WEEK 5: Analyzing the TechniCity – Part 1

MOOC Summaries - TechniCity - Analyzing the TechniCity

WEEK 5: Analyzing the TechniCity – Part 1

“Analyzing the TechniCity…Data Driven Delusions… Urban Data/Big Data… Open Data… Data Visualisation… Visual Survey Tools…Smart Cities The Untold Story… ” 
(Source)

Summaries

  • Context: Analyzing the TechniCity
  • Context: Data Driven Delusions
  • Context: Urban Data/Big Data
  • Context: Open Data
  • Context: Data Visualizations
  • Instructional: Visual Survey Tools
  • Case Study: Smart Cities - The Untold Story

Context: Analysing the TechniCity

  • There is a data revolution that is allowing us to gain much more detailed information – this includes financial data, statistics,  data with location specific information etc.
  • Big data is term used to describe data that is so large and complex that it is difficult to process using traditional data processing applications.
  • Now whether the data is big or small, in order to use the data we have to have a starting point, a question we want to answer.
    • How New York City used big data to catch restaurants that were illegally dumping cooking oil into sewers in neighborhoods.
  • Open data is the idea that data should be available for everyone to use without restrictions (of course not every piece of data should be open).
  • Open data helps to increase transparency – citizens want to know what their government is doing. Another reason for opening data is that it has value to our society. Open data can also be used to encourage participation in government.
  • There are open data initiatives across the globe:
    • The Nigerian Ministry of Communication Technology recently announced an open data development initiative to help encourage innovation and economic growth.
    • In France, a team of people make up Etalab, which is an open data initiative for reuse by everyone without any copyright restriction.
    • Chicago has also released more than 600 data sets to the public, and the open data portal has been accessed well over five million times with more than eight terabytes of data downloaded.
  • By opening more data, it makes it increasingly easy for everyone to engage in analyzing the city.
Chop Chop MOOCs’ summary of https://class.coursera.org/techcity-004/lecture

Context: Data Driven Delusions

  • Address issues of inequality as cities are developing, and the roles of technology and access.
  • One of the defining things about living in this modern age is that we think everything can be measured, but
    • what if we measured the wrong things?
    • what if there things that are hard to measure?
    • do we simply assume that we know the relationship between what we measure and what we want to change?
  • Example of measuring traffic and building roads – we did not always understand that building more roads actually leads to more traffic as more people decide the best way to travel is to use a car.
  • What we decide to measure has implications for power and inclusion in cities:
    • Bangalore and turning land records into electronic records available on the net, and the unintended consequence;
    • Spacesuits and how the little company won because they understood the human body;
    • New York City and the  misunderstanding about the size and location of their fire stations;
    • IBM has a partnership with the city of Rio and they have built this amazing control center with 22 of the city departments reporting and it is all in preparation for the World Cup and the Olympics; but some things are not measured (e.g. hospital beds and student enrolment), so the risk is that these things do not get appropriate attention.
  • The following could be useful guides:
    • what is being measured?
    • Who decides what should be measured?
    • Who is measuring it?
    • Who has access to the measurements and what is not being measured?
    • Who is being excluded?
    • What are the costs of that ex/inclusion?
Chop Chop MOOCs’ summary of https://class.coursera.org/techcity-004/lecture
 

Context: Urban Data/Big Data

  • Big data – not only from the analytics but also for data visualization (heard a good definition of big data the other day: big data is anything that won’t fit in an Excel spreadsheet).
  • There are emerging now some really quite impressive data sets in real-time that are quite useful to urban planners.
  • The richest data sets to date really come from automation in transit systems – example of London’s data for subway, heavy rail, and bus systems.
    • What we’re interested in terms of this big data are looking clearly at the behavior of the travelers.
    • We’re looking cycles in the data, we’re looking for diurnal, weekly, monthly cycles, etcetera.
    • From the data set, we really need to piece together trouble patterns.
  • Big data is as problematic as small data – big data has raised a large number of problems. There can be lots of missing bits and that data integration is major problem.
Chop Chop MOOCs’ summary of https://class.coursera.org/techcity-004/lecture

Context: Open Data

  • Open data initiatives from a grassroots advocacy perspective – ways in which governments and organizations make useful data available to mobilize public awareness programs.
  • The Open Data Handbook has a short definition of what open data is: for now let’s just say that it is data that a government (and in some cases non-government entities) has made available to anyone to use, and that what makes it open is that they have almost no restrictions on who can use the data or what can be done with it.
  • In a lot of cases, the most exciting data is actually geographic data about the locations of things that have allowed people to do analysis in in a spatial capacity.
    • Examples of uses of data in transit and food safety inspections.
  • What are some the reasons that make governments hesitant about making data open?
    • They may be making money off selling the data to large institutions, in order to recoup some of the costs of collecting or maintaining that data. So, they may be nervous about having that go away.
    • There may be concerns about how making data public may have implications for privacy, safety or abuse.
    • There may be a culture in place that’s skeptical of being open by default or as open as possible with data.
  • Data isn’t considered really and truly open if it comes with too many strings attached, and if it doesn’t support people transforming the data and building new things around it.
  • So how is open data being used? In many cases, governments are holding contests such as hackathons encouraging citizens to build either certain apps using certain data sets that they’ve released, or to concentrate on specific topics, and encouraging them to put their best ideas forward.
  • Some  concerns and critiques around open data initiatives:
    • Funding and sustainability of apps developed using open data (e.g. it’s one thing to make an app over a weekend, and it’s quite another to develop a business with a business model that’s, that can be relied upon in the long term).
    • Open data may only be empowering the empowered, and it may be limited in its ability to make a difference in some of the most challenging issues (and lulling us into thinking we may be making progress on problems when there actually isn’t any meaningful change).
    • Too much interest in whether or not data is open, and not whether or not the data is actually driving anything meaningful with regards to the most pressing needs that we have.
    • Whether or not open data is being made available primarily for the benefit of private interests.
  • Data is often created to serve some kind of public benefit. It’s in addressing these critiques that I think open data’s greatest potential lies.
 Chop Chop MOOCs’ summary of https://class.coursera.org/techcity-004/lecture

Context: Data Visualizations

  • Data visualization is simply defined as the visual representation of data.
  • With so much complicated data, it can be helpful to communicate information through maps, charts, or other visualizations.
  • So what does data visualization mean in the context of city planning? Cities have the challenge of taking everything from demographic data, to land uses, to water consumption, to transit ridership, and turning it into a format that can be easily understood by political leaders, the public, and even for the planners themselves.
  • In considering how and when to create data visualizations, there are several key questions.
    • How complex is the data?
    • What types of visualization are needed to address a particular issue or challenge?
    • What tools or techniques are available to address the issue or challenge?
    • How can complex data be presented to support decision making?
    • What role will the visualizations play in supporting knowledge, discovery, and dissemination?
    • What role would the visualization play in fostering communication and collaboration between stakeholders?
  • Case study of using visualizations in the city of Austin.
  • While there are definitely lots of great data visualizations, visualizing information is not an easy task.There are plenty of terrible examples of data visualization, whether it be a chart that doesn’t make sense or an overly complex map.
  • Useful to always reflect on the key questions:
    • Who is the audience for the information?
    • What is the most important information to convey?
    • What’s the simplest way to convey that information?
    • How will this information help support decision making?
Chop Chop MOOCs’ summary of https://class.coursera.org/techcity-004/lecture

Instructional: Visual Survey Tools

  • City planning is an inherently visual  discipline.
  • With the evolution of the internet, and things such as Google Street View, and other platforms, we’re able to engage in unique and new ways.
  • One of the tools that can be used for engaging in visual preference surveys, is called Beautiful Streets, which is a visual preference survey for Philadelphia. It draws images from Google Streetview and we can choose which street is more beautiful.
  • Another example is Place Pulse, which is a fun tool for engagement. Place Pulse is simply a tool to measure perception and study its outcomes. It takes images from Google street view and randomly pairs them and people just answer the question e.g. which place looks cleaner, one or two?
  • The really fun thing about Place Pulse is it is part of the creative commons and it’s open source.
Chop Chop MOOCs’ summary of https://class.coursera.org/techcity-004/lecture

Case Study: Smart Cities – The Untold Story

  • Funny presentation on unbundling the hype and focusing on the really important decisions cities have to make.
  • Big data is like teenage sex. Everybody talks about it, nobody really knows how to do it. Everyone presumes everybody else is doing it so everyone claims they’re doing it.
  • There’s more big data on big data than big data itself! And who uses big data today? Mainly executives, to spice up their Power point presentation.
  • Big data is only half the way we really want to go. It’s not about documenting and building statistics. It’s about actually doing something about it. Make it a small action, make it a nudging, make it a big action, but just make something out of it.
  • We know traffic is bad, we know pollution is appalling. We know this planet is dying. Stop documenting it. Do something about it, okay.
  • Open data is like adult sex. Now the big difference between having open data for money and having it for free is the one for money usually costs a lot less.
  • We forget someone has to pay for all of it (it’s not just forcing someone to open up the data so that someone else can profit from it). We forget that it’s not only those who actually need to make money out of the data on top of an infrastructure. We also need to support the infrastructure.
  • It cannot be a broken value chain. We need a very good license agreement framework in place which values the whole value chain along the open and big data flow.
  • Citizen engagement – many have no time to begin with. The message I want you to take away is really is, people are very poor engagers this is because they may have sympathy but they don’t have empathy. Empathy is what gets you engaged. Empathy means you have bleed at the problem at hand.
  • I would like cities to take away is that simply engaging 10,000 people in your decision process, still there’s no mega-democratic. Your citizen engagement goes from 0.01% to maybe 1%. You’re still losing out on 99% of the society.
  • Chances are very high that the only way of reaching these 10,000 people is electronically. So automatically, everybody who doesn’t use it is out. These are the elderly, these are the sick, these are the kids.
  • People are very good in complaining. If I were a city, I would make very easy for people to complain. And tell them how this complaint was addressed. So that very simple feedback loop of with accountability at the end makes magic.
Chop Chop MOOCs’ summary of https://class.coursera.org/techcity-004/lecture

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