Data Viz Camp
An Exploration of Open Source Data Viz Technologies
Sun. Nov. 19 from 09:00 am to 06:00 pm
Data Viz Camp is a community-run conference focused on open source data visualization technologies.
Data Viz Camp features a rich mix of presenters from across the open source community.
We will be adding schedule details in the next few days which specify the detailed order/timing of presentation on Sun. Nov. 19, between 9am - 6pm.
Data Viz Camp includes an interesting mix of presentation on best practices, emerging techniques, recent research and case studies regarding open source technologies.
Overview Attendees to the Building Location Aware Applications using Redis workshop will develop a location-aware client-server application that indexes the location of Bike sharing stations in the US and Europe. The primary focus of the workshop will be on using the geospatial indexing functions of Redis to build location aware functionality into mobile and web applications. As part of the workshop, attendees will learn to parse a web feed of bike share data, store and index that data in Redis, map the data using Google Maps, and build a simple API for answering geographic proximity queries. Agenda The workshop take a hands-on approach to learning the material by building a sample application in Python to parse and load a public feed of bike share information into Redis, then build an API to query that data to build a sharing and locating application. The main topics covered in the workshop are: * Introduction to Redis Geospatial Indexing * GBSF Feed Format * Parsing GBSF feed and storing in Redis * Mapping geographic data from Redis using KML file * Querying Redis geographic data * Building an API to access Redis geographic data Time will be allocated to understanding how to build a JSON feed parser as well as some the issues (technical, legal, and being a good citizen) of using a public data feed, as well as how to serve up the queries. Requirements: This workshop is intended primarily for beginner or intermediate programmers interested in learning how to build a location-aware application. Instruction and support materials will be provided in Python. Attendees are welcome to use any language/system of their choice but limited assistance will be available. Attendees should be comfortable with setting up a multiple file development project and familiar with the basics of client-server programming. Attendees should also be familiar with the basics of pulling and running Docker containers. Attendees should bring a laptop with the following software installed: * Text Editor or IDE of their choice * Python 3.6.0 (or later) interpreter * Docker Community Edition (support materials will be provided as a container)
This talk will cover translating complicated graphics from financial reports into stories that can be consumed by anyone, designing for different mediums so content can be delivered straight to where our readers already are and factors to consider when creating accessible graphics. The talk will draw from real examples and open source templates from the Financial Times.
When projections showed Hurricane Harey could bring a record setting amount of rain to Houston, the graphics desk at the New York Times started exploring ways of showing the water. What does record setting rainfall look like in different parts of the country? Where and when did it rain the hardest? And how can points on a map communicate people's experience of the rising water? This talk will describe the process of answering one of those questions, from parsing NASA's microwave data with R to creating a live-updating interactive map with d3 and canvas that showed both the accumulation and rate of rainfall.
Good graphs are extremely powerful tools for communicating quantitative information clearly and accurately. Unfortunately, many of the graphs we see today are poor graphs that confuse, mislead or deceive the reader. Since there are many more types of confusing and misleading graphs than can fit in a conference session, the different in the title refers to different from the graphical mistakes I talked about at the 2016 Data Viz Open. Although the title and format will be similar to the previous session, the content will be entirely different. After showing a number of graphical mistakes, we will end with a little-known graph form that is different from the ones shown last year.
On Friday, the ICIJ released the data behind the Paradise Papers investigation. In this session, I'll show you how graphs can be used to analyze and visualize this data of offshore corporations and their related officers and entities. In addition to running pattern-matching queries across the dataset to determine connections between companies and people, I'll also show how graph algorithms can be applied to find key people and addresses in the data. I'll show you some visualizations created within Neo4j's tools, plus some custom visualizations that ICIJ built to explore the Paradise Papers data.
The venue for Data Viz Camp 2017 is Convene's midtown NYC campus (237 Park Ave & 730 3rd Ave), where it will be hosted along with other Open Camps events. We'll be posting further venue details here as the event approaches, including access and check-in procedures.