Advancing Smart-City Infrastructures: Open Data, Big Data. Perspectives from the Field Mark S. Fox, University of Toronto

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2 : Open Data, Big Data Perspectives from the Field Mark S. Fox, University of Toronto 2

3 Meet the Presenter Mark S. Fox Distinguished Professor of Urban Systems Eng. University of Toronto Ontologies: ontology.eil.utoronto.ca 3

4 Perspectives from the Field: JMG2020: Big Data and Global Cities teams, composed of post-graduate social scientists and engineers, attempt to solve a Global City problem using publically available data. Predicting city financial health What attracts immigrants to cities Predicting sewer failures Community factors leading to better school performance Impact of fire service reduction Relationship between immigration and crime 4

5 Data From Many Sources Police Fire Tax Building Permits New York s Analytics team created a dataset combining data from 19 agencies: Buildings, preservation, police, fire, tax, and building permits. By combining data analysis with expertise gleaned from inspectors, able to rate complaints such that in 70% of inspector visits led to vacate orders. 5

6 How big is BIG Most organizations are making the conversion from gut-based to fact-based decision making, and any facts will do! Any Data megabytes Data Analytics gigabytes Big Data tera & peta bytes And the data? It s not so big. Big Data = Data Analytics 6

7 The Real Bottlenecks Assuming you know what data you need (Big IF: often we do not know without research, nor can you just dump all the data into a data mining tool and let the algorithm figure it out): Where can I find it? What are the attributes? What do they mean? Are they equivalent to the attributes from other data sets? Is the data correct? Complete? Can I trust it? 7

8 Perspectives from the Field: Reusable representations of City of Knowledge Identify the patterns of knowledge and their constituents found in Toronto s 311 website Represent using ontologies Analysis of City Performance Measure city performance using City Indicators, and determine root causes of performance. 8

9 ISO Primary Student Teacher Ratio "The student/teacher ratio shall be expressed as the number of enrolled primary school students (numerator) divided by the number of full-time equivalent primary school classroom teachers (denominator). The result shall be expressed as the number of students per teacher. Private educational facilities shall not be included in the student/teacher ratio. One part-time student enrolment shall be counted as one full-time enrolment; in other words a student who attends school for half a day should be counted as a full-time enrolment. If a city reports full-time equivalent (FTE) enrolment (where two half day students equal one full student enrolment), this shall be noted. The number of classroom teachers and other instructional staff (e.g. teachers aides, guidance counselors) shall not include administrators or other nonteaching staff. Kindergarten or preschool teachers and staff shall not be included. The number of teachers shall be counted in fifth time increments, for example, a teacher working one day per week should be counted as 0.2 teachers, and a teacher working three days per week should be counted as 0.6 teachers. 9

10 Toronto ISO for

11 Problem Toronto 14.6 Melbourne 24.1 City Indicator Supporting Data/City Knowledge 11

12 Data Safari 12

13 Goal: What to Publish Cities lack direction on what data sets should be published on their Open Data website. Our goal is to identify the content that needs to be published to support the analysis of city performance. 13

14 Goal: How to Publish Cities lack direction on the Format and Vocabulary for the indicator-related data they publish. Our goal is to provide standard ontologies for the publishing of indicator-related city data on the Semantic Web. 14

15 Goal: Automated Analysis Though the intent of Open Government and Open Data is to enable public oversight, the vast amount of information and the complexity of analysis limits what citizens can do (and they can t be bothered). Our Goal is to provide tools to automate the analysis of city performance Perform longitudinal and transversal analyses, and Determine the root causes of differences, using data from across the semantic web. 15

16 Ontological Requirements If we are to develop a software application that can determine the root causes of city performance based on city indicators, what does it have to know? Indicator Theme City Specific? Knowledge? Knowledge?? ISO Indicator Definition Text City Indicator and Supporting Data Spreadsheet GCIO: Global City Indicator Ontologies ISO Indicator Theme City Indicator and City Specific Indicator Definition Knowledge Supporting Data Knowledge Semantic? Web Semantic? Web Semantic? Web Semantic? Web??? Definition Consistency Axioms Intra Indicator Consistency Axioms Inter Indicator Consistency Axioms PolisGnosis Analysis Engine??? Quantitative Analysis Axioms Definitions Analysis Axioms (logic & spatial) Event Analysis Axioms 16

17 Ontological Requirement 1 Representation of the unwritten narrative (i.e., meta information) associated with an indicator value. Quantity: ISO Place: Toronto Time: Provenance: Validity: 0.5 Trust:

18 Ontological Requirement 2 Heterogeneous representation of city indicator definitions. Indicator unit of measure (measurement ontology) owl:subclassof "an object property" GCI_ Quantity Year of measurement (temporal ontology) gci: om:unit_of_measure Population_ ratio_unit Ratio Indicator gci:for_ SchoolYear SchoolYear Place of measurement (placename and geospatial ontologies) Statistic being measured (Statistics ontology) Population being measured (Population ontology) gci:numerator gci:for_city gci:population_size gci:city gci:cardinality_of gci:population gci:locatedin gci:defined_by gci:denominator gci:population_size gci:cardinality_of gci:population gci:defined_by Person being counted (Theme ontology) Person Person 18

19 Ontological Requirement 3 Representation of generic thematic knowledge. For the Education theme that includes Student & Enrollment, Education Programs, Grades & Courses, Education Staff & Placements, Education Facility, Performance. 19

20 Ontological Requirement 4 Representation of city specific thematic knowledge. Definition of UK Public School differs from the ISO of Public School in that it is privately owned and operated. 20

21 PolisGnosis: Bringing it Together ISO Indicator Definition Text Indicator Theme Knowledge City Specific Knowledge City Indicator and Supporting Data Spreadsheet GCIO: Global City Indicator Ontologies ISO Indicator Definition Semantic Web Indicator Theme Knowledge Semantic Web City Indicator and Supporting Data Semantic Web City Specific Knowledge Semantic Web Definition Consistency Axioms Quantitative Analysis Axioms Intra Indicator Consistency Axioms PolisGnosis Analysis Engine Definitions Analysis Axioms (logic & spatial) Inter Indicator Consistency Axioms Open Advancing Data, Big Smart-City Data July Infrastructures 29, 2015 Event Analysis Axioms 21 21

22 Perspectives from the Field: What we Need Adoption of standard format: Semantic Web Adoption of standard ontologies Future software RFPs include the incorporation of these standards Place the onus on the vendors to provide the capabilities to acquire, represent and publish city data using standards Vendors and/or Organizations need to provide leadership 22

23 Perspectives from the Field: What we Need Identify and publish relevant data 23

24 Thank you! 24

Variety in Big Data: A Cities Perspective

Variety in Big Data: A Cities Perspective Mark S. Fox Professor of Industrial Engineering & Computer Science Senior Fellow, Global Cities Institute University of Toronto msf@eil.utoronto.ca, www.eil.utoronto.ca