This document discusses the ethics of big data and smart cities. It notes that cities are generating vast amounts of data from various sources that can be used to understand and manage urban systems. However, it also notes several critiques of smart city approaches, including the risk of technocratic governance, exacerbating inequalities, and threats to privacy from extensive data collection and integration. The document outlines various privacy concerns around urban big data, such as enabling extensive surveillance and inference, weak anonymization allowing re-identification, lack of transparency and individual control. It argues notice and consent are often empty given the complexity of data flows. Overall, it calls for balancing the benefits of data-driven urbanism with minimizing its pernicious effects through governance,
The ethics and risks of urban big data and smart citiesrobkitchin
This slidedeck provides a brief introduction to the ethics and risks associated with urban big data and smart cities and was presented at the launch of the Institute for Data, Systems and Society at MIT, Sept 2016
Predictive Modelling is used by businesses, social organisations and governments to build winning products, eradicate poverty and preempt cyclones. Learn the fundamental principles of predictive modelling with illustrative case studies. Know how companies like Netflix, Tinder and Citi Group use predictive modelling to deliver user satisfaction and profitability.
Smart City Lecture 2 - Privacy in the Smart CityPeter Waher
Privacy is a basic human right that has been heavily eroded on the point of extinction in the current digital age, as the constant reports on security breaches tell us. With the help of the General Data Protection Regulation (GDPR), privacy has been brought back from the dead, and is at least discussed in most enterprises in Europe, and perhaps a large part of the world. This lecture introduces the GDPR and Privacy, as it relates to the Smart City. It presents concepts such as “Data Protection by design and by default”, “Consent”, “Legal Basis”, etc. It also presents technologies that make protecting Privacy more difficult, and why.
These technologies work against the basic principles of privacy by default, so you need to know the details of how they work, to avoid serious pitfalls. There are also technologies that are more Privacy neutral. While not making data protection easier, at least the technology does not work against the basic principles of privacy. Finally, technologies that intrinsically help you protect Privacy are presented. These technologies make it easier to protect Privacy and sensitive data in general.
Tracxn - Top Business Models - Blockchain Industry Applications - Mar 2022Tracxn
Today's top #BusinessModel report is on Blockchain - Industry Applications rebrand.ly/ivw9x2c
Get our free reports on geography of your interest to your mailbox regularly https://rb.gy/cx2upn
Big Data Management: What's New, What's Different, and What You Need To KnowSnapLogic
This presentation is from a recorded webinar with 451 Research analyst and thought leader Matt Aslett for a discussion about the growing importance of the right data management best practices and techniques for delivering on the promise of big data in the enterprise. Matt reviews the big data landscape, how the data lake complements and competes with the data warehouse, and key takeaways as you move from big data test and development environments to production. You can watch the webinar here: http://bit.ly/25ShiQu
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
The ethics and risks of urban big data and smart citiesrobkitchin
This slidedeck provides a brief introduction to the ethics and risks associated with urban big data and smart cities and was presented at the launch of the Institute for Data, Systems and Society at MIT, Sept 2016
Predictive Modelling is used by businesses, social organisations and governments to build winning products, eradicate poverty and preempt cyclones. Learn the fundamental principles of predictive modelling with illustrative case studies. Know how companies like Netflix, Tinder and Citi Group use predictive modelling to deliver user satisfaction and profitability.
Smart City Lecture 2 - Privacy in the Smart CityPeter Waher
Privacy is a basic human right that has been heavily eroded on the point of extinction in the current digital age, as the constant reports on security breaches tell us. With the help of the General Data Protection Regulation (GDPR), privacy has been brought back from the dead, and is at least discussed in most enterprises in Europe, and perhaps a large part of the world. This lecture introduces the GDPR and Privacy, as it relates to the Smart City. It presents concepts such as “Data Protection by design and by default”, “Consent”, “Legal Basis”, etc. It also presents technologies that make protecting Privacy more difficult, and why.
These technologies work against the basic principles of privacy by default, so you need to know the details of how they work, to avoid serious pitfalls. There are also technologies that are more Privacy neutral. While not making data protection easier, at least the technology does not work against the basic principles of privacy. Finally, technologies that intrinsically help you protect Privacy are presented. These technologies make it easier to protect Privacy and sensitive data in general.
Tracxn - Top Business Models - Blockchain Industry Applications - Mar 2022Tracxn
Today's top #BusinessModel report is on Blockchain - Industry Applications rebrand.ly/ivw9x2c
Get our free reports on geography of your interest to your mailbox regularly https://rb.gy/cx2upn
Big Data Management: What's New, What's Different, and What You Need To KnowSnapLogic
This presentation is from a recorded webinar with 451 Research analyst and thought leader Matt Aslett for a discussion about the growing importance of the right data management best practices and techniques for delivering on the promise of big data in the enterprise. Matt reviews the big data landscape, how the data lake complements and competes with the data warehouse, and key takeaways as you move from big data test and development environments to production. You can watch the webinar here: http://bit.ly/25ShiQu
On this slides, we tried to give an overview of advanced Data quality management (ADQM). To understand about DQ why important, and all those steps of DQ management.
지난 4월 3일에 대전 KAIST 증강현실연구센터 콜로키움에서 발표한 자료입니다.
‘Digital Twin’ is a digital replication of real world objects, processes, phenomena that can be used for various purposes. Digital twin concept backs to manufacturing industry in early 2000s for the PLM (Product Lifecycle Management) purposes. It is based on the idea that a digital informational construct about a physical system could be created as an entity on its own. As cities are going through digital transformation, there are many attempts to apply digital twin concept to manage urban issues. Those attempts look set to play an increasingly important role in the creation of smart cities around the world and in addressing major public health, safety and environmental issues. Bringing the virtual and real worlds together in this way can help to give better analysis, visualization, and simulation to decision-making process. This will be a multi-way process with iterative feedback among stakeholders. In this colloquium, I talked about the recent trends of Smart City from the perspective of digital twin.
10 Steps to create a sucessfull e-commerce strategy - 10 passi per creare un ...Federico Gasparotto
Quali sono i 10 passi per creare un e-commerce di successo? Value proposition, Business planning, Channels integration, organisation transformation, Direct marketing, Commercial strategy, assortment planning, Product datatsheet, User Experience
Smart Cities and Big Data - Research Presentationannegalang
Research presentation on smart cities (sensor technology) and big data, presented in a graduate course I took on Transmedia Design and Digital Culture.
Geospatial shares the opportunities and the challenges of both high performance computing and big data. With applications from visualising IoT data through to ingesting drone footage, it brings with it increasingly large data sizes, complicated data pipelines, HPC-level transformation demands, and often massive traffic from public users. From Smart Cities through to location-based marketing, rapid analysis of large volumes of geospatial data is becoming increasingly important. In this session you will hear how AWS customers build powerful, flexible, secure, and future-proof geospatial platforms. These systems deliver more business value from location-based data while minimising both compute costs and management overhead.
오늘(2017.05.24) 양재동 엘타워에서 있었던 <홈iot>에서 발표한 <홈iot> 자료를 공유합니다. 주된 내용은 제가 그동안 반복적으로 주장하던 것으로, 사물인터넷 디바이스 자체를 목적으로 하기 보다는 디바이스를 서비스화 하거나 디바이스와 관련된 서비스와 함께 활용하라는 것입니다.
이를 위해서는 이용자들이 수용할 수 있는 가격으로 디바이스를 보급해야 한다는 것입니다. 그러면 디바이스 제조사는 손해를 볼 수 있게 되는데, 이를 디바이스의 서비스화 혹은 관련 서비스를 제공하는 데서 추가 수익을 거둠으로써 벌충할 수 있다는 것입니다. 이런 사업화 전략을 5가지 유형으로 나누어서 소개하고 있습니다.
Collibra Data Citizen '19 - Bridging Data Privacy with Data Governance BigID Inc
This presentation was shown at the 2019 Collibra Data Citizen Event in New York City.
Presented by Nimrod Vax, Chief Product Officer & Co-Founder & Joaquin Sufuentes, Lead Architect, Metadata Managment and Personal Infomation Protection, enterprise Data Managment, Intel IT
Knowing your clients well and knowing when they need financial support is a key part of a bank’s success in lending. But it is challenging to gather and process information about your customers to know them all entirely. Our senior consultant Lukáš Dvořák will show you how to use data to drive your lending business and improve the conversion rate of loan offers.
This project was completed with my group as part of my M.S. in Interaction Design and Information Architecture. We worked with the crypto exchange platform BlockFi and conducted user research in order to improve upon their existing IA. This part of the project involved researching BlockFi competitors in order to find where they fit in the marketplace.
Big Data Characteristics And Process PowerPoint Presentation SlidesSlideTeam
We present you content-ready big data characteristics and process PowerPoint presentation that can be used to present content management techniques. It can be presented by IT consulting and analytics firms to their clients or company’s management. This relational database management PPT design comprises of 53 slides including introduction, facts, how big is big data, market forecast, sources, 3Vs and 5Vs small Vs big data, objective, technologies, workflow, four phases, types, information analytics process, impact, benefits, future, opportunities and challenges etc. Our data transformation PowerPoint templates are apt to present various topics such as information management concepts and technologies, transforming facts with intelligence, data analysis framework, data mining, technology platforms, data transfer and visualization, content management, Internet of things, data storage and analysis, information infrastructure, datasets, technology and cloud computing. Download big data characteristics and process PPT graphics to make an impressive presentation. Develop greater goodwill with our Big Data Characteristics And Process PowerPoint Presentation Slides. Folks feel friendlier towards you.
Paper presented at Code Acts in Education, an ESRC seminar at the University of Stirling, January 28th 2014, http://codeactsineducation.wordpress.com/seminars/
Data-driven urbanism (Amsterdam, Jan 2017)robkitchin
This talk details the shift from data-informed urbanism to data-driven urbanism, the use of urban big data and smart city technologies in urban governance, and outlines various concerns and critiques.
지난 4월 3일에 대전 KAIST 증강현실연구센터 콜로키움에서 발표한 자료입니다.
‘Digital Twin’ is a digital replication of real world objects, processes, phenomena that can be used for various purposes. Digital twin concept backs to manufacturing industry in early 2000s for the PLM (Product Lifecycle Management) purposes. It is based on the idea that a digital informational construct about a physical system could be created as an entity on its own. As cities are going through digital transformation, there are many attempts to apply digital twin concept to manage urban issues. Those attempts look set to play an increasingly important role in the creation of smart cities around the world and in addressing major public health, safety and environmental issues. Bringing the virtual and real worlds together in this way can help to give better analysis, visualization, and simulation to decision-making process. This will be a multi-way process with iterative feedback among stakeholders. In this colloquium, I talked about the recent trends of Smart City from the perspective of digital twin.
10 Steps to create a sucessfull e-commerce strategy - 10 passi per creare un ...Federico Gasparotto
Quali sono i 10 passi per creare un e-commerce di successo? Value proposition, Business planning, Channels integration, organisation transformation, Direct marketing, Commercial strategy, assortment planning, Product datatsheet, User Experience
Smart Cities and Big Data - Research Presentationannegalang
Research presentation on smart cities (sensor technology) and big data, presented in a graduate course I took on Transmedia Design and Digital Culture.
Geospatial shares the opportunities and the challenges of both high performance computing and big data. With applications from visualising IoT data through to ingesting drone footage, it brings with it increasingly large data sizes, complicated data pipelines, HPC-level transformation demands, and often massive traffic from public users. From Smart Cities through to location-based marketing, rapid analysis of large volumes of geospatial data is becoming increasingly important. In this session you will hear how AWS customers build powerful, flexible, secure, and future-proof geospatial platforms. These systems deliver more business value from location-based data while minimising both compute costs and management overhead.
오늘(2017.05.24) 양재동 엘타워에서 있었던 <홈iot>에서 발표한 <홈iot> 자료를 공유합니다. 주된 내용은 제가 그동안 반복적으로 주장하던 것으로, 사물인터넷 디바이스 자체를 목적으로 하기 보다는 디바이스를 서비스화 하거나 디바이스와 관련된 서비스와 함께 활용하라는 것입니다.
이를 위해서는 이용자들이 수용할 수 있는 가격으로 디바이스를 보급해야 한다는 것입니다. 그러면 디바이스 제조사는 손해를 볼 수 있게 되는데, 이를 디바이스의 서비스화 혹은 관련 서비스를 제공하는 데서 추가 수익을 거둠으로써 벌충할 수 있다는 것입니다. 이런 사업화 전략을 5가지 유형으로 나누어서 소개하고 있습니다.
Collibra Data Citizen '19 - Bridging Data Privacy with Data Governance BigID Inc
This presentation was shown at the 2019 Collibra Data Citizen Event in New York City.
Presented by Nimrod Vax, Chief Product Officer & Co-Founder & Joaquin Sufuentes, Lead Architect, Metadata Managment and Personal Infomation Protection, enterprise Data Managment, Intel IT
Knowing your clients well and knowing when they need financial support is a key part of a bank’s success in lending. But it is challenging to gather and process information about your customers to know them all entirely. Our senior consultant Lukáš Dvořák will show you how to use data to drive your lending business and improve the conversion rate of loan offers.
This project was completed with my group as part of my M.S. in Interaction Design and Information Architecture. We worked with the crypto exchange platform BlockFi and conducted user research in order to improve upon their existing IA. This part of the project involved researching BlockFi competitors in order to find where they fit in the marketplace.
Big Data Characteristics And Process PowerPoint Presentation SlidesSlideTeam
We present you content-ready big data characteristics and process PowerPoint presentation that can be used to present content management techniques. It can be presented by IT consulting and analytics firms to their clients or company’s management. This relational database management PPT design comprises of 53 slides including introduction, facts, how big is big data, market forecast, sources, 3Vs and 5Vs small Vs big data, objective, technologies, workflow, four phases, types, information analytics process, impact, benefits, future, opportunities and challenges etc. Our data transformation PowerPoint templates are apt to present various topics such as information management concepts and technologies, transforming facts with intelligence, data analysis framework, data mining, technology platforms, data transfer and visualization, content management, Internet of things, data storage and analysis, information infrastructure, datasets, technology and cloud computing. Download big data characteristics and process PPT graphics to make an impressive presentation. Develop greater goodwill with our Big Data Characteristics And Process PowerPoint Presentation Slides. Folks feel friendlier towards you.
Paper presented at Code Acts in Education, an ESRC seminar at the University of Stirling, January 28th 2014, http://codeactsineducation.wordpress.com/seminars/
Data-driven urbanism (Amsterdam, Jan 2017)robkitchin
This talk details the shift from data-informed urbanism to data-driven urbanism, the use of urban big data and smart city technologies in urban governance, and outlines various concerns and critiques.
Big data, new epistemologies and paradigm shiftsrobkitchin
This presentation examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines.
This paper was presented at the 'Towards a Magna Carta for Data' workshop at the RDS in Dublin, Sept 17th. It discusses how considerations of the ethics of big data consist of much more than the issues of privacy and security that it often gets boiled down to, and argues that the various ethical issues related to big data are multidimensional and contested; vary in nature across domains, and which ethical philosophy is adopted matters to the deliberation over data rights.
Praxis and politics of urban data: Building the Dublin Dashboardrobkitchin
This paper was presented at the Association of American Geographers meeting in Chicago, April 22nd 2015.
This paper critically reflects on the building of the Dublin Dashboard (www.dublindashboard.ie) from the perspective of critical data studies. The Dashboard is a website that provides citizens, planners, policy makers and companies with an extensive set of data and data visualizations about Dublin City, including real-time information, indicator trends, inter and intra-urban benchmarking, interactive maps, the location of services, and a means to directly report issues to city authorities. The data used in the Dashboard is open and available for others to build their own apps. One member of the development team was an ethnographer who attended meetings, observed and discussed with key actors the creation of the Dashboard and its attendant praxis and politics up to the point of its launch in September 2014. This paper draws on that material to consider the contextual, contingent, iterative and relational unfolding of the Dashboard and the emergent politics of data and design. In so doing, it reveals the contested and negotiated politics of smart city initiatives.
A short set of slides that accompanied my thoughts as a discussant on papers presented at the alt.conference on Big Data at the Conference of the Association of American Geographers, Tampa, April 8-12, 2014
Rob Kitchin Smart Cities 08th March 2016 (Smart Dublin)Mainard Gallagher
Rob Kitchin is a Professor and ERC Advanced Investigator in the National Institute of Regional and Spatial Analysis at Maynooth University, for which he was director between 2002 and 2013. He is one of Ireland's leading social scientists and was the 2013 recipient of the Royal Irish Academy's Gold Medal for the Social Sciences and received the Association of American Geographers ‘Meridian Book Award’ for the outstanding book in the discipline in 2011.
Urban indicators, city benchmarking, and real time dashboards: Knowing and go...robkitchin
Talk presented at the Conference of the Association of American Geographers, Tampa, April 8-12. First attempt at presenting a paper presently being written for publication.
This slide set examines the contention that opening data is an inherently good thing - that the case for open data is an open and shut case. It sets out a contrary view that whilst open data is desirable, much more critical thinking is required as to what this means in practice and the possible negative implications of opening data, and calls for a wider debate about the relative merits and politics of open data and how we go about opening data.
The Real-Time City? Data-driven, networked urbanism and the production of sm...robkitchin
Keynote talk presented at IGU Urban conference in Dublin, August 9th. The paper discusses the transition from data-informed to data-driven, smart cities and the impact of such a transition on city governance and wider society.
Slides from the Privacy: Insights from Lawyers and Technologiest at Maynooth University, July 1st 2015. The talk argues that privacy is multidimensional in nature; notions and practices of privacy are changing rapidly; has all kinds of direct and indirect effects; technology and industry are running ahead of legislators; there is no teleological inevitability to the emerging privacy landscape; it is incumbent on states to address privacy issues and to find a balance with respect to interests of citizens, states and industry.
Interactive Data Science From Scratch with Apache Zeppelin and Apache Sparkfelixcss
Apache: Big Data North America 2016 session
How do you find the needle in the haystack?
With Big Data, finding insight is a big problem. Visualization and exploratory analysis help convert on insights and Apache Zeppelin (incubating) is an essential tool for that.
In this tutorial, Felix Cheung will introduce you to Apache Zeppelin, and provide step-by-step guides to get you up-and-running with Apache Zeppelin to run Big Data analysis with Apache Spark.
This is going to be a heavily hands-on session, no previous experience with Zeppelin, Data Science, or Statistics necessary.
Big data and smart cities: Key data issuesrobkitchin
This presentation was delivered at the first meeting of the Irish Government Data Forum, July 14th 2015. It was designed to provide an overview of key data issues related to smart cities in order to set the scene for a discussion about the kinds of data issues the forum might explore across a range of domains.
Talk presented at TILT, Tilburg University, Netherlands, 14th March 2019. Relates to the book: Cardullo, P., di Feliciantonio, C. and Kitchin, R. (eds) (2019, June, in press) The Right to the Smart City. Emerald.
Cities around the world are pursuing a smart cities agenda in which digital technologies are used to manage cities. In general, these initiatives are promoted and rolled-out by governments and corporations and enact various forms of top-down, technocratic governance and reproduce neoliberal governmentality. Despite calls for the smart city agenda to be more citizen-centric and bottom-up in nature, how this translates into policy and initiatives is still weakly articulated and practiced. Indeed, there is little meaningful engagement by key stakeholders with respect to rights, citizenship, social justice, commoning, civic participation, co-creation, ethics, and how the smart city might be productively reimagined and remade. This talk advocates for the Right to the Smart City and considers how to produce a genuinely humanizing smart urbanism, both with respect to setting out a normative vision for smart cities rooted in ideas of fairness, equity, care, democracy and the public good, and enacting this vision through citizen-centric tactics.
Over the past three decades city infrastructure and services have increasingly become digitally networked, programmable and data-driven. Moreover, citizens now regularly use mobile spatial media to mediate their spatial behavior and urban experiences and share information via crowdsourced platforms. As a result we are ever more living in the era of smart urbanism — city systems can be operationally managed dynamically using algorithms processing urban big data, citizens can access and contribute live information about the city, and planners and policy makers can redeploy new streams of data to model and plan the city with increasing granularity. The development of smart urbanism poses opportunities and challenges for urban planning, reshaping how we come to know and govern cities, and this talk will examine these drawing on research conducted in Boston and Dublin.
Introduction to the Programmable City ProjectProgCity
Rob Kitchin, PI Programmable City Project, NIRSA, NUIM
An overview of The Programmable City project, the ideas underpinning the research and the prospective case studies.
Your mobile knows a lot about you and that brings a number of business risks – security breaches from company data held in emails or business apps, for example. We highlight the data and security risks of the phone in your pocket. -
See more at: http://www.grant-thornton.co.uk/en/Thinking/Beware-the-secrets-held-in-your-smartphone-/?previouspage=7260
Bigger and Better: Employing a Holistic Strategy for Big Data toward a Strong...IT Network marcus evans
Bigger and Better: Employing a Holistic Strategy for Big Data toward a Strong Value-Adding Proposition
by Patrick Hadley, Australian Bureau of Statistics at the Australian CIO Summit 2014
Mobile Devices: Systemisation of Knowledge about Privacy Invasion Tactics and...CREST
This presentation reviews privacy concerns for mobile devices and outlines the importance of privacy engineering in ensuring users have safe access to their devices.
SocIoTal: Creating a Citizen - Centric Internet of ThingsDunavNET
Contrary to the general approach of creating Internet of Things
(IoT) services from a business perspective, the project addresses the design of citizen centered IoT solution.
La telefonía móvil como fuente de información para el estudio de la movilidad...Esri España
Existe una multitud de sectores donde es necesario disponer de datos que permitan entender los patrones de comportamiento de la población: la planificación y la operación de los sistemas de transporte requiere información precisa, fiable y actualizada sobre la demanda de viajes; los patrones de actividad y movilidad de los turistas tienen profundas implicaciones para la planificación de infraestructuras, el desarrollo de la oferta turística y las estrategias de marketing turístico; entender el comportamiento espacial de los clientes es clave para optimizar las estrategias de distribución, comercialización y publicidad, determinar la localización de un nuevo comercio o punto de venta, o maximizar el retorno de la inversión en acciones de marketing. Las fuentes de datos tradicionales, basadas fundamentalmente en encuestas y registros administrativos, proporcionan información muy valiosa, pero no están exentas de inconvenientes. En general, las encuestas resultan caras y lentas de realizar, lo que limita el tamaño de la muestra y la frecuencia de actualización de la información, a lo que hay que añadir otras limitaciones intrínsecas, como las respuestas incorrectas e imprecisas, o la dependencia de la disposición a responder de los entrevistados. En los últimos años, la generalización del uso de dispositivos móviles ha abierto nuevas oportunidades para superar muchas de estas limitaciones. La posibilidad de recoger datos geolocalizados sobre la actividad de las personas, de manera dinámica y a un coste sensiblemente inferior al de los métodos tradicionales, abre la puerta a infinidad de aplicaciones. Las más evidentes son quizá las relacionadas con el transporte y la movilidad, pero el abanico es mucho más amplio, abarcando casi cualquier área que requiera información sobre los patrones de actividad y movilidad de la población. Las nuevas fuentes de datos plantean asimismo importantes retos, desde la necesidad de desarrollar nuevas metodologías de análisis, hasta la protección de la privacidad.
Vídeo de la ponencia: https://youtu.be/5PKC5Qm0eHM
This paper examines issues that are impeding the roll-out of smart city initiative and challenges the ideas, ideals and ideology of smart cities as presently conceived.
Citizenship, social justice, and the Right to the Smart Cityrobkitchin
This presentation was delivered at the Right to the Smart City workshop at Maynooth University, Sept 5-6 2017. It sets out a set of questions and theoretical concepts for thinking through issues of citizenship, social justice, and the right to the smart city.
Being a ‘citizen’ in the smart city: Up and down the scaffold of smart citize...robkitchin
This paper discusses the issue of citizens’ participation and rights in the smart city. It does so by drawing on and extending Sherry Arnstein’s seminal work (1969) conceptualising participation in planning and renewal programmes. We argue that citizenship in the smart city is rooted in a pragmatic and paternalistic discourse and practice, rather than in theories around rights and citizenship. Promoters of smart cities, including those advocating a citizen-centric version, tend to conflate limited forms of engagement as a user or consumer of services with citizenship and rights. We develop a modified version of Arnstein’s ladder ― the ‘Scaffold of Smart Citizen Participation’ ― as a conceptual tool to unpack the diverse ways in which the smart city frames citizens and measure smart citizen inclusion, participation, and empowerment in Dublin, Ireland.
Why the National Spatial Strategy failed and prospects for the National Plann...robkitchin
This talk delivered at the MacGill Summer School in Glenties, Donegal as part of a panel on the National Spatial Strategy and where next for spatial planning in Ireland. It sets out the history of spatial planning in Ireland and why the NSS failed and discusses the prospects for a new National Planning Framework
Funding models for open access digital repositoriesrobkitchin
Across jurisdictions and domains (academia, government, business) there has been much recent attention paid to open forms of knowledge production (e.g., open-source software, open data/metadata, open infrastructures) and the creation of open digital repositories for the unrestricted sharing of data, publications and other resources. This paper focuses on the latter, documenting and critically examining 14 different funding streams, grouped into six classes (institutional, philanthropy, research, audience, service, volunteer), being pursued by open digital repositories to support their endeavours, with a particular focus on academic research data repositories. Whilst open digital repositories are free to access, they are not without significant cost to build and maintain, and unstable and cyclical funding poses considerable risks to their futures and the digital collections they hold. While the political and ethical debate concerning the merits of open access and open data is important, we argue that just as salient are concerns with respect to long-term, sustainable funding for the operation and maintenance of open access digital repositories.
Housing in Ireland: From Crisis to Crisisrobkitchin
This paper provides a macro analysis of Irish housing since 1991 and makes the argument that it has been perpetually in crisis and that we need to find a way to treat housing as a sector, not simply a market, and to create stability that will mitigate against boom/bust cycles.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
1. The Ethics of Urban Big Data
and Smart Cities
Prof. Rob Kitchin
Maynooth University
2. Data and the city
• Rich history of data being generated about cities
• Urban data are a key input for understanding city life,
solving urban problems, formulating policy and plans,
guiding operational governance, modelling possible
futures, and tackling a diverse set of other issues
• For as long as data have been generated about cities
then, various kinds of data-informed urbanism has been
occurring
• Data-informed urbanism is increasingly being
complemented and replaced by data-driven, networked
urbanism
• Post-Millennium, the urban data landscape is being
transformed moving from small to big data
3. Urban big data
• Directed
o Surveillance: CCTV,
drones/satellite
o Scaled public admin records
• Automated
o Automated surveillance
o Digital devices
o Sensors, actuators,
transponders, meters (IoT)
o Interactions and transactions
• Volunteered
o Social media
o Sousveillance/wearables
o Crowdsourcing/neogeography
o Citizen science
4. Urban big data
• Diverse range of public and private
generation of fine-scale (uniquely
indexical) data about citizens and places in
real-time:
• utilities
• transport providers
• environmental agencies
• mobile phone operators
• social media sites
• travel and accommodation websites
• home appliances and entertainment
systems
• financial institutions and retail chains
• private surveillance and security firms
• remote sensing, aerial surveying
• emergency services
• Producing a data deluge that can be
combined, analyzed, acted upon
9. Data-driven, networked urbanism
• Cities are becoming ever more instrumented and
networked, their systems interlinked and integrated
• Consequently, cities are becoming knowable and
controllable in new dynamic ways
• Urban operational governance and city services are
becoming highly responsive to a form of networked
urbanism in which big data systems are:
• prefiguring and setting the urban agenda
• producing a deluge of contextual and actionable data
• influencing and controlling how city systems respond and
perform in real-time
10. Creating smart cities
• Tackle pressing issues
• New forms of operational governance
• More efficient, competitive and productive service delivery
• Increase resilience and sustainability
• More transparency and accountability
• Enhance participation in city life and quality of life
• Stimulate creativity, innovation, entrepreneurship and
economic growth
• Improve models and simulations for future development
11. Eight critiques of smart cities
• City as a knowable, rational, steerable machine
• Ahistorical, aspatial and homogenizing
• Technocratic governance and solutionism
• Corporatisation of governance
• Serve certain interests and reinforce inequalities
• The politics of urban data
• Social, political, ethical effects
• Buggy, brittle, hackable urban systems
12. The politics of urban data
• Big data and dashboards are not simply technical tools
• Nor are they are not pragmatic, neutral, objective,
non-ideological; nor can they speak for themselves
• Data do not exist independently of the ideas,
instruments, practices, contexts, knowledges and
systems used to generate, process & analyze them
• Big data and dashboards express a normative notion
about what should be measured, for what reasons, and
what they should tell us
• And they have normative effect - being used to
influence decision-making, modify institutional
behaviour, condition workers, etc
13. The politics of urban data
Material Platform
(infrastructure – hardware)
Code Platform
(operating system)
Code/algorithms
(software)
Data(base)
Interface
Reception/Operation
(user/usage)
Systems of thought
Forms of knowledge
Finance
Political economies
Governmentalities & legalities
Organisations and institutions
Subjectivities and communities
Marketplace
System/process
performs a task
Context
frames the system/task
Digital socio-technical assemblage
Places
Practices
14. Ethics of data-driven urbanism
• Data-driven, networked
urbanism raises all kinds of
ethical & related questions
• Data ownership and control
• Data integration and data
markets
• Data security and integrity
• Dataveillance and privacy
• Data quality and provenance
• Data uses
15. Privacy and big urban data
• Privacy debates concern acceptable practices with
regards to accessing and disclosing personal and sensitive
information about a person
• identity privacy (to protect personal and confidential data)
• bodily privacy (to protect the integrity of the physical
person);
• territorial privacy (to protect personal space, objects and
property);
• locational and movement privacy (to protect against the
tracking of spatial behaviour)
• communications privacy (to protect against the surveillance of
conversations and correspondence);
• transactions privacy (to protect against monitoring of
queries/searches, purchases, and other exchanges)
16. A Taxonomy of Privacy Harms (compiled from Solove 2006)
Domain Privacy breach Description
Information
Collection
Surveillance Watching, listening to, or recording of an individual’s activities
Interrogation Various forms of questioning or probing for information
Information
Processing
Aggregation The combination of various pieces of data about a person
Identification Linking information to particular individuals
Insecurity Carelessness in protecting stored information from leaks and
improper access
Secondary Use Use of information collected for one purpose for a different
purpose without the data subject’s consent
Exclusion Failure to allow the data subject to know about the data that others
have about her and participate in its handling and use, including
being barred from being able to access and correct errors
Information
Dissemination
Breach of Confidentiality Breaking a promise to keep a person’s information confidential
Disclosure Revelation of information about a person that impacts the way
others judge her character
Exposure Revealing another’s nudity, grief, or bodily functions
Increased Accessibility Amplifying the accessibility of information
Blackmail Threat to disclose personal information
Appropriation The use of the data subject’s identity to serve the aims and
interests of another
Distortion Dissemination of false or misleading information about individuals
Invasion Intrusion Invasive acts that disturb one’s tranquillity or solitude
Decisional Interference Incursion into the data subject’s decisions regarding her private
affairs
17. Privacy and big urban data
• Intensifies datafication
• The capture and circulation data are:
• indiscriminate and exhaustive (involve all individuals, objects,
transactions, etc.);
• distributed (occur across multiple devices, services and places);
• platform independent (data flows easily across platforms, services,
and devices);
• continuous (data are generated on a routine and automated basis).
• Much greater levels of intensified scrutiny and modes of
surveillance/dataveillance
• Tasks previously unmonitored or caught through disciplinary gaze
now routinely tracked and traced
• All but impossible to live everyday lives without leaving digital
footprints and shadows
• Mass recording, organizing, storing and sharing big data changes
the uses to which data can be put
18. Location/movement data
• Controllable digital CCTV cameras + ANPR + facial
recognition
• Smart phones: cell masts, GPS, wifi
• Sensor networks: capture and track phone identifiers
such as MAC addresses
• Wifi mesh: capture & track phones with wifi turned on
• Smart card tracking: barcodes/RFID chips (buildings &
public transport)
• Vehicle tracking: unique ID transponders for automated
road tolls & car parking
• Other staging points: ATMs, credit card use, metadata
tagging
• Electronic tagging; shared calenders
19. Data type Data permissions that can be sought by android apps (from Hein 2014)
Accounts log email log
App Activity name, package name, process number of activity, processed id
App Data Usage Cache size, code size, data size, name, package name
App Install installed at, name, package name, unknown sources enabled, version code, version
name
Battery health, level, plugged, present, scale, status, technology, temperature, voltage
Device Info board, brand, build version, cell number, device, device type, display, fingerprint, IP,
MAC address, manufacturer, model, OS platform, product, SDK code, total disk
space, unknown sources enabled
GPS accuracy, altitude, latitude, longitude, provider, speed
MMS from number, MMS at, MMS type, service number, to number
NetData bytes received, bytes sent, connection type, interface type
PhoneCall call duration, called at, from number, phone call type, to number
SMS from number, service number, SMS at, SMS type, to number
TelephonyInfo cell tower ID, cell tower latitude, cell tower longitude, IMEI, ISO country code, local
area code, MEID, mobile country code, mobile network code, network name,
network type, phone type, SIM serial number, SIM state, subscriber ID
WifiConnection BSSID, IP, linkspeed, MAC addr, network ID, RSSI, SSID
WifiNeighbors BSSID, capabilities, frequency, level, SSID
Root Check root status code, root status reason code, root version, sig file version
Malware Info algorithm confidence, app list, found malware, malware SDK version, package list,
reason code, service list, sigfile version
20. Privacy and big urban data
• Deepens inferencing
• Big data and predictive modelling enables a lot of inference
beyond the data generated
• can infer info about an individual not directly encoded in a
database but constitute PII which can produce ‘predictive
privacy harms’.
• For example, co-proximity and co-movement with others
can be used to infer political, social, and/or religious
affiliation.
• Also can produce ‘the tyranny of the minority’
21. Privacy and big urban data
• Weak anonymization and enables re-identification
• Key strategies for ensuring individual privacy is anonymization, either
through the use of pseudonyms or aggregation or other strategies.
• Pseudonyms simply mean that a unique tag is used to identify a person
in place of a name.
• Code is persistent and distinguishable from others and recognizable on
an on-going basis, meaning it can be tracked over time and space and
used to create detailed individual profiles.
• No different from other persistent identifiers such as social security
numbers and in effect constitutes PII.
• Some companies talking of ‘anonymous identifiers’ is thus somewhat of
an oxymoron, especially when the identifier is directly linked to an
account with known personal details
• Inference and the linking of a pseudonym to other accounts and
transactions means it can be potentially be re-identified.
• It is possible to reverse engineer anonymization strategies by combing
and combining datasets
22. Privacy and big urban data
• Opacity and automation creates obfuscation and reduces control
• The emerging big data landscape is complex and fragmented.
• Various smart city technologies are composed of multiple interacting systems
run by a number of corporate and state actors.
• Data are thus passed between ‘devices, platforms, services, applications, and
analytics engines’ and shared with third parties.
• Across this maze-like assemblage data can be ‘leaked, intercepted,
transmitted, disclosed, dis/assembled across data streams, and repurposed’ in
ways that are difficult to track and control
• Moreover, algorithmic processing is black-boxed, so it’s not clear how data are
being processed
• Opacity and automation undermine the FIPPs at the heart of privacy regulation
in a number of respects:
• making it difficult for individuals to seek access to verify, query, correct or
delete data, or to even know who to ask (tangled set of roles (as data
processors and controllers);
• to know how data collected about them is used; to assess how fair any
actions taken upon the data are;
• to hold data controllers to account
23. Privacy and big urban data
• Data are being shared and repurposed and used in unpredictable
and unexpected ways
• One of the key features of the data revolution is the wholesale erosion
of data minimization principles;
• that is, the undermining of purpose specification and use limitations
principles that mean that data should only be generated to perform a
particular task, are only retained as long as they are needed for that
task, and are only used to perform a particular task.
• Solution pursued by many companies is to repackage data by de-
identifying them (using pseudonyms or aggregation) or creating derived
data, with only the original dataset being subjected to data
minimization. The repackaged data can then be sold on and repurposed
in a plethora of ways
• The data and services that data brokers offer are used to perform a
wide variety of tasks for which the data were never intended, including
to predictively profile, socially sort, behaviourally nudge, and regulate,
control and govern individuals and the various systems and
infrastructures with which they interact
24. Privacy and big urban data
• Notice and consent is an empty exercise or absent
• Individuals interact with a number of smart city technologies on a daily basis,
each of which is generating data about them.
• Given the volume and diversity of these interactions it is simply too onerous for
individuals to police their privacy across dozens of entities, to weigh up the
costs and benefits of agreeing to terms and conditions without knowing how the
data might be used now and in the future, and to assess the cumulative and
holistic effects of their data being merged with other datasets
• In the case of some smart city technologies there is little mechanism to seek
notice and consent
• For example, CCTV, ANPR and MAC address tracking, and sensing by the Internet
of Things, all take place with no attempt at consent and often with little
notification
• Moreover, there is no ability to opt-out
• As such, there is no sense in which a person can selectively reveal themselves;
instead they must always reveal themselves.
• If a person is unaware that data about them is being generated, then it is
impossible to discover and query the purposes to which those data are being
put
25. R
Fair Information Practice Principles (OECD, 1980)
Principle Description
Notice Individuals are informed that data are being generated and the
purpose to which the data will be put
Choice Individuals have the choice to opt-in or opt-out as to whether and
how their data will be used or disclosed
Consent Data are only generated and disclosed with the consent of
individuals
Security Data are protected from loss, misuse, unauthorized access,
disclosure, alteration and destruction
Integrity Data are reliable, accurate, complete and current
Access Individuals can access, check and verify data about themselves
Use Data are only used for the purpose for which they are generated
and individuals are informed of each change of purpose
Accountability The data holder is accountable for ensuring the above principles
and has mechanisms in place to assure compliance
Redundant in the age of big urban data?
27. Suggested solutions
• Market:
• Industry standards and self-regulation
• Privacy/security as competitive advantage
• Technological
• End-to-end strong encryption, access controls, security controls, audit
trails, backups, up-to-date patching, etc.
• Privacy enhancement tools
• Policy and regulation
• FIPPs
• Privacy by design;
• security by design
• Governance
• Vision and strategy: (1) smart city advisory board and smart city strategy;
• Oversight of delivery and compliance: (2) smart city governance, risk and
compliance board;
• Day-to-day delivery: (3) core privacy/security team, smart city
privacy/security assessments, and (4) computer emergency response team
28. Conclusion
• We are entering an era of embedded and mobile computation
• Devices and infrastructures are producing vast quantities of data in
real-time, and are responsive to these data, enabling new kinds of
monitoring, regulation and control
• Cities are becoming data-driven and are enacting new forms of
algorithmic governance
• Whilst data-driven, networked urbanism undoubtedly provides a set of
solutions for urban problems, it also raises a number of ethical and
normative questions
• The challenge facing urban managers and citizens is to realise the
benefits of planning and delivering city services using urban data and
real-time responsive systems whilst minimizing pernicious effects
• At present, little serious thought has been expended on the latter