20190619CivicTechonlineAcademy12

* Code for Japanシビックテックオンラインアカデミー #12

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1. 2019/06/19 12 1 (tosseto) tosseto@csis.u-tokyo.ac.jp Knight Foundation (2013)
2. 2019/06/19 12 2 Civic Tech Local Gov. Tech My City Forecast AI My City Report Urban Data Challenge OpenStreetMap 
   
3. 2019/06/19 12 3 http://urbandata-challenge.jp/ 7/1( )13:30 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
4. 2019/06/19 12 4 Civic technology (Google Scholar 2014 600 ) 2016 11 2017 7 2018 4 • 8 • Civic tech Toronto • La civic tech
5. 2019/06/19 12 5 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) • Datafication and empowerment: How the open data movement re- articulates notions of democracy, participation, and journalism (2014)
6. 2019/06/19 12 6 (Open311 ) (ACM-CHI ) (
7. 2019/06/19 12 7 • 2014 – Civic Engagement ? • – =citizen science – =participatory sensing • ICT ➔
8. 2019/06/19 12 8 Civic Hackathons: Innovation, Procurement, or Civic Engagement? Peter Johnson Department of Geography and Environmental Management, University of Waterloo, Toronto, Ontario, Canada Pamela Robinson School of Urban and Regional Planning, Ryerson University, Toronto, Ontario, Canada Abstract At all levels, governments around the world are moving toward the provision of open data, that is, the direct provision to citizens, the private sector, and other third parties, of raw government datasets, controlled by a relatively permissible license. In tandem with this distribution of open data is the promotion of civic hackathons, or “app contests” by government. The civic hackathon is designed to offer prize money to developers as a way to spur innovative use of open data, more specifically the creation of commercial software applications that deliver services to citizens. Within this context, we propose that the civic hackathon has the potential to act in multiple ways, possibly as a backdoor to the traditional government procurement process, and as a form of civic engagement. We move beyond much of the hype of civic hackathons, critically framing an approach to understanding civic hackathons through these two lenses. Key questions for future research emphasize the emerging, and important, nature of this research path. KEY WORDS: civic hackathon, big data, geoweb, web2.0, civic engagement, procurement, apps, open data bs_bs_banner 349 Review of Policy Research, 31(4), pp.349 357, 2014
9. 2019/06/19 12 9 Communications of the ACM, Vol. 59 No. 1, pp.82-89, 2016
10. 2019/06/19 12 10 ........................................................................................................................................................................................................................................................................................................ ........................................................................................................................................................................................................................................................................................................ POLITICS SYMPOSIUM Civic Engagement and Civic Technology ........................................................................................................................................................................................................................................................................................................ Introduction Hollie Russon Gilman, Columbia University, School of International and Public Affairs For many scholars and practitioners of civic engagement, the 2016 elections made manifest the deeper symptoms of declining civic engagement and institutional trust. Along several indices are alarming signs about American democracy—from declining participation in membership based civic organiza- tions to a record low level of trust in government and turnout in elections, especially local elections. Embedded within these trends is an existential concern that American civic life is becoming more stratified by place, politics, and identity. Many digital tools are accelerating iden- tity politics, enabling people to live within their own online action, which is a critical component of democratic health. Each article offers a contribution towards an ecosystem of motivations, mechanisms, and tools for impact. Taken together, these articles suggest a more robust role for citizens in decision making than simply advisory or consultative. Instead, they offer a model for more robust participation in civic life and democratic governance—driven by citizens for citizens. Demand-driven civic engagement pushes scholars to expand the traditional boundaries that delineate what is and is not civic action in contemporary politics and work towards more rigorous empirical research to understand what works best under which conditions (see Rajani 2010). One critical aspect of civic engagement is the opportunity offered to work more directly with public officials, especially on the local level. There are opportunities to leverage the tools of civic technology for more collaborative governance. In my article, I outline three cases of civic tech effectively fostering collaborative governance in US cities: innovation units, open ............................................................................................................................................... D 4 75 :7 ,3 4C 697 , C7 7C D D7 3 3 34 7 3 : D 53 4C 697 C9 5 C7 7C D : D 6 C9 / 2 Political Science & Politics ,Vol.59(3),2017 https://doi.org/10.1017/S104909651700052X
11. 2019/06/19 12 11 Data, Design and Civics: An Exploratory Study of Civic Tech Kirsten Boehner Georgia Institute of Technology School of Literature, Media and Communication kirstenboehner@gatech.edu Carl DiSalvo Georgia Institute of Technology School of Literature, Media and Communication carl.disalvo@lmc.gatech.edu ABSTRACT Civic technology, or civic tech, encompasses a rich body of work, inside and outside HCI, around how we shape technology for, and in turn how technology shapes, how we govern, organize, serve, and identify matters of concern for communities. This study builds on previous work by investigating how civic leaders in a large US city conceptualize civic tech, in particular, how they approach the intersection of data, design and civics. We encountered a range of overlapping voices, from providers, to connectors, to volunteers of civic services and resources. Through this account, we identified different conceptions and expectation of data, design and civics, as well as several shared issues around pressing problems and strategic aspirations. Reflecting on this set of issues produced guiding questions, in particular about the current and possible roles for design, to advance civic tech. Author Keywords Civic technology; design; speculative design ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous. INTRODUCTION The intersection of civics and technology has a long, mutually deterministic, history. Traditionally, the idea of Although interest in civics and technology also has a long history in the HCI community, arguably the naming of “civic technology”, or civic tech, is a relatively new area of study loosely defined as the design and use of technology to support both formal and informal aspects of government and public services. This attention could be narrated as a logical step in HCI’s many articulated turns – from the cognitive, to the social, to the cultural, and now perhaps to the civic. What driving questions should shape our research into and the development of civic tech? We begin to explore this question with civic leaders in Atlanta, where we work with several community projects [31]. Greater Atlanta is growing, quickly, into one of the largest metro-regions in the US. Its resultant complexity is often blamed on factors such as the available space for sprawl, a perceived dearth of planning, and an automobile centric culture. Civic organizations here lack the geographic core of other cities’ downtown areas and the distribution of civic services often obscures which municipality is responsible public roads or schools or emergency response, for example. Atlanta’s historical roots of racial and economic segregation continues into the present day, while the city also lays claim to a legacy of advancing social justice and supports a contemporary group of grassroots activists capable of generating one of the largest urban revival programs in the US. Yet, some critics doubt the ceiling of Civic Tech, Participation and Society #chi4good, CHI 2016, San Jose, CA, USA Proceedings of CHI '16. https://doi.org/10.1145/2858036.2858326
12. 2019/06/19 12 12 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
13. 2019/06/19 12 13 • – – IoT AI engagement – • – – – NPO • – Open311 – – https://civichall.org/civicist/10-problems-with-impact-measurement-in-civic-tech/
14. 2019/06/19 12 14 Sustaining Civic Tech 1 KNIGHT FOUNDATION SCALING CIVIC TECH Paths to a Sustainable Future Knight Foundation & Rita Allen Foundation (2017) Knight Foundation & Network Impact (2015) Knight Foundation (2013) mySociety & Microsoft (2017)
15. 2019/06/19 12 15 • Johnson, P., & Robinson, P. (2014). Civic Hackathons: Innovation, procurement, or civic engagement? Review of Policy Research, 31(4), 349 357. https://doi.org/10.1111/ropr.12074 • Sieber, R. E., & Johnson, P. A. (2015). Civic open data at a crossroads: Dominant models and current challenges. Government Information Quarterly, 32(3), 308 315. https://doi.org/10.1016/j.giq.2015.05.003 • Lee, M., Almirall, E., & Wareham, J. (2015). Open data and civic apps. Communications of the ACM, 59(1), 82 89. https://doi.org/10.1145/2756542 • Robinson, P. J., & Johnson, P. A. (2016). Civic Hackathons: New Terrain for Local Government-Citizen Interaction? Urban Planning, 1(2), 65. https://doi.org/10.17645/up.v1i2.627 • Pogačar, K., & Žižek, A. (2016). Urban Hackathon - Alternative Information Based and Participatory Approach to Urban Development. Procedia Engineering, 161, 1971 1976. https://doi.org/10.1016/j.proeng.2016.08.788 • Kirsten Boehner and Carl DiSalvo. (2016). Data, Design and Civics: An Exploratory Study of Civic Tech. In Proceedings of CHI '16. https://doi.org/10.1145/2858036.2858326 • PS: Political Science & Politics ,Vol.59(3),2017 Symposium: Civic Engagement and Civic Technology https://doi.org/10.1017/S104909651700052X • Andrew May, A. and Ross, R. (2018). The design of civic technology: factors that influence public participation and impact, Ergonomics, 61:2, 214-225, https://doi.org/10.1080/00140139.2017.1349939 • Perng, S-Y. and Kitchin, R. (2018) Solutions and frictions in civic hacking: collaboratively designing and building wait time predictions for an immigration office, Social & Cultural Geography, 19:1, 1-20, https://doi.org/10.1080/14649365.2016.1247193
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17. 2019/06/19 12 17 • (2014) 119 pp.101-114 • 継, Teemu Tossavainen, 響, , 2014 2014 SSI , pp.371-374. • (2014) 79(706) pp.2711-2719 • 2016 Vol7.2 • 2016 70(6) pp.10-16 • 2017 JRI 42(3) • (2017) 100(1) pp.47-52 • 響 2017 9 pp.26-29 • 2018 53(3) pp.1515-1522 • (2018) IT , 452
18. 2019/06/19 12 18 http://urbandata-challenge.jp/ 18 / 32
19. 2019/06/19 12 19 (UDC) • = • ( 200 * = *: 2018
20. 2019/06/19 12 20 UDC Tokyo+ 5 2013 UDCT 2014 2015 2016 10 56 20 158 75 30 198 2017 40 232 2018 CC-BY4.0 T 49 200
21. 2019/06/19 12 21 -> http://urbandata-challenge.jp/category/2018/localhub18
22. 2019/06/19 12 22 -> http://urbandata-challenge.jp/category/2017/localhub17
23. 2019/06/19 12 23 UDC
24. 2019/06/19 12 24 UDC2018 (2/6 ( ) 132 3,200 ( 91 2,500 ) UDC 2018 1 3 5 1 0 1 1 1 0 1 9 5 1 4 1 1 7 8 1 3 2 1 2 2 1 0 0 1 3 4 2 1 3 2 5 6 2 1 1 2 5 6 2 2 2 2 2 2 2 0 1 2 3 5 2 2 3 3 2 3 3 3 3 3 5 3 3 2 4 3 6 12 3 3 3 3 11 7 4 2 0 4 1 3 4 3 2 4 1 1 4 7 2 4 6 1 4 10 4 4 1 0 4 0 8 5 1 1 5 1 0 5 0 1 5 1 0 5 1 0 5 1 0 5 4 1 5 4 7 5 3 12
25. 2019/06/19 12 25 • AED SOS 2013 Coaido X-HUB TOKYO • 2014 Code for XX 19 • MY CITY FORECAST 2014 1,670 WS ICT 2016 • Seseki 2015 IT 科 • 2016 2015 9 UDC 2 Mashup Award2016 Civic Wave 10 25
26. 2019/06/19 12 26 UDC 5 2019 2023 2019 7 1 13:30 2019 http://urbandata-challenge.jp/news/2019-1st-symposium UDC UDC
27. 2019/06/19 12 27 • • Open311 •
28. 2019/06/19 12 28 Thank you! tosseto@csis.u-tokyo.ac.jp http://researchmap.jp/tosseto https://speakerdeck.com/tosseto