CITY FEED: a Crowdsourcing System for City Governance

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1 2014 IEEE 8th International Symposium on Service Oriented System Engineering CITY FEED: a Crowdsourcing System for City Governance Gianmario MOTTA, Linlin YOU, Daniele SACCO, Tianyi MA Dept. of Industrial and Information Engineering University of Pavia Pavia, Italy {linlin.you01, daniele.sacco01, Abstract In city governance, participation and engagement of stakeholders are a critical issue. Many researches use crowdsourcing to foster such participation and collaboration. To make sure that such support is complete, an evaluation grid shall be developed. To serve such need, we introduce a framework to assess the maturity of crowd-sourced municipality services. To show a structured example of crowd based city governance, we present CITY FEED, a system consisting of transactional and analytical modules. CITY FEED has been designed, implemented and deployed as a pilot in Pavia, Italy. Keywords Crowdsourcing, Smart cities, Smart cities services, City governance, Smart cities service maturity model I. INTRODUCTION Smart cities are a global need, in front of increasing population, energy consumption and environment pollution[2] In order to achieve the goal of smart, green and integrated cities, several projects have been launched [1] Amsterdam Smart City is a cradle for twenty pilot projects developed with the 72 partners, aiming at reducing emissions of carbon dioxide.[3][4] Lyon carries on an integrated mobility project for people, travelers and freight, starting in 2012, with the goal of reducing emissions.[5] In Portugal, MOBI.E is a framework for modeling smart mobility, that includes a network of shared electrical vehicles.[6][7] In Malaga, a project is deploying a fleet of electrical vehicles.[8] Generally, these projects intend to save energy and reduce emissions with positive social and economic impacts. However, a city is not only an energy consuming engine, but also a complex service system. But the performance of cross-organization services may often result poor because of the information islands and costly because of inefficient procedures [9] [10]. Within city services, the Smartness is evaluated by the citizens. [11] However citizens 1) have no chance to be involved in designing the systems that support their daily life, 2) receive marginal information, and 3) scarcely interact with relevant government departments. All of these issues come from the barrier between government and citizens and from the isolation of IT systems. So there is a trend to equip citizens with smart applications, that could bridge citizens, governments, and relevant business stakeholders. [13][14][15][28] Generally, smart cities services should provide satisfying and happy city life.[16][17]. Services, by common sense, shall use a wide variety and a high volume of source information, that includes crowdsourcing, open data, sensor data and web systems. Let us say the wider the sources the better the result. these sources, crowdsourcing is becoming more and more important. Crowdsourcing is a type of participative online activity in which an individual, an institution, a nonprofit organization, or company proposes to a group of individuals of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a task. The user will receive the satisfaction of a given type of need while the crowdsourcer will obtain and utilize to their advantage depend on the type of activity undertaken. [12] Many researches use crowdsourcing to provide innovative services. [18][20] Crowdsourcing alters the paradigm of smart city services, with the following benefits: (a) cities provide services to people who really need them and benefit from the wisdom of the crowd, (b) citizens have a rich knowledge on their concerns, (c) citizens can participate and evaluate the service and (d) residents, government and relevant business stakeholders can easily communicate with each other, and they can form a virtual community [19][21][22][28] We here describe CITY FEED, that is a crowdsourcing system, designed, developed and deployed in Pavia. In section 2, we define the four stages of smart city maturity. In section 3, current works are summarized and evaluated against the maturity model. In section 4, various aspects of CITY FEED are described: 1) scope, 2) information life cycle, and 3) the city governance process. In section 5 we describe implementation. Finally, in section 6, we summarize results and sketch the future. II. SMART CITY SERVICE SYSTEM MATURITY MODEL To evaluate city services, we propose a four stage model. Stages include (1) publishing, (2) interacting, (3) transacting, (4) evaluating. With the growing maturity, Quality of Service /14 $ IEEE DOI /SOSE

2 (QoS) increases. Briefly, the four stage model is a roadmap of service evolution, that includes a) information display, b) online processing, c) online interaction, d) holistic analysis (Figure 1). Fig. 1 four stages maturity model A. Stage 1: Publishing The system is a platform to present information of government, by web systems running as information distributor. In order to be more user-oriented, systems have been adapted to citizens demands to provide user-oriented information and user-friendly functions, as searching and downloading. [25][26] B. Stage 2: Transacting In transacting stage, systems are integrated vertically and horizontally. [23] Vertically, systems run as one-stop shopping. [25] Horizontally, the system supports crossorganizational processes. Therefore, the system can reduce the service cost and improve the satisfaction of citizen. [24] C. Stage 3: Interacting In this stage, services enhance citizen-oriented processes [25] With Web 2.0, services are delivered in two ways instead of one-way. Also, social networks have greatly enhanced the service system by more effective and efficient information broadcasting and problem solving procedure. So service system now is not only the way to process request, but also the channel to communicate online. Therefore, service cost is reduced for different stakeholders [29], e.g. citizens do not need to stay in a long queue to make their applications, and officers can also process requests on their desks with the support of integrated tools. D. Stage 4: Evaluating In this stage, data generated from service systems or gathered from other sources (e.g. social network) are used to measure the performance of the end-to-end process. [30] Data are essential in this stage to gain city insights and discover hidden issues. Hence collective intelligence is required for a comprehensive data analysis. Then relevant stakeholders could discover the bottleneck of the service and make better decisions. [11] Furthermore an inter-cycle will start triggered by decisions, in which appropriate activities will be performed to improve the quality of city services and reduce service cost. Finally in this stage, data are to be shared as a service. III. CURRENT STATUS OF CROWDSOURCING CITY GOVERNANCE SERVICES Researches and projects use crowdsourcing to innovate services. Basically, upon the coordination of user-generated contents, web applications, and mobile devices, crowdsourcing services have been linked to tasks [27]. Some on-going projects and researches are summarized here below: FixMyStreet (FMS) [32] has been one of the first citizen-driven systems for local public services in UK, with the inspiration to make service "transformation". The system establishes a crowdsourcing network for citizens to report, view and discuss local problems (e.g. broken street lights). The system also tracks the resolution of issues concerned by the local government. After the system had been put online, in the first six months problem reports were generated and many complimentary comments from citizens were received. However Stephen, et al. [31] observe that FMS is lacking "Sense of Virtual Community" (SOVC) [35] that would encourage online communication. Furthermore, local government officers commented that the system made duplications (e.g. the same information appears in different systems) and also it did not directly update fixes and lacked collective intelligence. In conclusion, the integration with government systems and the information analysis are missing in FMS. With the pervasive usage of smart phones, crowdsourcing can benefit of mobile map-based applications [36]. PDX Reporter [37] and Citizen Connect [38] enable citizens to interact with local governments in Portland and Boston respectively. Both systems deal with issues as potholes, streetlights and graffiti, and intend to enhance the citizen s engagement and participation. [39] Differently from Fix My Street the "Sense of Virtual Community" (SOVC) is there; in fact, citizens can comment and discuss issues in a community. Specifically, Citizen Connect is integrated with Twitter to share contents. Moreover, information is not only shared among citizens, but it is also routed to appropriate government departments for resolution. However, there are no obvious linkages and interactions among citizens, business stakeholders and government. Finally, crowd data are not used to obtain collective knowledge. See Click Fix (SCF) [34] is an example of the growing Government 2.0 eco-system in USA. [35] This service encourages residents to take care of and improve their neighborhoods with reports on non-emergency issues. The system is available on various platforms, e.g. web interfaces, smart phones and Facebook. It is one step ahead Citizen Connect, because it integrates existing systems and provides utilities to different departments. Also, incentive mechanisms are introduced to foster "Sense of Virtual Community" (SOVC). However, data 440

3 analysis is still elementary and it does not provide insights to stakeholders. In conclusion, crowdsourcing has been adopted to improve city governance and encourage citizens to participate. Also, the "Sense of Virtual Community" (SOVC) relies on the interaction among users, e.g. citizens, workers and etc. Finally, crowdsourcing is integrated with relevant government systems. However, the collective intelligence is still not implemented. The insights from crowd data will greatly improve city visualization and better support decision making. We introduce CITY FEED as a crowd-sourced governance system with the two complementary modules i.e. CITY FEED Manager and CITY FEED Analyzer. CITY FEED Manager not only implements typical functions, but also supports an integrated city governance workflow with dedicated services for various stakeholders. In turn, CITY FEED Analyzer is a collective intelligence system to analyze crowd data according to different dimensions. In order to better assess the competitive advantage of CITY FEED, we use the four stages maturity model. The result is shown in Table I. TABLE I. EVALUATION RESULT Projects Publishing Transacting Interacting Evaluating FMS Web-based t really service integrated PDX Reporter Mobile service Partial: issues are routed to appropriate stakeholders citizens Citizen- Connect Mobile service Partial: issues are routed to appropriate stakeholders SCF Both Support the workflow and provide tools CITY FEED Both Supports an integrated and cost-effective city governance process citizens citizens with incentive mechanism different stakeholders, e.g. citizens, workers and municipality officers Analyzer for data analysis IV. CITY FEED A CROWDSOURCING CITY GOVERNANCE SYSTEM Generally, CITY FEED is an easy application for a wide range of users (i.e. citizens, contractors, municipality officers). It intends to foster the citizen s participation and collaboration. Specifically, citizens identify issues by a text-and-picture feed, that may concern infrastructure (e.g. potholes) and mobility (e.g. bus delay). A. CITY FEED Overall Scope Figure 2 illustrates the overall scope of CITY FEED, that includes a transactional system (CITY FEED manager) and an analytic system (CITY FEED analyzer) and serves residents, contractors (who are responsible for resolving issues) and municipality officers. The software runs on various platforms, e.g. web interface and smart phone. Fig. 2 the scope of CITY FEED CITY FEED Manager includes three sub-systems: 1) Event Manager: processes events generated by residents; it creates, updates, deletes and comments events. 2) Issue Aggregator: gathers and aggregates events into issues to eliminate duplications and provides a portal for municipality officers, who validate issues. 3) Trouble Ticket Manager: support the whole life cycle of trouble tickets generated by contractors: opening, updating, tracking, and commenting.. CITY FEED Analyzer is a collective intelligence system to analyze crowd data generated by CITY FEED Manager. The system extracts data from CITY FEED Manager, transfers them according to Dimensional Fact Model (DFM) [10] and loads them into the data warehouse. Data are shown in different forms, e.g. bar chart, and structured along different dimensions (e.g. location, time, event class etc.) B. Innovational City Governance Process Traditionally, city governance process requires staff to uncover issues, that can be replaced by crowdsourcing. So residents discover issues and send text-and-picture feeds with geo-reference information. Then feeds will be aggregated into issues in order to avoid duplications (e.g. the same issue can be posted by different people). After that, issues will be validated by municipality officers before dispatched to contractors through notifications. Then contractors can check issues, open trouble tickets and fix issues. In the meantime, municipality officers will receive operational results as a summary report of feeds in different statuses (e.g. discovered, checked, on-going and fixed). Also all users can comment and track feeds with interrelationship among each other. Such process is shown in Figure

4 visibility by crowd data analysis and 4) DaaS (Data as a Service) is implemented by web services APIs for useful analyses in third parties. V. CITY FEED IMPLEMENTATION The implementation of CITY FEED has been divided into steps parts according to the services. CITY FEED Manager provides mobile services for different stakeholders to post, view, comment, track, fix and control issues. CITY FEED Analyzer provides web-based services to reveal city conditions by analyzing crowd data in different dimensions. Fig. 3 city governance process supported by CITY FEED C. Feed Lifecycle In the city governance process, main data entities include events, issues, tickets and analysis result. Feed lifecycle of CITY FEED is shown in Figure 4. It has been divided into two parts covered by CITY FEED Manager and CITY FEED Analyzer respectively. A. CITY FEED Manager implementation CITY FEED Manager provides common services to citizens (contractors and municipality officers are citizens also). A citizen can sign in and sign up the system, as shown in Figure 5. He can also view the feed list and detail, comment and create feeds, as shown in Figure 6. Then he can view ticket list and detail, and comment tickets as shown in Figure 7. Finally he can manage his personal information and find the terms of CITY FEED as shown in Figure 8. Fig. 5 sign in and sign up Fig. 4 Feed lifecycle of CITY FEED 1) The feed lifecycle in CITY FEED Manager: First of all, Event is the entity that contains potential issues with text, image and geo-location information. Then Issue is the entity created by aggregating Events and shall be validated by municipality officers. After validation, Issues will be notified to relevant contractors. After that, Tickets will be opened (to create a trouble ticket) after contractors confirm them. During the fixing process, they will be updated gradually and finally closed. 2) The Feed lifecycle in CITY FEED Analyzer The above three entities will be retrieved and processed in CITY FEED Analyzer according to the pre-defined DFM. Data will be loaded into data warehouse and analyzed by multidimensions. Finally the result will be presented in different forms. Differently from other crowdsourcing systems, CITYFEED 1) supports a cost-effective process thanks to volunteers (e.g. citizens) who discover issues without any cost, 2) penetrates the mobile workforces by mobile services, 3) improves the city Fig. 6 Feed related services Fig. 7 Tickets related services Fig. 8 personal information management and terms 442

5 Furthermore, CITY FEED Manager provides specific services to contractors and municipality officers respectively. For contractors, services are views on notifications and tickets, create tickets, add progress and close tickets. (Figure 9). For municipality officers, services are view notifications and operational reports shown in Figure 10 accordingly. Fig. 9 specific services for contractors Fig. 12 issues status Fig. 13 issues in different types in different regions Fig. 10 specific services for municipality officers B. CITY FEED Analyzer implementation CITY FEED Analyzer is implemented on Pentaho [41], that is an open source business analysis platform, as analysis engine and PostgreSQL [42] as data warehouse. In order to give an example about how the crowd data is analyzed, we choose the analysis made upon Issue entities. First, data will be preprocessed for cleansing. After, crowd data are restructured according to DFM [10] in Figure 11. As we can see, the issue fact includes two measurements and 6 dimensions. Then data are loaded in a data warehouse. By implementing this structure in a CUBE, we can generate multi-dimensional analysis results, e.g. issues status distribution in a pie chart show in figure 12 and number of issues in different types in different regions in a table shown in figure 13. Fig. 11 Issues DFM I. CONCLUSION In conclusion, CITY FEED is a cloud-based crowdsourcing system to support city governance. If evaluating CITY FEED on our four stages maturity model, CITY FEED is more mature than any other crowdsourcing system (especially in interacting and evaluating stages) with a multi-directional communication channel and a collective intelligence system. Generally CITY FEED innovates the following: 1) A cost-effective and satisfactory process with strong participation and collaboration CITY FEED is aiming to fasten the process to discover city issues. By implementing crowdsourcing in the system, the goal is accomplished thanks to citizens who can report feeds. The process, that eliminates checking routines, reduces the cost by a free issue discovery, a full use of contractors and a balanced workforce. Moreover, the new process establishes a good "Sense of Virtual Community (SOVC) to connect multi-stakeholders, namely citizens, municipality officers and contractors. User can keep each other updated with multi-directional messages, e.g. new feeds to notifications for contractors and tickets with comments and progress statuses for citizens. Therefore, issues can be discussed and solved in a group and information can be shared among users without any cost and barriers. 2) Mobile services to penetrate the mobile workforces CITY FEED enhances the mobile staff by an easy access to information. Generally, CITY FEED provides specific georeferenced mobile services for contractors to manage their trouble tickets. The workflow is initialized by notification distribution mechanism and then a contractor can follow the ticket management procedure to open, fix and close a ticket 443

6 supported by the system. As a result, the performance of staff increases. 3) Improve the visibility of the city CITY FEED improves the visibility of the city dramatically. First of all, taking advantages of Crowdsourcing, a community has been built, in which CITY FEED keeps user updated with up-to-date city status via mobile services. Then crowd data on events, issues and tickets are gathered, integrated and analyzed to unfold city conditions by multi-dimensional analysis. Finally, fully access to CITY FEED data is managed in DaaS (Data as a Service) model for third parties to make useful analyses. Currently, CITY FEED as a part of a smart cities project IRMA[2] (Integrated Real-time Mobility Assistant). In the future, we will continue our work in the following parts: We will test CITY FEED on Pavia students, who are substantial commuters, with the support of Pavia municipality. CITY FEED needs to be enhanced to support more complex city governance process, which gathers crowd data and enables citizens to identify infrastructure (e.g. road, traffic lights, etc.) and mobility (e.g. delays, parades, strikes, etc.) issues with pictureand-text feeds. We will make CITY FEED integrate with main social networks, e.g. Facebook and Twitter. It will make CITY FEED data available in existing social networks to better propagate our city. Also crowd data from social networks will be collected and analyzed together with CITY FEED data to make comprehensive analysis of our city. REFERENCES [1] European Commission. "Horizon 2020 The Framework Programme for Research and Innovation." Communication, COM (2011) 808 (2011). [2] Motta, Gianmario, Daniele Sacco, Alessandra Belloni, and Linlin You. "A system for green personal integrated mobility: A research in progress." 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7 [33] Mergel, Ines. "Distributed democracy: Seeclickfix. com for crowdsourced issue reporting." Com for Crowdsourced Issue Reporting (January 27, 2012) (2012). [34] SeeClickFix, [35] Blanchard, Anita L. "Developing a sense of virtual community measure." CyberPsychology & Behavior 10, no. 6 (2007): [36] Alt, Florian, Alireza Sahami Shirazi, Albrecht Schmidt, Urs Kramer, and Zahid Nawaz. "Location-based crowdsourcing: extending crowdsourcing to the real world." In Proceedings of the 6th rdic Conference on Human-Computer Interaction: Extending Boundaries, pp ACM, [37] PDX Reporter, [38] CitizensConnect, [39] Crawford, Susan P., and Dana Walters. "Citizen-Centered Governance: The Mayor's Office of New Urban Mechanics and the Evolution of CRM in Boston." Berkman Center Research Publication 17 (2013). [40] Motta, Gianmario, Giovanni Pignatelli, Thiago Barroero, and Antonella Longo. "Service level analysis method-slam." In Computer Science and Information Technology (ICCSIT), rd IEEE International Conference on, vol. 5, pp IEEE, [41] Patil, Manoj R., and Feris Thia. Pentaho for Big Data Analytics. Packt Publishing Ltd, [42] Obe, Regina O., and Leo S. Hsu. PostgreSQL: Up and Running. O'Reilly,

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