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Collaboration centred cities through urban apps based on open and user-generated data final

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urban apps, query mapper, JSON, SQL, Smart Cities, Open APIs, Crowdsourcing, Data Validation, RESTful API, acceptance model

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Collaboration centred cities through urban apps based on open and user-generated data final

  1. 1. Collaboration-centred Cities through Urban Apps based on Open and User-generated Data Puerto Varas, Chile, 3rd December 2015 Diego López-de-Ipiña, Unai Aguilera, Jorge Pérez MORElab Research Group, DeustoTech – Deusto Institute of Technology, Faculty of Engineering, University of Deusto dipina@deusto.es http://paginaspersonales.deusto.es/dipina
  2. 2. The need for Smart Cities • Challenges cities face today: – Growing population • Traffic congestion • Space – homes and public space – Resource management (water and energy use) – Global warming (carbon emissions) – Tighter city budgets – Aging infrastructure and population
  3. 3. What is a Smart City? • Smart Cities improve the efficiency and quality of the services provided by governing entities and business and (are supposed to) increase citizens’ quality of life within a city – This view can be achieved by leveraging: • Available infrastructure such as Open Government Data and deployed sensor networks in cities • Citizens’ participation through apps in their smartphones – Or go for big companies’ “smart city in a box” solutions
  4. 4. What is a Smart Sustainable City? A smart sustainable city is an innovative city that uses information and communication technologies and other means to improve quality of life, efficiency of urban operation and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social and environmental aspects https://itunews.itu.int/en/5215-What-is-a-smart-sustainable-city.note.aspx
  5. 5. Open Data as Enabler of Open Government • Open government is the governing approach where citizens have the right to access the documents and proceedings of the government to allow for effective public oversight – Enables citizens to get more directly involved in the legislative process – Open Data brings about: 1. More efficient and effective government 2. Innovation and economic growth 3. Transparency and accountability and 4. Inclusion and empowerment • BUT, serious lacks on exploiting the potential of Open Data, since Governments: – Focused their attention only on implementing their open data portals – Low effort on bringing open data closer to entrepreneurs and citizens through suitable APIs, easily consumable by application developers
  6. 6. Why Collaborative Cities? • Not enough with the traditional resource efficiency approach of Smart City initiatives • “City appeal and dynamicity” will be key to attract and retain citizens, companies and tourists • Only possible by user-driven and centric innovation: – The citizen should be heard, EMPOWERED! » Urban apps to enhance the experience and interactions of the citizen, by taking advantage of the city infrastructure – The information generated by cities and citizens must be linked and processed » How do we correlate, link and exploit such humongous data for all stakeholders’ benefit? • We should start talking about Big (Linked) Data
  7. 7. Technical Ingredients for Smart Cities: Broad Data + Open APIs • BroadData: Linked Data + Social Data + IoT Data – Linked Data: recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF – Crowdsourced Data: citizens can be viewed as mobile sensors that monitor the variables of the city, and the data provided by them as crowd-sourced data • Open APIs: there are several initiatives trying to promote Open APIs for Smart Cities: CitySDK, Open311, Ushahidi
  8. 8. IES Cities Project • The IES Cities project promotes user-centric mobile micro-services that exploit open and user-supplied data – Fosters and accelerates the development and deployment of new urban services that exploit city knowledge • Its platform aims to: – Enable user supplied data to complement, enrich and enhance existing datasets about a city – Facilitate the generation of citizen-centric apps that exploit urban data in different domains European CIP project 36 month long, finishes in Feb 2016 http://iescities.eu
  9. 9. IES Cities Stakeholders
  10. 10. IES Cities Objectives • To create a new open-platform adapting the technologies and over taking the knowledge from previous initiatives. • To validate and test a set of predefined urban apps across the cities. • To validate, analyse and retrieve technical feedback from the different pilots in order to detect and solve the major incidences of the technical solutions used in the cities. • To adequately achieve engagement of users in the pilots and measure their acceptability during the validations. • To maximize the impact of the project through adequate dissemination activities and publication of solutions upon a Dual-license model. 10
  11. 11. IES Cities Platform (I) • IES Cities platform v2 ready for execution of 2nd pilots phase: – Query Mapper: eases app development: • Access to dataset information controlled using different mechanisms including ACL control • Platform automatically creates and publishes new datasets when an application developer specifies a schema of dataset for their app – Logging & Rating interfaces: enables to monitor usage & acceptance – IES Cities Entities Management: manages apps, datasets, users – IES Cities Player: broker among users and platform – IES Cities Web Interface: offers a web UI for all platform stakeholders and to manage all entities • Includes KPI graphical visualization • Business logic can rely on the client side (HTML5+JS) whilst data persistence hosting is done at the IES Cities back-end
  12. 12. IES Cities Platform (II) • User-support tools integrated to ensure platform sustainability: – IES Cities Forum: http://iescities.freeforums.org/ – IES Cities Contact Form in three supported languages – IES Cities Manual including support for installation, developers and users: https://iescities.com/IESCities/manual/index.html
  13. 13. IES Cities Platform (III) 13
  14. 14. IES Cities Platform Architecture (I)
  15. 15. IES Cities Platform Architecture (II) Mobile Apps Account REST Interface REST Interface IES Cities Player REST Interface User Interface WEB Interface Developer Interface Admin Interface Council Interface Apps & DataSet Stats REST Interface Data Wrapper Query Mapper Logging Module Account Interface Authentication: Login Authorization Scalaris JDO Data Interface PostgreSQL Scalaris IES Cities Player Virtuoso Ckan D2RQ Dataset Registration App Registration User Interface Developer Interface Admin Interface Council Interface User Management IES Cities Player Interface Apps Filter App Data REST interface Application Server Server-side app specific logic Data Validator
  16. 16. • All the functionality of the IES Cities platform is offered through a RESTful API which groups operations in the following categories: – Entities interface which offers CRUD operations to deal with the main entities tackled by the project; – Logging module which enables server-side components to register diverse events associated to apps life cycle (e.g. AppStart, AppProsumer and so on), player interactions (e.g. PlayerAppSearch), or dataset- related (e.g. DatasetRegistered); – Query Mapper which offers methods to enable the query and insertion of data through SQL http://iescities.com/IESCities/swagger/ IES Cities RESTful Open API
  17. 17. IES Cities RESTful Open API
  18. 18. Query Mapper • A key component of this platform devised to streamline the development of Open Data based mobile urban apps for web developers: – Supported datasource types: • JSON (new), CSV (new), SPARQL, Relational – User/local created datasets – Connection with external repositories – Permissions – Data response formats: • JSON and JSON-LD IES Cities Dataset Query Response Update Data source Data source type Mapping attributes Permission section
  19. 19. IES Cities Query Mapper
  20. 20. • Modus operandi: 1. Public administrations register datasets, including several metadata fields, e.g. mapping script between original format and JSON 2. A developer searches and selects a dataset against which develops his/her app and registers it with the solution 3. End-users/citizens with the help of the IES Cities Player browse, search, select and execute a desired urban app IES Cities in Use
  21. 21. • Datasets registered in the IES Cities platform require a mapping description in order to be connected with the data sources. – Supported data sources are: database, sparql, json and csv. – json_schema data type is used to create user-generated datasets • Let’s consider the following JSON file from Zaragoza about accommodation: "result": [ { "id": 1, "title": "FELISA GALu00c9, 6", "lastUpdated": "2013-04-30T00:00:00Z", "geometry": { "type": "Point", "coordinates": [ 678191.46, 4614794.52 ] }, ... ] Step 1: Public Administration Dataset Registration
  22. 22. • The following code shows the mapping to register the ZGZ dataset: – Observe that access control is enabled through the permissions field { "mapping": "json", "uri": "https://www.zaragoza.es/api/recurso/turismo/alojamiento.json", "root": "result", "key": "id", "refresh": 86400, "table": "hotel" "permissions": { "select": [ { "table": "hotel", "access": "ALL" }, { "table": "hotel_geometry", "access": "USER", "users": ["user1", "user2"] } ] } } Step 1: Public Administration Dataset Registration
  23. 23. • For application-specific datasets a the mapping type is "json_schema" and within the tables field, the schema of each underlying table has to be defined using JSON syntax "mapping":"json_schema", "schema":{ "tables":[ { "key":"id", "name":"Comments", "Comments":[ { "id":1, "text":"some_string", "author":"some_string", "rating":1, "app":"some_string", "date":"2015-01-01" } ] } ], Step 1: Public Administration Dataset Registration
  24. 24. Step 2: A developer searches and selects a dataset against which develops his/her app and registers it with the solution
  25. 25. Step 2: A developer searches and selects a dataset against which develops his/her app and registers it with the solution
  26. 26. Step3: End-users/citizens browse, search and select a desired urban app with IES Cities Player
  27. 27. IES Cities Apps
  28. 28. • 16 apps + IES Cities Player have been created: – E.g. Zaragoza Complains & Suggestions Apps validating IES Cities solution
  29. 29. • Thanks to the support of the IES Cities a platform, a web developer only needs to create a query in the standard SQL language and send it to the Query Mapper: – The query is submitted through a REST API to the IES Cities Query Mapper (data/query/{datasetid}/sql) which delegates to Zaragoza SPARQL endpoint and maps the results into JSON • For Zaragoza council enrichment of its datasets by third parties (userss) presented some issues: – Data does not need to be approved before being published – There is no mechanism to control the amount of data a citizen can add • Possible VERIFICATION solutions are: – IntelliSense techniques and other consolidation techniques (earlier submitted reports) – Social opinion: enable end-users to vote up or down reports • The adopted VERIFICATION solution has been: – End-user suggestions and complaints are first validated by an officer before they can be viewed and voted for by the final users Zaragoza Complaints & Suggestions
  30. 30. Apps Evaluation Methodology • The Compass Acceptance Model has been taken as reference for the evaluation process: feedback and assessment of the apps – The original CAM contains ease of use, usefulness, cost and mobility factors. • To replace mobility we have added “interaction with the city” Reported ease of use Reported usefulness Costs and Efforts Reported interaction with the city Short Term Short Term Short Term Short Term Long Term Long Term Long Term Low Neutral High
  31. 31. Apps Evaluation Methodology • Degree of acceptance of apps measured by: – Definition of a range of Key Performance Indicators (KPIs). • Defined regarding the types of users and for the different apps uses. • Some common KPIs defined across apps are: a) number of downloads, b) number of active users, c) users activity based on data consumption, d) users activity based on data contributions – Set-up of a range of data sources to feed the KPIs: • User questionnaires were obtained by asking users directly about their opinions and experiences with the application • Logging data was generated from logs of events generated by the app in use • Google Play, i.e. the marketplace where our apps have been uploaded has been checked to obtain online application distribution service; • Platform stats were extracted from other meta-data stored in the IES Platform – A mapping of data sources to KPIs has been performed. • Available data sources values assigned to KPI variables.
  32. 32. • IES Cities platform allows councils to manage their dataset and urban app ecosystem – Aimed to increase the quality of life of their citizens and to foster economy promotion – Allowing both administration provided and end-user generated data exploitation • The IES Cities’ Query Mapper component streamlines Open Data-based app development: – A SQL-based interface which returns as result the lingua franca of web developers, i.e. JSON • 16 apps across 4 European cities have been developed and are currently being tested. Conclusions
  33. 33. Collaboration-centred Cities through Urban Apps based on Open and User-generated Data Puerto Varas, Chile, 3rd December 2015 Diego López-de-Ipiña, Unai Aguilera, Jorge Pérez MORElab Research Group, DeustoTech – Deusto Institute of Technology, Faculty of Engineering, University of Deusto dipina@deusto.es http://paginaspersonales.deusto.es/dipina

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