E19e33bd30954fc1846f3d64863b7846?s=47 Toshikazu SETO
September 28, 2019


* 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


  1. 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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    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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    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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    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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    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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    2019/09/28 Code for Japan Summit ( )× 10 • 2014

    – Civic Engagement ? • – =citizen science – =participatory sensing • ICT – Open311 – [new]
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    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 sshanu@iitk.ac.in 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 e￿ort. Moreover, previous works have been limited to identifying a speci￿c 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 e￿cacy of our approach, and using human evaluation, we establish the appropriateness of our model at representing di￿erent 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 signi￿cant 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 pro￿t 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 tra￿c 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 speci￿c category of civic issues from images, such as garbage dumps [28] and road damage [24]. However, their methods are limited to the speci￿c 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 * sobolevsky@nyu.edu 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 a1111111111 a1111111111 a1111111111 a1111111111 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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    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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    2019/09/28 Code for Japan Summit ( )× 15 Civic Tech

    Together! tosseto@csis.u-tokyo.ac.jp http://researchmap.jp/tosseto https://speakerdeck.com/tosseto 11/1( )13:00 2019 https://udc2019-nagoya.peatix.com/