[Editor's note: Thanks Curt!]
Republished from information aesthetics.
Via Vizworld and Gizmodo.
Sam Loman [just-sam.com] created an original take on illustrating the inner structure and workings of the human body as the tight intertwining of different systems (e.g. arterial, digestive, musculetal, respiratory, etc.), by way of a subway map metaphor.
[Editor's note: This series of imaginative geographies from Christoph Niemann’s is playful and builds on the new Google Map symbolization conventions. Niemann's work previously featured here with Coffee Art and I Lego N.Y. Thanks Trevor!]
Republished from New York Times.
[Editor's note: Featuring the DMZ on the Korean peninsula and Chagos Islands (Diego Garcia) of the Indian Ocean. Both of these features can be found in Natural Earth, the free GIS map of the world.]
Republished from the Economist. Feb 8th 2010
THERE was a time when conservation meant keeping people away from nature. America’s system of national parks, a model for similar set-ups around the world, was based on the idea of limiting human presence to passing visits, rather than permanent habitation.
In recent years this way of doing things has come under suspicion. To fence off large areas of parkland is often impractical and can also be immoral—in that it leads to local people being booted out. These days, the consensus among conservationists is to try to manage nature with humans in situ. But there are still “involuntary parks”, to borrow a phrase from the writer and futurist Bruce Sterling, that serve to illustrate just how spectacularly well nature can do when humans are removed from the equation.
Some such “parks” are accidents of settlement, or its absence. Nature is preserved in those rare places that people just have not got round to overrunning—for example the Foja Mountains in western New Guinea, an area of rainforest that teems with an astonishingly rich variety of plants and animals. Others are accidents of conflict: places from which people have fled and where the fauna and flora have thrived as a result.
Continue reading at the Economist . . .
[Editor's note: Humorous but scientific take on "tree huggers" in the U.S. mid-Atlantic. Catching up on some old clippings as I roll back into DC.]
Republished from The Washington Post.
Jess Parker hugs trees.
In the woods of Anne Arundel County, he throws his arms around tulip poplars, oaks and American beeches, and holds them so tightly that his cheek presses into their bark. This is not some hiker on a lark: anybody, hopped up on campfire coffee and exercise endorphins, might hug a tree once.
This is science. Parker has done it about 50,000 times.
Parker, a forest ecologist at the Smithsonian Institution, has spent the past 22 years on a research project so repetitive, so time-consuming, that it impresses even researchers with the patience to count tree rings. Since 1987, he and a group of volunteers have embraced thousands of trees, slipped a tape measure behind them, and wrapped it around to measure the trees’ girth.
This year, after about 250,000 hugs between them, the work paid off.
Parker’s data, which showed the trunks gradually fattening over time, indicated that many of the trees were growing two to four times faster than expected. That raised questions about climate change’s impact on the age-old rhythms of U.S. forests.
It might also raise questions about Parker and members of his team, who say they enjoyed almost every minute of it.
Continue reading at The Washington Post . . .
[Editor's note: I'm pleased to announce the immediate availability of version 1.1 of Natural Earth! Three months after our initial launch, the project reaches a major milestone. The download manager will be updated the next couple weeks. In the meantime, please check out the ZIP and release notes below.]
Continue reading and download the data at NaturalEarthData.com . . .
No posts this week. I’m out and about speaking in Reno Wednesday, in SF rest of week.
However, for your viewing pleasure, Natural Earth 1.1 will be released tomorrow. Check back in this space.
[Editor's note: With over half of humanity now living in an urban environment, this exciting new remote sensing dataset can help planners better estimate global urban sprawl. I hope to use this as a foundation to refine Natural Earth's urban polygons and mash them up against GeoNames.org features. Thanks Annemarrie!]
Republished from UW-Madison.
Center for Sustainability and the Global Environment, University of Wisconsin-Madison
The MODIS 500-m global map of urban extent was produced by Annemarie Schneider at the University of Wisconsin-Madison, in partnership with Mark Friedl at Boston University and the MODIS Land Group. The goal of this project was generate a current, consistent, and seamless circa 2001-2002 map of urban, built-up and settled areas for the Earth’s land surface. This work builds on previous mapping efforts using Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1-km spatial resolution (Schneider et al., 2003; 2005), which was included as part of the MODIS Collection 4 (C4) Global Land Cover Product (Friedl et al., 2002). Here we addressed weaknesses in the first map as well as several limitations of contemporary global urban maps by developing a methodology that relies solely on newly released Collection 5 (C5) MODIS 500-m resolution data. Specifically, a supervised decision tree classification algorithm was used to map urban areas using region-specific parameters (see Schneider et al., 2009; 2010 for full details on methodology).
The intended audience for the MODIS 500-m map of urban extent is primarily the academic research community working at regional to global scales on questions related to the geophysical environment; please keep this in mind as you put the data to use.
More about the data »
Download the data »
Note: Email registration required
b. Land cover classes
In the global land cover map, the classes are defined according to the International Geosphere-Biosphere Programme (IGBP) 17-class scheme shown in Table 1.
|
No. |
Class name |
Description |
|
|
|
|
|
1 |
Evergreen Needleleaf Forest |
Lands dominated by woody vegetation with a percent cover > 60% and height exceeding 2 meters. Almost all trees remain green all year. Canopy is never without green foliage. |
|
2 |
Evergreen Broadleaf Forest |
Lands dominated by woody vegetation with a percent cover > 60% and height exceeding 2 meters. Almost all trees remain green year round. Canopy is never without green foliage. |
|
3 |
Deciduous Needleleaf Forest |
Lands dominated by woody vegetation with a percent cover > 60% and height exceeding 2 meters. Trees shed their leaves during the dry season; e.g. Siberian Larix. |
|
4 |
Deciduous Broadleaf Forest |
Lands dominated by woody vegetation with a percent cover > 60% and height exceeding 2 meters. Consists of broadleaf trees with an annual cycle of leaf-on and leaf-off periods. |
|
5 |
Mixed Forests |
Lands dominated by woody vegetation with a percent cover > 60% and height exceeding 2 meters. Consists of mixtures of either broadleaf or needleleaf trees and in which neither component exceeds 60% of landscape. |
|
6 |
Closed Shrublands |
Lands with woody vegetation with a height less than 2 meters. The total percent cover, including the herbaceous understory, exceeds 60%. The shrub foliage can be either evergreen or deciduous. |
|
7 |
Open Shrublands |
Lands with woody vegetation with a height less than 2 meters, and sparse herbaceous understory. Total percent cover is less than 60%. The shrub foliage can be either evergreen or deciduous. |
|
8 |
Woody Savannas |
Lands with and herbaceous understory, typically graminoids, and with tree and shrub cover between 30-60%. The tree and shrub cover height exceeds 2 meters. |
|
9 |
Savannas |
Lands with an herbaceous understory, typically graminoids, and with tree and shrub cover between 10-30%. The tree and shrub cover height exceeds 2 meters. |
|
10 |
Grasslands |
Lands with herbaceous types of cover, typically graminoids. Tree and shrub cover is less than 10%. |
|
11 |
Permanent Wetlands |
Lands with a permanent mosaic of water and herbaceous or woody vegetation. The vegetation can be present in either salt, brackish, or fresh water. Only wetlands covering extensive areas (i.e., more than 500 km2) will be mapped (e.g., Sud, Okavanga, Everglades). |
|
12 |
Croplands |
Lands where crops comprise > 60% of the total land cover. |
|
13 |
Urban Areas |
See (a) above. |
|
14 |
Cropland - Natural Vegetation Mosaic |
Lands with mosaics of crops and other land cover types in which no component comprises more than 60% of the landscape. |
|
15 |
Snow and Ice |
Lands under snow/ice cover for most of the year. |
|
16 |
Barren or Sparsely Vegetated |
Lands with exposed soil, sand or rocks and has less than 10% vegetated cover during any time of the year. |
|
17 |
Water Bodies
|
Oceans, seas, lakes, reservoirs, and rivers. Can be either fresh or salt water bodies. Coded as 0 in the MODIS-based maps. |
[Editor's note: It's possible to enjoy pervasive GPS and enjoy privacy, too. A congressional subcommittee held a joint hearing titled, “The Collection and Use of Location Information for Commercial Purposes” on Wednesday. Learn more in this white paper from the Electronic Frontier Foundation. Thanks GIS User!]
Republished from the Electronic Frontier Foundation.
By Andrew J. Blumberg and Peter Eckersley, August 2009
Also available as a PDF
in English and Bulgarian.
Over the next decade, systems which create and store digital records of people’s movements through public space will be woven inextricably into the fabric of everyday life. We are already starting to see such systems now, and there will be many more in the near future.
Here are some examples you might already have used or read about:
These systems are marvellously innovative, and they promise benefits ranging from increased convenience to transformative new kinds of social interaction.
Unfortunately, these systems pose a dramatic threat to locational privacy.
Locational privacy (also known as “location privacy”) is the ability of an individual to move in public space with the expectation that under normal circumstances their location will not be systematically and secretly recorded for later use. The systems discusssed above have the potential to strip away locational privacy from individuals, making it possible for others to ask (and answer) the following sorts of questions by consulting the location databases:
Of course, when you leave your home you sacrifice some privacy. Someone might see you enter the clinic on Market Street, or notice that you and your secretary left the Hilton Gardens Inn together. Furthermore, in the world of ten years ago, all of this information could be obtained by people who didn’t like you or didn’t trust you.
But obtaining this information used to be expensive. Your enemies could hire a guy in a trench coat to follow you around,but they had to pay him. Moreover, it was hard to keep the surveillance secret — you had a good chance of noticing your tail ducking into an alley.
In the world of today and tomorrow, this information is quietly collected by ubiquitous devices and applications, and available for analysis to many parties who can query, buy or subpeona it. Or pay a hacker to steal a copy of everyone’s location history.
It is this transformation to a regime in which information about your location is collected pervasively, silently, and cheaply that we’re worried about.
Continue reading at Electronic Frontier Foundation . . .
[Editor's note: Visualizing complex connection topologies is made easier with a new plugin for Microsoft Excel. Now someone needs to port it to Flash ActionScript 3!]
Republished from Visual Business Intelligence.
This blog entry was written by Bryan Pierce of Perceptual Edge.
The chances are good that you’ve seen network visualizations before, such as the one below in which the circles and octagons represent large U.S. companies and each connecting line represents a person who sits on the board of both companies.
(This image was created by Toby Segaran: http://blog.kiwitobes.com/?p=57)
While these types of graphs have become more common in recent years, there’s still a good chance that you’ve never created one yourself. This is because, traditionally, to create network visualizations, you’ve either needed specialized (and often unwieldy) network visualization software or a full-featured (and usually expensive) visualization suite. That’s no longer the case. A team of contributors from several universities and research groups, including the University of Maryland and Microsoft Research, recently released NodeXL, a free add-in for Excel that allows you to create and analyze network visualizations.
Using NodeXL you can import data from a variety of file formats and it will automatically lay out the visualization for you, using one of twelve built-in layout algorithms.
Continue reading at Visual Business Intelligence . . .
[Editor's note: The 9.3.1 release of ArcGIS adds the ability to embed GIS data in layer packages for easy sharing. This blog post from ESRI steps thru how to create and leverage layer packages. Natural Earth will soon be available as a layer package!]
Republished from ESRI ArcExplorer blog.
With the release of ArcGIS Desktop 9.3.1 the ability to create layer packages was introduced. Layer packages encapsulate the data, cartography, and other properties of the layer as it’s authored in ArcMap (or ArcGlobe) into one easily shareable package.
Layer packages can be shared with other ArcGIS Desktop users, shared on ArcGIS Online (public beta soon), and are also supported in ArcGIS Explorer 900 along with layer files. What’s significant for Explorer users is that now the cartographic capabilities of ArcGIS Desktop can be seen using Explorer. In the past only simple rendering options were available in Explorer for local data sources, now these are expanded to include ArcGIS Desktop cartography via layer files and layer packages.
ArcGIS 9.3.1 was released not long ago, and ArcGIS Explorer 900 is currently in Beta. But since you may want to begin to create layer packages now for use in Explorer 900 when it becomes available we thought we’d cover a few basic pointers on how to create good layer packages.
We began by downloading some data and an ArcMap document (.mxd file) from the USGS. The data we downloaded was from an open file report with data from the Engineering aspects of karst map.
We downloaded the data, started ArcMap, opened the provided map document, and this is where we started. Our goal for this post was to take the karst_polys_polygon layer in the map and share it as a layer package with ArcGIS Explorer 900 users.
You can see the data (from a personal geodatabase) is already symbolized so we have a good start. But there’s a few things we want to do during the process of authoring the layer package that will ensure those we share the layer package with have the best possible experience and that we present the data in the best possible way. We think authoring is a good way to think about this process, and we’ll step you through the basics of what to consider.
Continue reading at ESRI ArcGIS Explorer Blog . . .
