New York Tech Journal
Tech news from the Big Apple

#CivicHacking

Posted on January 26th, 2016

#NYCSmartCities & #EnergyData

01/26/2016 @UrbanFuturesLab, 19th floor, 15 MetroTech, Jay Street, Brooklyn

20160126_185040[1]

David Moore @PPF spoke about his organization’s projects to make it easier to access/track legislation at the Federal, state and local levels. The NY City Council has a web site, but it’s hard to find information. #Councilmatic provides a user-friendly interface that can be searched by committee meetings, your council member, bills being heard, etc. The site makes it easier to find all proposed legislation on an issue, track the hearings on the bill, submit comments to council members, etc.

NYC.councilmatic.org covers the New York city council and was launched on Sep 30, 2015. They have similar sites for Chicago and Philadelphia. PPF is acquiring the resources to add other U.S. cities.

Earlier, David created the OpenCongress site which is a user friendly interface on federal bills including bill summary, annotate bill text, etc. The data were acquired by screen scraping public sites.

Next, OpenState created similar sites for each state.

The Sunlight foundation provided some funding for these ventures, but now concentrates only on the Federal and State level. PPF concentrates on cities.

To standardize their data presentation, PPF also created a data standard: Open civic data.

posted in:  data, NYC Energy Data, NYC smart city and energy data, Open source    / leave comments:   No comments yet

Actuating human behavior in small cities

Posted on October 21st, 2015

NYC Smart City and Energy Data

10/21/2015 @Urban Future Labs, 15 MetroTech, Brooklyn

20151021_190345[1] 20151021_190848[1]

Matt Caywood @TransitScreen spoke about using information dissemination as a method to increase use of #PublicTransit.

He first noted the high cost of #transportation and the added costs of inefficiencies (such as traffic congestion) in the transportation network. In addition, efforts to remove these inefficiencies by adding supply have a mixed, and often counterproductive, outcome. As example of the failure of this approach he cited a freeway in Houston who’s “improvement” in 2014 slowed traffic by 33%, encouraged city sprawl and induced additional demand.

By contrast, his firm emphasized Transportation Demand Management (TDM) in which riders are encourage to move from one form of transportation to another or commute at off-peak times. (One very effective example of #TDM is congestion pricing)

TransitScreen focuses on increasing the availability of real-time public transit information. They place real-time transit screens in public locations and on mobile devices. The goal is to move the information out of the train stations and into buildings so riders can better plan their trips.

He cited several studies showing the benefits of providing better transit information.

  1. The Wilson Boulevard corridor in Arlington, VA showed an increase to 47% use of public transit by publicizing the availability of public transit.
  2. Real-time information at stations for buses
    1. NYC indicate 1.7% increase in ridership on routes
    2. Chicago significant but modest increase
    3. Tampa no significant effect
    4. But all showed improved rider satisfaction as measured by perceived reliability, convenient, more control, shorten wait time

Placing of screens outside the train stations has shown benefits

  1. Parkmerced in San Francisco replaced ads with real-time transit screens in the building lobbies and has seen increased viewer engagement along with a 5% decrease in driving.
  2. Seattle Children’s hospital has a transit screen in the lobby. Interaction increased as the coffee shop in the lobby saw a 33% increase in transactions after the screen was put in place.
  3. Barclays Center, Brooklyn saw a 9% shift to public transit based on increased publicity of available transit options in a survey of likely Nets game attendees

TransitScreen also studied the Capital Bikeshare program in D.C. advising riders on the probability of a bike arriving in the next 5 minutes to add to an empty bikeshare station.  They also computed the probably of a bike being available 5 minutes from now when there is only a single bike in the station now. The probabilities were computed for different times, days of the week and weather conditions.

Matt talked about the difficult of switching one’s default mode of transportation and how better information can help. In honor of the anniversary of “Back to the Future”, the Guardian had an article elaborating on the challenges of changing transportation modes for society as a whole.

 

posted in:  NYC Energy Data, NYC smart city and energy data    / leave comments:   No comments yet

Challenges and Opportunities of #HVAC Data – Gabriel from Ecorithm  

Posted on June 24th, 2015

NYC smart city and energy data #smartcitydata

06/23/2015 @Urban Futures Lab, 15 MetroTech, 19th Floor, NY

20150623_190253[1] 20150623_190539[1] 20150623_192139[1] 20150623_192323[1] 20150623_195031[1]

Gabriel Peschiera @ Ecorithm (a software startup that turns building data into actionable insights) spoke about the challenges of collecting and analyzing sensor data within large buildings. These analyses have the potential to improve the comfort of the occupants and the functioning of the building systems.

Ecorithm focuses on buildings with more than 100,000 sq ft of floor space. These buildings have a building maintenance system to monitor the function of the chiller -> air handlers -> Variable Air Volume

In older systems, they will work with 3rd parties to place sensors for input into JACE, the Java Application Control Engine. For newer systems, they will collect data from the current BMS. In a typical building, their system receives information from 3000 locations, with sensor readings every 5 minutes. Data are available at the end of each day.

Gabriel touched on some of the data analysis tools and challenges

  1. Data cleaning problems often center around mislabeled locations and types of sensor inputs
  2. Spectral analysis in frequency space shows the temperature fluctuations driven by daily schedules, control loops and weather. From frequency plots they can see periodic patterns that might indicate persistent problems. They can also see if disparate locations have similar frequency patterns which may be driven by a common source.
  3. Support vector regressions can detect nonlinear deviations from expected patterns. This method can be used to detect faults and fill in missing data.
  4. Model-based optimization may eventually be a technology to better tune systems.

He also presented screen shots of their monitoring control panel and fault reports.

posted in:  NYC Energy Data, NYC smart city and energy data    / leave comments:   No comments yet

#Innovating for #Cities: #OpenData, #Sensors, #InternetOfThings

Posted on May 4th, 2015

NYC Smart City and Energy Data

05/04/2015 @UrbanFuturesLab, 15 Metro Tech, Brooklyn, NY

20150504_185644[1]

Peter Madden @thepmadden talked about the #Catapults which were set up by the British government to convert ideas into commercial products. His company within the program was started 18 months ago with four objectives:

  1. urban innovation centre in London
  2. cities lab – data, models, visualization
  3. cross disciplinary teams
  4. projects across UK & globally

They have been active in several initiatives include:

  1. Make city data open, accessible and simple to use. This is structured work – collecting data and making it available to entrepreneurs (not hackathons). They have a transportation API. They also just received a grant to release urban-model data in a similar fashion.
  1. Sensing London in which they deploy low cost sensors throughout the royal parks. The sensors include those for light, pollution, sound, etc. + usage mapping by demographics, income, etc. The data are collected to see how parks are used. One of the sensors is a low cost air pollution sensor. They include Boris bike rental data. They overlay air quality with cycling data and eventually would like to see real-time cycle-rerouting to avoid the worst quality air.
  1. City-wide IoT demonstration in the small city of #MiltonKeynes. Sensors and beacons in each parking space within the city transmit the location of vacant spaces using the old TV spectrum. A smart parking app would have the potential to reduce the auto-generated air pollution by eliminating up to 25% of city driving which is done in search of parking spots.

Peter emphasized that projects are collaborations and at a city scale with the goal of studying and mitigating a problem. He said that there are many other initiatives described on their web site. Some further examples are

  1. smart litterbins that report when they are filled
  2. smart lampposts – moving to LEDs, but also including connectivity
  3. Using EEG data to analyze how the blind view the city and to identify the environments which create the most stress.

posted in:  NYC Energy Data    / leave comments:   No comments yet

Promoting #solar technology diffusion through data-driven behavior modeling

Posted on March 19th, 2015

NYC EnergyData

03/19/2015 @Urban Future Lab, 15 MetroTech, Brooklyn, NY

20150319_185434[1] 20150319_190656[1]

Kiran Lakkaraju @Sandia Labs presented models to better understand when people install #SolarPanels on their homes. He emphasized the importance of understanding the economic, social, technical, and cognitive factors when people decide to purchase solar panels.

He first talked about experimental studies showing different arguments for solar power appeal differently depending on one’s political view.

Kiran then proceeded to the main study which modeled household adoption rates for 440,000 homes near San Diego between 2007 and 2011 based on energy usage, demographics, size of house, unemployment rate, neighbors with solar panels, etc. When information was only available for those who had adopted solar panels, a model of factors were created to fill in the missing data. To create a model he projected the characteristics backwards in time to create a monthly-by-month data set and fit a logistic regression (hazard model) to the factors.

Significant variables predicting higher adoption rates included: owner occupy, household size, peer at ¼ mile, have a pool. These findings are noteworthy since financial incentives such as energy rebates are not in the list. This indicates that a policy of increased rebates will have only a marginal effect on adoption rates.

Kiran concluded by talking about another policy that might in theory cause a greater increase in adoptions: seed neighborhoods by randomly installing free systems. This would jump start the peer effect and would assist in the dissemination of information about home solar power.

He encouraged the audience to contact him (klakkar@sandia.gov) about the studies he presented and contact Eugene Vorobeychik @ Vanderbilt Univ (eug.vorobey@gmail.com) for copies of the behavioral studies.

posted in:  NYC Energy Data    / leave comments:   No comments yet