After two days in the studio I worked through so many of the conceptual questions that have been bugging me for months. And opened up a stack of new ones.

Basically, I managed to hack my way around the twotone file structure and get my bridge samples into their system, playing as instruments in the data sonification tool.

Trumpets now play the Rama VIII Bridge in Bangkok, and the glockenspiel plays the Golden Gate. Problem is, all of these bridge sounds are already so complex, once you start mapping them to different notes in response to the shifts in data, it’s pure sonic chaos! If I had a system that played a sample and shifted the pitch as the data changes, that would be way more seamless. I am enjoying the ad hoc nature of this process though and the way it is forcing me to consider at a much deeper level, the relationship between the data and the sounds.

As imagined, the one to one parameter mapping of sound sample to dataset is not actually that interesting. In terms of compositional complexity – it gets repetitive very quickly. And, extremely dense sonically if I haven’t chosen the initial samples well.

Something one note, simple, not too much going on, without multiple beats or tones.

Eventually I will upload some of these composition samples, but for now am still navigating how much of this process to share and what to keep private for the eventual ‘outcome’. Although as we discussed in the Publishing as Practice workshop today, having ways to show your artistic process can be both liberating and engaging.

Liberating, because it frees you from the grip of perfectionism + as my dear friend Ernest always says: finished is better than perfect! Engaging because while it may pierce the bubble of mystery around your work, it can also make you more approachable. Since this is a project that relies heavily on collaboration, for me it makes sense to make the process as transparent as possible. This allows potential creative partners to dive into the various threads of creative process, and gives a quick overview for anyone interested in working together. It’s also a little alarming, as nothing is ‘finished’ and I don’t feel nearly ready to make it public. Yet here I am, writing for you – whoever you are, dear reader – to lay my artistic soul bare.

There was something else. Ah yes, the constraints of the twotone platform mean that I have to take a very ‘zen’ approach to the work. Like the Tibetan Monks I sawy in New York City back in 1989, drawing sand mandalas. So intricate and beautiful, painstaking work that they released into the river once it was finished. You can’t stay attached to the outcome if you keep working through the process, over and over again.

Also that there is no ONE definitive work that will come from this. Many variations will emerge. And I am starting to make peace with that as part of the creative process.

I think perhaps I had envisaged – or ensounded? – a massive, global, all the bridges playing together event. But honestly, that is only possible as a conceptual frame. If you take even the 29 sensors on the ONE bridge and try to make a piece out of them, the sonic chaos resulting is going to be almost unbearable to listen to. So I need to find ways to pin it back into a context or reason for listening, and connecting. That is, the bridges have to relate to each other in some way, and to my own practice and experience. Otherwise it becomes totally random. I am starting to find more interesting questions through this process. And dealing with technical issues that I hadn’t even considered – like the sheer volume of data generated by a bridge sensor. And the compatability or otherwise of the various types of data with each other and the systems I need to use for creating sound compositions.

As an example, I have figured out that the selected storm data from the Hardanger Bridge structural monitoring sensors is only available in mat format but the csv files I need are massive and broken down by hour, throughout the day. So I needed to find out exactly what time did this storm hit? Hurricane Nina seems like a good place to start. Around 2-8pm on a Saturday, 10th January 2015 – now I have attempted to open those csv files but their compression is not playing nice with my computer. It takes another level of engagement now to connect with the engineers and find out if they are interested in the sonification process, and how possible it is to switch formats.

I am charmed to discover that the accelerometers used are made by Canterbury Seismic Instruments, in Christchurch New Zealand, where my grandmother was born. Which makes complete sense, given the magnitute and frequency of earthquakes NZ needs to monitor. Cusp-3 Series Strong Motion Accelerographs.

Technical Specifications PDF – curious, is it possible to convert to audio signal?

I have done this with the B&K accelerometers on the Green Bridge permanent installation in Brisbane, and it only took a simple adapter…

UNDER CONSTRUCTION by Jodi Rose

That brings us up to date, and my decision now to try selecting more subtle bridge samples as a starting point, and find out how they sound using the two datasets I am already working with. Then I need to get my head around the generative composition tools and work on mapping out the structure of the piece for the Church of Our Lady.

Thanks to the generous structural monitoring engineers at NTNU, I have access to an incredible range of accelerometer data from the Hardanger Bridge. It only took one more specific search term, and is published under a creative commons (cc-by) license.

Now the fun really starts – downloading the csv files: LowWind, HighFreq; MaxLift, LowFreq, MaxPitch, HighFreq (which I misread as MaxPatch and thought OMG they have sonified it already! Although perhaps they have. I still need to write and make contact) MaxDrag, LowFreq… The monitoring sensors are in place since 2013, there is seven years of data. And the storms – Storm Nina, Storm Ole, Storm Roar, Storm Thor!

Image Credit: NTNU Department of Structural Engineering, Trondheim

Wind and Acceleration Data from the Hardanger Bridge

By Aksel Fenerci, Knut Andreas Kvåle, Øyvind Wiig Petersen, Anders Rønnquist, Ole Øisethhttps://doi.org/10.21400/5ng8980s Published 18-08-2020 at Norges teknisk-naturvitenskapelige universitet 2764 views

The dataset consists of long-term wind and acceleration data collected from the Hardanger Bridge monitoring system. The data are collected through wind sensors (anemometers) and accelerometers that are installed on the bridge. The dataset includes both the raw data (in “.csv” format) and the organized data (with “.mat” extension based on hdf5 format). Downloadable zipped folders contain monthly data with different frequency resolutions, special events (storms, etc.) and the raw data. Details on the organization of the data can be found in the readme file and the data paper, both of which can be found in the dataset.

Resource type: Dataset

Category: Teknologi, Bygningsfag, Konstruksjonsteknologi

Process or method: GPS, Wi-Fi, accelerometers, anemometry, signal processing

Geographical coverage: Hardanger, Norway

Fenerci, A., Kvåle, K. A., Petersen, Ø. W., Rönnquist, A., & Øiseth, O. A. (2020). Wind and Acceleration Data from the Hardanger Bridge. https://doi.org/10.21400/5NG8980S

Datasets and Weather

Ok I’m breaking this down now – the CSV files are by year and month eg. Raw 2015 1.

Storms happen in January, Storm Nina: 10th Jan 2015, Storm Thor: 29th Jan 2016.

So to focus on the storms, go for the first month. I can’t use their smaller already selected and edited mat files in the data sonification tool. Maybe it’s possible to conver mat to csv? (oh that question that opens up a whole new can of worms!)

And have just discovered that my idea works to replace the audio files in the twotone sampler with my own bridge sounds… except that I have to go through the meticulously and make each NOTE as a bridge sound, as they move up and down on the scale while playing the data. I think that’s enough for today. Back to the sensors.

For now I’m taking the full month raw csv files and parsing them by date. You gotta start somewhere – Storm Nina go!

Poetic Storm Nina Video Homage

Storm Norway – Sandvikjo stormen Nina 10.01.2015 – Halsnøy by monica

MELLOM BAKKAR OG BERG
Ivar Aasen

Mellom bakkar og berg ut med havet
heve nordmannen fenge sin heim,
der han sjølv heve tuftene grave
og sett sjølv sine hus oppå dei.

Han såg ut på dei steinute strender;
der var ingen som der hadde bygd.
«Lat oss rydja og byggja oss grender,
og så eiga me rudningen trygt»

Han såg ut på det bårute havet,
der var ruskut å leggja utpå,
men der leikade fisk nedi kavet,
og den leiken, den ville han sjå.

Fram på vinteren stundom han tenkte:
«Gjev eg var i eit varmare land!»
Men når vårsol i bakkane blenkte,
fekk han hug til si heimlege strand.

Og når liane grønkar som hagar,
når det lavar av blomar på strå,
og når netter er ljose som dagar,
kan han ingen stad venare sjå.

Sud om havet han stundom laut skrida:
Der var rikdom på benkjer og bord,
men ikring såg han trelldomen kvida
og så vende han atter mot nord.

Lat no andre om storleiken kivast,
lat deim bragla med rikdom og høgd,
mellom kaksar eg inkje kan trivast,
mellom jamningar helst er eg nøgd.

Lyd Mellom bakkar og berg ut med havet

BETWEEN HILLS AND MOUNTAINS Ivar Aasen

Between hills and mountains out to sea raise the Norwegian get his home, where he himself raise the tufts dig and put their houses on top of them. He looked out on the rocky beaches; there was no one who had built there. “Let us clear and build our villages, and then own the rudder safely » He looked out on the stretcher sea, there was debris to lay on, but there were fish playing down the cave, and that toy, he wanted to see. Until the winter he sometimes thought: “I wish I were in a warmer country!” But when the spring sun on the slopes shone, he got the urge to say home-grown beach. And when liane greens like pastures, when it blooms of flowers on straw, and when nights are as bright as days, he can see no city venare. South of the sea he sometimes loudly slid: There was wealth on benches and tables, but around he saw the bondage quiver and then he turned north again. Let no others about the size kivast, let them brag with wealth and heights, between cookies I can not thrive, between jams preferably I am satisfied.

Sound: Between hills and mountains out to sea

Wind-induced response of long-span suspension bridges subjected to span-wise non-uniform winds: a case study

Master thesis – NTNU Norwegian University of Science and Technology. Department of Structural Engineering. [E.M. Forbord & H. Hjellvik]

The response has also been predicted using wind data from the Hardanger Bridge, and the predictions have been compared to the measured response. Uniform profiles of wind speed and turbulence have been given different values based on the measured data, more specifically the mean value of all sensors and the value from the midmost wind sensor. It is seen that the choice of value does not affect the accuracy of response predictions. No matter what values are chosen, the predictions are quite inaccurate in general. lntroducing a non-uniform profile of mean wind speed makes the predictions slightly better in some cases, but not noteworthy, and the accuracy is still relatively low. When also including the non-uniformity of turbulence in the response calculations, the predicted response is reduced and the accuracy worsened with respect to the measured response. Accounting for the non-uniformity of self-excited forces shows almost no effect on the predictions. It is concluded that non-uniform wind profiles do not improve the accuracy of predicted bridge response, and that other uncertainties in the calculation methods have larger impact on the predictions than whether the non-uniform profiles are included or not.

2.1 Random Vibration Theory

6.2 Influence of Non-Uniform Turbulence Standard Deviations

In this section, the influence of span-wise non-uniform turbulence standard deviations on the dynamic response will be presented. Three wind speed profiles have been analysed with different turbulence std profiles. The wind speed profiles used are the linear profile and the sinus profile shown in Figure 5.5a and 5.5d, in addition to a uniform wind speed profile. The three different turbulence std profiles shown in Figure 6.15 are studied. They all have the same integrated sum along the span to make them comparable. The two non-uniform turbulence std profiles chosen have the opposite shapes of the wind speed profiles used in this section, because this is often seen in the measurement data from the Hardanger Bridge. Both of these turbulence std profiles will be compared to uniform turbulence standard deviations, for all the three wind speed profiles. The horizontal turbulence std has a span­ wise mean value of 20% of the wind profile’s mean wind speed, and for the vertical component the corresponding value is 10%. The effect of turbulence std on the response is included in the calculations through the wind spectra, which have a quadratic dependancy on the turbulence std, as shown in Eg. (2.40). The span-wise variation of wind speed is also included in the formula. Therefore, to study the effect of the turbulence std profiles isolated, the response using a uniform wind speed profile and different turbulence std profiles has been calculated. In addition comes the linear and sinus wind profiles, to study if the same turbulence std profiles have different effect on these than on the uniform wind speed profile. The calculated response will only be presented for wind profiles with the mean wind speed of 10 mis, because the trends, the shape and differences of the response along the span are nearly the same for all mean wind speeds for the different wind speed profiles.

6.3 Influence of Non-Uniform Self Excited Forces

To study the influence of span-wise non-uniform self-excited forces on the dynamic response, several wind speed profiles have been numerically tested with both uniform and non-uniform self-excited forces. The non-uniform self-excited forces are caused by the non-uniform wind profile. The re­ sponse is predicted with uniform self-excited forces where the aerodynamic properties are dependent on the mean wind speed of the wind profile, and with non-uniform self-excited forces where the aero­ dynamic properties vary along the span with the wind speed. Toen the bridge response in both cases are compared. The wind profiles tested are presented in Figure 5.5. As in section 6.1, the standard de­ viations of turbulence components are span-wise uniform, such that the influence of the non-uniform self-excited forces are investigated separately. The horizontal and vertical turbulence standard devi­ ations have been set to 20 and 10%, respectively, of the horizontal mean wind speed.

The influence of the non-uniform turbulence standard deviation has connection to the shape of the wind speeds along the span. As discussed previously, the response shifts to where the wind speeds is largest. The same can be said about the turbulence std. It was seen that the wind is dominating and shifts the response more than the turbulence std, for this particular shapes and ratio between the mean wind speed and standard deviation of the turbulence components. The horizontal shift in the response due to the non-uniform turbulence std comes from the cross-spectral densities of the turbulence components which is high when two points with large turbulence std are considered.

The effect of including the non-uniform self-excited forces on the response increase with the mean wind speed of the wind profile. The difference between the response using non-uniform and uniform self-excited forces are largest for the highest mean wind speeds studied. The lateral response using non-uniform self-excited forces deviates less from the response using uniform self-excited forces compared to the vertical and torsional response. This is due to the aerodynamic derivatives which has been taken as zero. The reason for the large ratios in the vertical and torsional direction is the aerodynamic derivatives that reduce the total damping and stiffness of the structure as mentioned. For lower mean wind speeds, 10-20m/s, the difference is below 10% for all response components.

Masters Thesis NTNU 2017 permanent link

I think it’s safe to say they haven’t sonified it… yet!

Here are a few more links open from my research on the Hardanger Bridge

Official Website https://www.vegvesen.no/vegprosjekter/Hardangerbrua/InEnglish/the-hardanger-bridge

General Norway Bridges info https://www.vegvesen.no/en/roads/Roads+and+bridges/Bridges

The Neglected Bridges of Norway

https://www.vg.no/spesial/2017/de-forsomte-broene/kart/index-eng.php

AllplanInfrastructure

Data Sonification toolkit coming together! Today I’m learning about twotone and how to resucitate a dead web audio interface. The wonderful Øystein Fjeldbo comes by to help me navigate this brave data world, and talks me through some of the options I’m exploring to make a proof-of-concept. First up, based on a tutorial in the Brexification post from MCT (Music Commmunicationst Technology) Master’s student blog at NTNU turns out to be not that adapatble. It’s very handy that it comes embedded in Max for Live (Connection Kit) and I get the sense of how easy it could be to use.

I can change the API to another live data stream, but there is no simple way to swap their synthesised sounds for a sample player. It turns out to be a completely different process, applying paramater changes to a tone generated by the patch, rather than making changes to an audio sample. So, we take a look at the patch made by the students, who have adapted it to their own needs – but again, this is too specific and not quite what I want to do. Now I’m a little concerned that I will have to give the process over to a custom build, but when we look into the code for the mysteriously vanishing twotone app, it turns out this is something Øystein can help me rebuild.

What is it for?

TwoTone can be used for understanding data through listening. It makes data more accessible.

Data

TwoTone can be used by itself or in tandem with visualization. Just like in the cinema, sounds add another layer to understanding.

Music

TwoTone is a fun and intuitive way to make your own compositions without any prior musical or technical knowledge.

Google NEWS & DATAVIZED TECHNOLOGIES

Or so the twotone web audio app promises – sadly their domain is no longer active. Even though it was launched with great fanfare on the 5th March in 2019. Only two years on, and it’s already obsolete. “TwoTone is imagined and made by Datavized Technologies with support from Google News Initiative. We hope you like it.” Did Data Sonification fall out of fashion so fast? Luckily it’s open source, and thanks to GitHub, the code still exists. But I have no idea what to do with it, or where to start.

https://twotone.io/ introduction

Where did it go? Technology graveyard from two years ago. Google labs and all.

The talented Øystein, who teaches Data Sonification manages to get this zombie code going… from the graveyard of twotone.io https://github.com/datavized/twotone

It’s such a beautiful and simple concept, a data sonification web-based audio tool. You simply upload your dataset and then can choose instrumentation, key, duration, etc. Once we have it running on my browser, I start working my way through the tutorials.

TWOTONE TUTORIALS

Introduction: https://twotone.io/tutorials/introduction-to-twotone/

Advanced: https://twotone.io/tutorials/advanced-features-tutorial/

NODEJS

Nodejs – another thing we had to install to get twotone working!

https://nodejs.org/en/

https://nodejs.org/en/about/

I really have no idea what any of this means, but I trust that Øystein knows!

Despite not being able to swap out my own sounds with the pre-made samples, it’s satisfying working through the options to make interesting sonification compositions.

I’m pretty happy to be able to add my own csv data sets – a couple of examples from my research. Golden Gate Bridge accelerometers recorded on mobile phone by Stephen Wylie, via kaggle https://www.kaggle.com/mrcity/golden-gate-accel-20180512 This is one minute of data from the “Linear Accelerometer” of the Physics Toolbox Suite v1.8.6 for Android. The data was collected from a Pixel 2 phone on the east side of the Golden Gate Bridge at the midpoint between the two towers of the bridge at approximately 3:20 PM local time on May 12, 2018. CCO Public Domain.

and the composition looks like this…

adding sound later – the export is doing a glitchy thing where it only gives a minutes of the sound, not all 30 minutes… But it’s only a problem with this one, not my next attempt using pedestrian data from the Brooklyn Bridge.

NEW YORK CITY OPEN DATASETS

Transporation: Brooklyn Bridge Pedestrian Count

Towards Manhattan / Towards Brooklyn. Weather summary, precipitation:

Now it gets fun to start playing with filters: defining the key, speed and instruments<

I’ve figured out a hack to get my own samples in there – just have to name them exactly as the existing sets are named. Going to work on that tomorrow.

There’s a range of durations, from 02 seconds to 14 seconds, and I think the actual notes can be sacrificed for a variety of sounds. This is where it starts to get really creative! And sounds like some wild free jazz. Now I need to do some more study in order to really get a sense of the possibilities of sonification. Here’s the lecture series by Thomas Hermann – one of the people who literally wrote the book on sonification.

SONIFICATION AND SOUND DESIGN, MCT 2019

MCT Data Sonification course taught by Thomas Hermann: techniques beyond parameter mapping for applications in data mining and bio feedback

MCT4046 Sonification and Sound Design Seminar Series – Spring 2019
Speaker: Thomas Hermann

Facilitator: Shreejay Shrestha
Video production: MCT students 2018-2020
Video recording: Shreejay Shrestha, Guy Sion
Audio recording: Jørgen Nygård Varpe
A/V editing & processing: Sepehr Haghighi
Design: Karolina Jawad
Music: Sepehr Haghighi
Technical support: Robin Støckert, Anders Tveit, Alexander Refsum Jensenius
Administration: Maj Vester Larsen
Consultant: Sigurd Saue
Seminar Series Curation: Anna Xambó
Recorded in NTNU Trondheim 2019

ARNE NORDHEIM

Norwegian composer Arne Nordheim, recommended by Øystein to listen for generative compositions and experimental music techniques.

Hardanger Bridge monitoring

Next step: contact NTNU engineers and ask for access to their data, pretty please?!

NTNU Case study: The Hardanger Bridge

The Hardanger Bridge is a 1380 meters long suspension bridge, located on the western coast of Norway. It is the longest suspension bridge in Norway, and the 10th longest in the world, which makes it an interesting case study.  Our research in the field of suspension bridges requires knowledge from several engineering fields; such as aerodynamics, signal processing, finite element analysis and control theory. 

Experimental surveys

A comprehensive measurement system is operating on the Hardanger Bridge to improve the current understanding of the dynamic behaviour of long-span suspension bridges and their interaction with wind. This includes sensors for measurement of both response and environmental excitation. The system is described in detail under Structural monitoring (Structural monitoring – The Hardanger Bridge).

Source: https://www.ntnu.edu/kt/research/dynamics/monitoring/hardanger

Figure 2: Illustration of the front view of the Hardanger Bridge, showing accelerometer and anemometer positions. Illustration by NTNU/Heidi Kvåle.

The Hardanger Bridge monitoring project

The Hardanger Bridge, opened in 2013, is a 1380 m long suspension bridge crossing the Hardanger fjord in western Norway. The main span is 1310 m long, which makes it the longest suspension bridge in Norway and the 10th longest in the world. The two concrete bridge towers are 200 m high, made using the slip forming technique, and supports the steel bridge casing and the cables that are anchored in the mountain side.

The main objective with the monitoring project is to study the dynamic behaviour of the suspension bridge; especially the wind-induced vibrations. All data generated from the extensive measuring system is directly used in research related to the ferry-free costal highway E39 project; both PhD and master theses. 

The monitoring system consists of the following sensors (illustrated in Figure 2):

  • 20 triaxial accelerometers
  • 9 anemometers

System identification and modal analysis

Based on recordings established by the monitoring system, parameters characterizing the system behaviour; typically represented by natural frequencies, damping ratios, and mode shapes; are estimated (system identification). The results are a highly valuable asset for these applications:

  • Studying the dynamic behaviour of the bridge
  • Updating the numerical model, such that it better describes the real behaviour of the bridge
  • Verification and possible improvement of the current state-of-the-art methods used for numerical modelling
Modal shape of the Hardanger Bridge. Model and animation by NTNU/Øyvind Wiig Petersen.

Load modelling and identification

Modelling of the wind-induced forces on suspension bridges is crucially important for accurate prediction of the dynamic response.

The modelling of the environmental wind loads hinges on the description of the spatial and temporal characteristics of the wind field. The wind data from the field measurements will be used to characterize the wind field. It is aimed to test the performance of load models and reveal the uncertainty involved in response prediction.

The models for motion-induced loads most commonly used in bridge aerodynamics are linear engineering approximations. It has been shown in several case studies that the models are working well when the response of the bridge is dominated by one vibration mode in each direction. Taking into account that the principle of superposition does not hold in fluid dynamics, it is unknown if the models will be able to predict reliable results for a more complex motion. Thus, there is a need to test the accuracy of the linear assumption introduced in the modelling of the self-excited forces. This challenging task does not only require development of new experimental setup but also identification techniques able to work with an arbitrary motion.

People:

Postdocs and PhD candidates working with suspension bridges:

SOURCE: https://www.ntnu.edu/kt/research/dynamics/research/long-span/suspension-bridges

Golden Gate Bridge Accelerometer Data

Accelerometer Data collected by Android’s Physics Toolbox Suite on 12 May 2018

https://www.kaggle.com/mrcity/golden-gate-accel-20180512

Golden Gate Bridge Accelerometer Data Accelerometer Data collected by Android’s Physics Toolbox Suite on 12 May 2018www.kaggle.com

Context

I got the chance to walk across the Golden Gate Bridge in San Francisco, CA for the first time in 22 years on May 12, 2018. There have been a great many technological advancements since then, as now we are all walking around with powerful computers and sensors in our pockets. I decided it would be fun to measure the bridge and provide others the opportunity to analyze data as to its motion for a brief snippet of time.

Content

This is one minute of data from the “Linear Accelerometer” of the Physics Toolbox Suite v1.8.6 for Android. The data was collected from a Pixel 2 phone on the east side of the Golden Gate Bridge at the midpoint between the two towers of the bridge at approximately 3:20 PM local time on May 12, 2018.

Acknowledgments

This dataset is hereby owned by the community under the terms of a very lenient license in the condition that I have published it shortly after recording it.

Golden Gate Bridge G-Force Data

G-Force Data collected by Android’s Physics Toolbox Suite on 12 May 2018

https://www.kaggle.com/mrcity/golden-gate-gforce-20180512

Golden Gate Bridge G-Force DataG-Force Data collected by Android’s Physics Toolbox Suite on 12 May 2018 http://www.kaggle.com

Context

I got the chance to walk across the Golden Gate Bridge in San Francisco, CA for the first time in 22 years on May 12, 2018. There have been a great many technological advancements since then, as now we are all walking around with powerful computers and sensors in our pockets. I decided it would be fun to measure the bridge and provide others the opportunity to analyze data as to its motion for a brief snippet of time.

Content

This is one minute of data from the “g-force Meter” of the Physics Toolbox Suite v1.8.6 for Android. The data was collected from a Pixel 2 phone on the east side of the Golden Gate Bridge at the midpoint between the two towers of the bridge at approximately 3:15 PM local time on May 12, 2018.

Acknowledgments

This dataset is hereby owned by the community under the terms of a very lenient license in the condition that I have published it shortly after recording it.

Inspiration

Maybe this will inspire people to install sensors on the bridge, and other bridges, to monitor for things such as traffic (such as to find an optimum speed limit), dangerous fatigue, or dangerous wind conditions. At the very least, one could use this in comparison with a baseline stable motion to see how the bridge shakes. One could study effects of vehicles traversing the bridge (not that there’s any visual data for when that happened relative to this dataset, but I do believe at least one big bus drove by my device during this recording). One could study if there are periodic vibrations, and if so, at what frequencies. This would be even more interesting if correlated with wind data and run compared to several different wind speeds.

NY BRIDGES EAST RIVER BICYCLE CROSSINGS

https://www.kaggle.com/new-york-city/nyc-east-river-bicycle-crossings

New York City – East River Bicycle Crossings | Kaggle Context. The New York City Department of Transportation collects daily data about the number of bicycles going over bridges in New York City. This data is used to measure bike utilization as a part of transportation planning.www.kaggle.com

Context

The New York City Department of Transportation collects daily data about the number of bicycles going over bridges in New York City. This data is used to measure bike utilization as a part of transportation planning. This dataset is a daily record of the number of bicycles crossing into or out of Manhattan via one of the East River bridges (that is, excluding Bronx thruways and the non-bikeable Hudson River tunnels) for a stretch of 9 months.

Content

A count of the number of bicycles on each of the bridges in question is provided on a day-by-day basis, along with information on maximum and minimum temperature and precipitation.

Acknowledgements

This data is published in an Excel format by the City of New York (here). It has been processed into a CSV file for use on Kaggle.

Inspiration

  • In this dataset, how many bicycles cross into and out of Manhattan per day?
  • How strongly do weather conditions affect bike volumes?
  • What is the top bridge in terms of bike load?

https://data.cityofnewyork.us/Transportation/Bicycle-Counts-for-East-River-Bridges/gua4-p9wgCSV

OPEN DATA NEW YORK

OPEN DATASETS NEW YORK
https://data.ny.gov/

State of New York | Open Data | State of New York Browse, download, and analyze COVID-19-related data from the New York State Department of Health. The data will be updated on a daily basis.data.ny.gov

Brooklyn Bridge Datasets
https://data.ny.gov/browse?q=brooklyn%20bridge&sortBy=relevance
https://data.ny.gov/Transportation/Hourly-Traffic-on-Metropolitan-Transportation-Auth/qzve-kjga

Hourly Traffic on Metropolitan Transportation Authority (MTA) Bridges and Tunnels: Beginning 2010

Transportation View Data This dataset provides data showing the number of vehicles (including cars, buses, trucks and motorcycles) that pass through each of the bridges and tunnels operated by the MTA each hour of the day. The data is updated weekly.API https://data.ny.gov/resource/qzve-kjga.json

Hourly Traffic on Metropolitan Transportation Authority (MTA) Bridges and Tunnels: Beginning 2010 | State of New York – State of New York | Open Data | State of New York DATA.NY.GOV.

https://data.ny.gov/Transportation/Daily-Traffic-on-Metropolitan-Transportation-Autho/cwhc-n4ek

Daily Traffic on Metropolitan Transportation Authority (MTA) Bridges and Tunnels: Beginning 2010

View based on Hourly Traffic on Metropolitan Transportation Authority (MTA) Bridges and Tunnels: Beginning 2010Transportation View Data This dataset provides data showing the number of vehicles (including cars, buses, trucks and motorcycles) that pass through each of the bridges and tunnels operated by the MTA each hour of the day. The data is updated weekly.API https://data.ny.gov/resource/cwhc-n4ek.json

Brooklyn Bridge Automated Pedestrian Counts Demonstration Project

Transportation View Data DOT is testing automated technology to count pedestrians. The counter is located on the Manhattan approach of the Brooklyn Bridge. https://data.cityofnewyork.us/Transportation/Brooklyn-Bridge-Automated-Pedestrian-Counts-Demons/6fi9-q3ta
Columns in this Dataset

Column NameDescriptionType
hour_beginningDate and time of hourly countDate & Time
locationName of site where count was obtainedPlain Text
PedestriansTotal count (sum of directions)Number
Towards ManhattanPedestrians crossing towards ManhattanNumber
Towards BrooklynPedestrian crossing towards BrooklynNumber
weather_summaryOverall daily weather (cloudy, clear, rain, etc.)Plain Text
temperatureHourly temperature, in Fahrenheit degreesNumber

PITTSBURGH BRIDGE DATA

Pittsburgh Bridges on Kaggle

https://www.kaggle.com/davorbudimir/pittsburg-bridges

SOCRATA OPEN DATA

Socrata Open Data Transparency Information

https://www.tylertech.com/products/socrata
https://www.tylertech.com/resources/case-studies/pittsburghs-commitment-to-transparency

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info@tylertech.com https://www.tylertech.com/resources/case-studies/pittsburghs-commitment-to-transparency

Pittsburgh’s Commitment to Transparency – tylertech As with most cities, Pittsburgh is swimming in data. And while having this large quantity of details, databases, and stats can be advantageous, it can also overwhelm, especially if the data isn’t readily accessible. Data might be stashed in binders, files, or siloed on an employee’s desktop. However, by implementing a modern approach to its data management and analysis systems …www.tylertech.com

NY Dept of Transport Bridge Conditions

Explore Open Data from New York State

https://www.kaggle.com/new-york-state/nys-dept-of-transportation-bridge-conditions

Content

New York State inspectors assess all of the bridges every two years including a bridge’s individual parts. Bridges are analyzed for their capacity to carry vehicular loads. Inspectors are required to evaluate, assign a condition score, and document the condition of up to 47 structural elements, including rating 25 components of each span of a bridge, in addition to general components common to all bridges. The NYSDOT condition rating scale ranges from 1 to 7, with 7 being in new condition and a rating of 5 or greater considered as good condition. Bridges that cannot safely carry heavy vehicles, such as some tractor trailers, are posted with weight limits. Based upon inspection and load capacity analysis, any bridge deemed unsafe gets closed.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

  • Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata’s API and Kaggle’s API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Ben Dumond on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Seattle Spokane St Bridge Counter

From City of Seattle Open Data

https://www.kaggle.com/city-of-seattle/seattle-spokane-st-bridge-counter
https://data.seattle.gov/

City of Seattle Open Data portal© 2021 City of Seattle. Powered Bydata.seattle.gov

INFO ACTIVISM CAMP 2013

APPLY BY FRIDAY 19th APRIL

Tactical Technology Collective

Tactical Tech is an organisation dedicated to the use of information in activism. We aim to inspire innovative campaigns and provide practical support for collecting, investigating and curating evidence for advocacy. We do this through project partnerships, trainings and developing and distributing resources.

People around the world are using digital tools and visualisation techniques to expose injustice and abuse, creating subversive narratives to challenge the status quo and mobilising for action. We call the strategic, safe and creative use of digital tools and information in campaigning information activism.

We focus on the use of data, design and technology in campaigning through our Evidence & Action programme and on helping activists understand and manage their digital security and privacy risks through our Privacy & Expression programme.

 

 

Image from https://tacticaltech.org/evidence-influence-camp-2013
Image from https://tacticaltech.org/evidence-influence-camp-2013

Evidence + Influence

Bringing practitioners together to discuss and debate, share, critique and improve ideas, solve-problems, inspire each other and develop new practices for evidence-based advocacy.

You know about Wikileaks, but do you know how journalists from The Guardian turned thousands of leaked cables into information graphics? Or how Greenpeace used publicly available data to expose the funding connections between climate change skeptics and the oil industry in the US, through Exxon Secrets? And how Kazeboon, a group of young activists in Cairo used crowd-sourced video documentation of police brutality in street screenings to educate the public and contradict the state media monopoly?

There has been a surge of innovation in working with evidence across different sectors because of access to a diverse range of publishing platforms, availability of relatively easy-to-use information gathering, documentation and visualisation tools and the sheer amount of information publicly accessible online. The ability for activists to collect and use evidence in these ways presents an exciting threshold for political influence and campaigning beyond the scope of reports, petitions and demonstrations. How can we learn across disciplines and share some of these techniques and skills?

New techniques and approaches also throw up new challenges. How do you process and analyse thousands of documents in different formats and what are the ethical implications of releasing and using data whose contents you don’t actually know and can’t verify? How can you identify storylines in data and find creative ways of representing it that engages audiences? What about the double bind of working with evidence which can be used to expose abuse and misconduct, but can also be used to entrap you and others?

To explore the answers to questions like these Tactical Tech is hosting the ‘Info-Activism Camp 2013: Evidence & Influence’. The Camp is both a skillshare and peer-learning event and a space in which to foster collaboration. We will explore new ideas and solutions that will contribute to enhancing creative & technical practices and deepening critical debates in this inter-disciplinary field. Everyone who participates in the Camp will have something to teach and to share whether it be practical and thematic advocacy experience, data wrangling skills, information design, data security techniques, collecting and handling data or creative ideas for visualising evidence for advocacy.

Our tentative agenda will focus on three different themes:

  • Collecting, finding and investigating data
  • Curating, manipulating and working through data
  • Working creatively to find and present evidence

Important discussion about data ethics, legal considerations and digital privacy and security will also be weaved in. Read more on the Info-Activism Camp 2013 and more projects from TacticalTech.