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