Soheil Sabri , Christopher J Pettit, Ian Bishop, Abbas Rajabifard
02 October 2015
Challenges for Integrating Subjective and Objective Measures in Urban Quality of Life Appraisal for Future Smart Living.
ISO 37120 City Indicators
Indicators for City Services
and Quality of life
ISO 37120
Economy Education
Energy
Environment
Recreation
Safety
Finance
Fire and emergency response
Governance
Health
Shelter
Solid waste
Telecommunication and innovation
Transportation
Urban Planning
Wastewater
Water and sanitation
Standard ISO/DIS 37120 indicators for city services and quality of life can act as drivers for smart city planning, as they incorporate two key aspects: 1. The development and
communication of data needed for most of the ISO 37120 indicators requires geospatial data as smart technology .
2. Achieving these indicators needs innovative tools enabling the integration of geospatial data and ICT-derived data.
Information Viewpoint
Computational Viewpoint
Engineering
Technology Viewpoints
Optimized Design/Development
Enterprise Viewpoint
Community Objectives
• Indicators for city services and quality of life
• Smart City Applications • Enterprise Components
Abstract/Best Practices
RM-ODP Viewpoints
Information Models • GML • CityGML • IndoorGML • LandXML • BIM
OGC Smart Cities Spatial Information Framework
Indicator
Maintain a City Model
Recreation: Recreation space
Energy: Renewable energy
Environment: Noise pollution
Indicator
Common Operating Picture
Urban Economics
Big data Analytics
Crowdsourcing and VGI
Open Data
Services • OGC Web Services • Sensor Web (SWE) • Mobile and IoT • Crowdsourcing • Open Data
Percivall, G., Ronsdorf, C., Liang, S., McKenzie, D., & McKee, L. (2015). OGC Smart Cities Spatial Information Framework. Retrieved from http://www.opengeospatial.org/pressroom/pressreleases/2181
Clause 13; incl 13.1 & 13.2
Model showing the relationships between objective conditions, subjective responses and neighborhood satisfaction.
Robert W. Marans
Quality of urban life & environmental sustainability studies: Future linkage opportunities ☆
Habitat International, Volume 45, Part 1, 2015, 47–52
http://dx.doi.org/10.1016/j.habitatint.2014.06.019
Objective
Subjective
Quality of Life & Environmental Sustainability
Multi-scale & Multi-dimensional
Individual Land use
Neighborhoods
City & Regions
Quality of Life
Components of quality of life (from MITCHELL, 2000, p74).
Screenshot of the app, while playing the stop-signal reaction time game.
Brown HR, Zeidman P, Smittenaar P, Adams RA, McNab F, et al. (2014) Crowdsourcing for Cognitive Science – The Utility of Smartphones. PLoS ONE 9(7): e100662. doi:10.1371/journal.pone.0100662 http://127.0.0.1:8081/plosone/article?id=info:doi/10.1371/journal.pone.0100662
• The large sample size vastly outweighed the noise inherent in collecting data outside a controlled laboratory setting,
• This can lead to the collection
of richer data sets and a significant cost reduction as well as provide an opportunity for efficient phenotypic screening of large populations.
Crowd sourced data for cognitive science
Literature Theme Approach Scale of Analysis QoL Potential Planning
Outcome Reference (s)
Participatory sensing
Measuring satisfaction through real world trial prototype system (e-mail, online form, iPhone app, SMS message, or electronic kiosk); Custom-made smartphone apps (2Loud?)
Uni Campus; Neighbourhood and City
Physical Env. Design and regeneration of public spaces; participatory planning; Noise pollution assessment
(Simm et al., 2015; Marusic et al., 2014; Leao et al., 2014; Woodcock, Frankova, & Garton, 2012)
Georeferenced emotion extraction
user generated contents (Metadata of Fliker and panorama); Custom-made smartphone app (EmoMap) Crowdsourced data (Twittermood; Tweetbeat); integration of human and technical sensors; integrated virtual environment and reality; Custom-made smartphone app (Mappiness)
Neighbourhood; City and Region
Physical Env. ; Personal Dev.
Tourism Planning; pedestrian navigation systems; CPTED
(Hauthal, 2015; Resh et al., 2015; MacKerron & Mourato, 2013; Klenter et al., 2013; MacKerron, 2012; Long, 2012; Toet & van Schaik, 2012; Ahn et al., 2009)
Local Knowledge and Planning Process
Combining Social Media and Call Data Records (CDR); Web-based ranking locations based on individual familiarity with places based using social media data (Foresqauare); web questionnaire (SoftGIS)
City; Neighbourhood
Physical Env. Visualising city-scale events; integrating local knowledge and urban planning process
(Balduini et al., 2015; Wang, 2015; Rantanen & Kahila, 2009)
Voice Your View (vYv): Comparing Electronic devices and on-time analysis of
sentiment (Woodcock, Frankova, & Garton, 2012)
Woodcock, A., Frankova, K., & Garton, L. (2012). VoiceYourView: Anytime, anyplace, anywhere user participation. Work, 41(SUPPL.1), 997–1003. doi:10.3233/WOR-2012-0276-997
Participatory Sensing
Prof. Jon Whittle, Lancaster University: • Pensioners who walk their dog
every day. On the route, at dusk, they hesitate as they walk past a large shrub, fearing what is behind.
• Young people who enjoy keeping fit. Their jogging route takes them into areas of the park that are poorly lit and they are afraid.
• Parents who take their children to the park but is concerned that the bandstand is becoming a magnet for teenage drinking parties.
Source: http://www.jonwhittle.org/voice-your-view/
Mappiness application snapshots:
MacKerron, 2012 and http://www.mappiness.org.uk/meters/
MacKerron, G. (2012). Happiness and environmental quality. The London School of Economics and Political Science. Retrieved from http://etheses.lse.ac.uk/383/
Georeferenced Emotion Extraction
CitySensing, visual story telling to help the audience perceiving emergence patterns (Balduini et al., 2015)
Balduini, M., Della Valle, E., Ciuccarelli, P., Azzi, M., Larcher, R., & Antonelli, F. (2015). CitySensing: Fusing City Data for Visual Storytelling. MultiMedia, IEEE. doi:10.1109/MMUL.2015.54
Local Knowledge and Planning
Balduini, M., Della Valle, E., Ciuccarelli, P., Azzi, M., Larcher, R., & Antonelli, F. (2015). CitySensing: Fusing City Data for Visual Storytelling. MultiMedia, IEEE. doi:10.1109/MMUL.2015.54
Challenges
Challenges
Technical
Legal
Ethical
Policy
Challenges
Technical
Validity of crowdsourced
data for subjective
measurement
Custom-made apps rather than
social media
No Transaction, No Tagging: No
data (Aged population, children, …)
Remained Stand-alone and theoretical
Scale is city and Regional and less
focused on individual land
uses like Recreational areas
Integrating to Planning process
is not well-defined
Geo-semantic emotion extraction needs to be an integrated system of Technical sensors, Human sensors and Crowd sourced data (Resch et al., 2015).
Resch, B., Summa, A., Sagl, G., Zeile, P., & Exner, J.-P. (2015). Urban Emotions—Geo-Semantic Emotion Extraction from Technical Sensors, Human Sensors and Crowdsourced Data. In G. Gartner & H. Huang (Eds.), Progress in Location-Based Services 2014 SE - 14 (pp. 199–212). Springer International Publishing. doi:10.1007/978-3-319-11879-6_14
ISO 37120 City Indicators Clause 13: Integrated Approach
Source: https://www.spear.land.vic.gov.au/spear/pages/eplan/3d-digital-
cadastre/3dprototype/prototype.html
Sabri, S., Pettit, C. J., Kalantari, M., Rajabifard, A., White, M., Lade, O., & Ngo, T. (2015). What are Essential requirements in Planning for Future Cities using Open Data Infrastructures and 3D Data Models? In 14th Computers in Urban Planning and Urban Management (CUPUM2015) (pp. 314–1–314–17). Boston, MA: MIT.
Planning for future cities (Sabri et al., 2015).
ISO 37120 City Indicators Clause 13: Integrated to 3D Information models
Thank you