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Market Research Report - 247580

Smart City - Urban Data: A Pivotal Element in Digital Communities

Published by IDATE DigiWorld
Published Content info 70 Pages
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Smart City - Urban Data: A Pivotal Element in Digital Communities
Published: December 31, 2014 Content info: 70 Pages
Description

This report explores the topic of smart cities from the perspective of the urban data generated by the city's various stakeholders: public and private sector players and citizens . It inventories the different sources of urban data , and examines the issues surrounding their collection, storage and processing . The report then looks at the different ways these data can be utilised and monetised, and presents the main problems that have been identified by smart city initiatives: business models, governance, citizen involvement .

These different topics are described by drawing on multiple examples that have been tested or deployed in different cities around the globe.

Table of Contents

Table of Contents

1. Executive Summary

  • 1.1. Mass production of urban data
  • 1.2. Is it smart to pool urban data, and their collection?
  • 1.3. The challenge of making urban data, open data
  • 1.4. Do we need national governance for urban data?

2. Methodology & definitions

  • 2.1. IDATE's general methodology
  • 2.2. Methodology specific to this report
  • 2.3. Definitions

3. The what and why of urban data

4. The prospect of a huge surge in the production of urban data

  • 4.1. Government and citizen data
  • 4.2. Sensors and tags
    • 4.2.1. Infrastructure sensors
    • 4.2.2. Tags: RFID, NFC, QR code
  • 4.3. Citizen-generated data
    • 4.3.1. Personal sensors
    • 4.3.2. Examples of citizen-generated data
  • 4.4. External data
    • 4.4.1. The Internet
    • 4.4.2. Other possible sources of data

5. Collection urban data: from juxtaposed to shared networks

  • 5.1. Available networks
    • 5.1.1. WPAN technologies
    • 5.1.2. Ultra Wide-band (UWB)
    • 5.1.3. Proprietary technologies
    • 5.1.4. Cellular networks
  • 5.2. The challenge of streamlining backhaul networks

6. Storage and processing: the heart of any urban data utilisation project

  • 6.1. The challenge of making urban data, open data
    • 6.1.1. Open Data: first small forays
    • 6.1.2. Open data specialists
  • 6.2. Storing urban data
  • 6.3. Processing urban data
    • 6.3.1. Quality of the data and prior processing
    • 6.3.2. APIs: automating data transfers
    • 6.3.3. The emergence of big data
    • 6.3.4. Visualisation

7. Putting urban data to work: complementary approaches in search of a winning model

  • 7.1. Private sector approach to utilising urban data
    • 7.1.1. Developing businesses and creating new professions
    • 7.1.2. New specialised players
    • 7.1.3. How telcos and IT service companies are positioned in the market
  • 7.2. Public approach
    • 7.2.1. The city as decision-maker
    • 7.2.2. The city as data manager
  • 7.3. Public-private approach
  • 7.4. Managing the ecosystem
    • 7.4.1. Incubators
    • 7.4.2. Living labs
    • 7.4.3. Challenges, contests and other incentive measures

8. Urban data: still a number of unknownsâ€

  • 8.1. Legal and regulatory framework governing data
    • 8.1.1. Personal data
    • 8.1.2. Data from public actors
    • 8.1.3. Data from private actors
  • 8.2. Standardising urban data
  • 8.3. Business models
    • 8.3.1. Expected benefits
    • 8.3.2. Potential sources of financing

9. Conclusion: stepping stones to urban data governance

Report figures

  • Figure 1: Processing urban data
  • Figure 2: The five main sources of data for smart city applications
  • Figure 3: The different types of data collected by smart city sensors
  • Figure 4: Main types of network deployed in a city
  • Figure 5: String of networks used to collect sensor data
  • Figure 6: Open data portals around the globe
  • Figure 7: Urban data: revenue sources and externalities
  • Figure 8: Technical layers enabling the use of urban data
  • Figure 9: The main vertical services targeted by smart city initiatives
  • Figure 10: Some recent smart city market estimates
  • Figure 11: The main living labs around the globe
  • Figure 12: Estimated number of connected objects in use around the globe
  • Figure 13: Processing urban data
  • Figure 14: The five main sources of data for smart city applications
  • Figure 15: Open data portals around the globe
  • Figure 16: Examples of free parking space and full rubbish bin sensors
  • Figure 17: The different types of data collected by smart city sensors
  • Figure 18: The connected boulevard in Nice
  • Figure 19: QR code/NFC tags for accessing different services
  • Figure 20: QR code/NFC tags for accessing different services
  • Figure 21: The "Fix my Street" service in Brussels
  • Figure 22: How the Green Watch works
  • Figure 23: Map of connected Netatmo personal weather stations
  • Figure 24: How data is captured for the Urban Emotions project
  • Figure 25: "Rate my area" home screen
  • Figure 26: Map of the number of photos taken by location in New York
  • Figure 27: Live Singapore! taxis and rain screen
  • Figure 28: Example of the Waze - Berlin application interface
  • Figure 29: Main types of network deployed in cities
  • Figure 30: PAN ecosystem
  • Figure 31: Main wireless technology networks
  • Figure 32: Mobile technology specifications
  • Figure 33: String of networks used to collect sensor data
  • Figure 34: Storing and processing urban data
  • Figure 35: The datacentre ecosystem
  • Figure 36: The open data ecosystem
  • Figure 37: Dashboard for France's data.gouv.fr open data site
  • Figure 38: Functionalities offered on the OpenDataSoft site
  • Figure 39: A selection of datacentres that are part of PIN in France (non exhaustive list)
  • Figure 40: Using API for the different sources of urban data
  • Figure 41: Using tweets to indicate flooded areas in Jakarta
  • Figure 42: Priority targets for smart city projects
  • Figure 43: IBM Smart Water: the operator's view - selecting events, asset types and logical zones to display on a geospatial map
  • Figure 44: Schneider Electric: applying existing expertise to the smart city
  • Figure 45: INEO - GDF Suez: utilising data for the national benefit
  • Figure 46: Ondeo Systems: overview of the water data processing chain
  • Figure 47: M2ocity: overview of the water data processing chain
  • Figure 48: Deutsche Telekom: an integrated and centralised approach to the smart city
  • Figure 49: Traffic management system in Berlin
  • Figure 50: New York City: the DataBridge Store
  • Figure 51: The main mobility-related data made available by Metropolitan Lyon
  • Figure 52: Main public and private sector players involved in the latest edition of Datact
  • Figure 53: The Datalyse approach to processing big data
  • Figure 54: Approaches to smart cities
  • Figure 55: Tel Aviv - a map of start-ups
  • Figure 56: The "Tuba" living Lab in the Part-Dieu neighbourhood of Lyon
  • Figure 57: Data discovery challenge - Singapore December 2014
  • Figure 58: Public concern over how their personal information is used on the Internet (on the left Europe; on the right, the United States)
  • Figure 59: Percentage of people willing to share personal information depending on the reward they receive in exchange
  • Figure 60: Urban data: revenue sources and externalities
  • Figure 61: Home page for the Spacehive crowdfunding platform
  • Figure 62: Examples of crowdfunding platforms
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