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Deutsch, Owen Exercise #1: Case Studies in Sensing and Data Collection. Real-Time Cities: an Introduction to Urban Cybernetics Harvard Design School: SCI 0646900 Spring 2014. User or Used?. Questions asked here of data sensing and acquistion technologies :

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Real time cities an introduction to urban cybernetics harvard design school sci 0646900

Deutsch, Owen

Exercise #1: Case Studies in Sensing and Data Collection

Real-Time Cities: an Introduction to Urban Cybernetics

Harvard Design School: SCI 0646900

Spring 2014


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

User or Used?

Questions asked here of data sensing and acquistion technologies:

Voluntary and informed discolosure?

Public or private ownership?

Visualized and understandable or sparse and opaque?

Conceptual or pragmatic?

Collaborative or individualized?

The cases below survey some of the technologies and briefly attempt some answers.

1 | Disney MagicBands

2 | London Smart Parking

3 | UrbanflowInteractive Urban Kiosks

4 | Nextdoor Partnerships

5 | WaterWatchers


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

1 | Disney MagicBands

“Unlock the Magic with Your MagicBand or Card

Later this year, a simple touch of your card or MagicBand will let you enter parks, unlock your hotel room door and more.”


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

1 | Disney MagicBands


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

1 | Disney MagicBands

How was the data collected?

An HF Radio Frequency device and transmitter is embedded in each wristband, which sends and receives signals through a small antenna. These signals can be detected at short-range touch points or by long-range readers (unless the user opts-out of long-range info collection.

Why was the data collected? What is interesting about the data?

Disney can monitor the purchasing and movement patterns of its customers and supposedly better tailor the customer’s experience as a result. Disney can also use the data to adjust its pricing strategies. This is one of the first examples of this degree of monitoring put into practice as such a large scale, and with such a high percentage of adoption.

What stories about the urban dynamics can the collected data tell?

The data can be used to better predict how people move through a controlled area and how this relates to their purchasing habits. Although this example pertains to a corporate environment, the technology could be scaled to the urban level.

What sort of questions about urban dynamics can be answered by looking at the data?

Is there a correlation between what goods people buy and how long they stay in a certain area? What factors are most likely to encourage people to change their consumption habits? What factors influence the degree to which people congregate or not? Etc…

How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data?

Each MagicBand has a specific code which is referenced against a central database of customers’ names, birthdays, and browsing history on Disney websites or within the park (if they so choose). How specifically these data are analyzed is presumably proprietary information.


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

1 | Disney MagicBands

How are particular patterns highlighted through techniques for tagging the data in order of their importance?

Highlighting customers’ habits and preferences is the primary goal of this program, but whether there is any heirarchy to these patterns is unknown. Presumably customers who spend more would be tagged as being of greater importance in determining future Disney policies.

How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data?

Given the vast amount of data collected by the MagicBands, details which are already known, redundant, or otherwise unremarkable might be eliminated. Information collected on frequency of ride usage might be inherently unnecessary, but more useful if correlated with customers’ ages.

Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change?

The data are dynamic, and are continually updated. How frequently the data are parsed in unknown, but any sudden variation would presumably be of interest to park managers, and would allow them to respond quicker than otherwise.

Who is the target audience of the data presentation?

The target audience is data analysts, marketers, and executives at Disney, although the supposed beneficiaries also include Disney customers.

What are their goals when approaching the data presentation? What do they stand to learn?

As a for-profit company, any goals are ultimately financial.


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

2 | London Smart Parking

“How London Plans to Eliminate the Search for a Parking Spot”

Project Video: http://www.youtube.com/watch?v=9MSnD4DrUwo


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

2 | London Smart Parking


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

2 | London Smart Parking

How was the data collected?

Infrared sensors embedded in the road detect the presence of cars, and relate this to any corresponding meter payment.

Why was the data collected? What is interesting about the data?

By making these data publicly-accessible, the amount of time drivers spend cruising for parking spaces can be reduced. Municipal authorities may also be able to adjust parking prices or enforcement. This is the first time such a project has been implemented at a city-wide scale.

What stories about the urban dynamics can the collected data tell?

Frequency and use of monitored parking spaces is one urban dynamic that will be better known.

What sort of questions about urban dynamics can be answered by looking at the data?

Additional questions about driving and parking habits, and how these relate to the nearby built environment, road or traffic conditions might also be answered by making further inferences from these data.

How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data?

Data detailing which parking spaces are occupied are relayed back to drivers via a smartphone app. Additional data are presumably relayed to a municipal database where traffic engineers and others can parse the data as need be.


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

2 | London Smart Parking

How are particular patterns highlighted through techniques for tagging the data in order of their importance?

Individual cars and drivers are not tagged, but parking spaces with greater use or areas with greater traffic congestion may be of more importance to the recipients of these data.

How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data?

Any data collected from this program which do not reduce the amount of time drivers must spend parking, or serve any other municipal purpose, may be eliminated.

Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change?

The data are dynamic, and are updated whenever a driver enter or leaves a parking space. If the data are not updated and relayed to drivers faster than they can spot an open space as they normally would, the efficiency would have to be improved.

Who is the target audience of the data presentation?

The target audience includes drivers, primarily, and parking authorities and municipal officials.

What are their goals when approaching the data presentation? What do they stand to learn?

The data must be present in a way which is clear enough for drivers to comprehend quickly.


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

3| Urbanflow

“Urbanflow:

An Interactive Information Service for Urban Screens”

Project Video: http://www.youtube.com/watch?v=9MSnD4DrUwo


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

3 | Urbanflow


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

3 | Urbanflow

How was the data collected?

Citizens, municipal authorities, and anyone with local knowledge submits their data to a manager of a city’s informational kiosks.

Why was the data collected? What is interesting about the data?

By assembling various sources of local data and providing multiple interfaces around the city, citizens and visitors gain access to local information and have an opportunity to provide useful information of their own.

What stories about the urban dynamics can the collected data tell?

The Urbanflow kiosks would be designed to deliver updates on transit conditions, service outages, energy consumption, or numerous other urban metrics.

What sort of questions about urban dynamics can be answered by looking at the data?

By using an urbanflow kiosk a visitor might be able to more easily navigate the road network or know if any local events are occuring. The managers of these kiosks may also be able to infer broader patterns of citizen and visitor activity based on what information they submit or what queries they make.

How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data?

The designers and managers of Smartflow kiosks would have to curate which information is received and displayed. The philosophy espoused on the Smartflow website is that “data only becomes understandable and usefully actionable when it’s been designed.”


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

3 | Urbanflow

How are particular patterns highlighted through techniques for tagging the data in order of their importance?

Based on Smartflow concept videos, data relating to wayfinding and neighborhood geographies seem to be prioritized, with other data layered or annotated.

How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data?

Data which are not easily decipherable or which useful to a pedestrian’s urban experience are eliminated. However, there is no set limit to the number of data layers which might be included.

Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change?

Some of the data is dynamic, although most of it is static- transit routes or neighborhood boundaries, for example. If any of this changes, each kiosk may be updated from a central database at once, rather than having to manually make changes as with traditional kiosks.

Who is the target audience of the data presentation?

According to Urbanflow’s designers, most users of urban kiosks are visitors and tourists, and would compose the primary target audience. Local residents are another important audience, however, and are depended upon to update hyperlocal, on-the-ground information.

What are their goals when approaching the data presentation? What do they stand to learn?

According to the Smartflow website, the data should be, “couched in carefully-considered cartography, iconagraphy, typography and language”. Whether better designed, more current and informative kiosks substantially improve users’ experience remains to be seen.


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

4 | Nextdoor Partnerships

“Nextdoor:

The Private Social Network for your Neighborhood”

Project Video: http://www.youtube.com/watch?v=9MSnD4DrUwo


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

4 | Nextdoor Partnerships


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

4 | Nextdoor Partnerships

How was the data collected?

Neighborhood residents voluntarily submit data to neighborhood-specific message boards.

Why was the data collected? What is interesting about the data?

Nextdoor is designed to facilitate neighborhood interaction and information-exchange. Other similar social networks are not neighborhood specific, or at least have no verification mechanism.

What stories about the urban dynamics can the collected data tell?

The vitality of a neighborhood can be gauged by the quantity of discussion on its message boards. The content of conversations can also provide insight into what issues the neighborhood may be facing, such as public safety or services.

What sort of questions about urban dynamics can be answered by looking at the data?

The quantity and nature of how neighbors interact in a virtual space, and how this compares to traditional neighborhood interaction is one question which could be answered. Whether this improves neighborhood interaction or outcomes would be another question of interest.

How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data?

Since the data is qualitative and limited to a textual platform, there is not as much necessity to limit or abstract the data for it to be useful. But if a researcher wanted to deduct greater trends from the data, its organization and privacy measures might present an obstacle.


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

4 | Nextdoor Partnerships

How are particular patterns highlighted through techniques for tagging the data in order of their importance?

Patterns are not highlighted in order of importance other than whatever patterns or topics a neighborhood deems to be of enough importance to revisit or have prolonged discussions about.

How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data?

In keeping with the democratic nature of the discussion groups, there are no curators deciding which data are important or not.

Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change?

The data are dynamic, and reflect changing neighborhood conditions to the extent that neighborhood residents feel moved to comment about them. If the frequency of this data becomes overwhelming for a particular neighborhood group, it’s possible that that group may be subdivided into more manageable networks.

Who is the target audience of the data presentation?

The target audience, and supposedly the only group with access to the data, are neighborhood residents themselves.

What are their goals when approaching the data presentation? What do they stand to learn?

The data should be presented in a way that most neighborhood residents find it accessible and easy to modify.


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

5 | WaterWatchers

“Fixing South Africa’s water system

with citizen inspectors”

Project Video: http://www.youtube.com/watch?v=9MSnD4DrUwo


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

5 | WaterWatchers


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

5 | WaterWatchers

How was the data collected?

Citizens take a picture of leaks or other water issues and answer three questions, either via a smartphone app or by SMS text.

Why was the data collected? What is interesting about the data?

By crowdsourcing reports of water problems, authorities are able to better maintain safe water supplies. The app is uncommon in that it doesn’t require a smartphone.

What stories about the urban dynamics can the collected data tell?

Water usage and the quality of local infrastructure are the most relevant urban dynamics which the data would inform.

What sort of questions about urban dynamics can be answered by looking at the data?

Municipal authorities or hydrological engineers can better know where to focus their resources, or the level of investment which an area requires. Public health authorities might also have better information on the spread of water-borne disease.

How is the magnitude of the data is dealt with; limiting the collected data, limiting the dimensions in the data set, or abstracting the data?

IBM uses proprietary analytics to parse the data. Since qualitative information is submitted, presumably some customized algorithms would need to be used.


Real time cities an introduction to urban cybernetics harvard design school sci 0646900

5 | WaterWatchers

How are particular patterns highlighted through techniques for tagging the data in order of their importance?

Leak reports which are concentrated in a particular area, or multiple reports of the same incident would be tagged of greater importance. A faulty water supply which impacts a high number of people would also be prioritized.

How does the original question to be addressed operate as the benchmark for eliminating unnecessary details in the data?

Given concerns about having adequate WaterWatchers participation for the app to be useful, eliminating unnecessary details probably wouldn’t be a high priority.

Is the data of a static or dynamic nature? If dynamic, what is the frequency of change and what happens when it starts to change?

The data is dynamic and updated as often as citizen submissions are made. The proficiency of IBM with handling large datasets and its experience in managing a similar app suggest that any changes in the data could be assimilated.

Who is the target audience of the data presentation?

Citizen participants with water supply issues are the target audience, although the data could be valuable for researchers, as well.

What are their goals when approaching the data presentation? What do they stand to learn?

The primary goal is to improve the public water supply in this instance, and longer-term, to develop techniques for continuing this type of citizen engagement and generally making public services more efficient in various contexts.


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