Transforming Data into Visualization Data visualization explains a story to the user; a story that if told well should help the viewer discern information and relationships between the data. Data visualization, for a designer, is the process of taking a complex structure and breaking it down in a way that the reader can easily comprehend. It is a powerful tool used to translate complex data into accessible insights. In this article, I’ll explain the four critical steps we took to create a visualization graph for the World Economic Forum (WEF). Step 1: Decipher the Data The first step in any data visualization process begins with unpacking all of the elements and establishing the goal of the visualization or infographic. The Networked Readiness Index (NRI) is an index comprised of four sub indexes and ten pillars. Step 2: Iterate…Iterate…Iterate We embarked on an iterative process exploring a variety of graphic structures before we reached a solution where the viewer could unquestionably discern the data.
Data visualization: Science on the map Illustration by the Project Twins When linguist Lauren Gawne roams the valleys of Nepal documenting endangered Tibetan languages, she takes pains to distinguish each dialect's geographical origin. But when it came to producing maps of her results, for many years her cartographic methods were somewhat crude. “My old maps were [made] using MS Paint on top of some copyrighted map that I really shouldn't have been using,” she says. Lauren Gawne Lauren Gawne's maps: with mother's help (A) and in TileMill (B). So in 2013, she jumped at the chance to join a workshop on mapping and visualization at the University of Melbourne in Australia, where she was working on her PhD. TileMill is just one tool in the emerging field of customized mapping, where a bevy of open-source technologies and start-ups have given rise to an abundance of offerings for researchers and enthusiasts (see ‘Get on the map’). Get on the map The following tools may also be useful for specific mapping purposes: Storage hubs
8 tools for visualizing data with open source Data visualization is the mechanism of taking tabular or spatial data and conveying it in a human-friendly and visual way. There are several open source tools that can help you create useful, informative graphs. In this post we will take a look at eight open source, data visualization tools. Datawrapper Datawrapper was created by journalism organizations from Europe, designed to make data visualization easy for news institutes. To create a graph, click on the "New Chart" link on the top menu bar. Image provided by Nitish Tiwari. Chart JS Chart JS is a clean charting library. Charted Created by the product science team at Medium, this is one of most minimal charting tools available online. Image provided by Nitish Tiwari. D3 stands for data driven documents. Image provided by Nitish Tiwari. Dygraphs Dygraphs is a flexible, JavaScript-based charting library. Image provided by Nitish Tiwari. Raw Timeline Leaflet Mobile readiness is the key to high traffic and good conversion rates.
Wo Europas Bevölkerung wächst – und wo sie schrumpft Wo wächst und schrumpft Europa wirklich? Die Karte zeigt erstmals ein detailliertes Bild über die Bevölkerungsentwicklung in 119.406 Gemeinden aus 43 europäischen Staaten (mit der Türkei). Die Anwendung basiert auf den Berechnungen des Bundesinstituts für Bau-, Stadt- und Raumforschung (BBSR), das seine Analyse im Juni zusammen mit einer Karte veröffentlicht hatte. Die Berliner Morgenpost macht diese Erhebungen nun interaktiv sichtbar – mit Daten für jede einzelne Gemeinde und Schnellanalysen. Auffallend sind die besonders hohen Zuwächse im Westen Europas, während in weiten Teilen der östlichen und südlichen Länder die Bevölkerung schrumpft. Auch für Deutschland zeichnet sich ein deutliches Bild: Im 25. Ein solches Ausdünnen großer zusammenhängender Regionen ist sonst nur noch in Osteuropa (Rumänien, Bulgarien, Baltikum), im Nordosten Spaniens, in Portugal und in Teilen Nordeuropas zu sehen. Weitaus schwächer ausgeprägt ist dieser Trend ist Frankreich.
Population Lines Print I recently produced a map entitled “Population Lines”, which shows population density by latitude. The aim was to achieve a simple and fresh perspective on these well-known data. I have labelled a few key cities for orientation purposes but I’ve left off most of the conventional cartographical adornments. The data, from NASA SEDAC, have been mapped many times before and in many beautiful ways but none seem to me quite as compelling as the simple approach here of using only black and grey lines across the page. Following quite a lot of interest in the map, I’ve had some A2 prints produced for those who’d like to own a copy. Frame not included Small print: Print is unframed. For those interested Ryan Brideau has produced a version of the code for how to do this here.
Open Data Zürich - Stadt Zürich Open Data Zürich kümmert sich innerhalb der Stadt Zürich um die Umsetzung von Open Government Data (OGD). Bei Open Government Data handelt es sich um bereitgestellte Datensätze aus öffentlichen Verwaltungen für eine breite Öffentlichkeit in digitaler Form. Die veröffentlichten Datensätze sind maschinell lesbar, kostenlos und zur freien Weiterverwendung gedacht. Die Stadt Zürich hat im Juni 2012 das erste OGD-Portal der Schweiz lanciert. Mit dem von Open Data Zürich angebotene Portal bietet die Stadtverwaltung Zürich einen zentralen Einstiegspunkt für die Suche und Nutzung von offenen Daten der Stadt Zürich. OGD-Stammtische 2015 Sie finden wieder statt: Unsere Stammtische zum Gedankenaustausch in der entspannten Atmosphäre des Restaurants Karl der Grosse. Folgende Daten gilt es zu reservieren: Mittwoch 09.09.2015, 18:00Mittwoch 09.12.2015, 18:00 Anmelden könnt Ihr Euch per email an opendata@zuerich.ch Wir freuen uns auf Eure Gesellschaft! Anwendungen zu den Anwendungen Datenkatalog
Map of scientific collaboration (Redux!) | Olivier H. Beauchesne Several years ago, I created a map of scientific collaborations. The attention this map obtained surpassed my wildest expectations; it got published in the scientific and popular press all around the world! I had mainly forgotten about it until I received an email that rekindled my interest in this visualization and I thought it was high time to revisit this visualization. Unfortunately, scientific papers (and associated data) are closely guarded and only a handful of firms have full access to them. I now work in a very different field, so I lost access to this dataset. But while perusing my Twitter feed, I came across the very active feed of Scimago Lab. Read on for more maps and an overview of the methodology >> After a bit of back and forth, I spent a weekend programming a tool to draw large geographical graphs. Click here to open this map in a new window These following maps were rendered with different color schemes.
Reviving the Statistical Atlas of the United States with New Data Ever since I found out about the Statistical Atlas of the United States, historically produced by the Census Bureau, it annoyed me that there wasn't one in the works for the 2010 Census due to cuts in funding. The last one was for 2000. Actually, the 2000 edition was called the Census Atlas, but whatever. With more data than ever, it seems like there should be one. Maybe that's why there's isn't one. Too much data, too much of an undertaking, and too many bureaucratic decisions to make. The first Atlas, by Francis A. I got to thinking, hey, I could do that. Here are my results. GEOLOGY. Source: Geological Survey and the Department of Agriculture. WEATHER. Source: National Weather Service and National Oceanic and Atmospheric Administration. LAND COVER. Source: Department of Agriculture. POPULATION. Source: American Community Survey and the Department of Agriculture National Agricultural Statistics Service Cropland Data Layer FOREIGN POPULATION. Source: American Community Survey. AGE. RACE. P.S.
Free mapping data will elevate flood risk knowledge | Creating a better place For the past 17 years we have been capturing LIDAR (Light Detection & Ranging) data in England. LIDAR uses a laser to scan and map the landscape from above and is widely considered to be the best method for collecting very dense and accurate elevation data across the landscape. We use LIDAR to help the work of the Environment Agency in many ways, including creating flood models, assessing coastal change and analysing how land is used. We now have an extensive archive of aerial LIDAR data covering nearly three quarters (72%) of England – the data mainly covers flood plains, coastal zones and urban areas. As technology has improved and costs have fallen, LIDAR data is now being used by just about everybody who works with maps. In 2013, we made the data available for free for the first time for non-commercial use to anybody who wanted it. We are releasing two LIDAR products under the Open Government Licence and you will be able to access these through Datashare.
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