With the threat of Gustav increasing to a Category 4 hurricane we’ve been working on getting storm surge and wave height prediction data loaded into GeoCommons (many thanks to Raj for leading this up). According to CNN, FEMA’s models has predicted “4.5 million people will be in the storm’s path and 59,953 buildings will be destroyed. The path also ensnares about 170 hospitals and more than 1,100 police and fire stations….resulting in $29.3 billion dollars in damage.”

One of the big motivations for developing Geocommons was our experience doing emergency response work during Hurricane Katrina. Models like those conveyed in the CNN report were not shared in a manner that made the results actionable to those on the ground. Knowing that a storm surge is going to hit and do damage is one thing, knowing if that storm surge is going to specifically hit you or your assets. The Federal government creates the data but does a terrible job of sharing it in a manner that makes it personalized and actionable (our attempt at recommending fixes).

The concept is simple - if I was a local first responder responsible for managing shelters it would be very useful to know - is there a risk of my shelters being under water? If I was the electric power company - are any of my substations going to be under water? You can fill several pages with similar examples, but none of them can be answered if the data is not shared in a means that allows those responding to use the data.

To this end we’ve posted storm surge and wave height prediction data from NOAA onto GeoCommons, so that you can accomplish some of these simple but critical tasks. If you download the data in Google Earth you can have a map like the one below is a few seconds showing areas that are predicted to have a storm surge between 10 and 15+ feet.

You can also download the data as a “shapefile” for GIS analysis and do some more sophisticated mapping like the image below showing the storm surge impact in relation to energy infrastructure assets:

Using Maker! you can also do some lightweight analysis with the data:

Then you can add data on assets that could be impacted the threat. In the Maker! map below I added facilities regulated by the EPA that could be effected by the storm surge:

Looks like there is an Entergy facility that could potentially be dealing with a 21 ft storm surge.

All the datasets are available on Finder! tagged as “Gustav” and we’ll be adding more over the weekend and as the situation evolves:

Gustav Storm surge of 1ft or less

Gustav Storm surge of 2ft

Gustav Storm surge of 3 to 5 ft

Gustav Storm surge of 5 to 10 ft

Gustav Storm surge of 10 to 15 ft

Gustav Storm surge of 15 ft or more

Popularity: 11% [?]

As Gustav increases in intensity traveling through the Gulf the threat of it hitting Louisiana and potentially New Orleans appears to be increasing. It also looks like Gustav could intersect with a variety of critical US energy infrastructure. We’ve been tracking both the storm and the potential impact on energy infrastructure and thought we would share some of the data. The map below shows a few of the datasets we’ve put together thus far:

Gustav is the orange circles and they are sized by the wind speed of the storm for the predicted location. The blue squares are refineries sized by their production levels in barrels of oil refined per day. Finally the white circles are the locations of offshore oil and gas platforms.

The source data is all available on Finder! - here are a few of the datasets that might be of interest:

National Weather Service, Hurricane Gustav Movement, World, 8.27.2008 - 9.1.2008

MMS, Major Shipping Fairways in the Gulf of Mexico (Line), World, 2008

Wikipedia, Global Oil Refineries, World, 2.3.2004

MMS, Active Pipelines in the Gulf of Mexico, World, 2008

MMS, Pacific OCS Region: Platform/Rig Locations, Gulf of Mexico, 8/1/2008

Popularity: 8% [?]

Urban Mapping has created a multi layer paper map called Panamap. Powered by their “MapAction Technology,” users can view different layers of the map in different angles. The map provides three images that “are interlaced by alternating horizontal strips from each. The resulting compound image is calibrated to a specially designed polymer lens substrate. Lenses contain between 60 to 200 micro-lenses per inch, depending on the desired outcome. This is mounted to a backing, die cut and packaged.” Currently, you can get Panamaps for Chicago and New York City.

The world’s leading technology firm for GIS software, ESRI, has been named an approved prime contractor on the SeaPort Enhanced (SeaPort-e) online portal by The Naval Surface Warfare Center (NSWC). ESRI can now provide the U.S. Navy, the U.S. Marine Corps, and the Defense Threat Reduction Agency (DTRA) a broad range of engineering, technical, and programmatic services related to GIS and IT

MapQuest released a new beta version of their site in response to the growing competition from other mapping sites like Google Maps. The new MapQuest added a map on the home page, and now offers a “copy and paste address field.” Despite their efforts, users are still not satisfied with the “zoom in and out” function. Many question if this is “too little, too late?”

The London’s Metropolitan Police launched the first crime mapping test site. Based on Google Maps, the beta displays crime stats for neighborhood levels in a user-friendly interface. Users can also find local cops, report a crime, and learn about crime prevention, victim support, and details of their local criminals. The creation of the crime map brings ‘hope that this new service will continue to evolve for a long time as the ability to filter by crime type, hotspots, and trends would be exceptional. You can also get the raw data as well as look at the mashup on the UK site from Finder!

Google launched their Gears Geolocation API. This new service provides “a way to get a more precise estimate of a user’s location using the cell-ID of nearby towers or on-board GPS.” Mobile devices with Gears will be able to access their location more accurately. This new tool allows “third parties to plug into their sites and automatically take advantage of both tower and GPS-based positioning.” Currently, Gears only works on Internet Explorer and WinMo.

Popularity: 16% [?]

When Laurie was working on her blog post covering the geopolitics of oil, she asked Raj and I to help out with creating some maps. She had some nice data showing the known oil and natural gas reserves around the globe. Specifically, she wanted some 3D maps to really show the relative amounts of oil and natural gas in different geographies.

Creating the map presented us a classic cartographic decision - should we do a data visualization or a data analysis? While this is a very distinct difference, I think it gets largely lost by most GeoWeb users, and hopefully this little example will help illustrate the importance of the difference.

For Laurie’s maps she wanted to show the relative amounts of known natural gas and crude oil around the globe. The data set she had collected from the USGS provided polygons where petroleum and natural gas were located. The most straight forward way to map the data was to create a thematic map shading the polygons based on the amount of oil or natural gas contained in each. In cartographic circles this is called a choropleth map. Below is an example of a choropleth map in Maker! with the oil data from Laurie’s post:

Known Petroleum Reserves

You could also use the centroid of each polygon to make a proportional symbol map - where the size of the symbol is representative of the amount of oil or natural gas in the location. Here is an example of using the natural gas data set but rendered as proportional symbols:

Known Natural Gas Reserves

Proportional or graduated symbols are a great choice when you have small polygons with high values that might get lost in a choropleth map with many large polygons. For instance in the oil reserve map Venezuela has a very high oil reserve, but since the polygon is quite small it is easy not to notice it on the map.

Another technique for getting small places noticed is using extruded polygons, which are 3D. Bjorn Sandvik has done a great job promoting these techniques in Google Earth with his Thematic Mapping Engine. Below is an example of the oil reserve data as extruded polygons on a 3D globe:

3D globes have some shortcomings like accuracy and not being able to see the whole globe at once, but offer a great dynamic way to interact with data. For the geopolitics of oil, we decided to go with creating a thematic map using extruded polygons but on a 2D projection of the earth. That way we could see all the continents and curvature of the earth accuracy would be diminished. While not as cool as Bjorn’s virtual globe maps they got the job done:

A second option we looked at was doing a spatial analysis of the oil data. Instead of visualizing the data values for the polygons, we did an analysis of the spatial distribution of the data. In this case we thought it would be interesting to analyze the spatial density of the oil reserve locations. To do so we needed to convert the polygons to points based on the centroid of each polygon. Then we could run a kernel density analysis of the data. This sounds fancy, but it’s really just placing a grid over the data and tabulating counts for each cell with a bit of fancy math for smoothing to create a continuous surface. While this is not terribly complex, it is much different than just visualizing the data. The results of the density analysis for the oil data can be seen in the map below:

We ended up not using the density analysis because it was not really accurate since the source data had been polygons and not points. If the source data had been well locations weighted by oil production it would have been spot on.

The confusion between geodata visualization and analysis was one of the reasons we deprecated GeoIQ (sometimes called heatmaps for Google maps). Spatial density analysis has popped in lots of applications since the original work but I think it is still commonly misunderstood by users. We found many users that thought the hot spot was the highest single point value on the map, which often it was not. Few realized the hot spot was the cluster of points that were closest together with the highest aggregate value. I think with the right work flow or user interface it can be a great tool, but we are not quite there yet. The difference between the two only becomes apparent when users can have access to geodata visualization tools (i.e. thematic maps) and geodata analysis tools (i.e. density maps).

Popularity: 20% [?]