Posts Tagged ‘grid’

The 21st Century Grid: New Lines on the Horizon (National Geographic)

Friday, June 25th, 2010

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[Editor's note: Interactive map shows proposed renewable energy power plants and transmission lines in the United States and Canada. The print version by Martin Gamache and Sam Pepple is worth a look –– it composites the multiple themes into a single view, and compares with the current state of the power system in a separate map.]

Republished from National Geographic Magazine.

Can we fix the infrastructure that powers our lives?
By Joel Achenbach. Photograph by Joe McNally

We are creatures of the grid. We are embedded in it and empowered by it. The sun used to govern our lives, but now, thanks to the grid, darkness falls at our con­venience. During the Depression, when power lines first electrified rural America, a farmer in Tennessee rose in church one Sunday and said—power companies love this story—”The greatest thing on earth is to have the love of God in your heart, and the next greatest thing is to have electricity in your house.” He was talking about a few lightbulbs and maybe a radio. He had no idea.

Juice from the grid now penetrates every corner of our lives, and we pay no more attention to it than to the oxygen in the air. Until something goes wrong, that is, and we’re suddenly in the dark, fumbling for flashlights and candles, worrying about the frozen food in what used to be called (in pre-grid days) the icebox. Or until the batteries run dry in our laptops or smart phones, and we find ourselves scouring the dusty corners of airports for an outlet, desperate for the magical power of electrons.

Continue reading at National Geographic Magazine . . .

The Variety of American Grids (Greater Greater Washington)

Friday, June 4th, 2010

[Editor's note: Geeky urban geography with map diagrams! See related post discussing cultural background for US grids and the example block sizes in the Washington DC metro.]

Republished from Greater Greater Washington.
By Daniel Nairn   •   May 31, 2010 9:54 am

I wanted a nerdy planning-related poster for my wall (other than the periodic table of city planning), so I made one this week. I scoured Google Earth and measured that quintessentially American grid in about a hundred downtowns around the country.

Of course, there are variations in block proportions within downtowns, but I tried to pick cities that had more uniformity than average to come up with a single prototype. (Washington, DC has very little uniformity.)


Click for the poster-quality version (large PDF).

Exploring these grid proportions messed with my preconceptions. I assumed the more western and newer cities would have larger grids than the more eastern and older cities, but no obvious pattern is discernible to me. Mobile, AL, settled by French colonists in the early 18th century, Tulsa, OK, a 19th century farming town, and Anchorage, AK, a 20th century frontier town, all share the same 300′ x 300′ internal block (street widths vary a little). What compelled the early settlers of these towns to choose, say, 220′ over 440′ lengths? I can’t say I have any idea right now.

Manhattan is also a curious story. According to Witold Rybczynski, the expanding nation unequivocally chose the 1811 Commissioner’s Grid of New York City over L’Enfant’s baroque-influenced plan for Washington, DC as the model for new towns. While this is surely true, it begs the question: why are New York’s long and skinny blocks not found anywhere else in the country? You would think at least one group of western settlers would seek to emulate their home town of New York more exactly.

I’m leaving aside the interesting value questions around block size. Ever since Jacobs, conventional wisdom has held that smaller blocks are preferable for walkability, but urban designer Fannis Grammenos challenges the grid somewhat in a Planetizen post.

SatValMod Color-Greyscale image integration (Viljoen via ESRI)

Wednesday, October 28th, 2009

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[Editor's note: This VisualBasic script from David Viljoen, Geological Survey of Canada, for ArcGIS solves a transparency flattening issue when trying to blend color into a grayscale shaded relief image. Often the colors become distorted during the merge. This tool preserves the color (hue) and moderates the saturation and value instead. I've used HSV color adjustment layers in Photoshop with relief masks to accomplish the same thing, nice to know it's available in ArcMap, too. Aileen mentioned it at NACIS Sacramento earlier this month.]

Republished from ESRI.
By David Viljoen.

I developed the SatValMod (SVM) method to address the problems associated with traditional methods of integrating color with gray-scale raster data (e.g. layer transparency, multiplying color by gray-scale values, etc.).

The main problem with traditional methods is color loss or corruption. SVM does not change the original hue and modulates the saturation and value so that the final output has the same rich colors of your input data.

SVM does not require Spatial Analyst. It supports Grid, BIL, and TIF formats for input. It outputs a BIL file.

The SVM method involves a pixel-by-pixel transformation of RGB color coordinates to HSV space, modulation of the saturation and value color components, and transformation of the orginal hue and modulated saturation and value components back to RGB space. More details are available in the PowerPoint slide show included in the ZIP.

This technique can work with rasterized polygon layers. You will need to create a CLR file that relates pixel values to the polygon colors.

I hope you find SVM useful in creating your color/gray-scale image integration products.

Download from ESRI . . .

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Travellr: Behind the Scenes of our Region-Based Clusters (Google GeoDev)

Monday, July 6th, 2009

[Editor's note: The age-old rule for cloropleth mapping that suggests aggregation by multi-scale areal units based on the map's zoom level is slowly seeping into "clustering" for the point-based mashup geo community. This overview from Travellr published on the Google GeoDevelopers blog includes two illustrations that show the power of this technique. I used such a technique (different implementation) on The Washington Post's recent swine flu mapping.]

Republished from Google GeoDevelopers Blog.
Monday, June 22, 2009

Recently, there has been a lot of interest in clustering algorithms. The client-side grid-based MarkerClusterer was released in the open source library this year, and various server-side algorithms were discussed in the Performance Tips I/O talk. We’ve invited the Travellr development team to give us insight on their unique regional clustering technique.

Travellr is a location aware answers service where people can ask travel-related questions about anywhere in the world. One of its features is a map-based interface to questions on the site using Google Maps.

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Figure 1. An example of the Travellr Map, showing question markers for Australia.

Clustering for usability
We learned that the best way to display markers without cluttering our map was to cluster our questions depending on how far you zoom in. If the user was looking at a map of the continents, we would cluster our questions into a marker for each continent. If the user zoomed-in to France we would then cluster our questions into a marker for each region or city that had questions. By clustering our data into cities, regions/states, countries, and continents, we could display relevant markers on the map depending on what zoom level the user was looking at.

Optimizing for Clustering
Our next challenge was how to extract clustered data from our database without causing excessive server load. Every time the user pans and zooms on the map, we need to query and fetch new clustered data in order to display the markers on the map. We also might have to limit the data if the user has selected a tag, as we’re only interested in a questions related to a topic (ie: “surfing”). To execute this in real-time would be painstakingly slow, as you would need to to cluster thousands of questions in thousands of locations with hundreds of tags on the fly. The answer? Pre-cluster your data of course!

Step 1. Structure your location data
When a question is asked about a city on Travellr, we also know its region/state, country and continent. We store more than 55,000 location points as a hierarchy, with each location “owning” its descendent nodes (and all of their data). Our locations are stored in a Modified Preorder Tree (also called Nested Sets). Modified Preorder Trees are a popular method of storing hierarchical data in a flat database table, having a focus on efficient data retrieval, and easy handling of sub trees. For each location we also keep a record of its depth within the tree, its location type (continent, country, region/state, or city), and its co-ordinates (retrieved using the Google Maps geocoder).

Step 2. Aggregate your data
We calculate aggregate data for every branch of our locations tree ahead of time. By storing aggregate data for cities, regions/states, countries, and continents, we provide an extremely fast and inexpensive method to query our locations database for any information regarding questions asked about a particular location. This data is updated every few minutes by a server-side task.

Our aggregations include:

  • Total question count for a location
  • Most popular tags for that location
  • Number of questions associated with each of those tags.

How we query our structured, aggregate data on the map
Whenever the user zooms or pans the map we fire off a query to our (unpublished ;) API with the tags they are searching for, the current zoom level, and the edge co-ordinates of the map’s bounding box. Based on the zoom level (Figure 2) we work out whether we want to display markers for continents, countries, states, or cities. We then send back the data for these markers and display them on the map.

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Figure 2. Clustering at different zoom levels (blue = continents, countries, pink = states, cities)

Everyone Wins
So what is the result of structuring and aggregating our data in such a way? It means that we have nicely organized, pre-clustered data that can be read from cheaply and easily. This allows us to provide a super-fast map interface for Travellr that puts minimal load on our infrastructure. Everyone is happy!

Comments or Questions?
We’d love to hear from you if you have any questions on how we did things, or suggestions or comments about Travellr’s map. This article was written by Travellr’s performance and scalability expert Michael Shaw (from Insight4) and our client-side scripting aficionado Jaidev Soin.

You can visit Travellr at www.travellr.com, or follow us on Twitter at twitter.com/travellr.

Maya Lin: Systematic Landscapes (Corcoran in Washington, DC)

Friday, June 12th, 2009

[Editor's note: If you travel to DC this summer, check out this installation from renowned contemporary artist and architect Maya Lin of the Vietnam Veterans Memorial fame. There is a fee to enter the private museum.]

Republished from the Corcoran Gallery of Art, Washington D.C.

Run dates: March 14, 2009 — July 12, 2009

This spring, the Corcoran Gallery of Art will present Maya Lin: Systematic Landscapes—a dramatic installation of major new works by this renowned contemporary artist and architect.   On view from March 14 through July 12, the exhibition addresses contemporary ideas about landscape and geologic phenomena.  Lin’s second nationally-traveling exhibition in 10 years, Systematic Landscapes explores how people perceive and experience the landscape in a time of heightened technological influence and environmental awareness.

Lin (b. 1959) came to prominence in 1981 with her design for the Vietnam Veterans Memorial in Washington, D.C. and has since achieved a high degree of recognition for a body of work that includes monuments, buildings, earthworks, sculpture and installations.  Traversing Lin’s constructed landscapes in this exhibition—moving around, under, and through them—we encounter a world that has been mapped, digitized, analyzed, and then reintroduced by Lin as actual, physical structures.  Her work blends a typology of natural forms, from rivers to mountains to seas, with a visual language of scientific analysis represented by grids, models, and maps.  In doing so, Lin merges an understanding of the ideal and the real, encouraging an encounter with conceptual, sculptural and architectural modeling.

Systematic Landscapes is centered on a trio of large-scale sculptural installations: 2×4 Landscape (2006), Water Line (2006) and Blue Lake Pass (2006).  Each sculpture offers a different means for viewers to engage with and comprehend a schematic representation of landscape forms.  In these projects, Lin examines how people’s modern relationships to the land are extended, condensed, distorted and interpreted through new computer technologies.  She translates a series of dramatic landscape environments selected for their inspiring beauty and connection to life-supporting habitats into spatial environments where viewers can engage with them in an art gallery setting.

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