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20190928CodeforJapanSummit

Toshikazu SETO
September 28, 2019

 20190928CodeforJapanSummit

* Code for Japan Summit 2019
* アカデミア(研究者)×シビックテック

Toshikazu SETO

September 28, 2019
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  1. 2019/09/28 Code for Japan Summit ( )× 1
    (tosseto)
    [email protected]
    Knight Foundation (2013)

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  2. 2019/09/28 Code for Japan Summit ( )× 2

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  3. 2019/09/28 Code for Japan Summit ( )× 3
    Civic Tech Local Gov. Tech
    My City Forecast
    AI
    My City Report
    Urban Data Challenge
    OpenStreetMap

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  4. 2019/09/28 Code for Japan Summit ( )× 4
    http://urbandata-challenge.jp/
    11/1( )13:00
    2019

    53(3) pp.1515-1522 2018 https://doi.org/10.11361/journalcpij.53.1515

    70(6) pp.10-16 2016 https://doi.org/10.3169/itej.70.840

    2013 GIS 23(2) pp.23-30 2015 https://doi.org/10.5638/thagis.23.59

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  5. 2019/09/28 Code for Japan Summit ( )× 5
    Civic technology
    (Google Scholar 2014 600 )
    2016
    11
    2017
    7
    2018
    4

    8
    • Civic tech Toronto
    • La civic tech

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  6. 2019/09/28 Code for Japan Summit ( )× 6
    Web of Science Core Collection : 58
    ( civic tech* 2014 )
    • Civic Hackathons: Innovation, Procurement, or Civic Engagement?(2014)
    39
    • Institutions for Civic Technoscience: How Critical Making is Transforming
    Environmental Research (2014)
    • Data cation and empowerment: How the open data movement re-
    articulates notions of democracy, participation, and journalism (2014)

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  7. 2019/09/28 Code for Japan Summit ( )× 7
    https://arxiv.org/abs/1904.04104
    • シビックテックに関わる学術論⽂はほとん
    どない(計算機科学のソフトウェア分野)
    • ソフトウェア開発にシビックテックがどの
    ように関わるかを明らかにしたもの
    • 対象は,Code for Ireland
    • ⼀定評価しつつも、より良いものにする上
    で,マネジメント体制や,ソフトウェアの
    クオリティ・永続性に課題

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  8. 2019/09/28 Code for Japan Summit ( )× 8
    ACM-DGOV

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  9. 2019/09/28 Code for Japan Summit ( )× 9
    (Open311 )
    (ACM-CHI )
    (

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  10. 2019/09/28 Code for Japan Summit ( )× 10
    • 2014
    – Civic Engagement ?

    – =citizen science
    – =participatory
    sensing
    • ICT
    – Open311
    – [new]

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  11. 2019/09/28 Code for Japan Summit ( )× 11
    Open311:
    (Google Scholar 2014 400 )
    Adversarial Adaptation of Scene Graph Models for
    Understanding Civic Issues
    Shanu Kumar
    Indian Institute of Technology Kanpur, India
    [email protected]
    Shubham Atreja, Anjali Singh, Mohit Jain
    IBM Research AI, India
    {satreja1,ansingh8,mohitjain}@in.ibm.com
    ABSTRACT
    Citizen engagement and technology usage are two emerging trends
    driven by smart city initiatives. Governments around the world are
    adopting technology for faster resolution of civic issues. Typically,
    citizens report issues, such as broken roads, garbage dumps, etc.
    through web portals and mobile apps, in order for the government
    authorities to take appropriate actions. Several mediums – text,
    image, audio, video – are used to report these issues. Through a
    user study with 13 citizens and 3 authorities, we found that image
    is the most preferred medium to report civic issues. However, ana-
    lyzing civic issue related images is challenging for the authorities
    as it requires manual eort. Moreover, previous works have been
    limited to identifying a specic set of issues from images. In this
    work, given an image, we propose to generate a Civic Issue Graph
    consisting of a set of objects and the semantic relations between
    them, which are representative of the underlying civic issue. We
    also release two multi-modal (text and images) datasets, that can
    help in further analysis of civic issues from images. We present
    a novel approach for adversarial training of existing scene graph
    models that enables the use of scene graphs for new applications
    in the absence of any labelled training data. We conduct several
    experiments to analyze the ecacy of our approach, and using
    human evaluation, we establish the appropriateness of our model
    at representing dierent civic issues.
    KEYWORDS
    Civic Engagement, Scene Graph Generation, Adversarial Training,
    Smart Cities, Intelligent Systems on Web
    ACM Reference Format:
    Shanu Kumar and Shubham Atreja, Anjali Singh, Mohit Jain. 2019. Adver-
    sarial Adaptation of Scene Graph Models for Understanding Civic Issues. In
    Proceedings of ACM Conference (Conference’17). ACM, New York, NY, USA,
    Article 4, 11 pages. https://doi.org/10.475/123_4
    1 INTRODUCTION
    In recent years, there has been a signicant increase in smart city
    initiatives [9, 30, 31]. As a result, government authorities are em-
    phasizing the use of technology and increased citizen participation
    for better maintenance of urban areas. Various web platforms –
    SeeClickFix [27], FixMyStreet [1], ichangemycity [16] – have been
    Permission to make digital or hard copies of part or all of this work for personal or
    classroom use is granted without fee provided that copies are not made or distributed
    for prot or commercial advantage and that copies bear this notice and the full citation
    on the rst page. Copyrights for third-party components of this work must be honored.
    For all other uses, contact the owner/author(s).
    Conference’17, July 2017, Washington, DC, USA
    © 2019 Copyright held by the owner/author(s).
    ACM ISBN 123-4567-24-567/08/06.
    https://doi.org/10.475/123_4
    Figure 1: Comparison between Civic Issue Graph and Scene
    Graph for the same image. The scene graph provides a com-
    plete representation of all objects and relationships in the
    image, while the Civic Issue Graph only consists of relations
    representative of the civic issue.
    introduced across the world, which enable the citizens to report
    civic issues such as poor road condition, garbage dumps, missing
    trac signs, etc., and track the status of their complaints. Such ini-
    tiatives have resulted in exponential increase in the number of civic
    issues being reported [2]. Even social media sites (Twitter, Face-
    book) have been increasingly utilized to report civic issues. Studies
    have found the importance of civic issue reporting platforms and
    social media sites in enhancing civic awareness among citizens
    [36]. These platforms help the concerned authorities to not only
    identify the problems, but also access the severity of the problems.
    Civic issues are reported online through various mediums – textual
    descriptions, images, videos, or a combination of them. Previous
    work [10] highlights the importance of mediums in citizen partici-
    pation. Yet, no prior work has tried to understand the role of these
    mediums in reporting of civic issues.
    In this work, we rst identify the most preferred medium for
    reporting civic issues, by conducting a user study with 13 citizens
    and 3 government authorities. Using the 84 civic issues reported by
    the citizens using our mobile app, and follow-up semi-structured
    interviews, we found that images are the most usable medium for
    the citizens. In contrast, authorities found text as the most preferred
    medium, as images are hard to analyze at scale.
    To ll this gap, several works have proposed methods to auto-
    matically identify a specic category of civic issues from images,
    such as garbage dumps [28] and road damage [24]. However, their
    methods are limited to the specic categories that they address.
    arXiv:1901.10124v1 [cs.AI] 29 Jan 2019
    https://doi.org/10.475/123_4
    https://doi.org/10.3390/ijgi8030115
    RESEARCH ARTICLE
    Structure of 311 service requests as a
    signature of urban location
    Lingjing Wang1,2, Cheng Qian1,2, Philipp Kats1,3, Constantine Kontokosta1,4,
    Stanislav Sobolevsky1,5,6*
    1 Center for Urban Science and Progress, New York University, Brooklyn, New York, United States of
    America, 2 Tandon School of Engineering, New York University, Brooklyn, New York, United States of
    America, 3 Kazan Federal University, Kazan, Russia, 4 Department of Civil and Urban Engineering, New York
    University, Brooklyn, New York, United States of America, 5 Senseable City Laboratory, Massachusetts
    Institute Of Technology, Cambridge, Massachusetts, United States of America, 6 Institute Of Design And
    Urban Studies of The Saint-Petersburg National Research University Of Information Technologies,
    Mechanics And Optics, Saint-Petersburg, Russia
    * [email protected]
    Abstract
    While urban systems demonstrate high spatial heterogeneity, many urban planning, eco-
    nomic and political decisions heavily rely on a deep understanding of local neighborhood
    contexts. We show that the structure of 311 Service Requests enables one possible way of
    building a unique signature of the local urban context, thus being able to serve as a low-cost
    decision support tool for urban stakeholders. Considering examples of New York City, Bos-
    ton and Chicago, we demonstrate how 311 Service Requests recorded and categorized by
    type in each neighborhood can be utilized to generate a meaningful classification of loca-
    tions across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based
    classification of urban neighborhoods can present sufficient information to model various
    socioeconomic features. Finally, we show that these characteristics are capable of predict-
    ing future trends in comparative local real estate prices. We demonstrate 311 Service
    Requests data can be used to monitor and predict socioeconomic performance of urban
    neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions.
    1 Introduction
    Cities can be seen as a complex system composed of multiple layers of activity and interactions
    across various urban domains; therefore, discovering a parsimonious description of urban
    function is quite difficult [1–4]. However, urban planners, policy makers and other types of
    urban stakeholders, including businesses and investors, could benefit from an intuitive proxy
    of neighborhood conditions across the city and over time [5–7]. At the same time, such simple
    indicators could provide not only valuable information to support urban decision-making, but
    also to accelerate the scalability of successful approaches and practices across different neigh-
    borhood and cities, as urban scaling patterns have become an increasing topic of interest [8–
    12]. As the volume and heterogeneity of urban data have increased, machine learning has
    PLOS ONE | https://doi.org/10.1371/journal.pone.0186314 October 17, 2017 1 / 21
    a1111111111
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    OPEN ACCESS
    Citation: Wang L, Qian C, Kats P, Kontokosta C,
    Sobolevsky S (2017) Structure of 311 service
    requests as a signature of urban location. PLoS
    ONE 12(10): e0186314. https://doi.org/10.1371/
    journal.pone.0186314
    Editor: Yanguang Chen, Peking University, CHINA
    Received: December 4, 2016
    Accepted: September 28, 2017
    Published: October 17, 2017
    Copyright: © 2017 Wang et al. This is an open
    access article distributed under the terms of the
    Creative Commons Attribution License, which
    permits unrestricted use, distribution, and
    reproduction in any medium, provided the original
    author and source are credited.
    Data Availability Statement: The data underlying
    this study was obtained from a third party. The 311
    data for the three cities is publicly available and can
    be freely downloaded from: 1) New York City Open
    data Portal https://nycopendata.socrata.co 2)
    Boston Open data Portal https://data.cityofboston.
    gov 3) Chicago Open data Portal https://data.
    cityofchicago.org The census data is available at
    census.gov. Zillow data is available from https://
    www.zillow.com/research/data/. The authors did
    not have any special privileges in obtaining this
    data.
    https://doi.org/10.1371/journ
    al.pone.0186314

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  12. 2019/09/28 Code for Japan Summit ( )× 12


    – IoT AI engagement




    – NPO

    – Open311


    https://civichall.org/civicist/10-problems-with-impact-measurement-in-civic-tech/

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  13. 2019/09/28 Code for Japan Summit ( )× 13

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  14. 2019/09/28 Code for Japan Summit ( )× 14

    • Open311

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  15. 2019/09/28 Code for Japan Summit ( )× 15
    Civic Tech Together!
    [email protected]
    http://researchmap.jp/tosseto
    https://speakerdeck.com/tosseto
    11/1( )13:00
    2019
    https://udc2019-nagoya.peatix.com/

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