A very rich site with examples of cartography that I find hard to describe.
Rather than an inadequate description, here is an example of a custom map that the site generated at my request:
For best viewing, save the image to your computer and view in a browser.
See Your Calendar if you want to generate a custom calendar for yourself.
Don’t skip exploring the other projects at this site.
Are crowdsourced maps the future of navigation? by Kevin Fitchard.
From the post:
Given the craziness of the first two weeks in September in the tech world an interesting hire that should have gotten more attention slipped largely through the cracks. Steve Coast, founder of the OpenStreetMap project, has joined Telenav, signaling a big move by the navigation outfit toward crowdsourced mapping.
OpenStreetMap is the Wikipedia of mapping. OSM’s dedicated community of 1.3 million editors have gathered GPS data while driving, biking and walking the streets of the world to build a map from the ground up. They’ve even gone so far as to mark objects that exist on few other digital maps, from trees to park benches. That map was then offered up free to all comers.
Great story about mapping, crowd sourcing, etc., but it also has this gem:
For all of its strengths, OSM primarily has been a display map filled with an enormous amount of detail — Coast said editors will spend hours placing individual trees on boulevards. Many editors often don’t want to do the grunt work that makes maps truly useful for navigation, like filling in address data or labeling which turns are allowed at an intersection. (emphasis added)
Sam Hunting has argued for years that hobbyists, sports fans, etc., are naturals for entering data into topic maps.
Well, assuming an authoring interface with a low enough learning curve.
I went to the OpenStreetMap project, discovered an error in Covington, GA (where I live), created an account, watched a short editing tutorial and completed my first edit in about ten (10) minutes. I refreshed my browser and the correction is in place.
Future edits/corrections should be on the order of less than two minutes.
Care to name a topic map authoring interface that easy to use?
Not an entirely fair question because the geographic map provided me with a lot of unspoken context.
For example, I did not create associations between my correction and the City of Covington, Newton County, Georgia, United States, Western Hemisphere, Northern Hemisphere, Earth, or fill in types or roles for all those associations. Or remove any of the associations, types or roles that were linked to the incorrect information.
Baseball fans are reported to be fairly fanatical. But can you imagine any fan starting a topic map of baseball from scratch? I didn’t think so either. But on the other hand, what if there was an interface styled in a traditional play by play format, that allowed fans to capture games in progress? And as the game progresses, the associations and calculations on those associations (stats) are updated.
All the fan is doing is entering familiar information, allowing the topic map engine to worry about types, associations, etc.
Is that the difficulty with semantic technology interfaces?
That we require users to do more than enter the last semantic mile?
Cartographies of Time: A Visual History of the Timeline by Maria Popova.
Maria reviews Cartographies of Time: A History of the Timeline by Daniel Rosenberg and Anthony Grafton.
More examples drawn from the text than analysis of the same.
The examples represent events but attempt to make the viewer aware of their embedding in time and place. A location that is only partially represented by a map.
I mention that because maps shown on news casts, particularly about military action, seem to operate the other way.
News maps appear to subtract time and its close cousin, distance, out of their maps.
Events happen in the artificial area created by the map, where the rules of normal physics don’t apply.
More troubling, the maps become the “reality” for the viewing audience rather than a representative of a much bloodier and more ambiguous reality on the ground.
Just curious if you have noticed that difference.
40 Maps That Will Help You Make Sense of the World
From the post:
If you’re a visual learner like myself, then you know maps, charts and infographics can really help bring data and information to life. Maps can make a point resonate with readers and this collection aims to do just that.
Hopefully some of these maps will surprise you and you’ll learn something new. A few are important to know, some interpret and display data in a beautiful or creative way, and a few may even make you chuckle or shake your head.
If you enjoy this collection of maps, the Sifter highly recommends the r/MapPorn sub reddit. You should also check out ChartsBin.com. There were also fantastic posts on Business Insider and Bored Panda earlier this year that are worth checking out. Enjoy!
A must see collection of maps!
I’m not vouching for the accuracy of any of the maps.
After all, 20. Map of Countries with the Most Violations of Bribery shows none for the United States. Must have an odd definition of bribery.
United States Senators are paid $174,000 per year.
It cost $10.5 million on average to win a United States Senate seat.
Let’s see, six year term at $174,000 per year = $1,044,000. And you are going to spend more than 10X that amount to get the job? Plus that amount to retain it for another six years?
Or some public spirited person is going to give you > $10.5 million with no strings attached.
If you believe that last statement, please log off the Internet and never return. You are unsafe. (full stop)
Exploring LinkedIn in Neo4j by Rik Van Bruggen.
From the post:
Ever since I have been working for Neo, we have been trying to give our audience as many powerful examples of places where graph databases could really shine. And one of the obvious places has always been: social networks. That’s why I’ve written a post about facebook, and why many other graphistas have been looking at facebook and others to explain what could be done.
But while Facebook is probably the best-known social network, the one I use professionally the most is: LinkedIn. Some call it the creepiest network, but the fact of the matter is that professional network is, and has always been, a very useful way to get and stay in contact with other people from other organisations. And guess what: they do some fantastic stuff with their own, custom-developed graphs. One of these things is InMaps – a fantastic visualisation and colour coded analysis of your professional network. That’s where this blogpost got its inspiration from.
As Rik points out, you can view InMaps but you can do much else.
To fix that, Rik guides you through extracting data from InMaps and loading it into Neo4j.
For extra credit, try merging your data with data on the same people from other sources.
Could give you some insight into the problems faced by the NSA.
Interactive crime map of London (Map by James Cheshire.)
An interactive map of crime in London, May 2012 – April 2013.
Which should be titillating for tourists, etc.
A better interactive map would be of the London surveillance cameras and their fields of view.
In case you didn’t know, the number of surveillance cameras is increasing.
Ginny Sloan writes in Will More Video Surveillance Cameras Make Us Any Safer?:
In the wake of the Boston marathon bombing, Boston Police Commissioner Davis has called for more surveillance cameras, and press accounts report new calls for cameras from Richmond, Virginia to San Francisco. Mayor Emmanuel has said Chicago will keep adding cameras, and Mayor Bloomberg is warning New York City residents that more cameras are coming, scoffing at complaints that this will be “Big Brother,” and telling New Yorkers to “Get used to it!” But does the Boston investigation really teach us that what our major cities need is more cameras?
True, it was video surveillance footage from a department store camera that provided the first important clues leading to the suspects in the marathon bombing. Additional video footage from members of the public also helped police identify and apprehend the suspects. The law enforcement officials who sought and examined the video footage, and the businesses and individuals who provided their videos in response, all deserve our praise and gratitude.
But we must be careful in identifying lessons from this use of video evidence. Most importantly, we should recognize that video cameras did not, and cannot, prevent an attack like the Boston marathon bombing. Nor did the ubiquitous cameras in London, the most-surveilled city on the planet, prevent the devastating bombing attacks in that city in 2005. This is not to discredit the important role that surveillance footage has played in identifying suspects after the fact in these cases and others. Yet increasing the number of cameras in cities like Boston, or Chicago — which already has over ten-thousand cameras — would not convert the cameras into a terrorism-prevention tool. Nor is there any indication that Boston investigators were hampered by having too little video footage to examine.
I think Ginny is missing the point. Cameras are cheaper than police officers, don’t get sick, have insurance, paid vacation or retirement. No, cameras are not ever going to prevent any crimes, but then that isn’t the point.
The point is that cameras are an easy way to appear to be doing something, even if the something is ineffectual and an invasion of your privacy.
If you want to protect your privacy and the privacy of others, take pictures of surveillance cameras with a GPS enabled cellphone.
That won’t give you field of view but just having all of them located will be a major step forward.
Chicago has approximately 2.7 million residents. With 10,000 cameras, one out of every 270 people could take an image of one camera and all of their locations would be captured.
Hardly a secret, the cameras are in public view.
The freedom you regain may be your own.
GraphHopper Maps – High Performance and Customizable Routing in Java by Peter Karich.
From the post:
Today we’re proud to announce the first stable release of GraphHopper! After over a year of busy development we finally reached version 0.1!
GraphHopper is a fast and Open Source road routing engine written in Java based on OpenStreetMap data. It handles the full planet on a 15GB server but is also scales down and can be embedded into your application! This means you’re able to run Germany-wide queries on Android with only 32MB in a few seconds. You can download the Android offline routing demo or have a look at our web instance which has world wide coverage for car, bike and pedestrian:
The trip to the current state of GraphHopper was rather stony as we had to start from scratch as there is currently no fast Java-based routing engine. What we’ve built is quite interesting as it shows that a Java application can be as fast as Bing or Google Maps (in 2011) and beats YOURS, MapQuest and Cloudmade according to the results outlined in a Blog post from Pascal and with tests against GraphHopper – although OSRM is still ahead. But how can a Java application be so fast? One important side is the used algorithm: Contraction Hierarchies – a ‘simple’ shortcutting technique to speed up especially lengthy queries. But even without this algorithm GraphHopper is fast which is a result of weeks of tuning for less memory consumption (yes, memory has something to do with speed), profiling and tweaking. But not only the routing is fast and memory efficient also the import process. And it should be easy to get started and modify GraphHopper to your needs.
Contraction hierarchies are a very active area of graph research.
Contraction Hierarchies at Wikipedia has a nice coverage with a pointer to Robert Geisberger’s thesis, Contraction Hierarchies: Faster and Simpler
Hierarchical Routing in Road Networks.
You may also be interested in:
Efficient Route Planning by Prof. Dr. Hannah Bast. A wiki for a 2012 summer course on route planning. Includes videos, slides, exercises, etc.
Sketching D.C. Crime Data With R by Matt Stiles.
From the post:
A car burglar last week nabbed a radio from our car, prompting me to think (once again) about crime in Washington, D.C., where I live.
I wanted to know if certain crimes were more common in particular neighborhoods, so I downloaded a list of every serious crime in 2012 from the city’s data portal. The data contained about 35,000 reported incidents of homicides, thefts, assaults, etc., with fields listing the date, time and neighborhood associated with each case.
I used the statistical programming language R, which is great for quickly creating small multiples to examine data, to make some rough visual sketches.
First, since we’re talking about cars, the first grid shows thefts from vehicles, by hour and “advisory neighborhood commission“. These commissions are the small groups of officials who represent their respective D.C. neighborhoods on issues like real estate development and alcohol sales, among other things. (I live in Brookland, which is governed by ANC 5B). You can find your ANC here.
(…)
Matt charts a variety of crimes in DC and is sure to get your attention.
Occurs to me that a map of DC, color coded by crime and time of day, would be great conference swag for conference tote bags.
With the subway system marked with “Do Not Exit” here signs.
Resources for Mapping Census Data by Katy Rossiter.
From the post:
Mapping data makes statistics come to life. Viewing statistics spatially can give you a better understanding, help identify patterns, and answer tough questions about our nation. Therefore, the Census Bureau provides maps, including digital files for use in a Geographic Information System (GIS), and interactive mapping capabilities in order to visualize our statistics.
Here are some of the mapping resources available from the Census Bureau:
- TIGERweb allows data users to view our TIGER database and even offers a Web Map Service (WMS) for app developers and more advanced GIS users.
- The Census Data Mapper is a web mapping application that provides customers with a simple way to view and print county-based demographic maps for the United States.
- The newly released Census Flows Mapper shows county-to-county migration from the American Community Survey 5-year data.
- The Metropolitan and Micropolitan Area Population viewer shows 34 characteristics of the population for all metropolitan and micropolitan statistical areas by census tract and how they compare to the nation.
- In addition, the Small Area Income and Poverty Estimates (SAIPE) Interactive Tool combines data tables and mapping capabilities to display statistics at the state, county, and school district level.
- The county business pattern and demographic interactive maps combine economic and demographic data to provide a full profile of a particular area.
(…)
That listing is just some of the resources that Katy covers in her post.
Combining your data or public data along with census data could result in a commercially successful map.
Building A Visual Planetary Time Machine by by Randy Sargent, Google/Carnegie Mellon University; Matt Hancher and Eric Nguyen, Google; and Illah Nourbakhsh, Carnegie Mellon University.
From the post:
When a societal or scientific issue is highly contested, visual evidence can cut to the core of the debate in a way that words alone cannot — communicating complicated ideas that can be understood by experts and non-experts alike. After all, it took the invention of the optical telescope to overturn the idea that the heavens revolved around the earth.
Last month, Google announced a zoomable and explorable time-lapse view of our planet. This time-lapse Earth enables you explore the last 29 years of our planet’s history — from the global scale to the local scale, all across the planet. We hope this new visual dataset will ground debates, encourage discovery, and shift perspectives about some of today’s pressing global issues.
This project is a collaboration between Google’s Earth Engine team, Carnegie Mellon University’s CREATE Lab, and TIME Magazine — using nearly a petabyte of historical record from USGS’s and NASA’s Landsat satellites. And in this post, we’d like to give a little insight into the process required to build this time-lapse view of our planet.
Great imaging and a benchmark to compare future progress in this area.
Within three to five (3-5) years, this should be doable as senior CS project. Graduate students and advanced hackers will be using higher resolution “spy” satellite images.
From five to eight (5-8) years, software packages appear for the average consumer, processing on the local “grid.”
From eight to ten (8-10) years, mostly due to the long product cycle, appears in MS Office XXI. 😉
If not sooner!
Easy mapping with Map Stack by Nathan Yau.
Nathan writes:
It seems like the technical side of map-making, the part that requires code or complicated software installations, fades a little more every day. People get to focus more on actual map-making than on server setup. Map Stack by Stamen is the most recent tool to help you do this.
(…)
It’s completely web-based, and you edit your maps via a click interface. Pick what you want (or use Stamen’s own stylish themes) and save an image. For the time being, the service is open only from 11am to 5pm PST, so just come back later if it happens to be closed.
Over 3,000 maps have been made over the last four days! Examples.
Now to see semantic mapping interfaces improve.
From the webpage:
CLAVIN (*Cartographic Location And Vicinity INdexer*) is an open source software package for document geotagging and geoparsing that employs context-based geographic entity resolution. It combines a variety of open source tools with natural language processing techniques to extract location names from unstructured text documents and resolve them against gazetteer records. Importantly, CLAVIN does not simply “look up” location names; rather, it uses intelligent heuristics in an attempt to identify precisely which “Springfield” (for example) was intended by the author, based on the context of the document. CLAVIN also employs fuzzy search to handle incorrectly-spelled location names, and it recognizes alternative names (e.g., “Ivory Coast” and “Côte d’Ivoire”) as referring to the same geographic entity. By enriching text documents with structured geo data, CLAVIN enables hierarchical geospatial search and advanced geospatial analytics on unstructured data.
See http://clavin.bericotechnologies.com/ for an online demo, videos and other materials.
Your mileage may vary.
I used a quote from today’s New York Times (Rockets Hit Hezbollah Stronghold in Lebanon):
An ongoing battle in the Syrian town of Qusair on the Lebanese border has laid bare Hezbollah’s growing role in the Syrian conflict. The Iranian-backed militia and Syrian troops launched an offensive against the town last weekend. After dozens of Hezbollah fighters were killed in Qusair over the past week and buried in large funerals in Lebanon, Hezbollah could no longer play down its involvement.
Col. Abdul-Jabbar al-Aqidi, commander of the Syrian rebels’ Military Council in Aleppo, appeared in a video this week while apparently en route to Qusair, in which he threatened to strike in Beirut’s southern suburbs in retaliation for Hezbollah’s involvement in Syria.
“We used to say before, ‘We are coming Bashar.’ Now we say, ‘We are coming Bashar and we are coming Hassan Nasrallah,'” he said, in reference to Hezbollah’s leader.
“We will strike at your strongholds in Dahiyeh, God willing,” he said, using the Lebanese name for Hezbollah’s power center in southern Beirut. The video was still online on Youtube on Sunday.
Hezbollah lawmaker Ali Ammar said the incident targeted coexistence between the Lebanese and claimed the U.S. and Israel want to return Lebanon to the years of civil war. “They want to throw Lebanon backward into the traps of civil wars that we left behind,” he told reporters. “We will not go backward.”
The results from CLAVIN:
Locations Extracted and Resolved From Text
ID Name Lat, Lon Country Code # 272103 Lebanon 33.83333, 35.83333 LB 3 6951366 Lebanese 44.49123, 26.0877 RO 3 276781 Beirut 33.88894, 35.49442 LB 2 162037 Dahiyeh 38.19023, 57.00984 TM 1 6252001 U.S. 39.76, -98.5 US 1 103089 Qusair 25.91667, 40.45 SA 1 163843 Syria 35, 38 SY 1 163843 Syrian 35, 38 SY 1 294640 Israel 31.5, 34.75 IL 1 170062 Aleppo 36.25, 37.5 SY 1 (The highlight added to show incorrect resolutions.)
FYI:
RO = Romania
SA = Saudia Arabia
TM = Turkmenistan
Plus “Qusair” appears twice in the quoted text.
For the ten locations mentioned a seventy (70%) percent accuracy rate.
Better than the average American but proofing is still an essential step in editorial workflow.
I first saw this in Pete Warden’s Five short links.
Subway Maps and Visualising Social Equality by James Chesire.
From the post:
Most government statistics are mapped according to official geographical units. Whilst such units are essential for data analysis and making decisions about, for example, government spending, they are hard for many people to relate to and they don’t particularly stand out on a map. This is why I tried a new method back in July 2012 to show life expectancy statistics in a fresh light by mapping them on to London Tube stations. The resulting ”Lives on the Line” map has been really popular with many people surprised at the extent of the variations in the data across London and also grateful for the way that it makes seemingly abstract statistics more easily accessible. To find out how I did it (and read some of the feedback) you can see here.
James gives a number of examples of the use of transportation lines making “abstract statistics more easily accessible.”
Worth a close look if you are interested in making dry municipal statistics part of the basis for social change.
No more heatmaps that are just population maps! by Pete Warden.
From the post:
I'm pleased to announce that there's a brand new 0.50 version of the DSTK out! It has a lot of bug fixes, and a couple of major new features, and you can get it on Amazon's EC2 as ami-7b9df412, download the Vagrant box from http://static.datasciencetoolkit.org/dstk_0.50.box, or grab it as a BitTorrent stream from http://static.datasciencetoolkit.org/dstk_0.50.torrent
What are the new features?
The biggest is the integration of high resolution (sub km-squared) geostatistics for the entire globe. You can get population density, elevation, weather and more using the new coordinates2statistics API call. Why is this important? No more heatmaps that are just population maps, for the love of god! I'm using this extensively to normalize my data analysis so that I can actually tell which places actually have an unusually high occurrence of X, rather than just having more people.
If you use the DSTK (and you should), do send Pete a note of appreciation.
I can’t wait to start mapping tax dodgers!
Google Map Redesign by Caitlin Dempsey.
From the post:
Googles Maps is preparing to debut its newly revamped Google Maps. Terming it “smart recommendations” the new functionality of Google Maps is intended to be more interactive and custom tailored to the specific user. The more you use the map to search for locations, favorite items by starring them, and write location reviews, the more unique the map becomes. Clicking a specific business or feature will result in the map features adjusting to show roads and locations related to that place.
(…)
Previewing the new Google Maps is currently only available by invite at the moment. You can request your invite via the Preview page.
Technology could be exposing you to a broader view of the world, perhaps even as other see it.
Instead:
Ironic if you remember the original MacIntosh commercial:
Timothy Leary today would say:
Turn on, tune in, unplug.
Geography of hate against gays, races, and the disabled by Nathan Yau.
Nathan reports on the work of Floating Sheep who relied on 150,000 tags to create this map.
More details at Nathan’s site but as Nathan says, read the FAQ before you get too torqued about the map.
If nothing else, this should be a good lesson in the choices made collecting and mapping “objective” data (the tweets) and what questions you should ask about that process.
I found it interesting that the sea coast along the Gulf of Mexico seemed to have less hate.
How would you defend the choices you make when making a topic map?
Some information, that is important to someone will have to be left out. Was that out of religious, political, social or ethnic bias?
You can’t avoid that sort of question but you can be comfortable with your own answers should it arise.
My stock response is:
“The paying client is happy with the map. Become a paying client and you can be map happy too.”
Binify + D3 = Gorgeous honeycomb maps by Chris Wilson.
From the post:
Most Americans prefer to huddle together around urban areas, which raises all sorts of problems for map-based visualizations. Coloring regions according to a data value, known as a choropleth map, leaves the map maker beholden to arbitrary political boundaries and, at the county level, pixel-wide polygons in parts of the Northeast. Many publications prefer to place dots proportional in area to the data values over the center of each county, which inevitably produces overlapping circles in these same congested regions. Here’s a particularly atrocious example of that strategy I once made at Slate:
Two weeks ago, Kevin Schaul released an exciting new command-line tool called binify that offers a brilliant alternative. Schaul’s tool takes a series of points and clusters them (or “bins” them) into hexagonal tiles. Check out the introductory blog post on his site.
Binify operates on .shp files, which can be a bit difficult to work with for those of us who aren’t GIS pros. I put together this tutorial to demonstrate how you can take a raw series of coordinates and end up with a binned hexagonal map rendered in the browser using d3js and topojson, both courtesy of the beautiful mind of Mike Bostock. All the source files we’ll need are on Github.
I think everyone will agree with Chris, that is truly an ugly map. 😉
Chris’ post takes you through how to make a much better one.
The Map Myth of Sandy Island by Rebecca Maxwell.
From the post:
Sandy Island has long appeared on maps dating back to the early twentieth century. This island was supposedly located in the Pacific Ocean northwest of Australia in the Coral Sea. It first appeared on an edition of a British admiralty map back in 1908 proving that Sandy Island had been discovered by the French in 1876. Even modern maps, like the General Bathymetic Chart of the Oceans (the British Oceanopgraphic Dat Centre issued an errata about Sandy Island) and Google Earth, show the presence of an island at its coordinates. Sandy Island is roughly the size of Manhattan; it is about three miles wide and fifteen miles long. However, there is only one problem. The island does not actually exist.
Back in October 2012, an Australian research ship undiscovered the island. The ship, called the Southern Surveyor, was led by Maria Seton, a scientist from the University of Sydney. The purpose of the twenty-five-day expedition was to gather information about tectonic activity, map the sea floor, and gather rock samples from the bottom. The scientific data that they had, including the General Bathymetic Chart of the Oceans, indicated the presence of Sandy Island halfway between Australia and the island of New Caledonia, a French possession. The crew began to get suspicious, however, when the chart from the ship’s master only showed open water. Plus, Google Earth only showed a dark blob where it should have been.
When the ship arrived at Sandy Island’s supposed coordinates, they found nothing but ocean a mile deep. One of the ship’s crewmembers, Steven Micklethwaite, said that they all had a good laugh at Google’s expense as they sailed through the island. The crew was quick to make their findings known. The story originally appeared in the Sydney Morning Herald and prompted a large amount of controversy. Cartographers were the most puzzled of all. Many wondered whether the island had ever existed or if it had been eroded away by the ocean waves over the years. Others wondered if the island mysteriously disappeared into the ocean like the legendary city of Atlantis. An “obituary” for Sandy Island, reporting the findings, was published in Eos, Transactions of the Geophysical Union in April of 2013.
Rebecca details the discovered/undiscovered history of Sandy Island in rich detail.
It’s a great story and you should treat yourself by reading it.
My only disagreement with Rebecca comes when she writes:
Maps are continually changing and modern maps still contain a human element that is vulnerable to mistakes.
On the contrary, maps, even modern ones, are wholly human constructs.
Not just the mistakes but the degree of accuracy, the implicit territorial or political claims, what is interesting enough to record, etc., are all human choices in production.
To say nothing of humans on the side of reading/interpretation as well.
If there were no sentient creatures to read it, would a map have any meaning?
OpenStreetMap Editor Designed by MapBox Goes Live by Caitlin Dempsey.
From the post:
A new easy-to-use editor for OpenStreetMap has gone live. Called iD, the development of in-browser data editor was coordinated by MapBox and funded by a grant from the Knight Foundation. The Alpha version of iD was released in January of this year, but was only recently added as an option to the edit drop down menu on www.openstreetmap.org.
From the announcement on the OpenStreetMap blog:
The new editor, codenamed ‘iD’, boasts an intuitive interface and clear walk-throughs that make editing much easier for new mappers. By lowering the barrier to contributions, we believe that more people can contribute their local knowledge to the map – the crucial factor that sets OSM apart from closed-source commercial maps.
You really need to see this to appreciate the ease of adding information to a map.
Excellent!
Largest Atlas in the World Created using ArcGIS by Caitlin Dempsey.
From the post:
Earth Platinum, the largest atlas ever printed, was released in February 2012 by Millennium House, Australia. Only 31 copies of the 330 pound, leather-bound book exist and each are priced at $100,000. The book measures 6ft by 9ft and has been recognized by Chris Sheedy of the Guinness Book of World Records as the largest atlas in existence. The book contains 128 pages and requires at least two hands, or in some case multiple people, to turn the pages.
Earth Platinum has surpassed the previous holder of the world record for largest atlas, the famous Klencke Atlas (which measures about 5′ 9″ by 6′ 3″ when opened). The Klencke Atlas is housed in the Antiquarian Mapping Division of the British Library in London and held the title for largest atlas worldwide from 1660 until the publication of Earth Platinum. Published as a one-off over 350 years ago, the Klencke Atlas is reported to contain all geographical knowledge of that time, just as Earth Platinum does today.
Amazon doesn’t have it listed so I can’t say if you get a discount and/or free shipping or both. 😉
Interesting but only as a publishing oddity.
I would rather have a digital version that is a geographic interface into a general knowledge topic map.
Map Coloring Revisited by Lance Fortnow.
From the post:
Following the coloring theme from Bill’s last post, a few years ago I asked you readers for natural examples of maps that were and were not three colorable. Chris Bogart gave a nice non-trivial example of a three-colorable country, Armenia.
…
Here’s a simple 7-node graph with every interior node with even degree but not 3-colorable.
There must be some real world map that captures this graph.
I’ll make the same deal I made before, an autographed copy of my book for the best example of a real-world example of a non-three colorable map with interior regions with an even number of neighbors. Should be a real political unit–not just a collection of states.
I am assuming “real-world examples” includes historical maps.
How you would go about discovering such a map?
History of San Francisco street names mapped by Nathan Yau.
Nathan points to a project that has captured not only street names but the history of those names for part of San Francisco.
You don’t have to be there to appreciate the map.
Reminded me of a highway in a small town where I lived in Louisiana that was variously known as Hwy. 84, Winnfield Highway, “Front street” or simply the “front.”
Each of those names had a history, had anyone cared to capture them.
Atlas of Design by Caitlin Dempsey.
From the post:
Do you love beautiful maps? The Atlas of Design has been reprinted and is now available for purchase. Published by the North American Cartographic Information Society (NACIS), this compendium showcases cartography at some of its finest. The atlas was originally published in 2012 and features the work of 27 cartographers. In early 2012, a call for contributions was sent out and 140 entries from 90 different individuals and groups submitted their work. A panel of eight volunteer judges plus the book’s editors evaluated the entries and selected the finalists.
The focus of the Atlas of Design is on the aesthetics and design involved in mapmaking. Tim Wallace and Daniel Huffman, the editors of Atlas of Design explain the book’s introduction about the focus of the book:
Aesthetics separate workable maps from elegant ones.
This book is about the latter category.
My personal suspicion is that aesthetics separate legible topic maps from those that attract repeat users.
The only way to teach aesthetics (which varies by culture and social group) is by experience.
This is a great starting point for your aesthetics education.
From the introduction popup:
TweetMap is an instance of MapD, a massively parallel database platform being developed through a collaboration between Todd Mostak, (currently a researcher at MIT), and the Harvard Center for Geographic Analysis (CGA).
The tweet database presented here starts on 12/10/2012 and ends 12/31/2012. Currently 95 million tweets are available to be queried by time, space, and keyword. This could increase to billions and we are working on real time streaming from tweet-tweeted to tweet-on-the-map in under a second.
MapD is a general purpose SQL database that can be used to provide real-time visualization and analysis of just about any very large data set. MapD makes use of commodity Graphic Processing Units (GPUs) to parallelize hard compute jobs such as that of querying and rendering very large data sets on-the-fly.
This is a real treat!
Try something popular, like “gaga,” without the quotes.
Remember this is running against 95 million tweets.
Impressive! Yes?
Abstract Maps For Powerful Impact by Jim Vallandingham.
You can follow the abstraction, even from the bare slides.
Still, it is a slide deck that makes you wish for the video.
The OpenStreetMap Package Opens Up
From the post:
A new version of the OpenStreetMap package is now up on CRAN, and should propagate to all the mirrors in the next few days. The primary purpose of the package is to provide high resolution map/satellite imagery for use in your R plots. The package supports base graphics and ggplot2, as well as transformations between spatial coordinate systems.
The bigest change in the new version is the addition of dozens of tile servers, giving the user the option of many different map looks, including those from Bing, MapQuest and Apple.
Very impressive display of the new capabilities in OpenStreetMap and this note about OpenStreetMap and ggmap:
Probably the main alternative to OpenStreetMap is the ggmap package. ggmap is an excellent package, and it is somewhat unfortunate that there is a significant duplication of effort between it and OpenStreetMap. That said, there are some differences that may help you decide which to use:
Reasons to favor OpenStreetMap:
- More maps: OpenStreetMap supports more map types.
- Better image resolution: ggmap only fetches one png from the server, and thus is limited to the resolution of that png, whereas OpenStreetMap can download many map tiles and stich them together to get an arbitrarily high image resolution.
- Transformations: OpenStreetMap can be used with any map coordinate system, whereas ggmap is limited to long-lat.
- Base graphics: Both packages support ggplot2, but OpenStreetMap also supports base graphics.
Reasons to favor ggmap:
- No Java dependency: ggmap does not require Java to be installed.
- Geocoding: ggmap has functions to do reverse geo coding.
- Google maps: While OpenStreetMap has more map types, it currently does not support google maps.
Fair enough?
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Visualizing Biological Data Using the SVGmap Browser by Casey Bergman.
From the post:
Early in 2012, Nuria Lopez-Bigas‘ Biomedical Genomics Group published a paper in Bioinformatics describing a very interesting tool for visualizing biological data in a spatial context called SVGmap. The basic idea behind SVGMap is (like most good ideas) quite straightforward – to plot numerical data on a pre-defined image to give biological context to the data in an easy-to-interpret visual form.
To do this, SVGmap takes as input an image in Scalable Vector Graphics (SVG) format where elements of the image are tagged with an identifier, plus a table of numerical data with values assigned to the same identifier as in the elements of the image. SVGMap then integrates these files using either a graphical user interface that runs in standard web browser or a command line interface application that runs in your terminal, allowing the user to display color-coded numerical data on the original image. The overall framework of SVGMap is shown below in an image taken from a post on the Biomedical Genomics Group blog.
We’ve been using SVGMap over the last year to visualize tissue-specific gene expression data in Drosophila melanogaster from the FlyAtlas project, which comes as one of the pre-configured “experiments” in the SVGMap web application.
More recently, we’ve been also using the source distribution of SVGMap to display information about the insertion preferences of transposable elements in a tissue-specific context, which as required installing and configuring a local instance of SVGMap and run it via the browser. The documentation for SVGMap is good enough to do this on your own, but it took a while for us to get a working instance the first time around. We ran into the same issues again the second time, so I thought I write up my notes for future reference and to help others get SVGMap up and running as fast as possible.
Topic map interfaces aren’t required to take a particular form.
A drawing of a fly could be topic map interface.
Useful for people studying flies, less useful (maybe) if you are mapping Lady Gaga discography.
What interface do you want to create for a topic map?
Map Projection Transitions by Jason Davies.
A delightful world map that transitions between projections.
How many projections you ask?
Far more than I would have guessed. And I suspect this listing isn’t complete.
By analogy, how would you construct a semantic projection for a topic map?
Varying by language or names of subjects would be one projection.
What about projecting entire semantic views?
Rather than displaying Cyprus from an EU view, why not display the Cyprus view as the frame of reference?
Or display the sovereignty of nations, where their borders are subject to violation at the whim and caprice of larger nations.
Or closer to home, projecting the views of departments in an enterprise.
You may be surprised at the departments that consider themselves the glue holding the operation together.
I encountered the gvSIG site while tracking down the latest release of i3Geo.
From its mission statement:
The gvSIG project was born in 2004 within a project that consisted in a full migration of the information technology systems of the Regional Ministry of Infrastructure and Transport of Valencia (Spain), henceforth CIT, to free software. Initially, It was born with some objectives according to CIT needs. These objectives were expanded rapidly because of two reasons principally: on the one hand, the nature of free software, which greatly enables the expansion of technology, knowledge, and lays down the bases on which to establish a community, and, on the other hand, a project vision embodied in some guidelines and a plan appropriate to implement it.
Some of the software projects you will find at gvSIG are:
gvSIG Desktop
gvSIG is a Geographic Information System (GIS), that is, a desktop application designed for capturing, storing, handling, analyzing and deploying any kind of referenced geographic information in order to solve complex management and planning problems. gvSIG is known for having a user-friendly interface, being able to access the most common formats, both vector and raster ones. It features a wide range of tools for working with geographic-like information (query tools, layout creation, geoprocessing, networks, etc.), which turns gvSIG into the ideal tool for users working in the land realm.
gvSIG Mobile
gvSIG Mobile is a Geographic Information System (GIS) aimed at mobile devices, ideal for projects that capture and update data in the field. It’s known for having a user-friendly interface, being able to access the most common formats and a wide range of GIS and GPS tools which are ideal for working with geographic information.
gvSIG Mobile aims at broadening gvSIG Desktop execution platforms to a range of mobile devices, in order to give an answer to the needings of a growing number of mobile solutions users, who wish to use a GIS on different types of devices.
So far, gvSIG Mobile is a Geographic Information System, as well as a Spatial Data Infrastructures client for mobile devices. Such a client is also the first one licensed under open source.
I3Geo
i3Geo is an application for the development of interactive web maps. It integrates several open source applications into a single development platform, mainly Mapserver and OpenLayers. Developed in PHP and Javascript, it has functionalities that allows the user to have better control over the map output, allowing to modify the legend of layers, to apply filters, to perform analysis, etc.
i3Geo is completely customizable and can be tailor to the different users using the interactive map. Furthermore, the spatial data is organized in a catalogue that offers online access services such as WMS, WFS, KML or the download of files.
i3Geo was developed by the Ministry of the Environment of Brazil and it is actually part of the Brazilian Public Software Portal.
gvSIG Educa
What is gvSIG Educa?
“If I can’t picture it, I can’t understand it (A. Einstein)”
gvSIG Educa is a customization of the gvSIG Desktop Open Source GIS, adapted as a tool for the education of issues that have a geographic component.
The aim of gvSIG Educa is to provide educators with a tool that helps students to analyse and understand space, and which can be adapted to different levels or education systems.
gvSIG Educa is not only useful for the teaching of geographic material, but can also be used for learning any subject that contains a spatial component such as history, economics, natural science, sociology…
gvSIG Educa facilitates learning by letting students interact with the information, by adding a spatial component to the study of the material, and by facilitating the assimilation of concepts through visual tools such as thematic maps.
gvSIG Educa provides analysis tools that help to understand spatial relationships.
Definitely a site to visit if you are interested in open source GIS software and/or projects.
From the homepage:
i3Geo is an application for the development of interactive web maps. It integrates several open source applications into a single development platform, mainly Mapserver and OpenLayers. Developed in PHP and Javascript, it has functionalities that allows the user to have better control over the map output, allowing to modify the legend of layers, to apply filters, to perform analysis, etc.
i3Geo is completely customizable and can be tailor to the different users using the interactive map. Furthermore, the spatial data is organized in a catalogue that offers online access services such as WMS, WFS, KML or the download of files.
i3Geo was developed by the Ministry of the Environment of Brazil and it is actually part of the Brazilian Public Software Portal.
I followed an announcement about i3Geo 4.7 being available when the line “…an application for the development of interactive web maps,” caught my eye.
Features include:
- Basic display: fix zoom, zoom by rectangle, panning, etc.
- Advanced display: locator by attribute, zoom to point, zoom by geographical area, zoom by selection, zoom to layer
- Integrated display: Wikipedia, GoogleMaps, Panoramio and Confluence
- Integration with the OpenLayers, GoogleMaps and GoogleEarth APIs
- Loading of WMS, KML, GeoRSS, shapefile, GPX and CSV layers
- Management of independent databases
- Layer catalog management system
- Management of layers in maps: Change of the layers order, opacity change, title change, filters, thematic classification, legend and symbology changing
- Analysis tools: buffers, regular grids, points distribution analysis, layer intersection, centroid calculation, etc.
- Digitalization: vector editing that allows to create new geometries or edit xisting data.
- Superposition of existing data at the data of the Google Maps and GoogleEarth catalogs.
Unless you want to re-invent mapping software, this could be quite useful for location relevant topic map data.
I first saw this at New final version of i3Geo available: i3Geo 4.7.
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