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スマートシティ:デジタルコミュニティの中枢的エレメントである都市データ

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

発行 IDATE DigiWorld 商品コード 247580
出版日 ページ情報 英文 70 Pages
納期: 即日から翌営業日
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こちらの商品の販売は終了いたしました。
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スマートシティ:デジタルコミュニティの中枢的エレメントである都市データ Smart City - Urban Data: A Pivotal Element in Digital Communities
出版日: 2014年12月31日 ページ情報: 英文 70 Pages

当商品の販売は、2016年11月29日を持ちまして終了しました。

概要

当レポートでは、公的機関、民間、個人から集められる都市データについて調査し、都市データの収集、保存、処理に関連する課題、都市データの活用法と収益化の方法などについて検証します。

第1章 エグゼクティブサマリー

第2章 調査手法・定義

第3章 都市データとその有効性とは?

第4章 都市データ生成の急増の見通し

  • 政府および市民のデータ
  • センサーおよびタグ
    • インフラセンサー
    • タグ:RFID・NFC・QRコード
  • 市民の生成するデータ
    • パーソナルセンサー
    • 市民の生成するデータの例
  • 外部データ
    • インターネット
    • 可能性のあるその他のデータソース

第5章 都市データの収集:並列のネットワークから共有のネットワークへ

  • 利用可能なネットワーク
    • WPAN技術
    • Ultra Wide-band (UWB)
    • 専用技術
    • セルラーネットワーク
  • バックホールネットワークの合理化における課題

第6章 保存と処理:都市データの有効利用プロジェクトの中心

  • 都市データのオープンデータ化における課題
  • 都市データの保存
  • 都市データの処理
    • データ品質と前処理
    • API:データ移行の自動化
    • ビッグデータの出現
    • ビジュアル化

第7章 都市データの活用:成功モデルの模索における補足的アプローチ

  • 都市データ活用の民間によるアプローチ
  • 公的なアプローチ
  • 官民共同のアプローチ
  • エコシステムの管理

第8章 依然として未知数の都市データ

  • データのガバナンスに関する法規制上の枠組み
    • パーソナルデータ
    • 公的機関からのデータ
    • 民間からのデータ
  • 都市データの標準化
  • ビジネスモデル
    • 期待できるメリット
    • 潜在的財源

第9章 総論:都市データのガバナンスへの足掛かり

図表

目次
Product Code: M14448MR

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

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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