Centre for Social Services Engineering
The PolisGnosis Project
The PolisGnosis Project
The Problem
Cities use a variety of metrics to evaluate themselves. With the introduction of ISO 37120, that contains 100 indicators for measuring a city’s quality of life and sustainability, it is now possible to consistently measure and compare cities, assuming they adhere to the standard. With the adoption of Open Data policies, cities are now publishing vast amounts of data which can potentially be used to analyse their performance. But much of this open data lacks standard formats and vocabularies to enable cross department and cross city analysis. Nor do tools exist for automating the analysis of this data.
Goal
The goal of this research is to automate the longitudinal analysis (i.e., how and why a city’s indicators change over time) and transversal analysis (i.e., how and why cities differ from each other at the same time), in order to discover the root causes of differences. In particular, to develop a system that takes as input:
- All of the information and knowledge with respect to an indicator,
- A set of consistency axioms,
- A set of diagnosis axioms and apply the axioms to determine the root cause of differences in a cities performance over time, or in comparison to other cities.
Phase 1
The first phase of this project focused on the creation of standard representations of city
knowledge (i.e., Vocabularies and Ontologies) that can be used to represent indicators and their supporting data and publish them on the Semantic Web. These ontologies are referred to as the Global City Indicator Ontologies (GCIO). GCIO is used to:
1. Represent the complete definition of each indicator in the ISO 37120 standard.
2. Represent each city’s indicator value (for a particular year), including the supporting data used to derive it, using the aforementioned ontologies.
3. Represent indicator theme-specific knowledge, such as basic knowledge about education, such as school, teacher, student, grade, etc.
4. Represent a city's theme-specific indicator knowledge. For example, in order to diagnose the education theme of indicators, PolisGnosis needs to understand concepts such as what grades comprise a Primary school in the city, and what schools are defined as Public schools.
The following depicts the flow of data within the PolisGnosis System:
A series of master’s projects has resulted in ontologies spanning many of the ISO 37120 themes, including:
Fox, M.S., (2013), “A Foundation Ontology for Global City Indicators”, Working Paper, Enterprise Integration Laboratory, University of Toronto, Revised: 13 October 2017.
Fox, M.S., (2014), “An Education Ontology for Global City Indicators (ISO 37120)”, Working Paper, Enterprise Integration Laboratory, University of Toronto, Revised: 14 May 2015.
Wang, Y., and Fox, M.S., (2015), “A Shelter Ontology for Global City Indicators (ISO 37120)”, Working Paper, Enterprise Integration Laboratory, University of Toronto, Revised: 28 August 2015.
Forde, A., and Fox, M.S., (2015), “A Telecommunication and Innovation Ontology for Global City Indicators (ISO 37120)”, Working Paper, Enterprise Integration Laboratory, University of Toronto, Revised: 13 August 2015.
Dahleh, D., and Fox, M.S., (2016), “An Environment Ontology for Global City Indicators (ISO 37120)”, Working Paper, Enterprise Integration Laboratory, University of Toronto. Revised 12 October 2016.
Wang, Z., and Fox, M.S., (2016), “A Finance Ontology for Global City Indicators (ISO 37120)”, Working Paper, Enterprise Integration Laboratory, University of Toronto.
Komisar, A., and Fox, M.S., (2017), “An Energy Ontology for Global City Indicators (ISO 37120)”, Working Paper, Enterprise Integration Laboratory, University of Toronto. 30 May 2017.
Rauch, N. and Fox, M.S., (2017), “Fire and Emergency Ontology for Global City Indicators (ISO 37120)”, Working Paper, Enterprise Integration Laboratory, University of Toronto.
Khazei, K., and Fox, M.S., (2017), “A Public Safety Ontology for Global City Indicators (ISO 37120)“, Working Paper, Enterprise Integration Laboratory, University of Toronto.
Abdulai, T., and Fox, M.S., (2017), “Recreation Ontology for Global City Indicators (ISO 37120)”, Working Paper, Enterprise Integration Laboratory, University of Toronto, Revised: 30 September 2017.
Yousif, W., and Fox, M.S., (2018), “A Transportation Ontology for Global City Indicators (ISO 37120)”, Working Paper, Enterprise Integration Laboratory, University of Toronto, eil.utoronto.ca.
Falodi, J., and Fox, M.S., (2018), “A Healthcare Ontology for Global City Indicators (ISO 37120)“, Working Paper, Enterprise Integration Laboratory, University of Toronto, eil.utoronto.ca.
Navalta, R., and Fox, M.S., (2019), “A Water and Sanitation Ontology for Global City Indicators (ISO 37120)“, Working Paper, Enterprise Integration Laboratory, University of Toronto, eil.utoronto.ca.
The results of our research are having a practical impact. ISO/IEC JTC1/Working Group 11 “Smart Cities” recently published a new standard, ISO/IEC 21972 – “An Upper Level Ontology for City Indicators”, based on our Foundation Ontology for city indicators. A new set of ISO/IEC city data standards are currently being developed based on our work.
Phase 2
The second phase focused on the development of consistency axioms that automate the determination of whether a city's indicators and supporting data are consistent with the ISO 37120 definitions, and whether they are longitudinally and transversally consistent.
Yetian Wang's master's thesis provides a method for detecting inconsistencies in city data. Three types of inconsistencies were investigated:
1. Definitional consistency evaluates if data used to derive a city indicator is consistent with the indicator’s definition (e.g. ISO 37120). For example, if the indicator is a student/teacher ratio, then a city’s reported indicator is inconsistent if it includes teachers that do not satisfy the ISO37120 definition, e.g., administrative staff.
2. Transversal consistency evaluates if city indicators published by two different cities are consistent with each other. For example, if the indicator measures homeless population, then indicators are transversally inconsistent if the homeless definition used by each differs. Note that each city’s indicator can be definitional consistent but not transversal consistent.
3. Longitudinal consistency evaluates if an indicator published by a city is consistent over different time intervals. For example, if the indicator measures a city’s PM10 air pollution, then the indicator is longitudinally inconsistent if the geospatial dimensions of the city have changed, which may arise through amalgamation.
In order to automate the process of inconsistency detection, our approach relies on two assumptions: 1) indicator definitions and the data used to derive their values are represented using standard data models, i.e., ontologies as defined in Phase 1, and 2) the data used to derive the indicators are openly published. Given an indicator definition, an indicator’s value and supporting data, both represented using the GCIO, the equivalent graph representation is analysed to detect inconsistencies. The algorithm performs sub-graph matching seeking out mismatches such as measurement, temporal, geospatial, and population definition differences. The algorithm detects actual inconsistencies, e.g., incorrect population definitions such as administrators vs. teachers, or potential inconsistencies, such as temporal differences of measurements.
Wang, Y., and Fox, M.S., (2017), "Consistency Analysis of City Indicator Data", in S. Geertman et al. (eds.), Planning Support Science for Smarter Urban Futures, Lecture Notes in Geoinformation and Cartography, DOI 10.1007/978-3-319-57819-4_20.
Project Team
Mark S. Fox
Yetian Wang
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Recent Publications
Fox, M.S., (2018), “The Semantics of Populations: A City Indicator Perspective”, Journal of Web Semantics, Vol. 48, pp. 48-65.
Wang, Y., (2017), "Consistency Analysis of City Indicator Data", in S. Geertman et al. (eds.), Planning Support Science for Smarter Urban Futures, Lecture Notes in Geoinformation and Cartography, pp. 355-369.
Wang, Y., and Fox, M.S., (2017), "Households, The Homeless and Slums: Towards a Standard for Representing City Shelter Open Data", Proceedings of the AAAI Workshop on AI or OR for Social Good.
Fox, M.S., (2015), "The PolisGnosis Project: Representing and Analyzing City Indicators", Working Paper, Enterprise Integration Laboratory, University of Toronto, 11 March 2015.