Titiana Petra Ertiö
email: titiana.ertio@utu.fi

Sampo Ruoppila
email: sampo.ruoppila@utu.fi

Sarah-Kristin Thiel
email: sarah-kristin.thiel@ait.ac.at

Motivations to use a mobile participation application

Keywords: mobile application, citizen participation, urban planning, digital divide

This paper reports results on presumably the first city-wide mobile participation trial (Living Lab) examining citizen participation in urban planning, conducted in Turku, Finland, in 2015. The questions examined are the socio-economic characteristics of the application users, as well as their motivations to participate. The inclusion of online participation has been discussed widely in literature on e-participation and the digital divide, arguing for a great influence of socio-economic factors, interest in the topic, and users' online skills. The results reveal that this application, too, was used predominantly by young adults and middleaged, highly educated, and technology savvy citizens, who were already interested in urban planning. Their use of the application was motivated primarily by the opportunity to bring their own ideas and issues to the city authorities' attention, rather than participating in missions given by the municipality or discussing planning issues with fellow citizens, indicating a rather individualistic usage interest. The location-based features and ease of use of a mobile application were valued highly. Those who idled reported predominantly technical challenges with the app.

Introduction

Digital inclusion is a key question for governance, since many public engagement opportunities and services have moved online. One area of interest has been urban planning, in which variations of Public Participation Geographic Information Systems (PPGIS) have become widely used for collecting citizens' viewpoints. Recently, the increasing usage of smartphones and tablets has created new possibilities for mobile participation [START_REF] Korn | Situating engagement: Ubiquitous infrastructures for in situ civic engagement[END_REF][START_REF] Ertiö | Supporting "Participation" in Mobile Participation[END_REF][START_REF] Kleinhans | Using Social Media and Mobile Technologies to Foster Engagement and Self-Organization in Participatory Urban Planning and Neighbourhood Governance[END_REF][START_REF] Ertiö | Participatory Apps for Urban Planning -Space for Improvement[END_REF][START_REF] Desouza | Citizen Apps to Solve Complex Urban Problems[END_REF][START_REF] Evans-Cowley | There's an App for That[END_REF]. A plethora of so-called reporting apps have emerged, enabling citizens to send maintenance requests to the local authorities. Another type of participation app employs the so-called citizen sensing approach, where built-in sensors in citizens' mobile devices collect the data and submit it to a back-end database [START_REF] Höffken | Mobile Participation: Citizen Engagement in Urban Planning via Smartphones[END_REF]. These applications tie "the problem to be solved" to a place, thereby situating engagement [START_REF] Korn | Situating engagement: Ubiquitous infrastructures for in situ civic engagement[END_REF]. In fact, situated (in-situ) engagement is one of the biggest promises of mobile participation: it enables citizens to discuss topics of interest at the particular location where they occur [START_REF] Ertiö | Participatory Apps for Urban Planning -Space for Improvement[END_REF]. However, little is known about which citizens would be interested in using mobile participation apps for civic engagement and why. Neither have the questions of digital inclusion yet been investigated in the realm of mobile participation.

This paper is based on -to the best of our knowledge -the first city-wide trial of mobile participation in urban planning carried out in the world to date. The mobile application Täsä ("here" in Turku dialect), developed in the research project "Building Pervasive Participation" (b-Part; www.b-part.eu), was tested in a Living Lab run in cooperation with the City of Turku (Finland) between June and October 2015. The app enabled citizens to add and discuss geo-referenced urban development ideas (referred to as contributions in the app) visible to all users. Any contribution could be enhanced by adding a title, short description, photo, feeling, location, and tags (e.g. green areas, transportation, culture) (see Figure 1). Most of the features were implicitly recognisable to users familiar with social media. Contributions could be commented and voted upon, forming the bi-directional communication between city officials and citizens, as well as among citizens. Users could further choose to participate in so-called missions, which were in-app tasks given by the municipality to solicit feedback on certain topics. In fact, both city officials and citizens could create missions, but in practice only the officials did. For example, one mission invited citizens to vote and comment on the question of whether to build a new light-traffic bridge in a particular place in Turku.

The Täsä application was built on the participatory sourcing approach, meaning that users were encouraged to contribute their own ideas and voice concerns (bottom up), which were then processed by authorities to eventually inform decisions. Täsä extended this approach by putting a main focus on the communication between citizens and city authorities. In this respect, city officials and urban planners responded to citizens' input, but also asked for input on specific topics (missions) themselves (top down). During the Living Lab, Täsä was formally one official participation channel of the municipality.

Täsä was available for Android, iOS, and Windows phones free of charge. 780 users downloaded Täsä, of which one third (32%) produced content (contributions, comments, votes, or likes) within the app. The Täsä website was visited by more than 1 700 unique users. Täsä was marketed through social media (predominantly Facebook) by the municipality, as well as by the research team, using flyers, mailing lists, and electronic newsletters. It was also featured in several stories published in local newspapers and on radio. Information was also disseminated via speaking engagements and events organised for neighbourhood associations. Täsä had its own website with video tutorials on how to download the app and how to use it. In addition, users could contact the research team and ask for instructions, if needed.

Drawing on the results of the Täsä user surveys, this paper discusses what added value mobile applications can bring to citizen participation. More precisely, we will discuss who chose to participate in Turku's urban planning through the Täsä application, and why. The study contributes to the growing literature on digital inclusion in terms of the motivations for adopting new technologies, the factors encouraging their usage, barriers to adoption, and benefits of implementation.

Inclusive e-participation and motives for adopting new technologies

The inclusion of self-selected participants online has been discussed widely in literature on e-participation and -more broadly, including adopting new technologies -on the digital divide, arguing for a great influence of socio-economic factors, interest in the topic, and users' online skills.

Inclusive e-participation

Perhaps the greatest promise of e-participation is its potential to broaden the pool of participants. Compared to traditional (off-line) forms of participation, e-participation can involve almost a limitless number of participants. Meanwhile, the greatest challenge remains the inclusion of disengaged citizens and also giving less articulated groups a voice in the political arena.

The popularity of e-participation channels has been on the rise. For example, in Germany, online petitions have been increasing in number, as have registered users of official participation portals [START_REF] Lindner | Broadening Participation Through E-Petitions? An Empirical Study of Petitions to the German Parliament[END_REF]. Compared to face-to-face participation, e-participation has a number of other advantages. For instance, [START_REF] Brabham | Crowdsourcing the Public Participation Process for Planning Projects[END_REF] enumerates the anonymity of participants, as well as the speed and reach of ideas posted online. The adoption of new technologies has been boosted with the shift towards Web 2.0, which has emphasised the role of citizens as active knowledge producers [START_REF] Kubicek | The Potential of E-Participation in Urban Planning: A European Perspective[END_REF]. These new skills that citizens have learned have proven to be transferable to many fields of activity, including participatory urban planning [START_REF] Saad-Sulonen | Combining Participations. Expanding the Locus of Participatory EPlanning by Combining Participatory Approaches in the Design of Digital Technology and in Urban Planning[END_REF].

The empirical results of various e-participation platforms, however, have given little support for enhancing inclusive participation, in terms of representativeness at least. The "usual suspects" in online participation are well-educated men, already active in other areas of political life [START_REF] Lindner | Broadening Participation Through E-Petitions? An Empirical Study of Petitions to the German Parliament[END_REF][START_REF] Jonsson | Democratic Innovations in Deliberative Systems -The Case of the Estonian Citizens Assembly Process[END_REF]. While they may propose useful arguments and various relevant viewpoints, the challenge remains that this may also reinforce previously existing power imbalances [START_REF] Carman | Barriers are Barriers: Asymmetric Participation in the Scottish Public Petitions System[END_REF]. Conversely, the characteristics of non-participants (feminine gender, low social-economic status, age, ethnic background, disability) have significant consequences for the process and the outcomes [START_REF] Aitken | E-Planning and Public Participation: Addressing or Aggravating the Challenges of Public Participation in Planning?[END_REF]. The empirical studies on e-participation indicate that interest in it reflects interest in political participation in general. Interests in politics, time, money, and (civic) skills are the most important predictors of political participation [START_REF] Brady | Beyond SES-A Resource Model of Political Participation[END_REF]. The specific novel technology, however, may require the effort of learning new skills, which might raise the participation barrier [START_REF] Steinmann | Analysis of Online Public Participatory GIS Applications with Respect to the Differences between the US and Europe[END_REF]. On the other hand, new technologies may also be a tempting factor. For instance, some studies indicate that citizens would prefer to use social media tools to engage with planning [START_REF] Evans-Cowley | The New Generation of Public Participation: Internet-Based Participation Tools[END_REF][START_REF] Williamson | Web 2.0 and Social Media Growth in Planning Practice: A Longitudinal Study[END_REF].

Despite benefits and pitfalls, scholars and pundits alike agree that e-participation tools have gained momentum and interest [START_REF] Lindner | Broadening Participation Through E-Petitions? An Empirical Study of Petitions to the German Parliament[END_REF] especially because they support deliberative processes among participants, and between citizens and public officials [START_REF] Aitamurto | Crowdsourced Deliberation: The Case of the Law on Off-Road Traffic in Finland[END_REF]. The lack of statistical representativeness "does not necessarily mean a poverty of views, information, and arguments and low quality deliberation" [19:2].

The digital divide in the mobile Internet era

The exclusion caused by the adoption of new technologies has been discussed in the literature on the digital divide. The traditional topic of this discussion has been access to the Internet and, more recently, differentiation in its usage. The discussion is useful in examining the gradual diffusion of Internet-related participation technologies. It has been asserted that socio-economic background factors (notably education, income, age) are associated with Internet access [START_REF] Van Dijk | The Deepening Divide Inequality in the Information Society[END_REF][START_REF] Van Deursen | Rethinking Internet Skills: The Contribution of Gender, Age, Education, Internet Experience, and Hours Online to Medium-and Content-Related Internet Skills[END_REF]. As access to the Internet became more common, the focus of the discussion shifted to differences in what the Internet is used for, mediated by online skills (and indirectly education), a phenomenon known as the second-level digital divide [START_REF] Hargittai | Second-Level Digital Divide: Differences in Peoples Online Skills[END_REF][START_REF] Van Deursen | The Digital Divide Shifts to Differences in Usage[END_REF]. The current debate is focused on differences in digital skills and the consequent differentiation between types of users [START_REF] Van Deursen | Rethinking Internet Skills: The Contribution of Gender, Age, Education, Internet Experience, and Hours Online to Medium-and Content-Related Internet Skills[END_REF][START_REF] Van Deursen | Internet Skills, Sources of Support, and Benefiting from Internet Use[END_REF][START_REF] Van Dijk | Digital Skills: Unlocking the Information Society[END_REF][START_REF] Zillien | Digital Distinction: Status-Specific Types of Internet Usage[END_REF]. [START_REF] Wei | Numbers Matters: The Multimodality of Internet Use as an Indicator of the Digital Inequalities[END_REF] summarizes how the variety of activities online is a critical indicator of digital inequalities. Far from being dismissed, socio-economic factors influence usage far beyond skills; more affluent people engage in more competence-enhancing activities compared with lower income people, whose usage concentrates on entertainment [START_REF] Van Deursen | Internet Skills, Sources of Support, and Benefiting from Internet Use[END_REF][START_REF] Van Dijk | Digital Skills: Unlocking the Information Society[END_REF][START_REF] Hargittai | Digital Inequality. Differences in Young Adults Use of the Internet[END_REF]. Consequently, the benefits are spread unequally; "those who are already in more privileged positions are more likely to use the medium for activities from which they may benefit" [28: 615]. Considerable effort has been put into investigating the users of online technologies and the disadvantages of low or non-usage [START_REF] Reisdorf | Non-adoption of the Internet in Great Britain and Sweden: A Crossnational Comparison[END_REF][START_REF] Reisdorf | Internet (Non-)use Types and Motivational Access: Implications for Digital Inequalities Research[END_REF].

Nonetheless, overall Internet access continues to improve, and consequently basic digital skills are becoming increasingly common sense. Moreover, the mobile Internet offers an access breakthrough for lower socio-economic groups worldwide [START_REF] Pearce | Digital Divides from Access to Activities: Comparing Mobile and Personal Computer Internet Users[END_REF]. If users' skills are limited or the technology is modest, disadvantaged groups might not be able to exploit the potential of mobile phones to the fullest, which can lead to consecutive exclusions online [START_REF] Hargittai | The Prevalence of Smartphone Use Among a Wired Group of Young Adults[END_REF]. The mobile Internet might reduce the access divide but widen the usage gap [START_REF] Mascheroni | The Mobile Internet: Access, Use, Opportunities and Divides Among European Children[END_REF]. Digital skills are directly associated with online participation, including expressing an opinion, or participating in consultation or petitioning [START_REF] Vicente | An Empirical Analysis of E-Participation. The Role of Social Networks and E-Government over Citizens' Online Engagement[END_REF].

In addition to material access, skills, and usage, motivation continues to be the most important factor mediating digital inclusion [START_REF] Van Dijk | The Deepening Divide Inequality in the Information Society[END_REF]. Hence, there is a constant concern that if users do not see value in using the Internet for a particular purpose, they leave it unused despite having access and skills [START_REF] Van Dijk | The Deepening Divide Inequality in the Information Society[END_REF]. [START_REF] Courtois | With a Little Help From My Friends: An Analysis of the Role of Social Support in Digital Inequalities[END_REF] identify an emotional (technophobia) and a rational factor (no need for a specific technology) in such a lack of usage motivation. Further, they found that the relation between access quality and skills fully mediates motivation in content-related skills [START_REF] Courtois | With a Little Help From My Friends: An Analysis of the Role of Social Support in Digital Inequalities[END_REF].

The perceived usefulness of a technology affects users' motivation to try it, leading to its use or not. [START_REF] Porter | Using the Technology Acceptance Model to Explain How Attitudes Determine Internet Usage: The Role of Perceived Access Barriers and Demographics[END_REF] extended the Technology Acceptance Model (TAM) and applied it to Internet use and non-use. [START_REF] Reisdorf | Internet (Non-)use Types and Motivational Access: Implications for Digital Inequalities Research[END_REF] found that non-users have negative attitudes towards online technologies and feel uncomfortable using them. Such attitudes have been attributed to lower perceived skills in handling technologies (indirectly mediated by education), coupled with a lack of confidence [START_REF] Helsper | The Emergence of a Digital Underclass. Digital Policies in the UK and Evidence for Inclusion[END_REF]. [START_REF] Reisdorf | Internet (Non-)use Types and Motivational Access: Implications for Digital Inequalities Research[END_REF] also found that education is the strongest predictor when differentiating between broad and narrow uses. Another study showed that positive attitudes towards online technologies were significant predictors for differentiating between low-users and non-users [START_REF] Reisdorf | Non-adoption of the Internet in Great Britain and Sweden: A Crossnational Comparison[END_REF].

The rapidly increased use of the mobile Internet, coupled with increased social media usage, has also opened new avenues for electronic participation. Through the myriad of social media applications, citizens learn new mobile usage skills. Social media, including the likes of Facebook and Twitter, are already ranked third as the preferred interaction channels between citizens and a municipality, after email and municipal websites [START_REF] Johannessen | Choosing the right medium for municipal e-participation based on stakeholder expectations[END_REF]. [START_REF] Xenos | The great equalizer? Patterns of social media use and youth political engagement in three advanced democracies[END_REF] found that social media use explains more variance than socio-economic factors in the political participation of young people. In part, this finding is explained by the ease of engagement: liking, sharing, and retweeting entail less effort than commenting on a post [START_REF] Bonsón | Citizens' engagement on local governments' Facebook sites. An empirical analysis: The impact of different media and content types in Western Europe[END_REF]. Attaching a photo as well as posting about public services (transportation, urban planning) also elicits a high level of engagement [START_REF] Bonsón | Citizens' engagement on local governments' Facebook sites. An empirical analysis: The impact of different media and content types in Western Europe[END_REF]. The effects of attaching a location to a post are unknown. Especially on mobile phones, attaching a location to a social media post is easy and quick, thanks to the GPS embedded in the phone. The skills and mobile communication practices that citizens appro-priate through social media are easily transferable to other contexts, including participatory urban planning.

Research questions and data

The research question leading this inquiry is what characterised the citizens who adopted the new mobile participation tool, and what were their motivations for using it? Specifically, we are interested in RQ1. Who signed up for the Täsä application trial? RQ2. What was their initial motivation to download the app? RQ3. What factors encouraged usage of the app? RQ4. What factors discouraged usage of the app?

The first two questions are answered based on the data of a pre-survey of registered users of the Täsä application. After downloading the app and registering, a pop-up window appeared on their device containing the survey, with questions regarding their social background (gender, age, educational achievement), experience with mobile devices, and attitudes towards urban planning and politics. During the trial, reminders were sent to complete the survey. 185 people, out of 780 who downloaded the app, answered the pre-survey.

Answers to the last two questions are based on the data of a post-survey, which was sent out to all registered users at the end of the trial. In the postsurvey, respondents were asked about their experiences in using the application, including motivating factors, evaluations of the app and mobile participation, and improvement ideas. This survey was answered by 97 users. The overlap of those who answered both the pre-and post-survey was rather small (39 users). In addition, to address the last question more profoundly, we conducted, during a period late in the trial, 12 telephone interviews with passive users, meaning those who had downloaded the app and created user accounts but had not produced any content in the app. These people consisted of onlookers, who used the app just for observing other people's input, and those who quickly abandoned usage altogether. Passive users were sampled randomly and approached by e-mail initially. Interviews were conducted until data saturation was achieved. Interviews were recorded after the participants were informed and had consented. Participants were asked about their impression of the app, factors that hampered their participation, and features that would make the application more appealing.

Findings

Täsä application users

The self-selected users of the mobile participation application were mostly young adults and middle-aged people with a high level of education -like a number of previous e-participation trials. Men outnumbered women, but not greatly. Compared with the inhabitants of Turku (Figure 2), for Täsä the 21-40 age group was considerably over-represented (72%), but Täsä lacked participants younger than 21 and especially senior citizens. The average education level of participants was high: more than half had an academic degree -another considerable difference compared to the city population overall. Most respondents reported good to excellent perceived skills in using mobile phones (89%), which also presumably differentiates them from the average.

Although the trial was intended for all smartphone users (more than 70% of the Finnish population by the time of the trial), the users needed to own a device with a rather recent operating system for the application to work well, which favoured the participation of people of active working age, with a good socio-economic status, and good mobile phone usage skills (and/or requirements for their phones), which is a classic restricting access factor of the digital divide.

Motivation to download the app

The results indicate the two most important reasons to download and start using the application. First, almost half of the respondents were initially motivated fairly or very much by curiosity to test the mobile participation application (Figure 3). Citizens also rated highly their desire to be informed about ongoing discussions about Turku's urban planning. Interestingly, taking action by using the Täsä app to communicate a specific idea of something in need of change to the City of Turku, or getting engaged in dialogue on planning issues with fellow citizens, was reported as less important. Secondly, when we asked about respondents' general interest in Turku's urban planning, almost all of them were very (64%) or fairly interested (30%), which indicates that they did not download the app solely because of their curiosity in testing the app.

Factors encouraging usage

If one of the main reasons to download the application was curiosity to test a new tool, at the end of the trial, we inquired what factors actually motivated its usage (Figure 4). In hindsight, the greatest motivator was the opportunity to bring one's own idea to the attention of city authorities, which 45% of respondents reported as influencing their participation very much. On the other hand, receiving feedback from the authorities motivated people much less than anticipated [START_REF] Ertiö | Supporting "Participation" in Mobile Participation[END_REF]. In addition, participating in missions (tasks) given by the city administration in the app and communicating with peers were not rated highly. In fact, people stated a will to use (and also used) the app largely as a reporting tool, making little use of its many interactive features, which functioned akin to social media. This finding indicates a more individualistic usage mode than expected.

Affordances specific to the mobile participation tool were also given great importance, which is a promising result regarding further development. The ability to give in-situ feedback, as well as ease of mobile participation usage, motivated the participants highly.

When asked, as many as 46% of post-survey respondents reported they had learned fairly or very much about how a mobile app could assist urban planning, and 71% reported that the experience increased their faith in the applicability of mobile apps in (participatory) urban planning. The application users made use of their existing skills in handling the new application, rather than being forced to learn new ones, since most people (86%) perceived no change in their general mobile device using skills after participating in the trial.

Factors discouraging usage

In order to understand which factors discouraged the use of the application, interviews were conducted with passive users, meaning those who had created user accounts, but had not produced any content in the app. The interviews confirmed that these people were partly onlookers and partly those who had quickly abandoned usage altogether. Unsurprisingly, the main reason for abandoning usage was technical challenges in their many forms. Some had trouble downloading and registering, some complained about the bugs in the app, while others would have preferred to participate through a webpage using a personal computer rather than a mobile phone. Even when active participation was not the case, interviews revealed that users still read and followed up on content posted in Täsä because they were interested in accessing topical information regarding Turku's urban planning. When we asked interviewees what it would take for them to use the app, the theme of the webpage -mobile optimized webpage -surfaced again. Another feature that would enhance participation would be a better structure for the content.

Conclusions and Discussion

The rapid development of mobile technologies has raised interest in broadening the realm of e-participation towards mobile participation [START_REF] Kleinhans | Using Social Media and Mobile Technologies to Foster Engagement and Self-Organization in Participatory Urban Planning and Neighbourhood Governance[END_REF]. However, applying new technologies to societal matters, such as participatory urban planning, comes with concerns regarding the "digital divide", meaning limiting factors influencing their usage.

This paper has reported results on the socio-economic characteristics of participants and their motivations to participate, analysed through surveys conducted when they registered as users and after the large mobile participation trial (Living Lab) conducted in Turku, Finland, in 2015.

The self-selected users of our mobile participation application were mostly young adults and middle-aged people with a high level of education, and a great interest in urban planning topics. As in a number of other previous eparticipation initiatives, Täsä users insufficiently represented Turku's inhabitants. However, while Täsä might have given a voice to people in advantageous positions, it certainly gave it to those who have been notoriously absent from traditional face-to-face participation. For those young adults and middle-age people, perhaps at the busiest stages of their lives, juggling job and family responsibilities, mobile participation proved its potential for civic engagement. Mobile participation's affordances, such as ubiquity and simplicity, appealed to this particular group, to bring their issues to the municipality's attention. Hence, the results suggest that mobile participation can have a complementary role in developing tools for citizen engagement.

Aiming for inclusive participation, the easiest step forward would be to introduce a mobile-optimised web-based participation platform alongside the mobile application. Websites are well-suited to involve those with older phones or those who take time to ponder by the keyboard. This idea surfaced in the feedback received during the trial, the interviews, and open questions in the post-survey. Our results are in line with recommendations that in-situ engagement should be complemented with ex-situ engagement [START_REF] Korn | Situating engagement: Ubiquitous infrastructures for in situ civic engagement[END_REF]. Unsurprisingly, we found that the technical challenges quickly displaced the initial motivation to try a new application, and led to passive usage or giving up use of the app altogether. Thus user-friendliness, ease of use, and technical reliability are certainly drivers of loyalty.

The self-selection process of the users was driven by curiosity towards the new application, confirming the "novelty effect" [START_REF] Korminouri | The Novelty Effect: Support for the Novelty-Encoding Hypothesis[END_REF] as one motivator. Courtois and Verdegem regard confidence in one's own skills to be indirectly supported by motivation [START_REF] Courtois | With a Little Help From My Friends: An Analysis of the Role of Social Support in Digital Inequalities[END_REF]. In a similar manner, most of our users reported excellent mobile usage skills from the beginning. We can only estimate how much the "second-order digital divide" [START_REF] Hargittai | Second-Level Digital Divide: Differences in Peoples Online Skills[END_REF] played a role among our young adults and middle-aged user groups, and how much their skills and their trust in them affected their self-selection. Nonetheless, the same users reported learning a lot about how a mobile application can contribute to developing participatory urban planning. This result indicates that early adopters of a technology expand their knowledge-base, which in turn helps them to better navigate future participation opportunities.

Participation in the Täsä trial happened mainly out of an interest in the city's urban planning. The results indicate that most citizens were motivated to use the mobile participation opportunity primarily as a tool to bring their own ideas to the attention of the city authorities. Despite the fact that the app had many features affording interaction among citizens, as well as between citizens and city officials, the respondents were much less interested in receiving feedback from the municipality, participating in missions (tasks) given by the municipality, or discussing planning ideas with fellow citizens. These findings on the motivation to use the app primarily independently are slightly surprising. Moreover, they also conflict with previous studies, which have found that citizens are eager to engage with planners using social media tools [START_REF] Evans-Cowley | The New Generation of Public Participation: Internet-Based Participation Tools[END_REF][START_REF] Williamson | Web 2.0 and Social Media Growth in Planning Practice: A Longitudinal Study[END_REF]. Living labs are highly context specific and their rollouts might have unexpected turns [START_REF] Åström | Potentials and Challenges of a Living Lab Approach in Research on Mobile Participation[END_REF]. Perhaps the technical difficulties confronted by many users might explain why the application did not develop into a more socially active platform. On the other hand, as many as half of the post-survey respondents (52%) were so-called passive users, who might have reflected upon an intention rather than the actual experience of using the app. Providing further insights into the relation between intentions and actual usage (based on saved back-end data) is beyond the scope of this paper, but will be addressed in a future analysis. Nevertheless, the large number of passive users who responded to the post-survey, giving constructive critique and encouraging comments, indicates a broad interest in an improved app. Such findings are consistent with the Technology Acceptance Model (TAM) and suggest a positive future usage intention, after realising the potential of mobile apps as participatory tools in urban planning.

Acknowledgements

The research project Building Pervasive Participation was funded by JPI Urban Europe. The authors would like to thank the two anonymous reviewers for their constructive comments.

Fig. 1 .

 1 Fig. 1. Screenshots of the Täsä application used in Turku. Map view (left) and contribution list (right)

Fig. 2 .

 2 Fig. 2. Täsä users compared with the Turku population: gender, education, and age. Source: Pre-survey of registered users of the application (N=185) and the City of Turku population statistics.

Fig. 3 .

 3 Fig. 3. Initial motivation to download the Täsä application. Source: Pre-survey of registered users of the application (N=185).

Fig. 4 .

 4 Fig. 4. Motivations for using Täsä. Source: Post-survey of registered users of the application (N=97).


Marios Poulos
email: mpoulos@ionio.gr

Vassilios S Belesiotis

Nikolaos Alexandris
email: alexandr@unipi.gr

A Classroom Observation Model Fitted to Stochastic and Probabilistic Decision Systems

Keywords: Probabilistic Decision Systems, Normalization Data, Education, Formative Assessment in the Classroom, Classroom Observation

This paper focuses on solving the problems of preparing and normalizing data that are captured from a classroom observation, and are linked with significant relevant properties. We adapt these data using a Bayesian model that creates normalization conditions to a well fitted artificial neural network. We separate the method in two stages: first implementing the data variable in a functional multi-factorial normalization analysis using a normalizing constant and then using constructed vectors containing normalization values in the learning and testing stages of the selected learning vector quantifier neural network.

Introduction

Background and Motivation

The preparation stage of data processing is the most significant procedure in stochastic and probabilistic decision systems [START_REF] Yu | An integrated data preparation scheme for neural network data analysis[END_REF], an important subclass of which is neural networks. Modeling has an enormous impact on the success of a wide range of such complex data analyses as data mining and feature-extraction procedures [START_REF] Yu | An integrated data preparation scheme for neural network data analysis[END_REF][START_REF] Hu | DB-H Reduction: A Data Preprocessing Algorithm for Data Mining Applications[END_REF], mainly because the quality of the input data in neural network models strongly influences their data analysis's results [START_REF] Yu | An integrated data preparation scheme for neural network data analysis[END_REF][START_REF] Sattler | A Data Preparation Framework Based on a Multidatabase Language[END_REF] and the efficiency of their performance, as wrongly prepared data is likely to make the data analysis problematic [START_REF] Yu | An integrated data preparation scheme for neural network data analysis[END_REF]. The appropriate pre-processing of input data is therefore a significant procedure [START_REF] Yu | An integrated data preparation scheme for neural network data analysis[END_REF][START_REF] Pastor-Satorras | Epidemics and immunization in scale-free networks[END_REF]. A study of the procedure for grouping collected data [START_REF] Yu | An integrated data preparation scheme for neural network data analysis[END_REF] that addresses the selection of the data's variables with genetic algorithms and those algorithms' linkage to significant relevant properties would therefore be valuable.

Problems with the Preparation Data Stage

Grouping these categories with the specific multi-scale and normalization practices in the non-structural data's category may, however, be overly simplistic [START_REF] Yu | An integrated data preparation scheme for neural network data analysis[END_REF], as doing so cannot provide solutions for significant multi-factorial unconstructed data that are linked with significant relevant properties. A characteristic application of this method is the domain of classroom observation [5], [START_REF] Flanders | Analyzing Teaching Behaviour[END_REF], because data collected from this process fall into the aforementioned category.

Existing solutions have focused on the consolidation phase, during which data management systems consist of a continuous process. The capture procedure stores unconstructed data in a well-constructed database [START_REF] Tsumoto | Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model[END_REF], but their basic problem is in the pre-processing training setting, as the number of possible interaction factors grows exponentially with the inclusion of each additional main effect in the logistic regression model. Logistic regression's ability to deal with interactions involving many factors is therefore limited, as having too many independent variables in relation to the number of observed outcome events is problematic [START_REF] Ritchie | Genetic Programming neural networks: A powerful bioinformatics tool for human genetics[END_REF], [START_REF] Moore | New strategies for identifying gene-gene interactions in hypertension[END_REF].

Aims and Scope

This paper attempts to solve the problems of preparing and normalizing data linked with significant relevant properties and captured from many sources by adapting these factors to a first phase using a Bayesian model that creates normalization conditions with an equitable distribution between the significant relevant properties of such types of data. It also aims to adapt this normalization to a well-fitted neural network so it can accept pre-processing data for training and testing procedures. It does this using two separate procedures, first implementing the data variable in a functional multifactorial normalization analysis using a normalizing constant and then using constructed vectors containing normalization values in the learning and testing stages of the selected learning vector quantifier neural network.

For application purposes of this model we focus on the educational domain and more specifically on classroom observations in order to construct a decision system able to learn to evaluate the quality of a teaching process given as input a set of observations describing the events that took place during this process. The required responses of the system are boolean (yes or no) and form a statistical model which follows the t-test. The proposed method will focus on the normalization of the input data, enabling the effective training of a neural network in order to provide evaluation results for any given set of data. The evaluation of a teaching process can be any value from the fuzzy set: excellent, good, adequate, poor.

This paper is divided in four sections. Section 2 describes the integrated data preparation method as well as the architecture of the neural network with the original classroom observation model. In Section 3, the fitted model is presented, adapting the model to the classroom observation data. Finally, Section 4 concludes and explains our plans for future work.

Proposed Method

Figure 1 outlines the schedule of this approach. Following our method, captured classroom observation data are normalized using a Bayesian model and adapted to a well-fitted neural network.

Education Model

The original model is based on Systematic Observation [START_REF] Ritchie | Genetic Programming neural networks: A powerful bioinformatics tool for human genetics[END_REF], which presents a process for categorizing classroom observations regarding events describing the teacher's and pupils' behavior in a well constructed form. The pioneer of this method Flanders introduced a system [9] which has formed the basis for a number of studies in this field [START_REF] Ritchie | Genetic Programming neural networks: A powerful bioinformatics tool for human genetics[END_REF]. He defines teaching behavior as 'acts by the teacher which occur in the context of classroom interaction' [START_REF] Moore | New strategies for identifying gene-gene interactions in hypertension[END_REF]. Flanders devised a system which is divided into 3 broad areas: teacher talk, pupil talk and silence. This classification is overviewed in Table 1. However, in order to apply our method we consider that the expected value for the number of occurrences of each characteristic is equal to S/n, where S is the total number of observations and n is the number of characteristics. Furthermore, we consider a confidence probability of a=0.025, ranging the area between x 2 0,975,ν and x 2 0,025,ν with degree of freedom ν=10-1=9.

Integrated Data Preparation Method

According to previous study [START_REF] Yu | An integrated data preparation scheme for neural network data analysis[END_REF], the proposed technique to formulate related features of non-structural data is based on extracting related features from text data or documents by semantic analysis and formulates an event-specific summary. But this technique doesn't support our cases which are based on % related results. In this case, the solution of this conversion could be found in the Bayes's theorem according to the posterior probability measure is proportional to the product of the prior probability measure and the likelihood function. Proportional to implies that one must multiply or divide by a normalizing constant to assign measure 1 to the whole space, i.e., to get a probability measure. In a simple discrete case we have [START_REF] Yu | An integrated data preparation scheme for neural network data analysis[END_REF] where P(H 0 ) is the prior probability that the hypothesis is true; P(D|H 0 ) is the conditional probability of the data given that the hypothesis is true, but given that the data are known it is the likelihood of the hypothesis (or its parameters) given the data; P(H 0 |D) is the posterior probability that the hypothesis is true given the data. P(D) should be the probability of producing the data, but on its own is difficult to calculate, so an alternative way to describe this relationship is as one of proportionality:

P(H 0 | D)P(D | H 0 )P(H 0 ). (2)
Since P(H|D) is a probability, the sum over all possible (mutually exclusive) hypotheses should be 1, leading to the conclusion that [START_REF] Sattler | A Data Preparation Framework Based on a Multidatabase Language[END_REF] In this case, the reciprocal of the value (4)

In our case according to Table 1 the prior probability is given by P(H 0 ) =S1/S. The first conditional probability is given by P(D|H 0 ) = P(H 0 )/α*100. k(j)=1/P(D)

(5)

is the normalizing constant [START_REF] Feller | An Introduction to Probability Theory and its Applications[END_REF] where j=1..9 is number of the parameters. Vector k(j) is of size 1xj.

Neural network Architecture

We adopt as the ideal neural network to accept an appropriate fitted Bayesian vector k(j), since the main applications of RBF have been shown to be in pattern with a Bayesian classifier [START_REF] Traven | A neural-network approach to statistical pattern classification by semiparametric estimation of a probability density functions[END_REF]. For this purpose we select an RBF neural network classifier from a variety of available neural networks architectures, for the following reasons:

The RBF network was preferred to a Multilayer Perceptron (MLP), because it can solve a given problem using fewer neurons and in sorter time, yet with the same success.

The RBF network has the ability to classify incoming vectors into classes that are not linearly separable because it uses static Gaussian function as the nonlinearity for the hidden layer processing elements [START_REF] Anita | Transvaginal ultrasound measurement ofendometrial thickness as a biomarker for estrogen exposure[END_REF]. The Gaussian function responds only to a small region of the input space where the Gaussian is centered. Successful application of this classifier relies on finding suitable centers for the Gaussian functions. An unsupervised approach usually produces better results than supervised learning.

Using n classes according to the referred problem we design the node characteristics and the RBF network topology.

An RBF neural network can be considered as a special three-layered network. The input nodes pass the input values to the internal nodes that formulate the hidden layer. The nonlinear responses of the hidden nodes are weighted in order to calculate the final outputs of network in the third (output) layer [START_REF] Sarimveis | A classification technique based on radial basis function neural networks[END_REF].

A typical hidden node in an RBF network is characterized by its center, which is a vector with dimension equal to the number of inputs to the node. The activity νl(x) of the lth node is the Euclidean norm of the difference between the input vector k(j) and the node center and is given by: ( )

i i v x x x = - (1)
The output function of the node is a radially symmetric function. A typical choice, which is also used in this work, is the Gaussian function [START_REF] Belesiotis | A software system for the didactic utilization of classroom observation for Informatic courses[END_REF]:

2 2 ( ) exp f ν ν σ   = -    
(2) where σ is the width of the node.

Fitted Model

According to Sections 2.1, 2.2 and 2.3 we constructed the vector of the normalization data k(j). For the training procedure we constructed a satisfied number n vectors k(j), where n-h (h<n h E N) is the number of vectors used in the training procedure. It must be noted that the number of the vectors n is distributed in the four target classes of the RBF neural network. These classes correspond to a decision Liker scale (excellent, good, satisfied, fair). For implementation purposes we used 4 neurons and 2 layers. In the testing procedure we used the sigmoid function and h vectors k(j).

For the statistical evaluation of this model we used t-test criterion in order to evaluate the significant probability by considering as hull hypothesis that the system yields a random results aiming to the significant rejection.

Conclusion-Futures

In this paper we focus on a method solving problems related to preparing and normalizing data linked with significant relevant properties, which have been captured from classroom observations by adapting these factors to a first phase using a Bayesian model that creates normalization conditions with an equitable distribution between the significantly relevant properties of such types of data. Moreover, we described how the normalization can aid the training of a well-fitted neural network.

In the future, we would like to perform an extensive statistical evaluation of our model with real classroom observation data obtained through experimental questioners. This process can be supported by the software environment proposed in [START_REF] Belesiotis | A software system for the didactic utilization of classroom observation for Informatic courses[END_REF], which provides a system aiding the recording of observed classroom events. Finally, we would like to extend [START_REF] Belesiotis | A software system for the didactic utilization of classroom observation for Informatic courses[END_REF] by enhancing it with decision making capabilities in order to help the teacher identify problems and provide formative assessment.

Fig. 1 .

 1 Fig. 1. The schedule of the proposed method is analyzed

Table 1 :

 1 Flanders' Interaction Analysis categories[9: 34], where T: Teacher, R: Response, I: Initiation, S is the total number of observations and S i (i=1..9) is the number of observations for each characteristic.

	Parameters	Characteristics	Groups	Expected Result	Total=S	Observation Result	Total=S	Α=0.025
	1 Teacher, accepts feeling. Accepts and clarifies an
	attitude or the feeling tone of a pupil in a non-
	threatening manner. Feelings may be positive or
	negative. Predicting and recalling feelings are included.
			T-I	S/10 S4/S
	5 Teacher, Lecturing. Giving facts or opinions about
	content or procedures: expressing his own ideas, giving his own explanation or citing an authority other than a	T-I	S/10 S5/S
	pupil.
	6 Teacher, Giving directions. Directions, commands or orders to which a pupil is expected to comply.	T-I	S/10 S6/S
	7 Teacher, Criticizing or justifying authority.
	Statements intended to change pupil behavior from non-
	acceptable to acceptable pattern; bawling someone out;	T-I	S/10 S7/S
	stating why the teacher is doing what he is doing;
	extreme self-defense.
	8 Pupil talk -response. Talk by pupils in response to
	teacher. Teacher initiates the contact or solicits pupil statement or structures the situation. Freedom to express	P-R S/10 S8/S
	own ideas is limited.
	9 Pupil talk -initiation. Talk by pupils which they
	initiate. Expressing own ideas; initiating a new topic;
	freedom to develop opinions and a line of thought, like	P-I	S/10 S9/S
	asking thoughtful questions: going beyond the existing
	structure.
	10 Silence or confusion. Pauses, short periods of silence
	and periods of confusion in which communication	S
	cannot be understood by the observer.

T-R S/10 S1/S 2 Teacher, Praises or encourages. Praises or encourages pupil action or behaviour. Jokes that release tension, but not at the expense of another individual: nodding head, or saying 'Um hm?' or 'Go on' are included. T-R S/10 S2/S 3 Teacher, accepts or uses ideas of pupils. Clarifying, building or developing ideas suggested by a pupil. Teacher extensions of pupil ideas are included but as the teacher brings more of his own ideas into play, shift to category five. T-R S/10 S3/S 4 Teacher, Asks questions. Asking a question about content or procedure, based on teacher ideas, with the intent that a pupil will answer.

N. Alexandris. Visiting Professor at the University of Canberra, Faculty of Information Sciences and Engineering, is Professor of University of Piraeus in the Department of Informatics.


Don Casey
email: caseyd@lsbu.ac.uk

Phillip Burrell
email: phillipburrellphillb@lsbu.ac.uk

Lasso : Linkage Analysis of Serious Sexual Offences A Decision Support System for Crime Analysts and Investigators

Keywords: fuzzy systems, decision support, crime linkage

One of the most important considerations when investigating a serious sexual offence is to find if it can be linked to other offences. If this can be done then there is a considerable dividend in terms additional evidence and new lines of enquiry. The central problem is the construction of a satisfactory typology of these crimes, but little progress has been made. It is the authors' contention that difficulties arise from the inadequacy of the adoption of the classical or 'crisp set' paradigm. Complex events like crimes cannot be described satisfactorily in this way and it is proposed that fuzzy set theory offers a powerful framework within which crime can be portrayed in a sensitive and perceptive manner that can enhance the search for associations between offences

Introduction

The most influential crime classification system has been that proposed in the Crime Classification Manual [START_REF] Douglas | Crime classification manual: A standard system for investigating and classifying violent crime[END_REF], which is the work of senior F.B.I agents and advances the notion of an organized / disorganized dichotomy in serious offences and was developed from interviews with offenders [START_REF] Ressler | Crime Scene and Profile characteristics of organized and disorganized murderers FBI Law Enforcement[END_REF]. The basis of this approach is that crimes can be differentiated by the level of planning associated with them. The authors extend this to assert that the dichotomy can be applied to the offender so that organized and disorganized crimes are committed by individuals who can be divided into discrete groups with distinct characteristics.

Very serious objections have been made to the methodology employed by the F.B.I: only 36 offenders were interviewed, no attempt was made to ensure this group was representative and the interviews conducted were not structured or consistent. An evaluation of the typology by distinguished psychologists working in the field [START_REF] Canter | The Organized/Disorganized Typology of Serial Murder. Myth or Model ? Psychology[END_REF] applied to 100 serial murderers provided no support for it.

In the most comprehensive research programme into the linkage of serious sexual offences by Grubin et al [START_REF] Grubin | Linking Serious Sexual Assault through Behaviour Home Office Research Study 215[END_REF], the authors propose Our starting premise is that rape attacks can be organized into distinct types It is certainly the aim of investigation into any field to initially classify the objects contained within it but it is the contention of this paper that that although rapes can be organized into types that these will be far from 'distinct'. And that the attempt to discriminate between crimes in this way is likely to be not only barren but actively misleading in that they will be forced in to mutually exclusive groups that will misrepresent their complexity; a view arrived at after many years of research by Canter [START_REF] Canter | Offender profiling and criminal differentiation[END_REF] one of the area's foremost investigators

The actions of any individual criminal may therefore be thought of as a subset of all the possible activities of all criminals. Some of this subset overlaps with the subsets of many other criminals, and some with relatively few. It therefore follows that assigning criminals or crimes to one of a limited number of 'types' will always be a gross oversimplification Canter and his associates who are identified with Investigative Psychology have published numerous studies [START_REF] Santilla | Inferring the Crime Scene Characterstics of an Arsonist[END_REF], [START_REF] Hakkanen | Crime Scene Actions and offender characteristics in a sample of Finnish stranger rapes[END_REF] on sexual assault, homicide and other serious crimes but have been unable in any of them to construct a satisfactory typology with even the most relaxed rules of assignment [START_REF] Salfati | Differentiating Stranger Murders: profiling offender characteristics[END_REF], [START_REF] Canter | Differentiating Sex Offences[END_REF].

Grubin is forced to propose a highly redundant 256 element taxonomy of serious sexual assault. A classification system in which many, if not most, elements will never occur cannot be satisfactory. The assumption of the crisp set paradigm in this research appears to be the cause of the problems relating to these difficulties. This can be illustrated by a simple description of a crime such as 'a very violent assault on a middle-aged woman by a young man' which cannot be properly expressed in terms of crisp sets. It can lead to either the misallocation of fundamentally different offences to the same place or to crimes that bear strong resemblances to each other being regarded as entirely unconnected, a phenomenon referred to as linkage blindness [START_REF] Grubin | Linking Serious Sexual Assault through Behaviour Home Office Research Study 215[END_REF] of which researchers are fully aware but have been unable to address.

Applicability of Fuzzy Systems

Geographic Profiling as a Fuzzy System

There has been only one area of research into crime clustering that has been widely acknowledged to have been successful in linking crimes and it is instructive that this can be regarded as relying on fuzzy sets, although this has not as yet been acknowledged.

There are various schools of geographic profiling [10] [11] but all are based on the idea proposed by environmental criminology [START_REF] Brantingham | Environmental Criminology[END_REF] relating to 'mental maps', i.e. that individuals, including criminals, are much more likely to conduct their activities in areas known to them. In terms of offenders this means that they are likely to live close to, or have some other association with the locations of their crimes. This has been adapted to construct a variety of systems so that the co-ordinates of a number of offences, known or believed to be linked can be input to a function that returns a 'jeopardy surface'. This not only returns information regarding the offender's lifestyle but presents an area in which further crimes committed by him may be discovered although they are presently unlinked. Most importantly it indicates those areas where the offender is likely to live or have some other strong association with such as employment or previous address. Slightly different techniques are employed to differentiate the 'most likely' from the 'less likely' areas.

We can regard this, the most successful approach in the area as a fuzzy system in that a set of discrete values, geographical coordinates, are input to an algorithm that assigns a degree of membership of the fuzzy set 'offender has an association with' , or something similar, to other co-ordinates in the region of the offences. Typically the result will be a map that resembles a series of concentric circles or clusters that are strongly reminiscent of a family of fuzzy sets.

Crime and Fuzzy Sets

Fuzzy set theory [START_REF] Zadeh | Fuzzy Sets[END_REF] allows us to represent crimes and criminals as highly descriptive objects in the concept space and to undertake experimental procedures to discover what the most significant differentiating features are, using mathematically and logically sound methods. We have been fortunate in being successful in obtaining data on 574 serious sexual offences from the Serious Crimes Analysis Section of the U.K National Policing Improvement Agency. We have excluded those offences that do not relate to serial stranger rapes, by which we mean a set of rapes committed by a single individual, unknown to the victim. This results in a much narrower dataset (n = 110, development set n = 83, test = 27). The development set which has provided the results in this paper consists of 28 series of average length 2.96.

Fuzzy Similarity

We can define the universe of crimes as a data set (X) of n elements

X =  x 1 , x 2, x 3, …. x n  ( 1 )
Where each crime (x i ) is defined by j features

x i =  x i1 , x i2, x i3, …. x i j  ( 2 )
In this case they could be the characteristics identified as significant by Grubin. We can then regard a crime as a datapoint in j dimensional space. If we introduce an index crime (x m ) into this space for comparison with another crime x k we can define a fuzzy relation that captures the concept 'x k is close to x m ', by using a membership function that measures the Euclidean distance between crimes and divides it by some value 'c', where c is a positive real number whose value is chosen as a reasonable representation of the concept 'close' in that application.

We have used c = d / 2 where d = the average distance between crimes. As a result the introduced crime becomes the centre of its own cluster. We can then define a number of crisp subsets around this centre by restricting membership of these sets to those elements that have a degree of membership which is greater than or equal to some value  in [0, 1]. This results in a crisp subset  A of the fuzzy set A which is itself defined on the universal set X. This crisp subset is known as an -cut of X:

 A = { x  X | A(x)  ) } ( 3 )
Here the crisp set  A contains all the elements of the universal set X whose membership degrees are greater than or equal to the value of . In this case we would generate a number of nested sets around the index crime, membership of which would reflect their 'closeness' to it. This illustrates very graphically the search strategy for crime analysts and investigators when one of these very serious crimes occurs and it is required to look for other crimes committed by the same offender.

Fuzzy c-means clustering

Fuzzy c-means clustering [START_REF] Bezdek | Pattern Recognition with Fuzzy Objective Function Algorithms[END_REF] is the most widely used fuzzy clustering strategy and effectively addresses the problem raised by Canter of exclusive types by defining a family of fuzzy sets on the universe X so that the sum of degrees of membership of all the classes of any datapoint is unity, there will be no empty classes and no class that contains all the datapoints. This is an iterative optimisation technique of the objective function below where a degree of fuzziness 1 ≤ m < ∞ is specified and elements assigned degrees of membership of the clusters until some termination criterion has been reached.

Initial results

This data is extremely rich and comprises over 370 fields with dichotomous values relating to every feature of the crime. By employing only those variables that inform the domains identified as significant by Grubin and excluding ill-defined or poorly recorded data we have reduced this to 41: sex [START_REF] Canter | Predicting serial killers' home base using a decision support system[END_REF], escape [START_REF] Canter | Predicting serial killers' home base using a decision support system[END_REF] and control (19). These reflect the offender / victim transaction that is at the heart of this serious offence. A problem that arose was that these concepts, unlike those usually identified with fuzzy membership functions like age, height etc are cumulatively or hierarchically scaled. This makes the use of a conventional membership function difficult. In order to overcome this we have proposed that the amount of these activities can be measured, i.e. the number of separate sexual, controlling or escape-centred actions.

The membership function we have employed derives closely from the techniques used by Canter [START_REF] Canter | Differentiating Sex Offences[END_REF] and Investigative Psychology which emphasizes the frequency of variables and their co-occurrence within crimes. Each variable is assigned a value c / n where c is the number of times it occurs in the dataset n, so a variable occurring 14 times has a value 14/83, this simple technique ensures a commonly occurring variable has a lower weighting than a more unusual action. It also means that a very simple form of learning analogous to experience is enabled as crimes are added and the distribution of variables changes. These values are then summed for all the variables for each dimension of the index crime. This value is then normalized using the highest sum in the dataset as divisor and as a result a degree of membership of each dimension can be assigned to each offence.

Our initial results have been encouraging using both c-means clustering and fuzzy similarity measures .By varying the fuzzy exponent ( m ) from the crispest fuzzy value of 1.25 to 3 and the number of clusters from 2 to 5 we have been able to evaluate the significance of these variables, something which has not before been done in this field. For instance using a fuzzy exponent of 1.25, 3 dimensions and 3 clusters, 15 of the 28 series of crimes where assigned a membership value ≥0.9 in one cluster. However it is unlikely that the greatest utility of this technique will be at this low level of fuzziness. Table 1 indicates an example of the very high level of consistency across the dimensions that can be achieved in linked series ( bold underline) and the resulting strong level of clustering at m = 1.25 in the clusters we identify as A,B and C.

c x u J j i N i C j m ij m     
This differs from previous attempts to classify crimes in that it is as empty as possible of psychological precepts, as is reflected in the cluster names. As a result the assignment of crimes to classes is as a result of their positions in the concept space rather than their perceived association with wider psychological principles. If clustering is successful then there undoubtedly are psychologically meaningful alternatives for these labels but this would lie within the remit of criminal psychologists Overall where m=1.25 88% of crimes are assigned to a set with > 0.80 degree of membership: type A = 12%, type B = 20%. Type C = 53%. 15 of the 28 series are assigned to a single set In terms of the closeness of crimes we have looked at the overall number of possible comparisons between the 83 offences and then at the relative differences in closeness between the linked and unlinked crimes, i.e. those committed by different individuals and those committed by the same. The relatively small number of comparisons of linked offences is as a result of the high number of series of two crimes table 2.

number of comparisons between crimes

All

Unlinked Linked total 3403 3393 110

The very marked difference in the degrees of closeness between linked and unlinked crimes is encouraging, particularly as the gap between the two widens very significantly as degree of closeness increases. So at the average degree of closeness of 0.23 a linked crime is a little less than twice as likely to appear while at the highest degree of closeness, > 0.9, a linked crime is seven times as likely to figure.

The implication in terms of assisting crime analysts and investigators is clear: at the highest levels of closeness the greatest possibility of a match occurs and this declines in line with closeness. It provides the clearest and most effective search strategy for analysts in order to maximise their chances of a positive 'hit'.

Conclusion

The problem of rigid typology that has hampered this area of research is precisely the one that fuzzy sets avoids. Because of the nature of the area under investigation any crisp classification method is bound to fail. Either a large number of crimes elude classification as in Investigative Psychology or an enormous system that specifies 256 type of stranger rape, which is itself a small subset of rape has to be proposed. These crimes must be placed somewhere if research is to be fruitful. The answer may be that instead of belonging nowhere or in a tiny compartment of a huge structure, that they belong in several places at the same time to differing degrees.

The success of geographic profiling in modelling criminal conduct is illuminating. An algorithm that can be regarded as a membership function uses a number of geographic locations, of linked crimes, as input in order to assign degrees of membership to a larger set of geographic points and thereby construct a fuzzy set. And in so doing effectively assist crime investigators in highlighting areas in which to find the offender. In this case longitude and latitude are the relevant dimensions on which the system operates. If one generalizes from this and is able to identify the pertinent dimensions that describe a landscape of actions rather than the physical landscape associated with crime then the achievements of geographic profiling may be possible. There is also an interesting symmetry in that the input to geo-profiling systems is a set of linked crimes and the desired output of LASSO is also a set of linked offences.

The 'set of meaningful numbers' called for in the earliest days of research [START_REF] Canter | Facet theory : approaches to social research New York[END_REF] into this area can be achieved by using fuzzy set theory and in thus allow empirical research rather than the experiential and anecdotal or hypothetical approaches that have so dominated the field for so long and so unproductively.

Fig. 1 .

 1 Fig. 1. closeness to the index crime

table 1 .

 1

		Dimensions		Membership
	Crime	Control	Sex	Escape	A	B	C
	x53	44	16	6	0.99	0	0.01
	x54	44	2	6	0.95 0.01 0.04
	x50	87	2	32	0.12 0.05 0.83
	x51	59	2	32	0.29 0.04 0.67
	x52	87	2	32	0.12 0.05 0.83

table 3 .

 3

	degree of closeness between crimes
		All	Unlinked	Linked
	above ave	1387	1309	78
	> 0.23	40.76%	38.58%	70.91%
	above 0.5	674	632	45
		19.81%	18.63%	40.91%
	above 0.6	464	427	37
		14%	12.58%	33.64%
	above 0.7	286	256	30
		8.40%	7.54%	27.27%
	above 0.8	124	110	14
		3.60%	3.24%	12.73%
	above 0.9	36	29	7
		1.00%	0.85%	6.36%


Efi Papatheocharous
email: efi.papatheocharous@cs.ucy.ac.cy

Andreas S Andreou
email: andreas.andreou@cut.ac.cy

On the Problem of Attribute Selection for Software Cost Estimation: Input Backward Elimination Using Artificial Neural Networks

Keywords: Software Cost Estimation, Artificial Neural Networks, Connection Weights, Garson"s Algorithm

Many parameters affect the cost evolution of software projects. In the area of software cost estimation and project management the main challenge is to understand and quantify the effect of these parameters, or "cost drivers", on the effort expended to develop software systems. This paper aims at investigating the effect of cost attributes on software development effort using empirical databases of completed projects and building Artificial Neural Network (ANN) models to predict effort. Prediction performance of various ANN models with different combinations of inputs is assessed in an attempt to reduce the models" input dimensions. The latter is performed by using one of the most popular saliency measures of network weights, namely Garson"s Algorithm. The proposed methodology provides an insight on the interpretation of ANN which may be used for capturing nonlinear interactions between variables in complex software engineering environments.

Introduction

Software effort estimation is the process of predicting the required effort to support the software development process by utilising attributes of cost, often called "cost drivers". This process usually involves activities after product specification and until software implementation and delivery, and is usually performed at the initiation of a project. The accurate computation of the development effort in software organisations is critical since it enables project managers to effectively deal with uncertainties and risks associated with resource planning and allocation. Specifically, cost overestimations result, in over allocation of resources and budget increase, which may cause loss of contracts and interruption of negotiations. On the other end, cost underestimations cause loss of money upon project completion, misallocation of project resources, quality compromises or budget and schedule extensions.

Artificial Intelligence (AI) techniques are quite popular in software cost estimation and are used for building models and calculating the effort factor. Especially, Artificial Neural Networks (ANN) that have the ability to provide a non-linear mapping among the inputs and the output have been used extensively. Nevertheless, in previous related works ANN are commonly used only as predictors and very rarely quantitative analysis is conducted regarding the influence of the network inputs on the output. In this work we focus on the ability of ANN to capture interactions between the influencing cost factors and effort and, in addition, the input"s degree of influence built within the network is examined using Garson"s Algorithm [START_REF] Garson | Interpreting Neural-Network Connection Weights[END_REF]. The overall purpose is to examine the prediction accuracy of development effort through the utilisation of different models and variable contributing factors. The values of the cost drivers are located within two widely-known and public databases, namely the Desharnais and the ISBSG, which are selected for experimentation. The contribution of inputs is assessed through a random sampling approach and using the resulting values of internal weights from the ANN. Gradually the contributing weights of inputs whose values do not significantly affect the output of the ANN are removed from the initial complete set of cost factors. The experiments conducted show that in software cost estimation there are several factors which are not critically significant but are commonly used for predicting effort in related research work. The input analysis conducted using Garson"s Algorithm helps in removing factors in a backward manner, starting from the least significant ones and until half of the initial cost factors are left in each dataset whereas during this process ANN"s prediction performance is continuously assessed.

The rest of this paper is organised as follows: Section 2 discusses the recent work on ANN utilisation for the problem of software cost estimation and also presents common approaches used for simplification and interpretation of ANN in other problem domains. Section 3 specifies the modeling technique and theory behind Garson"s Algorithm. Section 4 presents in detail the methodology proposed, accuracy measures used in the experimental process and discusses the main results obtained. Finally, Section 5 summarises the conclusions and future research steps.

Related Work

This section initially presents the latest applications of Artificial Neural Networks (ANN) in the software cost estimation literature and identifies that even though the approach is considered promising, one of the most important steps, the identification and inspection of the dominant cost attributes, is not given proper thought.

Recent work of Tronto et al. [START_REF] Tronto | An Investigation of Artificial Neural Networks based Prediction Systems in Software Project Management[END_REF] investigates the application of ANN and stepwise regression for software effort prediction. The experiments were conducted on the COCOMO dataset employing categorical variables whose impact was identified based on the work of Angelis et al. [START_REF] Angelis | Building A Software Cost Estimation Model Based On Categorical Data[END_REF] forming new categorical values. The authors identified a strong relationship between the success of each technique and the size of the learning dataset, the nature of the cost function and dataset characteristics, such as existence of outliers, collinearity and number of attributes.

In software cost estimation the comparison of models is a common research goal. Kaur et al. [START_REF] Kaur | Neural Network -A Novel Technique for Software Effort Estimation[END_REF] prove the effectiveness of ANN models for the NASA dataset compared to the Halstead, Walston-Felix, Bailey-Basili and Doty models, all of which are popular legacy models used in software cost estimation. Backpropagation ANN were used and reported as the most generalised networks currently in use that present good estimation capabilities. In addition, Reddy and Taju [START_REF] Reddy | A Concise Neural Network Model for Estimating Software Effort[END_REF] used the popular COCOMO model in software cost estimation mapped to an ANN with minimal number of layers and nodes to increase the performance of the network. They employed a feedforward backpropagation MLP and obtained improved predictions for effort using the COCOMO dataset compared to the COCOMO model. Rao et al. [START_REF] Rao | A Novel Neural Network Approach for Software Cost Estimation using Functional Link Artificial Neural Network (FLANN)[END_REF] used a Functional Link Artificial Neural Network (FLANN) which does not contain any hidden layers so that the network architecture becomes simple and training does not involve full backpropagation, thus reducing computational complexity. Their method provides more accurate results compared to other methods for software cost estimation on the NASA dataset.

Many researchers applied ANN on software cost estimation and yielded very accurate results. However, when using ANN one crucial step is to identify the dominant cost factors, or attributes, that affect development effort [START_REF] Park | An Empirical Validation of a Neural Network Model for Software Effort Estimation[END_REF]. A number of measures exist to determine the significance of ANN input attributes [START_REF] Garson | Interpreting Neural-Network Connection Weights[END_REF][START_REF] Belue | Determining Input Features for Multilayer Perceptrons[END_REF][START_REF] Glorfeld | A Methodology for Simplification and Interpretation of Backpropagation-Based Neural Network Models[END_REF], 10] but we identified that they have never been applied for software cost drivers. For example, sensitivity analysis, fuzzy curves, MSE change, weight elimination and node pruning, and optimal brain damage (OBD) methods are measures that rank input feature importance. Some of these measures are heuristic (forward and backward selection), sensitivity index-based, are based on pseudo weights, rely on Garson"s algorithm and some of its modified and extended versions that appear in the literature [START_REF] Zhang | Neural Networks for Classification: A Survey[END_REF]. More specifically, in this work the concepts described in the following methodologies have been adopted for software cost drivers: Garson [START_REF] Garson | Interpreting Neural-Network Connection Weights[END_REF] proposed a method for partitioning the ANN connection weights to determine the relative importance of each input variable in the network (for more details see Section 3.2). Glorfeld [START_REF] Glorfeld | A Methodology for Simplification and Interpretation of Backpropagation-Based Neural Network Models[END_REF] presented a methodology to simplify ANN using a backward selection process to eliminate input variables that are not contributing to the predictive power of accurate networks. According to the author this enables decision makers to understand the resulting effect of each contributing variable in producing accurate predictions. The application is on two classification examples, a commercial loan and a cheque overdraft problem.

Modeling Technique and Methodology for Input Elimination

Artificial Neural Networks

One of the primary applications of ANN involves models to forecast a dependent variable from a given set of independent variables. These are non-linear, model-free and alternative to traditional statistical methods. ANN consist of basic computational elements called neurons organized in groups forming layers. Certain types of neurons organised in multiple layers form the Multi-Layer Perceptron (MLP) [START_REF] Mcculloch | A Logical Calculus of the Ideas Immanent in Nervous Activity[END_REF] which is one of the most popular networks. The number of neurons in the input (first) layer is equal to the number of attributes used as independent variables. The last layer is the network output. Each subsequent layer uses the weights coming from the previous layers and adjusts them so that the accuracy performance error between the actual and predicted values for the dependent variable represented by the output is diminished.

Methodology with ANN and Garson's Algorithm

There are many methods for measuring the contribution of independent variables within a neural network, but most of which are very complicated and thus are rarely used in the area of software cost estimation. Garson"s algorithm [START_REF] Garson | Interpreting Neural-Network Connection Weights[END_REF] is considered a good trade-off example among complexity and effectiveness. It partitions the hidden layer weights into components associated with each input node. Next, the percentage of all hidden nodes weights associated with a particular input node is used to measure the relative importance of that attribute. The interested reader may refer to [START_REF] Olden | Illuminating the "Black Box": a Randomization Approach for Understanding Variable Contributions in Artificial Neural Networks[END_REF] for a step-by-step example on the algorithm.

A variety of ANN architectures were implemented, starting with a topology which contains a number of neurons in the hidden layer equal to the number of attributes used as inputs in each experiment and continuing with topologies resulting from increasing the number of hidden neurons by 1 until their number becomes twice the size of the input attributes. In addition, the "weakest" attribute is removed from the sample until the inputs are reduced to half the initial size. Moreover, the following randomisation process was followed for each sample: The initial weights and biases of the network were randomly set and the dataset used was randomly divided into three holdout subsets, training, validation and testing, with the percentages of 60%, 20% and 20% of the total available samples respectively, where each sample participates in only one subset.

The scaled conjugate gradient training function was used which is based on the derivative functions of weights, net inputs and transfer functions. The training process is repeated ten times so that the optimal network that minimizes the prediction error is identified and the weights of each input-hidden-output path are stored for further use by Garson"s algorithm. Evaluation of the networks was performed using the testing data samples based on the well known MMRE and pred(.25) accuracy measures. For each experiment ten holdout random samples were chosen so that validation on random data is performed.

After training is executed and the network is stabilized, for each input j, j=1,2,…,i, the Relative Importance (RI j ) is calculated using equation [START_REF] Garson | Interpreting Neural-Network Connection Weights[END_REF], where N i and N h are the number of input and hidden neurons, respectively and w is the connection weight, the superscripts "i","h" and "o" refer to input, hidden and output layers, respectively and subscripts "k", "m" and "n" refer to input, hidden and output neurons (in our case n=1 as there is only one output neuron). According to Garson"s algorithm, for each input node j the relative contribution of j to the outgoing signal of each hidden neuron is calculated and converted to a percentage, which serves as a measure of importance for each input node representing each variable. According to the proposed methodology, each input that makes the smallest contribution to the final output of the network, as this is reflected through the weight connections, is eliminated. Thus, in each repetition the initial number of variables utilised is lowered gradually by one until the necessary number of variables are left in the dataset.

4 Experiments and Results

Datasets Description

The Desharnais (1989) dataset [START_REF] Desharnais | Analyse Statistique de la Productivite des Projects de Development en Informatique a Partir de la Technique de Points de Fonction[END_REF] included 81 observations for systems developed by a Canadian Software Development House. The second dataset ISBSG R9 [START_REF]The International Software Benchmarking Standards Group[END_REF] provided the International Software Benchmarking Standards Group contains an analysis of the cost and other measurements for a large group of software projects, approximately 3,024. The projects come from a broad cross section of industry and range in size, effort, development platform, language, etc. These projects underwent a series of quality checks and pre-processing to create filtered versions of the datasets that do not contain null values and conform to the standards we set for homogeneity and integrity before feeding them as inputs to the ANN. The filtered datasets contained 77 and 113 in the Desharnais and ISBSG datasets and the attributes selected and used in this work, along with their abbreviations are summarised in Table 1.

Results

The results reported in this section include the initial and final lowest performance values of the best obtained network architectures in terms of prediction accuracy (MMRE value). The results of the training and testing phases of the various network architectures created are sensitive to the initialisation of weights, bias values and random division of the data samples used for training and testing. Initially, the number of input attributes for each experiment is reduced gradually by one according to importance of the inputs suggested by Garson"s algorithm; then, the network is trained again with the reduced variables and the new performance is traced. This process is repeated ten times (on random holdout samples) and Tables 2 and3 report the MMRE and Pred(.25) ANN performance figures for the training and testing phases using the Desharnais and ISBSG datasets respectively. The "Initial" and "Final" column results report accuracy having the number of inputs being equal to the initial number of attributes in each dataset p and being reduced to p/2 respectively. The order in which attributes are removed in each experiment repetition (first column) is given in the second column, while the rest columns present the forecasting performance observed after removing the "less important" attributes. The experiments indicate that quite accurate and successful predictions were obtained, as suggested by the consistently low MMRE values in both the Desharnais and ISBSG cases throughout the random holdout validation sampling process. Moreover, comparing the initial and final values of the accuracy measures we observe some performance degradation, something which indicates that maybe a part of useful information is lost when reducing the number of the participating attributes. One may argue that this is expected as the information contributing to the ANN learning process is truncated and hence prediction accuracy is gradually lowered as we move from the initial to the final network state. More specifically, the accuracy degree of the Desharnais dataset decreases 0-29% in the training phase depending on the experiment repetition, while during testing accuracy increases in some cases by 20% and in others decreases by 16%. This also occurs with the ISBSG dataset, where by removing attributes the performance accuracy increases by 6% and decreases by 6% in the training phase depending on the experiment, while during the testing phase accuracy increases by 16% and decreases by 24%. Another interesting finding is that the attributes that seem to be the "weakest" effort contributors in the majority of the experiments are TE, ME, DU, SC for the Desharnais case and RL, FC, CC, INC, OC for the ISBSG. We should also report, though, that there were validation cases in which the general picture of input significance was a bit disrupted. This may be considered as a weakness of the method as it relies on ANN models that behave differently when the initial conditions (initialisation) and the training/testing data samples change. Therefore, the results must be interpreted cautiously and only after a satisfactory number of repetitions that will enable a statistically safe conclusion.

Conclusions

This work investigates the ability of ANN to capture interactions between the influencing cost factors and effort within empirical software engineering project samples and attempt to provide cost predictive models. The main contribution is the understanding of the explanatory value of the inter-relationships between the input variables and the final output (effort) which is extracted from the internal network weights. Thus, it may provide an insight regarding each variable"s contribution to the overall prediction. We performed a backward elimination strategy to minimise the initial inputs and progressively evaluated the significance of connection weights and input variables. The approach was based on Garson"s Algorithm which exploits the various ANN models created, trained and tested over ten random sets of training and testing distinct samples. Moreover, from the various architectures created, trained and tested the results obtained from the best networks in terms of MMRE of actual vs. the prediction testing samples, i.e. result of the simulation phase of the process provide quite promising results. The approach enables decision makers to understand the resulting effect of each contributing variable in producing accurate predictions.

An interesting issue for future research is the comparison of the contributions of other saliency measures reported in the literature along with Garson"s over the same or newer datasets. In addition, a cost-benefit analysis of accuracy declination and the fewer attributes leading to less cost for collecting data and faster cost estimation modeling should be carried out to prove the validity of this work.

Table 1 .

 1 Summary of the attributes in the datasets used

	Desharnais		ISBSG
	Team Experience (years)	TE	Functional Size	FS
	Manager Experience (years)	ME	Adjusted Function Points	AFP
	Duration (months)	DU	Project Elapsed time	PET
	Transactions	TR	Project Inactive time	PIT
	Entities	EN	Resource Level (ordinal)	RL
	Points Adjusted	PA	Maximum Team Size	MTS
	Scope	SC	Input count	INC
	Points Non Adjusted	PNA	Output count	OC
			Enquiry count	EC
			File count	FC
			Interface count	IFC
			Added count	AC
			Changed count	CC
			Deleted count	DC

Table 2 .

 2 Random sampling and first four attributes removed from the Desharnais dataset.

	#	Order of	ANN Training Phase	ANN Testing Phase
		Attributes
		Removed

Initial MMRE Initial Pred Final MMRE Final Pred Initial MMRE Initial Pred Final MMRE

										Final
										Pred
	1	TE,DU,SC,ME	0.384	0.936	0.559	0.936	0.536	0.867	0.600	0.867
	2	ME,DU,TR,TE	0.280	0.979	0.343	1.000	0.409	0.933	0.487	0.933
	3	SC,EN,TE,DU	0.557	0.936	0.583	0.936	0.198	1.000	0.335	1.000
	4	TE,TR,PA,SC	0.474	0.915	0.485	0.894	0.364	1.000	0.387	1.000
	5	ME,PA,TE,DU	0.361	0.957	0.360	0.979	1.264	0.867	1.060	0.867
	6	TE,SC,DU,ME	0.512	0.915	0.784	0.915	0.386	0.933	0.346	0.933
	7 ME,SC,PNA,PA	0.472	0.957	0.572	0.957	0.569	0.800	0.512	0.800
	8	TE,ME,SC,EN	0.509	0.957	0.572	0.957	0.351	0.800	0.512	0.800
	9	TE,ME,EN,DU	0.358	0.936	0.356	0.936	0.507	0.933	0.578	0.933
	10 TE,SC,EN,DU	0.482	0.894	0.768	0.894	0.312	0.933	0.293	0.933
		Mean	0.439	0.938	0.538	0.940	0.490	0.907	0.511	0.907

Table 3 .

 3 Random sampling-first seven attributes removed from the ISBSG dataset.

#

Order

of Attributes Removed ANN Training Phase ANN Testing Phase Initial MMRE Initial Pred Final MMRE Final Pred Initial MMRE Initial Pred Final MMRE

							Final
							Pred
	1 CC,AFP,MTS,PET,OC,PIT,EC 0.223	0.957	0.329	0.928 0.358	1.000	0.578 1.000
	2 DC,CC,PET,RL,PIT,INC,EC	0.280	0.986	0.352	0.971 0.418	0.955	0.575 0.955
	3 INC,CC,DC,RL,OC,AC,FC	0.337	1.000	0.404	0.986 0.255	0.955	0.265 0.909
	4 MTS,CC,RL,INC,FC,FS,AC	0.350	0.957	0.493	0.957 0.199	0.955	0.191 0.955
	5 FS,RL,FC,EC,IC,INC,CC	0.367	0.971	0.411	0.986 0.202	0.955	0.298 0.955
	6 RL,DC,CC,INC,OC,EC,FS	0.362	1.000	0.303	1.000 0.346	0.909	0.385 0.864
	7 CC,OC,RL,FC,INC,AFP,IC	0.210	1.000	0.271	0.986 0.377	0.909	0.257 0.909
	8 FC,OC,AC,AFP,RL,IC,FS	0.247	0.957	0.344	0.957 0.188	1.000	0.424 1.000
	9 FC,IC,RL,AFP,AC,EC,PIT	0.261	0.957	0.305	0.971 0.662	1.000	0.500 1.000
	10 FS,IC,PIT,OC,AFP,RL,FC	0.338	0.986	0.279	0.986 0.249	0.955	0.270 0.955
	Mean	0.297	0.977	0.349	0.972 0.325	0.959	0.374 0.950