This document discusses key issues around big data and smart cities. It outlines different types of urban big data like directed data from surveillance cameras and automated data from digital devices. It also discusses how single systems can become integrated across a whole city and different sectors. The document then critiques smart cities and discusses concerns around data ownership, privacy, hacking, and how data could reinforce inequalities. It also outlines technical data concerns regarding access, integration, quality, analysis, and skills.
1. Prof. Rob Kitchin
NIRSA , Maynooth University
Rob.Kitchin@nuim.ie @robkitchin
Big data and smart cities
Key data issues
Government Data Forum, 14 July 2015
2. Urban big data
• Directed
o Surveillance: CCTV,
drones/satellite
o Scaled public admin records
• Automated
o Automated surveillance
o Digital devices
o Sensed and scanned data
o Interaction and transactional data
o IoT (Internet of things) and M2M
(machine to machine)
• Volunteered
o Social media
o Sousveillance/wearables
o Crowdsourcing
o Citizen science
6. Smart ...
• Economy
• entrepreneurship, innovation, productivity, competiveness
• Government
• e-gov, open data, transparency, accountability, evidence-informed
decision making, better service delivery
• Mobility
• intelligent transport systems, multi-modal inter-op, efficiency
• Environment
• green energy, sustainability, resilience
• Living
• quality of life, safety, security, manage risk
• People
• more informed, creativity, inclusivity, empowerment, participation
7. Seven critiques of smart cities
• Ahistorical, aspatial and homogenizing
• The politics of urban data
• Technocratic governance and solutionism
• Corporatisation of governance
• Buggy, brittle, hackable urban systems
• Serve certain interests and reinforce inequalities
• Social, political, ethical effects
8. The politics of urban data
Material Platform
(infrastructure – hardware)
Code Platform
(operating system)
Code/algorithms
(software)
Data(base)
Interface
Reception/Operation
(user/usage
Systems of thought
Forms of knowledge
Finance
Political economies
Governmentalities & legalities
Organisations and institutions
Subjectivities and communities
Marketplace
System/process
performs a task
Context
frames the system/task
Data assemblage
9. Data concerns
• Corporatisation of governance
• Data ownership
• Data control
• Buggy, brittle, hackable urban systems
• Data security, data integrity
• Social, political, ethical effects
• Data protection and privacy
• Dataveillance/surveillance (anonymous vs personalised
data – ANPR, CCTV, facial recognition, travel card &
bluetooth tracking, smart metering, etc)
• Data uses: Social sorting, control creep, dynamic pricing,
anticipatory governance, official statistics
10. Technical data concerns
• Data coverage and access
(openness)
• Data integration and
interoperability (data standards)
• Data quality and provenance:
veracity (accuracy, fidelity),
uncertainty, error, bias,
reliability, calibration, lineage
• Quality, veracity and
transparency of data analytics
• Ecological fallacy and
interpretation issues
• Skills and organisational
capabilities and capacities
