{"contributors":[],"created":"2026-07-10T10:54","description":"Can you imagine ways to improve public transport in your city using Linked Open Data? ","homepage":"http://zurich-r-user-group.github.io/hackathon.html","keywords":[["dribdat","hackathon","co-creation"]],"licenses":[{"name":"ODC-PDDL-1.0","path":"http://opendatacommons.org/licenses/pddl/","title":"Open Data Commons Public Domain Dedication & License 1.0"}],"name":"event-7","resources":[{"data":[{"aftersubmit":"","boilerplate":"","certificate_path":"","community_embed":"

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\"CreativeThe contents of this website, unless otherwise stated, are licensed under a Creative Commons Attribution 4.0 International License.

","community_url":"http://opendataday.org/#map","custom_css":".category-info, .category-info .category-container, .nav-categories, .navbar .section-header-content h4 { display: none; }\r\n\r\n.event-resources { padding-top: 2em; }\r\n\r\n.list-data .list-group-item {\r\n border-left: none; border-right: none; border-top: none;\r\n border-bottom: 1px solid #333; border-radius: 0px;\r\n margin-right: 1em;\r\n}\r\n.list-data .list-group-item img {\r\n height: 1em;\r\n padding-right: 0.5em;\r\n}\r\n@media (min-width: 1000px) {\r\ndiv[role=main] { background: white; padding:2em; }\r\n.list-data .list-group-item {\r\n width: 48%;\r\n display: inline-block;\r\n overflow: hidden;\r\n height: 3em;\r\n line-height: 2em;\r\n padding-top: 0.4em;\r\n}\r\n.list-data a.list-group-item:nth-child(2) {\r\n border-top: 1px solid #333;\r\n}\r\n}\r\n.list-data a.list-group-item:nth-child(1) {\r\n border-top: 1px solid #333;\r\n}","description":"Can you imagine ways to improve public transport in your city? Do you know or wish to learn how to wrangle, analyse, visualize or communicate with data? Join our upcoming hackathon in Z\u00fcrich, meet people from the open data community on occasion of **[Open Data Day](http://opendataday.org/)** - and make an impact together on all the ways we get around!","ends_at":"2017-03-04T19:00","gallery_url":"","has_finished":true,"has_started":false,"hashtags":"","hostname":"Zurich R User Group","id":7,"instruction":"
\r\n\r\nThe following project ideas were [submitted via GitHub](https://github.com/OpenDataDayZurich2016/ideas):\r\n\r\n
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\r\n\r\n\r\n\r\nThe event will include time for hacking but we will also invite some speakers for talks. Food and drinks will be provided. All you have to bring is your laptop.\r\n\r\nYou do not need to be a data science superhero yet. It is completely fine if you have basic skills in one of the following:\r\n\r\n- Combining and cleaning data sets\r\n- Creating graphs and visualisations\r\n- Creating statistics or models\r\n- Creating slides, documents, websites or anything else to communicate the results\r\n\r\nFor analyses you can use any open data you like. VBZ provides transport delay and passenger count data via the [Zurich Open Data Portal](https://data.stadt-zuerich.ch/dataset?sort=score+desc%2C+metadata_modified+desc&tags=vbz). The goal is it to use open source software ([such as R](https://www.r-project.org/)) to get interesting new insights and share them with the world.\r\n\r\nHackathon teams may also find our [Translations of attributes](https://github.com/OpenDataDayZurich2016/translations) and [Download scripts](https://github.com/OpenDataDayZurich2016/download_vbz_data) to be useful.","location":"University of Zurich","location_lat":0.0,"location_lon":0.0,"logo_url":"","name":"Open Data Day Zurich","starts_at":"2017-03-04T10:00","summary":"Can you imagine ways to improve public transport in your city using Linked Open Data? ","webpage_url":"http://zurich-r-user-group.github.io/hackathon.html"}],"name":"events"},{"data":[{"autotext":null,"autotext_url":"https://github.com/OpenDataDayZurich2016/ODDPredictDelays","category_id":"","category_name":"","contact_url":"https://github.com/OpenDataDayZurich2016/ODDPredictDelays/issues","created_at":"2017-03-10T14:22","download_url":"","event_name":"Open Data Day Zurich","event_url":"https://hack.opendata.ch/event/7","excerpt":"## Synopsis\r\n\r\nIn this project, we fit a simple linear model to predict delays in arrival times of VBZ public transportation vessels using data of 4 weeks. The accompanying shiny app can be found [here](https://lorenzwalthert.shinyapps.io/odd_predict_delays/).\r\n\r\nTo fit the model, we use the predictors 'weekday', 'vehicle type', 'temperature' and 'precipitation'. 'weekday' and 'vehicle type' are categorical predictors. 'temperature' and 'precipitation' are continuous predictors. \r\n\r\nWe obtain da...","hashtag":"","id":81,"ident":null,"image_url":"https://avatars1.githubusercontent.com/u/24478198?v=3","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"## Synopsis\r\n\r\nIn this project, we fit a simple linear model to predict delays in arrival times of VBZ public transportation vessels using data of 4 weeks. The accompanying shiny app can be found [here](https://lorenzwalthert.shinyapps.io/odd_predict_delays/).\r\n\r\nTo fit the model, we use the predictors 'weekday', 'vehicle type', 'temperature' and 'precipitation'. 'weekday' and 'vehicle type' are categorical predictors. 'temperature' and 'precipitation' are continuous predictors. \r\n\r\nWe obtain data for the predictors 'weekday' and 'vehicle type' from Open Data Zurich (https://www.stadt-zuerich.ch/opendata) and data for the predictors 'temperature' and 'precipitation' from http://www.tecson-data.ch/zurich/mythenquai/.\r\n\r\nThe delay in arrival times, which is the quantity we want to predict, we obtain from Open Data Zurich as well.\r\n\r\nThe data set we use for fitting the model contains ca. 6 mio data points. \r\n\r\nTo run our model:\r\n\r\n - Clone the project\r\n - Make directory 'raw' in project root directory\r\n - Move data into dir 'raw'. If you have the data on a USB stick 'Stadt Zurich Open Data' move data from USB into directory 'raw'. \r\n - Open the RProject in RStudio. \r\n - Hit Ctrl+Shift+B to start the Makefile-based project build.\r\n - Enter remake::create_bindings() in R console to bind to the data object from within R.\r\n","maintainer":"oleg","name":"Predict Delays","phase":"Share","progress":50,"score":91,"source_url":"https://github.com/OpenDataDayZurich2016/ODDPredictDelays","stats":{"commits":0,"during":0,"people":0,"sizepitch":1340,"sizetotal":1367,"total":1,"updates":1},"summary":"OpenDataDay 2017 repository","team":"oleg","team_count":0,"updated_at":"2017-03-10T14:23","url":"https://hack.opendata.ch/project/81","webpage_url":"https://lorenzwalthert.shinyapps.io/odd_predict_delays/"},{"autotext":null,"autotext_url":"https://github.com/OpenDataDayZurich2016/one-day-in-vbz-world","category_id":"","category_name":"","contact_url":"https://github.com/OpenDataDayZurich2016/one-day-in-vbz-world/issues","created_at":"2017-03-10T14:21","download_url":"","event_name":"Open Data Day Zurich","event_url":"https://hack.opendata.ch/event/7","excerpt":"The idea of this project is to visualize on a map all VBZ vehicles as the move across the city during the day, with special focus on dates of special events that affect the public transportation network.\r\n\r\n[Play with it yourself here!](https://opendatadayzurich2016.github.io/one-day-in-vbz-world/)\r\n\r\n## Z\u00fcriF\u00e4scht 2016\r\n\r\nThe following is a fraction of the 1st July 2016 visualized, the first day of Z\u00fcriF\u00e4scht 2016, and it is clearly visible how at around 18:35 all vehicles leave the central are...","hashtag":"","id":80,"ident":null,"image_url":"https://avatars1.githubusercontent.com/u/24478198?v=3","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"The idea of this project is to visualize on a map all VBZ vehicles as the move across the city during the day, with special focus on dates of special events that affect the public transportation network.\r\n\r\n[Play with it yourself here!](https://opendatadayzurich2016.github.io/one-day-in-vbz-world/)\r\n\r\n## Z\u00fcriF\u00e4scht 2016\r\n\r\nThe following is a fraction of the 1st July 2016 visualized, the first day of Z\u00fcriF\u00e4scht 2016, and it is clearly visible how at around 18:35 all vehicles leave the central area of Z\u00fcrich because the event is starting.\r\n\r\n![VBZ in Motion](https://github.com/OpenDataDayZurich2016/one-day-in-vbz-world/raw/master/assets/images/vbz-in-motion.gif \"VBZ in Motion\")\r\n","maintainer":"oleg","name":"One day in VBZ world","phase":"Share","progress":50,"score":88,"source_url":"https://github.com/OpenDataDayZurich2016/one-day-in-vbz-world","stats":{"commits":0,"during":0,"people":0,"sizepitch":684,"sizetotal":684,"total":1,"updates":1},"summary":"","team":"oleg","team_count":0,"updated_at":"2017-03-10T14:21","url":"https://hack.opendata.ch/project/80","webpage_url":"https://opendatadayzurich2016.github.io/one-day-in-vbz-world/"},{"autotext":null,"autotext_url":"https://github.com/OpenDataDayZurich2016/no.8_passengers_visualization","category_id":"","category_name":"","contact_url":"https://github.com/OpenDataDayZurich2016/no.8_passengers_visualization/issues","created_at":"2017-03-10T14:27","download_url":"","event_name":"Open Data Day Zurich","event_url":"https://hack.opendata.ch/event/7","excerpt":"This repository collects the code produced at ODD Zurich 2017 for visualizing the passenger spatial-temporal data on the station level.\r\n\r\nIt is connected to Issue #8\uff1a number of passengers traveling visualization https://github.com/OpenDataDayZurich2016/ideas/issues/8.\r\n\r\nA brief prototype of our work is shown here: https://invis.io/ZDAPT1M82.\r\n\r\nThere are passengers data (in and out) of stations at every stop and we try to analyze the spatial temporal pattern of stations on certain days (Monday...","hashtag":"","id":84,"ident":null,"image_url":"https://avatars1.githubusercontent.com/u/24478198?v=3","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"This repository collects the code produced at ODD Zurich 2017 for visualizing the passenger spatial-temporal data on the station level.\r\n\r\nIt is connected to Issue #8\uff1a number of passengers traveling visualization https://github.com/OpenDataDayZurich2016/ideas/issues/8.\r\n\r\nA brief prototype of our work is shown here: https://invis.io/ZDAPT1M82.\r\n\r\nThere are passengers data (in and out) of stations at every stop and we try to analyze the spatial temporal pattern of stations on certain days (Monday-Thursday, Friday, weekend). We try to cluster stations based on their passenger volume change pattern through the day. Using Dynamic Time Warping (DTW) Distances as features allow the analysis of time series data, (Kate, 2016). The hierarchical cluster analysis of data on day type 6 is implemented in R programming. The clusters of example stations are visualization on the map in QGIS.\r\n","maintainer":"oleg","name":"No.8 passengers visualization","phase":"Publish","progress":40,"score":68,"source_url":"https://github.com/OpenDataDayZurich2016/no.8_passengers_visualization","stats":{"commits":0,"during":0,"people":0,"sizepitch":888,"sizetotal":888,"total":0,"updates":0},"summary":"","team":"oleg","team_count":0,"updated_at":"2017-03-10T14:27","url":"https://hack.opendata.ch/project/84","webpage_url":""},{"autotext":null,"autotext_url":"https://github.com/OpenDataDayZurich2016/odd-zurich-stops-classification","category_id":"","category_name":"","contact_url":"https://github.com/OpenDataDayZurich2016/odd-zurich-stops-classification/issues","created_at":"2017-03-10T14:26","download_url":"","event_name":"Open Data Day Zurich","event_url":"https://hack.opendata.ch/event/7","excerpt":"The goal was to classify public transport stops by their surrounding landmarks, like universities or coworking_spaces, extracted from [OpenStreetMap](http://openstreetmap.org).\r\n\r\n# usage\r\n- The tool chain uses make and python 3.6.\r\n- create a ``data`` folder or change the Makefile variable.\r\n- add the ``data-examples/haltepunkt.csv`` file to ``data``.\r\n- run ``make``.\r\n\r\nThe Make script will\r\n- extract all active stop point coordinates\r\n- retrieve OpenStreetMaps landmarks with around these coor...","hashtag":"","id":83,"ident":null,"image_url":"https://avatars1.githubusercontent.com/u/24478198?v=3","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"The goal was to classify public transport stops by their surrounding landmarks, like universities or coworking_spaces, extracted from [OpenStreetMap](http://openstreetmap.org).\r\n\r\n# usage\r\n- The tool chain uses make and python 3.6.\r\n- create a ``data`` folder or change the Makefile variable.\r\n- add the ``data-examples/haltepunkt.csv`` file to ``data``.\r\n- run ``make``.\r\n\r\nThe Make script will\r\n- extract all active stop point coordinates\r\n- retrieve OpenStreetMaps landmarks with around these coordinates.\r\n- eliminates duplicates.\r\n- classifies the landmarks with the given classifiers.\r\n","maintainer":"oleg","name":"Stops classification","phase":"Prototype","progress":30,"score":58,"source_url":"https://github.com/OpenDataDayZurich2016/odd-zurich-stops-classification","stats":{"commits":0,"during":0,"people":0,"sizepitch":590,"sizetotal":590,"total":0,"updates":0},"summary":"","team":"oleg","team_count":0,"updated_at":"2017-03-10T14:27","url":"https://hack.opendata.ch/project/83","webpage_url":""},{"autotext":null,"autotext_url":"https://github.com/OpenDataDayZurich2016/visualization_delays","category_id":"","category_name":"","contact_url":"https://github.com/OpenDataDayZurich2016/visualization_delays/issues","created_at":"2017-03-10T14:26","download_url":"","event_name":"Open Data Day Zurich","event_url":"https://hack.opendata.ch/event/7","excerpt":"This repository collects the code produced at ODD 2017 for visualizing the delays.\r\n\r\nIt is connected to\r\n- [Issue #11 Visualisation of vehicle delays](https://github.com/OpenDataDayZurich2016/ideas/issues/11) and\r\n- [Issue #2 Visualization (map) of delay-causing streets](https://github.com/OpenDataDayZurich2016/ideas/issues/2)\r\n","hashtag":"","id":82,"ident":null,"image_url":"https://avatars1.githubusercontent.com/u/24478198?v=3","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"This repository collects the code produced at ODD 2017 for visualizing the delays.\r\n\r\nIt is connected to\r\n- [Issue #11 Visualisation of vehicle delays](https://github.com/OpenDataDayZurich2016/ideas/issues/11) and\r\n- [Issue #2 Visualization (map) of delay-causing streets](https://github.com/OpenDataDayZurich2016/ideas/issues/2)\r\n","maintainer":"oleg","name":"Visualization of delays","phase":"Training","progress":20,"score":41,"source_url":"https://github.com/OpenDataDayZurich2016/visualization_delays","stats":{"commits":0,"during":0,"people":0,"sizepitch":329,"sizetotal":370,"total":0,"updates":0},"summary":"code of working group to visualize delays","team":"oleg","team_count":0,"updated_at":"2017-03-10T14:27","url":"https://hack.opendata.ch/project/82","webpage_url":""},{"autotext":null,"autotext_url":"https://github.com/OpenDataDayZurich2016/bus-bunching","category_id":"","category_name":"","contact_url":"https://github.com/OpenDataDayZurich2016/bus-bunching/issues","created_at":"2017-03-10T14:29","download_url":"","event_name":"Open Data Day Zurich","event_url":"https://hack.opendata.ch/event/7","excerpt":"See Source link for R code.","hashtag":"","id":85,"ident":null,"image_url":"https://avatars1.githubusercontent.com/u/24478198?v=3","is_challenge":false,"is_webembed":null,"logo_color":"","logo_icon":"","longtext":"See Source link for R code.","maintainer":"oleg","name":"Bus bunching","phase":"Research","progress":10,"score":27,"source_url":"https://github.com/OpenDataDayZurich2016/bus-bunching","stats":{"commits":0,"during":0,"people":0,"sizepitch":27,"sizetotal":101,"total":0,"updates":0},"summary":"A project to analyse the effect of \"bus bunching\" due to delays in traffic","team":"oleg","team_count":0,"updated_at":"2017-03-10T14:29","url":"https://hack.opendata.ch/project/85","webpage_url":""}],"name":"projects"}],"sources":[{"path":"https://hack.opendata.ch/","title":"dribdat"}],"title":"Open Data Day Zurich","version":"0.9.4"}