zoom=12) +

+ geom_point(aes(x=long,y=lat,colour=bouwjaar),

+ dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat=sec_bag,size=0.5) +

+ scale_colour_gradientn(colours=c("black","green"),trans="sqrt")

 

D9-fig4.jpg
 

Figure 4: Monthly income per address, unavailable data are shown in gray.

 

D9-fig5.jpg

 

Figure 5: Year at which the building of each address was built, unavailable data are shown in gray.

 

In Figure 5 you can see that Amsterdam clearly grew from the inside out, and that the buildings on the harbor islands are the newest developments. This in combination with Figure 4 shows why there is no conclusive correlation result. Rich people live in the new harbor estates, but also in the old area in Amsterdam zuid, south of the Vondelpark.

 

Data Aggregation

Until now we have dealt with concrete, unaggregated dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat. However, if we want to answer questions like ‘Which streets have the highest average income?’, we have to average the income over the addresses per street. There are a few ways to accomplish this in R, such as the wonderful plyr and reshape packages, but here we will show the simplest way to aggregate dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat, the built-in aggregate function. The following code applies the mean function to all values in the Fiscaalmaandincome column with the same straatnaam.

 

> # aggregate the location and income by street name

> agg_inkomen <- aggregate(Fiscaalmaandinkomen ~ straatnaam,

+ dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat=sec_bag,mean)

> head(agg_inkomen,head=5)

 

straatnaam                     Fiscaalmaandinkomen

1                     Aaf Bouberstraat       1819.643

2                     Aagtdorperpad            2600.000

3                     A.A.H. Struijckenkade                     1700.000

4                     Aakstraat                        2342.105

5                     Aalbersestraat             1599.673

6                     Aalsmeerplein              2700.000

 

We can do the same aggregation on latitude and longitude to calculate the centroids of the streets. These can then be attached to the aggregated income values with the merge function we introducted in Section 5.

 

> # aggregate geocoordinates by street name

> # and join with the aggregated income

> agg_lat <- aggregate(lat ~ straatnaam, dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat=sec_bag,mean)

> agg_long <- aggregate(long ~ straatnaam, dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat=sec_bag,mean)

> agg_street <- merge(agg_inkomen,

+ merge(agg_lat,

+ agg_long,

+ by="straatnaam"),

+ by="straatnaam")

> head(agg_street,n=5)

 

straatnaam                     Fiscaalmaandinkomen                   lat                  long

1                     Aaf Bouberstraat       1819.643  52.35684  4.820258

2                     Aagtdorperpad            2600.000  52.39764  4.956488

3                     A.A.H. Struijckenkade                     1700.000  52.38203  4.812593

4                     Aakstraat                        2342.105  52.40763  4.911949

5                     Aalbersestraat             1599.673  52.37904  4.797936

 

Let’s look at the top-25 richest streets of Amsterdam. We can sort a data frame by the values of a column with the order function. If we need them in decreasing order, we can simply add a minus before the column name.

 

> # show the top-25 streets with the highest average income

> top_agg <- head(agg_street[order(-agg_street$Fiscaalmaandinkomen),],+ n=25)

> top_agg

 

straatnaam Fiscaalmaandinkomen lat long

487               Chopinstraat                                         10000.000                      52.34760  4.879134

589               David Ricardostraat                                                10000.000                      52.34157  4.826554

1150            Herinkhave 10000    10000.000                      52.33059  4.860133

1151            Herman Gorterstraat                     10000.000                      52.34648  4.883591

1433            Johannes Vermeerplein                 10000.000                      52.35545  4.883748

1505            Karthuizersplantsoen                    10000.000                      52.37931  4.881732

1562            Koerierstersplein      10000.000                      52.36949  4.907647

1680            Lassusstraat                 10000.000                      52.35265  4.865783

2165            Ouborg      10000.000                      52.32339  4.887754

2442            Richard Wagnerstraat                   10000.000                      52.34656  4.880149

2500            Rossinistraat                10000.000                      52.34574  4.879246

2788            Tesselschadestraat  10000.000                      52.36204  4.879180

3027            Verdistraat                    10000.000                      52.34791  4.880510

3225            Willem Royaardsstraat                 10000.000                      52.34627  4.884454

657               Diepenbrockstraat 9531.250    52.34542  4.882581

2371            Prins Hendriklaan 9390.323     52.35390  4.862630

1733 Lijnbaansstraat                     9100.000  52.36950  4.879062

1057 Hacquartstraat                      8890.000  52.35293  4.874257

3250 Withoedenveem                    8700.000  52.37615  4.925104

265 Bernard Zweerskade            8588.235  52.34563  4.878846

1904 Memlingstraat                        8515.789  52.34969  4.874836

154 Artemisstraat    8478.947  52.34548  4.852965

929 Gaffelaarspad      8400.000  52.33324  4.855981

2979 Van Limburg Stirumplein                                              8300.000  52.38431  4.874713

1043 Guido Gezellestraat 8200.000             52.34585  4.883473

 

As a final visualization task, let’s plot the top-25 richest streets, labeled with their street name, on the map, rescaling the dots to make them more visible.

 

> # The top-25 highest income streets plotted on the map

> qmap(location='Amsterdam', zoom=12) +

+ geom_point(aes(x=long,y=lat,color=Fiscaalmaandinkomen,size=1),

+ dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat=top_agg) +

+ scale_colour_gradientn(colours=c("black", "green")) +

+ scale_size(range=8,guide="none") +

+ geom_text(aes(x=long,y=lat,label=straatnaam),

+ dataWeergave van een feit, begrip of aanwijzing, geschikt voor overdracht, interpretatie of verwerking door een persoon of apparaat=top_agg,

+ size=3,vjust=0,hjust=0)

 

Conclusion

In this tutorial we have treated how to load Linked Open DataInformatie gepubliceerd als Open Data en dat aan andere data linkt, wordt Linked Open Data (LOD) genoemd. Data wordt nog beter ontsloten, omdat men zoveel mogelijk verwijzingen aanbrengt naar andere kennisbronnen en anderen gemakkelijk naar informatie kunnen verwijzen. from a SPARQL endpoint into R and how to join it with non-linked data from tabular files, such as tabseparated value files, to combine knowledge from multiple sources to answer questions that could not be answered with one data set alone. We have treated basic distribution exploration, correlation between two propertiesAls in OWL properties of RDFS properties. with missing values, and visualization of point data on a map. Finally, we have shown how to do simple aggregation in R.

 

D9-fig6.jpg

 

Figure 6: Monthly income aggregated at street level. Shown are the 25 streets with the highest average income.

 

The data and the R code used for this tutorial (in Sweave format) can be found on the Web, along with other tutorials on SPARQL and R, in the tutorials section of the Linked Science weblog: http://linkedscience.org/.

 

Acknowledgements

Thanks go to Jorik Blaas, Niels Willems, Jesper Hoeksema, for their support with the triple store, data gathering, and visualization.

 

References