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Who is using Open Government Data - Analysis
 

Who is using Open Government Data - Analysis

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Exploratory data analysis carried out for Open Data Impacts study, MSc dissertation submitted to Oxford Internet Institute, University of Oxford, July 2010. ...

Exploratory data analysis carried out for Open Data Impacts study, MSc dissertation submitted to Oxford Internet Institute, University of Oxford, July 2010.

See http://www.practicalparticipation.co.uk/odi/survey for details and underlying data.

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Who is using Open Government Data - Analysis Who is using Open Government Data - Analysis Presentation Transcript

  • Exploratory data analysis: OGD user motivations The Potential of Open Government Data (OGD) as a Tool in Democratic Engagement and Reform of Public Services: The Case of Data.gov.uk A presentation of data as an online appendix to Section 4.1 of the written research report. To be used in conjunction with the written report only. Tim Davies, MSc Student, Oxford Internet Institute Exploratory Analysis: July 2010. www.practicalparticipation.co.uk/odi/ | tim@timdavies.org.uk | @timdavies
  • What is this presentation I have used this presentation as a working notebook whilst bringing together my exploratory analysis of the motivations of OGD users according to the survey data from the Open Data Impacts Survey. It is not a presentation of findings, but shows the process I’ve gone through in exploratory analysis. Stages are left in, even if they don’t illuminate the final conclusions in order to help ensure a transparent process. R commands Analysis carried out in R. Relevant commands given throughout. These are mainly an aide-memoir for my own use, but, in conjunction with the shared data, should also support replication of the results.
  • Overview: • Question: What motivates users of Open Government Data (OGD) • Data source: 72 responses from an opportunistically sampled online survey delivered between May 11th and June 14th 2010 targeted at users of OGD. • Data: Responses to a range of ‘Strongly Agree -> Strongly Disagree’ based likert scales on ‘Attitudes’ towards OGD and ‘Motivations’ for working with OGD. Also responses to a range of statements about OGD use projects from 44 individuals within the sample. • Analysis: Using correlation analysis, exploratory factor analysis (Bartholomew et al. 2008, 7 - 9; Costello & Osborne 2005; Lawley & Maxwell 1962) and bootstrapped cluster analysis (Suzuki & Shimodaira 2006) in R. Bartholomew, D.J. et al., 2008. Analysis of Multivariate Social Science Data, Second Edition (Chapman & Hall) 2nd ed., Chapman and Hall. Becker, R.A., Chambers, J.M. & Wilks, A.R., 1988. The new S language: a programming environment for data analysis and graphics. Costello, A.B. & Osborne, J.W., 2005. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, (7), 1–9. Lawley, D.N. & Maxwell, A.E., 1962. Factor Analysis as a Statistical Method. Journal of the Royal Statistical Society. Series D (The Statistician), 12(3), 209-229. Suzuki, R. & Shimodaira, H., 2006. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics, 22(12), 1540.
  • Caveats • Sample size: Too small to allow any strong conclusions to be drawn • Items: Survey items were based on an initial literature review and exploratory research, but, due to time constraints, were not subjected to prior pilot testing. Refinements of the items and new data collection would strengthen the available data for future analysis. • Holistic interpretation: The interpretation presented draws upon contextual information gathered during exploratory research. • Interactive presentation: I’ve tried to include enough detail in this presentation to account for my analysis in Section 4.1 of the related dissertation. However, if the reader feels any analysis is not fully explained or does not provide adequate justification, questions are welcome on the blog post relating to this presentation at http://www.practicalparticipation.co.uk/odi/
  • 1: Correlation between motivations • Q: “People are interested in open data for many different reasons. Thinking about your own engagement with open government data, how important are the following motivations for working with or using open government data?” • A: ‘Not at all important’ to ‘Very Important’ coded on a scale -2 to +2. • Missing answers: Coded as 0 (neutral) • Data: OpenDataImpacts-SurveyMotivationData.csv (Available online as Google Spreadsheet) • Analysis: R correlation matrix, coded and ordered for strength or correlation. Clusters interpreted. R commands > library(gclus) > dta<-motivations > dta.r<-abs(cor(dta)) > dta.col<-dmat.color(dta.r,colorRampPalette(c("white","orange","red"))(100)) > dta.o<-order.single(dta.r) > cpairs(dta,dta.o,panel.colors=dta.col,gap=.5,main="Variables ordered and colored by correlation")
  • 1: Correlation between motivations
  • 1: Correlation between motivations 1
  • 1: Correlation between motivations 1) Government focussed Understanding, Efficiency, Accountability 1
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 1
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 1
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 1 2
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed Creating platforms, with semantic web 1 2
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed Creating platforms, with semantic web 1 2 3
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed Creating platforms, with semantic web 3) Reward focussed Recognition or profit 1 2 3
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed Creating platforms, with semantic web 3) Reward focussed In open tech community recognition is key asset for Recognition or profit gaining future work... 1 2 3
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed Creating platforms, with semantic web 3) Reward focussed In open tech community recognition is key asset for Recognition or profit gaining future work... 1 4 2 3
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed Creating platforms, with semantic web 3) Reward focussed In open tech community recognition is key asset for Recognition or profit gaining future work... 4) Digitising government focus A computerisation movement? 1 4 2 3
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed Creating platforms, with semantic web 3) Reward focussed In open tech community recognition is key asset for Recognition or profit gaining future work... 4) Digitising government focus A computerisation movement? 1 4 2 3 5
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed Creating platforms, with semantic web 3) Reward focussed In open tech community recognition is key asset for Recognition or profit gaining future work... 4) Digitising government focus A computerisation movement? 1 5) Problem solving focus Develop new skills to meet interesting challenge 4 2 3 5
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed 6 Creating platforms, with semantic web 3) Reward focussed In open tech community recognition is key asset for Recognition or profit gaining future work... 4) Digitising government focus A computerisation movement? 1 5) Problem solving focus Develop new skills to meet interesting challenge 4 2 3 5
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed 6 Creating platforms, with semantic web 3) Reward focussed In open tech community recognition is key asset for Recognition or profit gaining future work... 4) Digitising government focus A computerisation movement? 1 5) Problem solving focus Develop new skills to meet interesting challenge 6) Social/public sector enterprise 4 Companies providing local focussed services 2 3 5
  • 1: Correlation between motivations Also linked to make a 1) Government focussed difference... Understanding, Efficiency, Accountability 2) Technology innovation focussed 6 Creating platforms, with semantic web 3) Reward focussed In open tech community recognition is key asset for Recognition or profit gaining future work... 4) Digitising government focus A computerisation movement? 1 5) Problem solving focus Develop new skills to meet interesting challenge 6) Social/public sector enterprise 4 Companies providing local focussed services E.g. Environmental 2 mapping; Transport service using OGD. 3 5
  • 2: Cluster analysis of motivations • Bootstrapped cluster analysis shows similar patterns. • But no strong p values. I.e. weak & overlapping clusters as seen in correlation analysis. • Adds curiosity to government cluster. Fits with participant-observation evidence that interest in government is secondary to interest in technology for many OGD users. R commands > library(pvclust) > motclust<-pvclust(motivations,nboot=100000) > plot(motclust)
  • 3: Additional analysis R commands: non-graphical screen test solutions > library(nFactors) • Whilst a number of exploratory factor analysis solutions > ev<-eigen(cor(motivations)) can offer insights during interactive exploration of the data, > ap<- parallel(subject=nrow(motivations),var=ncol(motivations),re the non-graphical scree test solution suggests between 8 p=100,cent=0.05) and 10 factors, almost as many as there are items: thus > nS <- nScree(ev$values, ap$eigen$qevpea) Factor analysis is of limited additional use. > plotnScree(nS) R commands: factor analysis > factmot3<-factanal(motivations,3, rotation="promax", • kmeans analysis can be used to look at cluster sizes by scores="regression") # The three factor solution is the most intuitively interpretable. discretely dividing the data - but the overlapping nature of > print(factmot3,cutoff=0.3,sort=T,digits=2) the clusters we are identifying suggests this is also of limited utility. Interactively exploring different cluster sizes and the R commands: kmeans analysis size of those clusters does however provide some additional > factkmeans5<-kmeans(t(motivations),5) > #5 kmeans factors are interpretable insights which can then be checked against other survey data. • Qualitative reading of particular individual cases in each cluster, and additional exploratory statistical analysis can support further identification & description of the different forms of motivations OGD users have. • E.g. Those motivated by ‘Specific problems’ want ‘bulk Data’ and are not interested in SPARQL and building platforms; whereas those interested in platform building and developing new skills have strong interest in semantic web technologies.
  • Analysis of motivations • O’Reilly blogger Nat Torkington (2010) has suggested there are five types of people with an interested in OGD: “… [1] low-polling governments who want to see a PR win from opening their data, [2] transparency advocates who want a more efficient and honest government, [3] citizen advocates who want services and information to make their lives better, [4] open advocates who believe that governments act for the people therefore government data should be available for free to the people, and [5] wonks who are hoping that releasing datasets ... will deliver…economic benefits to the country”. • However, we find quite different clusters of motivations in the survey data:
  • Analysis of motivations • O’Reilly blogger Nat Torkington (2010) has suggested there are five types of people with an interested in OGD: “… [1] low-polling governments who want to see a PR win from opening their data, [2] transparency advocates who want a more efficient and honest government, [3] citizen advocates who want services and information to make their lives better, [4] open advocates who believe that governments act for the people therefore government data should be available for free to the people, and [5] wonks who are hoping that releasing datasets ... will deliver…economic benefits to the country”. • However, we find quite different clusters of motivations in the survey data: 1) Government focussed Understanding, Efficiency, Accountability
  • Analysis of motivations • O’Reilly blogger Nat Torkington (2010) has suggested there are five types of people with an interested in OGD: “… [1] low-polling governments who want to see a PR win from opening their data, [2] transparency advocates who want a more efficient and honest government, [3] citizen advocates who want services and information to make their lives better, [4] open advocates who believe that governments act for the people therefore government data should be available for free to the people, and [5] wonks who are hoping that releasing datasets ... will deliver…economic benefits to the country”. • However, we find quite different clusters of motivations in the survey data: 1) Government focussed 2) Technology innovation focussed Understanding, Efficiency, Accountability Creating platforms, with semantic web
  • Analysis of motivations • O’Reilly blogger Nat Torkington (2010) has suggested there are five types of people with an interested in OGD: “… [1] low-polling governments who want to see a PR win from opening their data, [2] transparency advocates who want a more efficient and honest government, [3] citizen advocates who want services and information to make their lives better, [4] open advocates who believe that governments act for the people therefore government data should be available for free to the people, and [5] wonks who are hoping that releasing datasets ... will deliver…economic benefits to the country”. • However, we find quite different clusters of motivations in the survey data: 1) Government focussed 2) Technology innovation focussed Understanding, Efficiency, Accountability Creating platforms, with semantic web 3) Reward focussed Recognition or profit
  • Analysis of motivations • O’Reilly blogger Nat Torkington (2010) has suggested there are five types of people with an interested in OGD: “… [1] low-polling governments who want to see a PR win from opening their data, [2] transparency advocates who want a more efficient and honest government, [3] citizen advocates who want services and information to make their lives better, [4] open advocates who believe that governments act for the people therefore government data should be available for free to the people, and [5] wonks who are hoping that releasing datasets ... will deliver…economic benefits to the country”. • However, we find quite different clusters of motivations in the survey data: 1) Government focussed 4) Digitising government focus 2) Technology innovation focussed Understanding, Efficiency, Accountability A political or computerisation movement? Creating platforms, with semantic web 3) Reward focussed Recognition or profit
  • Analysis of motivations • O’Reilly blogger Nat Torkington (2010) has suggested there are five types of people with an interested in OGD: “… [1] low-polling governments who want to see a PR win from opening their data, [2] transparency advocates who want a more efficient and honest government, [3] citizen advocates who want services and information to make their lives better, [4] open advocates who believe that governments act for the people therefore government data should be available for free to the people, and [5] wonks who are hoping that releasing datasets ... will deliver…economic benefits to the country”. • However, we find quite different clusters of motivations in the survey data: 1) Government focussed 4) Digitising government focus 2) Technology innovation focussed Understanding, Efficiency, Accountability A political or computerisation movement? Creating platforms, with semantic web 3) Reward focussed Recognition or profit 5) Problem solving focus Develop new skills to meet interesting challenge
  • Analysis of motivations • O’Reilly blogger Nat Torkington (2010) has suggested there are five types of people with an interested in OGD: “… [1] low-polling governments who want to see a PR win from opening their data, [2] transparency advocates who want a more efficient and honest government, [3] citizen advocates who want services and information to make their lives better, [4] open advocates who believe that governments act for the people therefore government data should be available for free to the people, and [5] wonks who are hoping that releasing datasets ... will deliver…economic benefits to the country”. • However, we find quite different clusters of motivations in the survey data: 1) Government focussed 4) Digitising government focus 2) Technology innovation focussed Understanding, Efficiency, Accountability A political or computerisation movement? Creating platforms, with semantic web 6) Social/public sector enterprise 3) Reward focussed Companies providing local focussed services Recognition or profit 5) Problem solving focus Develop new skills to meet interesting challenge
  • Further analysis R commands: identify clusters & create new matrix > c1<-c(7,9,2) > c2<-c(13,10,3) • We can take these clusters and create a new matrix of > c3<-c(3,11,4) > c4<-c(9,2,13,10) individuals rankings against them by summing the individual > c5<-c(14,8) variables. > c6<-c(12,6) > mclusts<- cbind(as.vector(by(motivations[,c1],c(1:72),sum)),as.vector( • We can then look at correlations between these and other by(motivations[,c2],c(1:72),sum)),as.vector(by(motivations[, variables. c3],c(1:72),sum)),as.vector(by(motivations[,c4],c(1:72),sum) ),as.vector(by(motivations[,c5],c(1:72),sum)),as.vector(by(m otivations[,c6],c(1:72),sum))) > colnames(mclusts)<-c("GovFocus", "TechFocus", • For example, looking at correlations between preferred "RewardFocus", "DigitalGov", "ProblemSolving", methods of data access and user motivation. "Enterprise") The additional data used is available in Google Docs linked from http://www.practicalparticipation.co.uk/odi/survey Download the relevant file as CSV and then import into the correct variable name with the command > egassociations<-read.csv<-(file=”filename.csv’,headers=T) To remove the line numbers, you can use: > egassociations<-egassociations[,2:length(assoctest[1,])]
  • Further analysis R commands: identify clusters & create new matrix > c1<-c(7,9,2) > c2<-c(13,10,3) • We can take these clusters and create a new matrix of > c3<-c(3,11,4) > c4<-c(9,2,13,10) individuals rankings against them by summing the individual > c5<-c(14,8) variables. > c6<-c(12,6) > mclusts<- cbind(as.vector(by(motivations[,c1],c(1:72),sum)),as.vector( • We can then look at correlations between these and other by(motivations[,c2],c(1:72),sum)),as.vector(by(motivations[, variables. c3],c(1:72),sum)),as.vector(by(motivations[,c4],c(1:72),sum) ),as.vector(by(motivations[,c5],c(1:72),sum)),as.vector(by(m otivations[,c6],c(1:72),sum))) > colnames(mclusts)<-c("GovFocus", "TechFocus", • For example, looking at correlations between preferred "RewardFocus", "DigitalGov", "ProblemSolving", methods of data access and user motivation. "Enterprise") The additional data used is available in Google Docs linked from http://www.practicalparticipation.co.uk/odi/survey Download the relevant file as CSV and then import into the correct variable name with the command > egassociations<-read.csv<-(file=”filename.csv’,headers=T) To remove the line numbers, you can use: > egassociations<-egassociations[,2:length(assoctest[1,])] 1) Government focussed Understanding, Efficiency, Accountability
  • Further analysis R commands: identify clusters & create new matrix > c1<-c(7,9,2) > c2<-c(13,10,3) • We can take these clusters and create a new matrix of > c3<-c(3,11,4) > c4<-c(9,2,13,10) individuals rankings against them by summing the individual > c5<-c(14,8) variables. > c6<-c(12,6) > mclusts<- cbind(as.vector(by(motivations[,c1],c(1:72),sum)),as.vector( • We can then look at correlations between these and other by(motivations[,c2],c(1:72),sum)),as.vector(by(motivations[, variables. c3],c(1:72),sum)),as.vector(by(motivations[,c4],c(1:72),sum) ),as.vector(by(motivations[,c5],c(1:72),sum)),as.vector(by(m otivations[,c6],c(1:72),sum))) > colnames(mclusts)<-c("GovFocus", "TechFocus", • For example, looking at correlations between preferred "RewardFocus", "DigitalGov", "ProblemSolving", methods of data access and user motivation. "Enterprise") The additional data used is available in Google Docs linked from http://www.practicalparticipation.co.uk/odi/survey Download the relevant file as CSV and then import into the correct variable name with the command > egassociations<-read.csv<-(file=”filename.csv’,headers=T) To remove the line numbers, you can use: > egassociations<-egassociations[,2:length(assoctest[1,])] 1) Government focussed 2) Technology innovation focussed Understanding, Efficiency, Accountability Creating platforms, with semantic web
  • Further analysis R commands: identify clusters & create new matrix > c1<-c(7,9,2) > c2<-c(13,10,3) • We can take these clusters and create a new matrix of > c3<-c(3,11,4) > c4<-c(9,2,13,10) individuals rankings against them by summing the individual > c5<-c(14,8) variables. > c6<-c(12,6) > mclusts<- cbind(as.vector(by(motivations[,c1],c(1:72),sum)),as.vector( • We can then look at correlations between these and other by(motivations[,c2],c(1:72),sum)),as.vector(by(motivations[, variables. c3],c(1:72),sum)),as.vector(by(motivations[,c4],c(1:72),sum) ),as.vector(by(motivations[,c5],c(1:72),sum)),as.vector(by(m otivations[,c6],c(1:72),sum))) > colnames(mclusts)<-c("GovFocus", "TechFocus", • For example, looking at correlations between preferred "RewardFocus", "DigitalGov", "ProblemSolving", methods of data access and user motivation. "Enterprise") The additional data used is available in Google Docs linked from http://www.practicalparticipation.co.uk/odi/survey Download the relevant file as CSV and then import into the correct variable name with the command > egassociations<-read.csv<-(file=”filename.csv’,headers=T) To remove the line numbers, you can use: > egassociations<-egassociations[,2:length(assoctest[1,])] 1) Government focussed 2) Technology innovation focussed Understanding, Efficiency, Accountability Creating platforms, with semantic web 3) Reward focussed Recognition or profit
  • Further analysis R commands: identify clusters & create new matrix > c1<-c(7,9,2) > c2<-c(13,10,3) • We can take these clusters and create a new matrix of > c3<-c(3,11,4) > c4<-c(9,2,13,10) individuals rankings against them by summing the individual > c5<-c(14,8) variables. > c6<-c(12,6) > mclusts<- cbind(as.vector(by(motivations[,c1],c(1:72),sum)),as.vector( • We can then look at correlations between these and other by(motivations[,c2],c(1:72),sum)),as.vector(by(motivations[, variables. c3],c(1:72),sum)),as.vector(by(motivations[,c4],c(1:72),sum) ),as.vector(by(motivations[,c5],c(1:72),sum)),as.vector(by(m otivations[,c6],c(1:72),sum))) > colnames(mclusts)<-c("GovFocus", "TechFocus", • For example, looking at correlations between preferred "RewardFocus", "DigitalGov", "ProblemSolving", methods of data access and user motivation. "Enterprise") The additional data used is available in Google Docs linked from http://www.practicalparticipation.co.uk/odi/survey Download the relevant file as CSV and then import into the correct variable name with the command > egassociations<-read.csv<-(file=”filename.csv’,headers=T) To remove the line numbers, you can use: > egassociations<-egassociations[,2:length(assoctest[1,])] 1) Government focussed 4) Digitising government focus 2) Technology innovation focussed Understanding, Efficiency, Accountability A computerisation movement... Creating platforms, with semantic web 3) Reward focussed Recognition or profit
  • Further analysis R commands: identify clusters & create new matrix > c1<-c(7,9,2) > c2<-c(13,10,3) • We can take these clusters and create a new matrix of > c3<-c(3,11,4) > c4<-c(9,2,13,10) individuals rankings against them by summing the individual > c5<-c(14,8) variables. > c6<-c(12,6) > mclusts<- cbind(as.vector(by(motivations[,c1],c(1:72),sum)),as.vector( • We can then look at correlations between these and other by(motivations[,c2],c(1:72),sum)),as.vector(by(motivations[, variables. c3],c(1:72),sum)),as.vector(by(motivations[,c4],c(1:72),sum) ),as.vector(by(motivations[,c5],c(1:72),sum)),as.vector(by(m otivations[,c6],c(1:72),sum))) > colnames(mclusts)<-c("GovFocus", "TechFocus", • For example, looking at correlations between preferred "RewardFocus", "DigitalGov", "ProblemSolving", methods of data access and user motivation. "Enterprise") The additional data used is available in Google Docs linked from http://www.practicalparticipation.co.uk/odi/survey Download the relevant file as CSV and then import into the correct variable name with the command > egassociations<-read.csv<-(file=”filename.csv’,headers=T) To remove the line numbers, you can use: > egassociations<-egassociations[,2:length(assoctest[1,])] 1) Government focussed 4) Digitising government focus 2) Technology innovation focussed Understanding, Efficiency, Accountability A computerisation movement... Creating platforms, with semantic web 3) Reward focussed Recognition or profit 5) Problem solving focus Develop new skills to meet interesting challenge
  • Further analysis R commands: identify clusters & create new matrix > c1<-c(7,9,2) > c2<-c(13,10,3) • We can take these clusters and create a new matrix of > c3<-c(3,11,4) > c4<-c(9,2,13,10) individuals rankings against them by summing the individual > c5<-c(14,8) variables. > c6<-c(12,6) > mclusts<- cbind(as.vector(by(motivations[,c1],c(1:72),sum)),as.vector( • We can then look at correlations between these and other by(motivations[,c2],c(1:72),sum)),as.vector(by(motivations[, variables. c3],c(1:72),sum)),as.vector(by(motivations[,c4],c(1:72),sum) ),as.vector(by(motivations[,c5],c(1:72),sum)),as.vector(by(m otivations[,c6],c(1:72),sum))) > colnames(mclusts)<-c("GovFocus", "TechFocus", • For example, looking at correlations between preferred "RewardFocus", "DigitalGov", "ProblemSolving", methods of data access and user motivation. "Enterprise") The additional data used is available in Google Docs linked from http://www.practicalparticipation.co.uk/odi/survey Download the relevant file as CSV and then import into the correct variable name with the command > egassociations<-read.csv<-(file=”filename.csv’,headers=T) To remove the line numbers, you can use: > egassociations<-egassociations[,2:length(assoctest[1,])] 1) Government focussed 4) Digitising government focus 2) Technology innovation focussed Understanding, Efficiency, Accountability A computerisation movement... Creating platforms, with semantic web 6) Social/public sector enterprise 3) Reward focussed Companies providing local focussed services Recognition or profit 5) Problem solving focus Develop new skills to meet interesting challenge
  • Further analysis: motivations and attitudes • The attitudes dataset records responses against a number of claims about OGD. • The table below shows how different motivational clusters correlate with particular statements about OGD. Note the strong correlation between ‘Reward focus’ and statements about commercial benefit; and the view in Government focussed and problem solving that OGD can drive reform of public services. • Only Government focussed motivations have any real correlation with claims about accountability. • The ‘Enterprise’ correlations suggest a stronger ‘Social Enterprise’ interpretation may be appropriate. Correlation matrix of generated motivational clusters and response to ‘attitudes 1’ questions This row is curious. Little evidence elsewhere in study that OGD actually is helping errors to be identified... R commands: > cor(attitudes,mclusts) #display formatted in excel
  • Further analysis: attitudes 2 • The attitudes 2 set of questions have far lower correlation. Overall the consensus on these items was far stronger, thus the information that can be extracted from their correlation with particular motivations less. • The correlations are not easily interpretable - but suggest that the views within each motivational cluster are not homogenous. The small (but greater than others) correlation of Tech and DigitalGov clusters against ‘Data only open when clear demand’ is surprising. • Attitudes 2 does little to illuminate the interpretation of these motivational clusters. Correlation matrix of generated motivational clusters and response to ‘attitudes 2’ questions R commands: > cor(attitudes2,mclusts) #display formatted in excel
  • Further analysis: associations • Correlations against associations provide some further confirmation of the clusters, suggesting the diversity of the ‘Gov Focus’ users, but relatively strong identity of the tech and reward focussed clusters. Correlation matrix of generated motivational clusters and response to ‘associations’ questions R commands: > cor(associations,mclusts) #display formatted in excel
  • The digital government connection • Using multi-dimensional scaling can support visualisation of the relationship between the different motivational clusters. • Given the data feeding into the clusters is overlapping (1 overlaps 4; 2 overlaps 3 and 4) we may expect some clustering of these when MDS is applied. However, we do see (6) closer to (1), (4) and (2) than may be anticipated, and (5) distant from Digitising Government focus. R: Using a manually created distance matrix of 1-correlation matrix value as dist > fit<-cmdscale(dist,2) > plot(fit[,2],fit[,1]) > text(fit[,2],fit[,1],labels=colnames(mclusts),pos=4,offset=0.2)
  • The digital government connection • Using multi-dimensional scaling can support visualisation of the relationship between the different 1) Government focussed motivational clusters. Understanding, Efficiency, Accountability • Given the data feeding into the clusters is overlapping (1 overlaps 4; 2 overlaps 3 and 4) we may expect some clustering of these when MDS is applied. However, we do see (6) closer to (1), (4) and (2) than may be anticipated, and (5) distant from Digitising Government focus. R: Using a manually created distance matrix of 1-correlation matrix value as dist > fit<-cmdscale(dist,2) > plot(fit[,2],fit[,1]) > text(fit[,2],fit[,1],labels=colnames(mclusts),pos=4,offset=0.2)
  • The digital government connection • Using multi-dimensional scaling can support visualisation of the relationship between the different 1) Government focussed motivational clusters. Understanding, Efficiency, Accountability • Given the data feeding into the clusters is overlapping (1 overlaps 4; 2 overlaps 3 and 4) we may expect some clustering of these when MDS is applied. However, we do see (6) closer to (1), (4) and (2) than may be anticipated, and (5) distant from Digitising Government focus. 2) Technology innovation focussed Creating platforms, with semantic web R: Using a manually created distance matrix of 1-correlation matrix value as dist > fit<-cmdscale(dist,2) > plot(fit[,2],fit[,1]) > text(fit[,2],fit[,1],labels=colnames(mclusts),pos=4,offset=0.2)
  • The digital government connection • Using multi-dimensional scaling can support visualisation of the relationship between the different 1) Government focussed motivational clusters. Understanding, Efficiency, Accountability • Given the data feeding into the clusters is overlapping (1 overlaps 4; 2 overlaps 3 and 4) we may expect some clustering of these when MDS is applied. However, we do see (6) closer to (1), (4) and (2) than may be anticipated, and (5) distant from Digitising Government focus. 2) Technology innovation focussed Creating platforms, with semantic web 3) Reward focussed Recognition or profit R: Using a manually created distance matrix of 1-correlation matrix value as dist > fit<-cmdscale(dist,2) > plot(fit[,2],fit[,1]) > text(fit[,2],fit[,1],labels=colnames(mclusts),pos=4,offset=0.2)
  • The digital government connection • Using multi-dimensional scaling can support visualisation of the relationship between the different 1) Government focussed motivational clusters. Understanding, Efficiency, Accountability • Given the data feeding into the clusters is overlapping (1 overlaps 4) Digitising government focus 4; 2 overlaps 3 and 4) we may A computerisation movement? expect some clustering of these when MDS is applied. However, we do see (6) closer to (1), (4) and (2) than may be anticipated, and (5) distant from Digitising Government focus. 2) Technology innovation focussed Creating platforms, with semantic web 3) Reward focussed Recognition or profit R: Using a manually created distance matrix of 1-correlation matrix value as dist > fit<-cmdscale(dist,2) > plot(fit[,2],fit[,1]) > text(fit[,2],fit[,1],labels=colnames(mclusts),pos=4,offset=0.2)
  • The digital government connection • Using multi-dimensional scaling can support visualisation of the relationship between the different 1) Government focussed motivational clusters. Understanding, Efficiency, Accountability • Given the data feeding into the clusters is overlapping (1 overlaps 4) Digitising government focus 4; 2 overlaps 3 and 4) we may A computerisation movement? expect some clustering of these when MDS is applied. However, we do see (6) closer to (1), (4) and (2) than may be anticipated, and (5) distant from Digitising Government focus. 2) Technology innovation focussed Creating platforms, with semantic web 5) Problem solving focus Develop new skills to meet interesting challenge 3) Reward focussed Recognition or profit R: Using a manually created distance matrix of 1-correlation matrix value as dist > fit<-cmdscale(dist,2) > plot(fit[,2],fit[,1]) > text(fit[,2],fit[,1],labels=colnames(mclusts),pos=4,offset=0.2)
  • The digital government connection • Using multi-dimensional scaling can support visualisation of the relationship between the different 1) Government focussed motivational clusters. Understanding, Efficiency, Accountability • Given the data feeding into the clusters is overlapping (1 overlaps 6) Social/public sector enterprise 4) Digitising government focus 4; 2 overlaps 3 and 4) we may Companies providing local focussed services A computerisation movement? expect some clustering of these when MDS is applied. However, we do see (6) closer to (1), (4) and (2) than may be anticipated, and (5) distant from Digitising Government focus. 2) Technology innovation focussed Creating platforms, with semantic web 5) Problem solving focus Develop new skills to meet interesting challenge 3) Reward focussed Recognition or profit R: Using a manually created distance matrix of 1-correlation matrix value as dist > fit<-cmdscale(dist,2) > plot(fit[,2],fit[,1]) > text(fit[,2],fit[,1],labels=colnames(mclusts),pos=4,offset=0.2)
  • Analysis • To be written up....