The Future of Cities: Big Data and the Urban Economy Piyushimita Thakuriah (Vonu) UNIVERSITY OF...

The Future of Cities:Big Data and the Urban Economy

Piyushimita Thakuriah (Vonu)

UNIVERSITY OF GLASGOW, UK

October 7, 2015

July 10, 2015

Urban Big Data Centre

Concentrations of innovation and economic growth

BUT

facing extreme challenges

Cities in the 21st Century

Human and social

Environmental and physical

Economic

adding significant complexities to urban governance


How to operate cities effectively and efficiently How should we evaluate potential consequences of complex

social policy change on urban areas What makes the economy resilient and strong – how to

develop shock-proof cities How different cities recovered from man-made or natural

disasters What interventions are needed for healthy behavior What strategies are needed for lifelong learning, civic

engagement and community participation How does one generate hypothesis about historical

evolution of social exclusion to impact current-day practice

Big questions facing decision-makers


Resources – having to prioritize in making allocations Lack of timely, sustained and agile strategies to change Little incentive to change direction – policy choices are pre-

determined Vested interest by industry and limited meaningful

involvement by advocacy groups and citizens

Its too hard!! Why? - There is lack of high-quality information to operate, plan, or evaluate urban strategies Fragmented and siloed information Lack of timely, granular information that gives meaningful

insights into contextual peculiarities and local experiences Lack of understanding of value derived from data-derived

strategies

Constraints in addressing “tough” urban problems

Setting the Stage

Courtesy ETSI

An explosion of data – 3.010 active Internet users, 3.65 unique mobile phone users, 2.079 active social media accounts*Governments, business and others collecting data

http://wearesocial.net/blog/2015/01/digital-social-mobile-worldwide-2015/




A wide spectrum of data:

Generated through transactional, operational, planning and social activities not all of which are specifically designed for research

Complexities: information management technology, analysis methods, political economy surrounding data, scientific paradigm and epistemological questions

Big Data

Big Data in the Urban ContextUrban Big Data ExamplesSensor systems (infrastructure-based or moving objects)

Environmental, water, transportation, building management; connected systems; Internet of Things

User-Generated Content (“social” or “human” sensors)

Participatory sensing systems, citizen science projects, social media, web use, GPS, online social networks and other socially-generated data

Administrative data (open or confidential microdata)

Open administrative data on transactions, taxes and revenue, payments and registrations; confidential person-level microdata

Private Sector Data (customer and transactions record)

Customer transactions data from store cards and business records; product purchases and terms of service agreements

Arts and Humanities Data Cultural repositories of text, images, sound recordings, linguistic data, film, art and material culture, and digital objects, and other media

Hybrid Data Sources and Synthetic Data

Linked data including linkage of survey-sensor, census-administrative records and simulated data

Thakuriah, P., N. Tilahun and M. Zellner (forthcoming). Big Data and Urban Informatics: Innovations and Challenges to Urban Planning and Knowledge Discovery. In edited volume Seeing Cities through Big Data: Research Methods and Applications in Urban Informatics, to be published by Springer.


Big Data as DATA/types of data and their characteristics (size, rate of generation, unstructured etc)

Big Data as APPROACH/data-driven analysis and decision-making

IMPACT

Adoption/ Implementation

Value-Proposition and Actionable

Strategies

Knowledge Discovery

Data Analytics

Urban Data Infrastructure Urb

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The Big Data Process


Analyzing, visualizing, simulating, understanding, interpreting such structured and unstructured data on cities and urban areas:

1) Dynamic resource management2) Knowledge discovery and understanding3) Urban engagement and civic participation4) Urban planning and policy analysis

Urban Informatics


1) Dynamic Resource Management

Urban Big Data Centre Urban Studies Seminar 2013

Thakuriah, P. and N. Tilahun (2013). Incorporating Weather Information into Real-Time Speed Estimates: Comparison of Alternative Models. Journal of Transportation Engineering, Vol. 139, No. 4, pp. 379-389.

Connecting the transport and weather sensor networks

Short-term prediction of traffic speeds given weather predictions

Machine-learning models & simulations to evaluate effects of data & forecast inaccuracies

Traveller decision-support and city management

Connected Systems – Weather Responsive Traffic Management – Fusion of real-time transport & weather data


Known incident from transportation sensor dataNegative tweets – tweets posted when there is no incidentPositive tweets – tweets posted when there is an incident

Detecting Events and Disruptions from Big Data – e.g., Twitter data


2) Knowledge Discovery and Analysing Social and Economic

Aspects of Cities


Clusters of bus stops showing spatial variations in bus ridership in response to real-time bus arrival system

Spatially-segmented Urban Management Strategies

Cluster 1 – Highest average gains in bus boarding after Bus Tracker launch on routes- Digitally savvy, high-income areas with high population densities located close to city center

Cluster 4 – Lowest average levels of boardings after Bus Tracker – Areas with low level of bus service usage located far from the city center and with a “car culture”


Integrated Multimedia City Data (iMCD) Primary survey of 1500 households in Glasgow and household

members (about 3000 persons) Questionnaire-based survey – transport, education,

energy use, ICT use, attitude and personal preferences Sensing survey (GPS and lifelogging use by participants)

Significant Information Retrieval for a year (data from various text-based and multimedia social data from the Internet, eg social media data)

Very High Resolution satellite data and LiDAR data to construct dynamic Digital Surface Model for Glasgow

Linking together a number of sensor networks: transportation, emissions, crime detection CCTV, weather etc

Potentially link to British Geological Survey (BGS) data on ground conditions beneath Glasgow

Urban Big Data Centre To model and simulate “life in the city” and links to behaviours and activitiesActivity

Locations

Mobility Patterns

Events in the city

4) Urban Planning and Policy Analysis


Labour Market Accessibility – increasingly travel costs estimated with “Big” Data such as cellular phone, GPS etc, job openings scraped from websites

How do patterns of labour market accessibility vary between UK cities?

How have these patterns changed over time?

What effect did the recession have?


Real Estate Data***

Listings for Glasgow

Sentiment mining of letting agent language (create thesaurus)

Linkage to wider set of urban indicators

Link to

Sales data – Land registry

*** This approach is indicative only and experimental ,and subject to agreement with data owner.


Detecting Changes to Land-use, Physical and Built Environment

Urban and Regional Planning and Policy Models + Data

Science Approaches

Immense need for integrated set of tools & technologies

for urban data management

VHR Longitudinal Satellite data & 3D Modelling – Detecting

Changes in the Built Environment

BGS Clyde Valley Bedrock Geology – linkage to social

science data to study environmental justice


Spatial Urban Data System (SUDS)

Geographical database on UK’s largest built-up areas and settlements;

Inputs: census, surveys, sensors, social media, specialised data programmes

Processes: simple processing to complex urban models and simulations

Outputs: Simple to complex indicators describing cities and communities (eg, transport accessibility, PM2.5 emissions, fuel poverty, walkability etc)

Started with: ISO 37120:2014 – 16 categories – 72 total; economy, education, environment, etc, starting with England and Wales (output areas)

Aspirations: small-area estimates (within Data Protection Act) and longitudinal, with common geographic identifiers


Java-based software server that allows users to view and edit geospatial data. Using open standards set forth by the Open Geospatial Consortium (OGC)

Access levels will vary depending on depending on spatial resolution, data owner and licensing requirements - some through open data portal, many likely through secure access

Open Source GIS (GeoServer) Portal to access SUDS Indicators

3) Urban Engagement, Civic Participation and Data

Entrepreneurship


Minecraft System (current) – Interactive Tools for Citieshttp://ubdc.ac.uk/our-services/research-services/data-systems/minecraft-server/ Urban Engagement,

civic participation – starting with children!

http://ubdc.ac.uk/our-services/research-services/data-systems/minecraft-server/

http://ubdc.ac.uk/our-services/research-services/data-systems/minecraft-server/


Open Data Initiatives

“Open Data” initiatives driven by open government strategies, reflecting accountable and transparent government, collaborative government, and innovative government

Governmental data should be accessible for everyone to use and to republish without copyright or other restrictions in order to create a knowledgeable, engaged, creative citizenry


Open Data Initiatives and Civic Hacking*

* Survey currently underway

Reasons for being involved in using Open Data Portal and in Civic Hacking

Percent who agreed

To promote urban engagement and improve

85

To improve social inclusion by strengthening access to information

90

To improve own knowledge of data, programming and development

70

To generate employment opportunities and improve resume

30


General-purpose ICT Infomediaries Smart City Companies Multiple-service ICT Companies

Urban Information Service Provider Infomediaries

City Information Services Location-Based Services Location-Based Social Networks

Urban Open and Civic Data Infomediaries Open Data Organizations Civic Hacking Organizations Community-Based Information Service

OrganizationsIndependent and Open Source Developer Infomediaries

Independent App Developers Open Source Developers

Urban Digital Infomediaries -4 Major Groups with 10 Urban Digital Infomediaries

Traditional Urban Data Users

Planning organizationsOperational agenciesResearch organizations and universitiesConsulting firms

Emerging Information Users

Thakuriah et al (2014) Emerging Urban Digital Infomediaries and Civic Hacking in an Era of Big Data and Open Data Initiatives


M Naphade, G Banavar, C Harrison, J Paraszczak, and R Morris. Smarter Cities and theirinnovation challenges. Computer, 44(6):32 –39, June 2011

Related Concepts: U-City Pervasive

Urban Spaces Etc.

Smart and Connected Cities to “Future” Cities


Big Data Challenges – Technological and Methodological, yes, but equally importantly..

Challenges Characteristics

Theoretical and

epistemological

1) Understanding metrics, definitions, and changing ideologies and methods to understanding “urban”

2) Determining validity of approaches and limits to knowledge from data-driven approach

3) Deriving visions of future cities and the links to sustainability and social justice

Political economy

1) Data entrepreneurship, innovation networks and power structures

2) Value propositions and economic implications3) Data access and governance framework 4) Privacy, security and trust management5) Responsible innovation and emergent ethics


Potential New Metrics and Motivations

Impacts, outcomes, cost-effectiveness, return-on-investment, efficiency, equity, Social interestingness,

exploration and urban discovery, inclusiveness, sharing and engagement; augmented mindfulness for self-awareness


Big Data and the Future of Cities: 5 Themes in the Way Forward

New norms of digital literacy requirements -Understanding digital exclusion, consumer awareness and rights and responsibilities

Data Systems –

Building and sustaining urban Big

Data Infrastructure

Analytics – specialized knowledge discovery,

operational and planning solutions

Capacity-building – Entrepreneurial strategies, workforce development Raising

awareness and making value case for data-derived decision-making - deriving policy & planning implications

_________

Partners

Innovations for sustainable and socially-just cities

Disciplines – Urban Social Science + Data Science Urban studies, planning & policy Statistics Economics Computer science Education Geography Mathematics Civil engineering

UBDC Portfolio

What we do Build Links among Cities, Local Governments, Businesses and Universities through Data

Data Products

Data Service

Research

Outreach/KE Training

Run a national data service to facilitate urban research

The Future of Cities: Big Data and the Urban Economy Piyushimita Thakuriah (Vonu) UNIVERSITY OF...

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