BSC Smart Cities Initiative

Transcription

1 BSC Smart Cities Initiative José Mª Cela CASE Director

2 CITY DATA ACCESS 2

3 City Data Access 1. Standardize data access (City Semantics) Define a software layer to keep independent Data and Applications taken into account Data dispersion Data errors Performance to access data 2. Prepare some specific critical data (City geometry) Simulations require geometry to work (2D or 3D) 3. Big Data Solutions if needed for performance

4 City Semantics RDB SQL query $ Ontology for smartcities (OWL-DL) XML IoT Xpath, Xquery Internet RDF graph SPARQL query Data (RDF, XML, GML, JSON, etc) Urban City Model Urban model Ontology Instantiation engine (Open Linked Data repositories) SPARQL queries Integration with opengis 4

5 City Semantics Urban parameters: sustainability, mobility model, economic diversity, etc RDB SQL query $ Ontology for smartcities (OWL-DL) Numerical simulation: noise, pollution, floods, earthquakes, tsunamis, etc XML IoT Xpath, Xquery Internet RDF graph SPARQL query Data (XML, GML, JSON, etc) Agent simulation: economic activities, social behaviour, transport, pandemics, etc Visualization 5

6 An ontology is Conceptually a graph Precise semantics Standard representation to allow interoperability (triples/ Linked Open Data) Same URI means same resource Models an open world, i.e. allows incomplete information Adding edges / vertices is easier than adding information into tables (which may require refactoring) Understandable and uniformly accessible from anywhere Amenable data structure for Defining data constraints Discovering unknown relationships via logic inference

7 Integration of Open Linked Data from any source Composition of RDF graphs is another RDF graph

8 SUM (Semantic Urban Model): Goals Urban Management and Planning Tool Integration from heterogeneous data sources Query and exploration functionality GIS Visualization Ecological Urbanism Paradigm Environmental, social, and economic sustainability of cities Evaluated by set of specific indicators Standard-based Semantic Data Model for Smart Cities

9 SUM Prototype Web oriented GIS Functionality Keyword search and related concept exploration Query wizard Sustainability metrics

10 Urban Ecology Agency of Barcelona Ecological Urbanism: degree that a city adheres to the following principles Efficient land use Quality of Public Space Mobility And Services Habitability In Housing and Buildings Biodiversity Organization Self- Sufficiency Social Inclusion and Interaction

11 Prototype Architecture Ontology Functionality: API Jena Application Server: Apache Tomcat Client Browser Semantic Model: SCRIBE + BCNEcology GIS Functionality: Parliament Triple Store JS GIS API: Open Layers Data process and mapping: Custom software OFFLINE Open Street Maps

12 Query Example Proximity to bicycle parking for population aged > 15, by neighbourhood Bicycle parking and neighbourhood data is georeferenced (e.g. shapefiles) Population age and residence Extract Urban Data Integrate with GIS Measure Query and Visualize

13 Explore the city 1. Select predefined areas (landlots, blocks) or draw a polygon 2. Select urban concepts Results: Selected urban concepts within area displayed on map

14 Explore the data Select urban concepts and explore its attributes and the values of the concept instances on map Click elements in the map and display the values of its attributes

15 Explore the model Display concepts in the model related by any type of relationship or by a specific type Display relationships in the model that contains given text

16 Data - Query Query wizard Average year of refurbishing of building units Ratio of number of building use units refurbished before 1990 per area (sum of landlot surface where the building units are located) Text transcription of the query

17 Data - Query Results Highlight elements that match Numeric results for each query. Total results if queries are connected by mathematical operators

18 Expert Query Sparql Query Highlight elements that match Sparql prefixes to help writing the query Query Results

19 Next Steps Semantic Model: SCRIBE + BCNEcology Heterogeneous data types Data not contemplated at the time of building the model may lead to model extension Other cities Semi-automatic process Data process and mapping

20 Mobility pattern identification Tourism Services in the City Pattern discovery from massive urban data sets Use case: Mobility patterns of visitors of Barcelona

21 The objectives Large economic impact of tourism (leisure & business) 7.5 Million visitors/year in hotels (5 th in Europe) 2.5 Million visitors/year in cruises (4 th in world) Economic impact of 20 M /day Objectives: Know where, when, and how visitors move Cross with geo-referenced info of city (shops, public transportation, touristic places, credit card use, etc) Visualize for easy understanding 21

22 Leverage current infrastructure 450 Wifi APs of Barcelona Wifi + other Wifis managed by the municipality Analyze tuples (MAC@, AP, timestamp,...) to Differentiate visitors/residents and discover mobility patterns Locate more frequently visited areas with respect time Historical series and new trends Use current available muni/business info with a unified data model (urban ontology) to cross results with georeferenced info

23 Potential Results Pattern discovery Mobility patterns Data repository City data Integrated Urban Data Analysis & Visualization tool

24 CITY GEOMETRY AND PHYSICAL PHENOMENA 24

25 City Geometry Data LIght Detection And Ranging (LIDAR)

26 LIDAR Geometry example

27 Barcelona LIDAR Data

28 Barcelona LIDAR Data

29 City Geometry for Simulation

30 City Geometry for Simulation

31 Atmospheric Transport

32 CFD Indoors/Outdoors

33 CFD Indoors/Outdoors

34 CFD Indoors/Outdoors

35 CFD Indoors/Outdoors

36 Water Flooding 36

37 CITY DYNAMICS 37

38 Agent Based Models Useful for Social simulations Crowd simulations Mobility simulations (cars+pedestrian+ ) Epidemic evolution... But ABM scale bad in parallel computers, then there are serious limits in: Agents complexity Number of agents

39 PANDORA PANDORA: Scalable execution of agent-based models Able to run models with millions of agents C++ framework for large-scale social simulation Automated generation of parallelized code GIS support (Grass), Statistical support (R package) Python scripting to define ABM Cassandra: an ABM visualization tool 39

40 Agent Based Models Crowd Simulations 40

41 Conclusions In the next future, ICT applied to cities will transform deeply the urban style of live. For ICT industry this will be a significant source of bussiness. BSC is committed to focalized its research capacity in this area. 47

42 Thank you for your attention! José Mª Cela CASE Director