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lofos

#makeopendata


~ PITCH ~

Electric mobility makes it easy and fun to get from point A to point B, but our range is limited by battery capacities. Personal mobility devices use lightweight batteries and need regular charging. Users of such devices have to rely on the availability of power outlets along the way to further destinations.

At the Swiss Open Energy Data Hackdays in April 2016 we sketched an app, prototyped a basic idea, and proposed some open/crowdsourced datasets that could allow users of personal mobility new levels of freedom of movement.

~ README ~

Lófos

Electric mobility makes it easy and fun to get from point A to point B, but our range is limited by battery capacities. Personal mobility devices use lightweight batteries and need regular charging. Users of such devices have to rely on the availability of power outlets along the way to further destinations.

At the Swiss Open Energy Data Hackdays in April 2016 we sketched an app, prototyped a basic idea, and proposed some open/crowdsourced datasets that could allow users of personal mobility new levels of freedom of movement.

The prototype is made using Ionic Framework, Raphaël and Google Maps Elevation API.

Demo: http://soda.camp/workshops/2016/lofos

Next data sources

Our next steps are plug in open data sources and/or create a crowdsourced databases to collect the following information:

Elevation data

Currently we use Google Maps API to obtain accurate elevation profiles of streets. We would welcome suggestions of other sources of such information. Users of apps like Ride With GPS help to create accurate GPS profiles, but this data is not shared with third parties.

Electric Vehicle battery data

While there are plenty of individual community projects (e.g. Segway Battery FAQ), there does not seem to be any wider effort underway to collect specifications about the power characteristics of personal mobility. Information such as the different ranges, top speeds, drive system (motor) power (wattage), maximum inclines, could be compiled and cross-referenced.

Public concerns about "electronic mobility aids" catching fire in transport that have led to bans on most flight carriers could also be potentially mitigated or better discussed publicly with access to comprehensive and accurate information about their engineering.

Public electric sockets

PlugShare ("the world's largest electric vehicle (EV) charging network with a database of 50,000+ charging stations" -FAQ) connects EV users with charging locations. According to data.gov, much of the data is sourced in the USA from the Energy Department's Application Programming Interface (API) for the Alternative Fuels Data Center.

In Europe, the non-profit association LEMnet.org collects and distributes this kind of data. We only discovered them after the hackathon and will look into using it next.

Traffic

Construction sites and other obstacles to personal mobility, status of traffic lights, special lanes for electric vehicles - there is a world of data possibilities in this area. The Opendata.ch Transport Working Group in Switzerland and similar efforts worldwide are opening data sources to enrich applications like this one. For example, see Geneve Velo, another hackathon project.

Next interfaces

We are fascinated by data visualisation in Virtual/Augmented/3D/Printed/QR/NFC and other "tangible" contexts, and are keen to explore interfaces for our application that allow viewing such information on the go. Here are some inspiring commercial and research projects which make this connection of personal mobility and new data sources and new data interfaces: Daqri, RideOnVision, Catapult (dailydot), In-place Augmented Reality (researchgate.net)

Team

Trivia

Sidenote: the name of the project comes from the Greek λόφος, meaning 'hills'. We are inspired by Odysseus and his hilly birthplace Ithaka.

Port Bathy and Capital of Ithaca by Edward Dodwell, Public Domain.


Swiss Energy Balance


~ PITCH ~

The goal of this project is to evaluate the cost of energy transactions between Cantons comparing their energy consumption and production. We want to simulate the economical impact related to the geographical distribution of production vs consumption.

We already found a database containing production/consumption values per Cantons and we are in the process of creating the visualisation

Unfortunately,we only found aggregated production data and we would like to differentiate the sources of production, so if anyone has any ideas/data please let us know!

We are also looking for ideas on how to model the price of infrastructure related to geographies of production and consumption. Our first idea would be to set a coefficient dependant on the geographical distance between the centres of each canton.



Challenges