Cartographica

Mapping 7.6 Earthquake in Costa Rica

There was a large (7.6) earthquake yesterday in Costa Rica. The earthquake signaled tsunami warnings in the region, but fortunately there was no tsunami. So far there have been three deaths reported. In the past 40 years Costa Rica has experienced more than 30 earthquakes of 6.0 magnitude or higher. So, needless to say Costa Ricans are accustomed to these types of events.

The purpose of this post is two-fold. The first purpose is to draw people's attention to the major geologic event that occurred in Costa Rica yesterday. The second purpose is to show a couple quick methods for creating concentric rings, which we have had questions about from some of our customers in the past. Concentric rings are used in a variety of ways, but one way you often see them used is in reporting the location of earthquakes.

Start by Adding a Live Map by choosing File > Add Live Map. After you add a live map choose Layer > Include in Map Extent. Including the Live Map layer in the map extent is necessary because it will enable the zoom functions to operate beyond the extent of the point layer that you are about to create in the following steps.

According to reports the earthquake occurred just off the cost of Costa Rica. To add a point representing the location of the earthquake choose Layer > New Layer and then choose Edit > Add Feature. A window will appear allowing you to choose the type of feature you would like to create. Select Point and then place the point by holding down the option key and clicking on the location about 50 miles off the coast. See below for an example.

There are two options for creating concentric rings. The first method involves using the buffer tool to create rings around a point at specific distances. To create a buffer choose Tools > Create Buffers for Layer's Features. A window will appear that will allow you to set the parameters of the buffer. Set the Uniform Width distance to 25 Miles. See below for an example.

To create new buffers at different distances select the Earthquake point layer in the Layer Stack, and then repeat the steps above to create buffers around the Earthquake point at 10, 50, and 75 miles. (Note: Be sure after each buffer is completed that you re-select the Earthquake point layer before creating the next buffer). When completed you will have four layers that are represented on the map by white circles. Double-click on the top buffer layer in the layer stack, click on the Fill box and then change the layer opacity to zero. Next click on the stroke box and change the color to red. See below for an example.

 To make all of the buffer layers have the same layer styles drag the top layer in the layer stack (the one you have already changed) on top of the other buffer layers. This will automatically match the layer styles. See below for a final image of the concentric rings created by using buffers.

The other method for creating concentric rings is to use an image from the internet. This method is not as precise as the buffer method because you cannot designate the distances for each ring. However, it is a quick alternative method for creating concentric rings. The first step is performing an image search for "concentric rings" using Google or a similar search engine. Find a clean looking example of concentric rings. Double-click on the earthquake layer in the layer stack and then click and drag your concentric ring image to the symbol box in the Layer Styles Window. Finally, change the stroke color to red and increase the point to a desired size . See below for an example of the Layer Styles window.

 Uncheck the four buffer layers that you created previously to reveal to new concentric rings that were created with an image from the internet. See below for an example of the final map.

Cartographica 1.2.8 ready for Mountain Lion and Retina Display

ClueTrust is happy to announce today the immediate availability of Cartographica 1.2.8.   This release fixes a number of small bugs and includes compatibility and feature fixes for both the Retina Display MacBook Pros and Mountain Lion.

Full release notes are available on the macgis.com site.

The new Retina Display MacBook Pros are gorgeous, and we're happy to say that things are really looking good on those displays, and with the advent of Mountain Lion's new Gatekeeper system, we have increased security by implementing Apple's new signature system to show that Cartographica comes from us, so you won't be warned about Unknown software.

The update is available now for current users using Check for Updates… from the Cartographica menu, or by downloading at the download page (starts automatically).

Mapping Idea Tolerance in U.S. Cities

Richard Florida is the well-known author of the book "The Creative Class" which describes the rise and the importance of a socio-economic class of individuals that Florida believes drives economic and social development. According to a Wikipedia, the creative class is made up of two separate groups of workers that make up about 30 percent of the U.S. workforce. The first of these groups is the Super-Creative Core: This group comprises about 12 percent of all U.S. jobs. It includes a wide range of occupations (e.g. science, engineering, education, computer programming, research), with arts, design, and media workers forming a small subset. Florida considers those belonging to this group to “fully engage in the creative process” (Florida, 2002). The Super-Creative Core is considered innovative, creating commercial products and consumer goods. The primary job function of its members is to be creative and innovative. “Along with problem solving, their work may entail problem finding” (Florida, 2002). The second group in the creative class is the Creative Professionals: These professionals are the classic knowledge-based workers and include those working in healthcare, business and finance, the legal sector, and education. They “draw on complex bodies of knowledge to solve specific problems” using higher degrees of education to do so (Florida, 2002).

Part of Florida's Creative Class philosophy is that those people who largely make up the creative class are most commonly found in cities. Because of this cities are central to economic and social growth and a hugely important moving forward in the future. In an article posted today in the Atlantic, Florida has used his team of experts to create a new index that measures how tolerant cities are of other people's ideas and innovations. The article, which can be found at this website, describes Florida's three T's of innovation which are technology, talent, and tolerance. His most recent article shows a chloropleth map of U.S. metros with color shades indicating levels of tolerance within each city. According to the article the tolerance index "ranks U.S. metros according to three key variables—the share of immigrants or foreign-born residents, the Gay Index (the concentration of gays and lesbians), and the Integration Index, which tracks the level of segregation between ethnic and racial groups." See below for a screenshot of Dr. Florida's Tolerance index.

Unfortunately, the article does not provide a link to the Tolerance Index dataset, but it does provide a short list of the top 20 most tolerant cities in the United States. We can use this list of the top 20 most tolerant cities as an example of how to quickly create our own map from a list such as the one provided. The first step in creating your map of the 20 most tolerant cities is to use a spreadsheet application to create a dataset that you can import into Cartographica. This requires a bit of data entry, but it's fairly painless. Once the data are entered save the file in .csv format. I provide an image below of my spreadsheet.

To create the chloropleth map of the U.S. metros you need to have a basemap that contains the U.S. metro areas of interest. To download a shapefile containing the metropolitan statistical areas visit this U.S. Census Bureau webpage. Once on the webpage, under the Nation-Based Shapefiles, select Metropolitan/Micropolitan Statistical Areas, and then click on the Downloaded Selected Files button. After the file is downloaded import the file by choosing File > Import Vector Data. To provide more context to the map add a Live Map as well by choosing File > Add Live Map. Be sure the Metro Areas map is on top of the Live Map in the Layer stack. See my map below for the full set of U.S. MSAs.

To join the top 20 Tolerance Index scores to the MSA map choose File > Import Table Data. This will bring up the import file window. Select the Join tab in the top right, change the Target Layer to the US MSA Map layer, change the Map To option to Name, check the box under Key for the Metro field, and then click on the import button. I provide an image below to show the set up. 

Because the .csv file you imported does not have data for all of the MSAs in the MSA file you can use Cartographica's Filter Bar to identify only those cities for which we have Tolerance Index data. Click on the magnifying glass inside of the Filter Bar and then select the Tolerance Index Score field. Type in 0 in the filter bar, a small window will appear that allows you to choose selection criterion. Select the option "is greater than". This will select only those cities that have values greater than 0 for the Tolerance index score. Notice the number of MSA visible on the map is much fewer than the original map. See the map below for an example. 

Finally, use the identify tool to select all of the remaining MSAs and then choose Layer > Create Layer from Selection. This will automatically add a new layer to the layer stack that contains only the top 20 tolerance index cities. Rename the new layer "Top 20 Tolerance Index Cities". You can create your own cholopleth map of the top 20 cities by double-clicking on the Top 20 layer in the layer stack, click on the + button five times to add five categories and then select a classification scheme (I used Equal Interval) by clicking on the gear box. Add a color scheme by choosing Window > Show Color Palettes and then click and drag the color ramp to the table in the Layer Styles Window to add the color scheme. Also, you can add a legend by choosing Window > Show Legend.  I provide an example of my final map below. 

 

Exploring Sea Level Change with Cartographica

The Pacific Institute has recently released a set of GIS data on measures of Sea Level change on the West Coast. This is a topic that has been in the news recently as scientists are investigating changing sea levels along coasts around the world. We are always trying to point our customers to new sources of data that they can use in their own professional work, or for the purpose of exploring the world around them with publicly available data. To download the Pacific Institute data visit the GIS Data Downloads section. Notice when you go to the website there are a number of files available that cover many different topics. Below I describe the methods for importing and exploring the data using Cartographica.

The first step for exploring these data is adding a Live Map, which we will use as the basemap for all of the Pacific Institute's data. To add a Live Map choose File > Add Live Map.

To create the following maps download the Dataset described as "Areas inundated by unimpeded Pacific coastal flooding under a scenario of 1.4-meter (55-inch) sea-level rise." To download the data click on the Ca_coast yr2100_flood.zip file. The file will automatically download. Once the data are downloaded save them to an desired location and then choose File > Import Vector Data… and open the shape file. When the data are imported they will have a name that is not so descriptive and will be colored white. Rename the layer so that it was a bit more descriptive. Rename the layer 55 Inch Sea Level Rise by clicking on the layer in the layer stack and retyping the layer name. Change the color to red to make the layer more visible on the map. To change the color double-click on the 55 Inch Sea Level Rise layer in the layer stack and then change the fill color to red by clicking on the fill box and then using the color wheel to choose a redish color. See the image below for an example of my map.

In addition to the geographic data, the files also include attribute data that provides a description of the locations at risk for flooding. To view the attribute file view the data in the data viewer at the bottom of the Cartographica window. Notice that the at-risk areas are named by which county they are in and there is data on the area and length of the individual locations.

For the next map, download the available data files that contain vulnerable public service buildings. Download the files for: vulnerable hospitals, schools, fire stations, and police stations. Once the files are imported change the names and color schemes to make a more effective map. Rename the layers Vulnerable Hospitals, Vulnerable Schools, etc. To make the symbols a bit more exciting you can use publicly available images of symbols representing each type of public service. To do this, go to Google Images to search for each type of symbol. For example, to find the school symbol use the search term "School Symbols". Once you find the image you would like to use double-click on the Vulnerable Schools layer in the layer stack and then simply click and drag the school symbol image into the symbol box within the layer styles window. Once the image is placed in the symbol box make sure to uncheck the stroke box, otherwise the image will be outlined and it will be hard to see. I provide an image below as an example.

 

See my final map below for an example of the point symbols for public service buildings at risk of coastal flooding in California. The image below shows the San Francisco Bay Area. I should mention that each of the public service building files also has accompanying attribute data that provides more information about the locations of each of the points.

Because you have a points and polygons added to the map you can use Cartographica's Count Points in Polygons tool to determine which of the flood prone areas have the most at risk public service buildings. To count points in polygon first select the 55 Inch Sea Level Rise layer in the layer stack, and then choose Tools > Count Points in Polygons. At this point a new window will appear, choose to count the number of at risk schools within each of the areas at risk of flooding. Once the count is complete you can see the number of at risk schools in each polygon by looking in the data viewer window. Another way you can check the number of at-risk schools in each polygon is by creating a chloropleth map. To create a chloropleth map double-click on the 55 Inch Sea Level Rise layer in the layer stack to bring up the layer styles window. Change the Based on option to Vulnerable Schools, click on the + button five times to add five categories, click on the gear box and choose distribute with Natural Breaks (Jenks). Finally, select a color scheme by choosing Window > Show Color Palettes, select a color scheme and then click and drag it to the table within the layer styles window. This will automatically apply the color scheme to the distributed values. I provide an image of the layer styles window and my final map below. 

As you can see from my map below it appears that the Orange County area in Southern California has some coastal areas that have many schools at risk of flooding if the sea level were to rise by 55 inches. 

 

Streamlining Workflow when Adding Features

When creating maps that involve drawing new features such as points, lines, and polygons it is often the case that you may want to create several new features that are exactly the same. Essentially, you may want to create identical features without having to re-draw each one. Cartographica has multiple functions that allow you to perform this task. I will illustrate with an example using polygons.  

There is a pavilion structure in downtown Lexington, Ky that is used for various events, one being its hosting of the local Farmer's Market on specific days throughout the week. In Lexington, one of the goals of the local Farmer's Market is to create a permanent location that would include multiple outdoor structures like the pavilion along with other features that perform other functions such as bathrooms, offices, etc. In this scenario I wanted to know if there was a downtown location that could fit several structures of similar size to the already present pavilion venue to potentially host a permanent Farmer's Market. So My first task was creating a new polygon that outlined the shape and size of the already existing pavilion location. To do this I first added a live map by choosing File > Add Live Map. This gives me the base image to use to draw the new polygon. See the image below of the pavilion that we are going to use to trace the new polygon. 

To draw the new polygon the first step is to click on the + button at the bottom of the layer stack. This will add a new layer that you can use to make the polygon. Once the new layer is added to the layer stack choose Edit > Add Feature. This will give you the option to choose the type of layer you would like to create. For this example we are going to create a new polygon layer. Select polygon from the window. See the image below for the window. 

At this point the background image will dim a bit and you will be able to create the new polygon. To draw the new polygon hold down the option key and click to place points in locations that will draw the appropriate polygon shape. Once the points are in the appropriate location hit the enter key. See the following image for an example. 

Now that you have the new polygon drawn, rename the layer in the layer stack to a more fitting name. For this example I named the new polygon Pavilion Polygon (Clever, huh?). Also, select the Bing Live layer in the layer stack and then under the Layer menu check the Include in Map Extent option. This will allow us to expand the extent of the map which will be helpful on the next step. When the map zooms outs, choose View > Zoom to Layer, this wlll automatically return you to the layer features you have drawn. Finally, move the Pavilion Polygon layer above the Bing Live layer in the Layer stack. We want our new polygon on top of the base map. 

There are multiple methods for creating identical polygons features depending on what your goals are. The duplicate function which is used to create additional polygons on the same layer. This is most often of use when you are interested in creating features that share the same geometry (and/or attributes). The second way to make identical polygons is the "create layer from selected feature option". This is used when you want to create shapes on a new layer that are identical to shapes on the old layer. Most often, this is when "clipping" items from one layer to another, but it can also be used to create a subset of features for purposes of planning. Finally, there is the copy/paste method that is used for moving data between layers. Often, you will use this mechanism when you are trying to relocate things onto another layer. To make these features more clear I will demonstrate each within the example mentioned above. 

Duplicate Function:

To create an Identical polygon on the same layer select the previously created polygon by using the identify tool. Next, choose Edit > Duplicate. Next, use the identify tool to select the duplicated polygon (hint: it's right on top of the old one) and drag it to a new location. Notice in the image below we still only have one Pavilion layer in the layer stack, but a new polygon is now shown in the open field to the South.

 

Create Layer from Selection:

To create a new layer that contains an identical polygon select the original pavilion polygon feature by using the identify tool. Next, choose Layer > Create Layer from Selection. This will automatically add a new layer to the layer stack that contains an identical set of selected polygons (again, the new polygon is directly on top of the original pavilion polygon). Use the identify tool to select the new polygon layer and then choose Edit > Edit Selected Feature and then click and drag the polygon to its new location. Notice in the image below I have place the new polygon layer next to the one from the duplicate step in the open field to the South.

 

 Copy and Paste:

To copy and paste features from one layer to another first select the pavilion polygon layer in the layer stack. Next, use the identify tool to select the original pavilion polygon. Next, choose Edit > Copy and then select the Pavilion Polygon Selection layer in the layer stack and then choose Edit > Paste. This will add an identical polygon to the Pavilion Polygon selection layer. Next, use the identify tool to select the copied feature (again, it's on top of the original pavilion polygon feature) and then choose Edit > Edit Selected Feature and then click and drag the copied polygon to its new location. Again, notice that the polygon has been moved to the open field to the South. The result of this method is that we now have two separate layers that each contain two identical copies of the original pavilion polygon. 

These features are all very useful for creating identical polygon features in the ways shown above. However, in performing the tasks above you have also completed a secondary task that deserves mentioning. This secondary task is the addition of feature attributes. Notice in the image below that the Pavilion Polygon selection layer has two rows which represent the two polygons on that layer. We can add additional columns of data to the polygon layers using several functions available in Cartographica. To add the Name column shown in the image below choose Layer > Add Columns. Next, choose Window > Show Layer Info, this will bring up a small window where you can change the name of the New Column to an appropriate name. To add the data to the new column simply click inside of the attribute window and type in the desired values. There are many other ways for adding additional data found under the Tools menu, or like we have seen here you can manually add data to your dataset. Depending on what your goals are Cartographica has a bevy of options that can help streamline your workflow, and help you produce professional quality maps and datasets.   

Georeferencing Detailed Cold War Era Maps using Cartographica

According to a recent article on Burrito Justice, during the Cold War the Russian government spent a lot of time and effort to create detailed maps of the many places in the world as potential targets for their military. The maps detail many different aspects such as elevation, water ways, and even streets that would be passable by tanks and other military vehicles. Now that the Cold War is over we can talk about the absurdity of it all and enjoy the hard work that the Russians put forth to make a series of really nice maps. 

The best location to find these maps for download are at maps.vlasenko.net. There are options to download maps at five different scales 1:50000, 1:100000, 1:200000, 1:500000, 1:1000000. The difficulty with finding the right map is that there is no good reference map for determining where the listed maps are located in the world. The author of the Burrito Justice article mentions that the 1:200000 map filed as J-10-22 is the San Francisco Bay area. So to use the same map in Cartographica, I downloaded the map, and then did some work georeferencing the image for use with Cartographica. 

To georeference an image the first step is to add a base map to compare the raster file to. For this, I used the Live Maps available in Cartographica. Choose File > Add Live Map to perform this action. 

Next, Zoom in to the San Francisco Bay area. Next, Choose File > Import Raster Data. This will import the downloaded map of San Franscisco. Notice there is a yellow triangle next to the raster file in the Layer Stack. This indicates that the layer is missing a coordinate reference system. You should see the window below after you import the raster data and click on the yellow triangle.

 

To set the coordinate reference system click on Set CRS. Because the Live Map is in the WGS84 Psuedo Mercator CRS we want to match the Russian raster file to the same CRS. To do this select the WGS84-Psuedo Mercator CRS and then click on the Set button. See the following image for an example.

Next, choose Layer > Include in Map Extent. This will allow you to see the entire Live Map rather than focusing only on the recently added Raster data (which right now isn't in the right place). 

Next, zoom in to the San Francisco Bay area using the zoom tool. Once you have zoomed in in to the San Francisco Bay area click on the Russian San Francisco layer in the layer stack and then choose Edit > Georeference Image. The Georeferencing panel will appear. 

Click on Fit Display to automatically move and resize the imported San Francisco map to the San Francisco Bay area that we have just zoomed to. Next, click on Flip Vertical to flip the raster image upright. The image below shows about what your map should look like. 

Next, we can use the Ground Control Points to adjust to location of the  Russian map to the underlying Live Map that we are using as reference. To do this, click on the Ground Control Points tab and the move the points to locations that help match the Russian map to the Live Map.

Hint: While doing this focus on geographic features that are easily identified and match them up. San Francisco has a unique pensula shape which is easy to see, but also there are many bridges and coast lines to help make the georeferencing process as accurate as possible. Also, as you attempt to match the two layers adjust the layer transparency so that you can get clear looks at what you are matching up. See the image below for the more accurately placed Russian Map. 

Once you have the map accurately placed you're finished! See a my final map below. 

 

 

Investigating International Incidents with Cartographica

The recent troubles in Syria have escalated a little farther when Syrian Forces shot down an American made F-4 Phantom Jet flown by Turkey. Multiple news sources are reporting on this issue and there have been some inconsistencies about what actually happened. According to an article written in the Telegraph, the Turkish Foreign Minister has stated that the F-4 was shot down in international airspace after briefly entering Syrian airspace. The attack came without warning to the pilots of the jet. Secretary of State Hillary Clinton has condemned the attack and has been quoted as saying that the attack was "brazen". See the image below for an example of the F-4 jet. 

According to numerous sources the Syrian airspace extends 12 miles from its border and the Turkish jet may have entered the airspace. According to this report on the Syrian Arab New Agency, the plane was heading toward Syria and was shot down only 1km off of the Syrian border. However, numerous other news agencies are reporting that the plane was shot down in international airspace more than 12 miles from the Syrian border. To highlight the situation further I have created a few maps using Cartographica to provide more context to the reports that are currently swirling. 

The first map below show the 12 mile airspace buffer around the Syrian border. To create this buffer I first created a new polygon layer based on the underlying Live Map. First, add the Live Map by choosing File > Add Live Map. Then click the + button at the bottom of the layer stack to add the new layer, then choose Edit > Add Feature. Choose Polygon, and then draw the polygon based on the boarder shown on the Live Map. Next, select the Syrian border layer in the layer stack, then to create the buffer, choose Tools > Create Buffer for Selected Feature. You will be prompted to set the buffer parameters. I selected Miles and typed in 12. The image below shows the results. 

The second image below shows the site where I have deduced the plane was shot down. According to the Turkish Foreign Minister it was located about 13 miles off of the Syrian border in international airspace. I highlight the location using a yellow star. To add the yellow star click on the + button at the bottom of the layer stack. Then, choose Edit > Add Feature. Choose to add a point feature and place it on the map at the appropriate location. According to the report the plane was shot down west of the city of Latakia. 

Keep an eye out on this situation as it unfolds. According to news reports Turkey is due to meet with NATO representatives on Tuesday to discuss the possible responses to be taken against Syria for its actions. Thus far the situation remains calm, but possible military action in the region is not impossible. The situation in Syria has continued to deteriorate over the past few months with the uprisings against Syrian President Bashar Al-Assad. This incident is only adding fuel to the fire.  

Fun with Cartographica at Davis-Monthan Air Force Base

Usually, our blog posts are all about the business of using Cartographica for analysis or for some other professional application. But, I am here to tell you that Cartographica can be used for exploring, and for viewing some of the most interesting locations on the planet. In a recent CNN article titled "Where Planes go to Die" author Thom Patterson points out some of the best aerospace sights on the planet. In checking out some of Patterson's suggestions I realized that Cartographica really allows you to explore and view interesting places without needing to do much work. Cartographica can be used for fun too! 

Patterson's article mentions the 309th Aerospace Maintenance and Regeneration Group based at Davis-Monthan Air Force Base in Tucson, Arizona. The 309th is tasked with maintaining and in some cases regenerating old Air Force planes for future use or for sell to other countries. To maintain the planes they are protected from the harsh desert climate using specially designed paints and other methods for reducing damage to the planes. The Airbase contains more than 4,000 airplanes, which would make it one of the largest air forces on the planet. According to the Air Bases official website for every $1 spent at the airbase the U.S. government recoups $11 that would otherwise be squandered. The airbase has also been made famous for appearing in popular culture. For example, the base was used in the recent Transformers: Revenge of the Fallen movie, and is replicated in the video game Call of Duty: Modern Warfare 2. The airbase actually consists of two separate parts, the air port sections and the boneyard. The boneyard is the storage site for the 4,000+ airplanes.  For this post I decided to use Cartographica to explore the Air Force base a little more. See below for some of the amazing aerial images and some features I created of this air base.

The first image below shows the location of the air base within Tucson, AZ.

The next image shows "the Boneyard" which is the storage area for most of the planes. Notice the measurement box that shows the storage area is nearly 500 acres. 

The next series of images highlights a few of the planes that I was able to identify. I created polygons using the Edit > Add Feature to create the highlighted areas. The first image below shows some retired B-52 bombers. There are many B-52s on site because of the START I treaty with Russia that was designed to significantly reduce the number of strategic bombers within both country's arsenals. 

 

The Next images shows retired C-130 Cargo planes which are still in service as a main cargo plane for all branches of the U.S. military. 

The next image shows a row of A-10 Warthogs which are known as "tank busters". The A-10s are flown by the 355th Fighter Wing which also works to provide air support for the Warthogs. The 355th is stationed at Davis-Monthan AFB. 

The final image shows a full view of the Boneyard with the individual sections dedicated to the various types of planes. Note that size of the site and that there are many other types of planes located there. I find the Davis-Monthan Boneyard very fascinating. 

Using MapQuest satellite imagery

MapQuest has recently made available, through information on their developer website, access to their tile servers for OpenStreetMap-style tiles of satellite imagery (mostly from government sources) and their street databases.   Because both CartoMobile and Cartographica support customized OpenStreetMap-style tile sources, you can now add these to your maps.

Cartographica Desktop

  1. Within an existing mapset, add a new live layer by choosing File > Add Live Map…
  2. Choose Other OpenStreet Map Server:  from the list of sources
  3. Type the following into the URL box:
    http://oatile1.mqcdn.com/tiles/1.0.0/sat
  4. Click Add

A new layer named OpenStreetMap Live will appear in the layer stack, and you're ready to go.

CartoMobile

  1. Create a new map or edit an existing map using Change Map in the gear menu
  2. Tap Add Custom OSM Server to add the new Base Map
  3. Type a label in the Layer Label box
  4. Type the following into the URL box:
    http://oatile1.mqcdn.com/tiles/1.0.0/sat
  5. Tap Done

Once you have re-entered the map in CartoMobile, you can use the Base Map by choosing it from the Base Maps list in the Gear menu.

Credit where it is due

MapQuest provides these tiles from a few different sources, mostly the US government and some from the Japanese space agency.

Tiles Courtesy of MapQuest

Map Diabetes in the United States with CDC data

The Center for Disease Control monitors various diseases within the United States. Often the CDC produces data for public use that can be used for scholarly and educational purposes. For this post I downloaded diabetes data from 2009 from this CDC website. The CDC are available for download in .xls format, so they need to be converted for use in Cartographica. After the data are downloaded, load the files into a spreadsheet program and save the file as a .csv. According to CDC data more than 25 million Americans have been diagnosed with diabetes and the rates of diabetes have continued to grow over the past decade. The CDC data are recorded at the county level so you should download the Current County and Equivalent county shapefile from the U.S. Census to join with the CDC data. Import the downloaded county shapefile into Cartographica by choosing File > Import Vector Data. 

To join the CDC data to the county file, choose File > Import Table Data. This will bring up the Import File window. Select the Join tab in the top right. This tells Cartographica that we are going to join the table data to another file. Next, change the Target Layer to the U.S. County file. In order to join the CDC data to the U.S. Census county file we need to join based on County FIPS and State FIPS codes. The CDC data has two columns that contain the County and State FIPS codes. Sometime FIPS codes are given as a single number that includes both county and state FIPS codes. However, the FIPS codes are also given in two separate columns, which is the case with the CDC data. Cartographica can join data based on one column or based on more than one column which is very helpful in the present situation. To select multiple columns change the Map to option to the appropriate column in the U.S. Counties file and the check the Key boxes next to the columns in the Import File Window. I provide an image below of the set up. 

After the data are joined to the County file they are viewable in the data viewer below the map window. I show an image of the data viewer with the CDC data joined below.

Finally, we can reproduce the maps shown on the CDC website by double-clicking on the U.S. Counties layer in the layer stack. This will bring up the Layer Styles window. To create my maps I used six categories, which are added to the table in the Layer Styles window by clicking on the plus button six times. To determine the interval sizes of the categories you can use any of the classification methods, but for these maps I used the Natural Breaks (Jenks) classification. To change the color I chose Window > Show Color Palettes and then I dragged the color scheme to the table in the Layer Styles window. I provide an image of the Layer Styles window and several maps I created using the CDC data. 

The distribution of Diabetes across U.S. Counties

The distribution of diabetes across Southern U.S. Counties. 

Here is the same map, after applying a different color scheme. 

 

Mapping the Appalachian Trail

The Appalachian Trail is a hiking trail in the Eastern United States that ranges from Springer Mountain, Georgia and Mount Katahdin, Maine. The trail is more than 2,000 miles long and it passes through more than 14 states. The trail includes numerous locations for viewing sights, camping, and lodging and typically takes several months to hike. The website TopoFusion has .gpx data that can be downloaded and imported into Cartographica to map the Appalachian Trail. The TopoFusion website says that the data are designed to be uploaded to GPS devices. gpx files can be imported by connecting your GPS device to you computer or by downloading .gpx files from another source like the internet or a local network. Download the at_full_gpx.zip file from the TopoFusion website and then import it into Cartographica by choosing File > Import Vector data. Also, add a live map by choosing File > Add Live Map. I show my map below.

 

The .gpx file contains some data related to the location of the start and end of the trail. However, we can add additional information to further enhance the .gpx file. To add a length column that will contain the data on the length of the trail choose Tools > Add Length Column and then check the data viewer for the added column. 

Notice that the length is given in meters. To convert the length to miles you can create a new column by choosing Layer > Add Column. Next, you can assign an equation to the column by choosing Window > Show Layer Info. In the Layer Info widow find the new column and rename it "length_Miles". I show an image below of the set up. 

Next, we can convert the length in meter column to length in miles by setting the formula for the new length_miles column. To set the formula click on the Set Formula button and then multiply the length column by 0.000621371192. To find the number to use for the conversion between meters and miles I did a google search and found the conversion. Once complete, click OK. I provide an image below of the Set Formula window. 

You can check to see the conversion in the length_miles column in the data viewer at the bottom of the data viewer. I provide an image of the data viewer below. The conversion shows that the trail is 2764 miles long, which matches the lengths I have seen in publication on the Appalachian Trial. 

Where are those FAA-approved Unmanned Drones?

In an article released last week it was reported that the Federal Aviation Administration has released the names and locations of Organizations approved for flying drones within the U.S. Drones have become infamous over the past several years due to their use for military purposes in Afghanistan, Pakistan, Iraq, and many other places. The use of the military technology for domestic purposes should not be unexpected as we have seen many military technologies cross-over for domestic purposes. Many argue that the use of such Military technology in local police agencies may be problematic and seen as an increase in domestic law enforcement power. Many argue that the use of these types of technology against the citizenry represents an unnecessary use of military strength.  However, it can also be argued that use of military technology in civilian life has been beneficial. One glaring example is the use of GPS. Did you know that GPS was originally a military development? 

In the article liked above a map is shown that can be quickly and easily reproduced in Cartographica. If you click on the map in the article it will take you to Google Maps which then allows you to explore the data. However, we can also download the data and import it into Cartographica. To download the data click on the KML link on the left side of the Google Maps window. This will automatically download the data. I provide an image below of the Google Maps page and highlight where the KML link is located. 

 

Next, open Cartographica and then import the KML file by choosing File > Import Vector Data. This will automatically import the KML file into Cartographica. You can also add a base map for context by choosing File > Add Live Map. I provide an image below of the data. 

 

Next, we can use Cartographica for more than just viewing the points. We can also create interesting additions to the map. To create a Kernel Density Map use the identify tool to select only the locations in the lower 48 states (notice there is one location in Alaska, we will ignore that location for now). Next, hold down the option button and choose Tools > Make Kernel Density for Selection... Next, you can select the type of Kernel you would like to use. For the map below I used the Negative Exponential kernel. To make the map more transparent you can double click on the KDM layer in the layer stack and change the opacity. Also you can change the color palette by choosing Window > Show Color Palettes. Choose the color palette and then drag it to the KDM layer in the layer stack. The color palette I used was downloaded from Color Brewer. For more information on how to use Color Brewer with Cartographica see this previous blog post.  My KDM map is shown below. 

 

Satellite Imagery of the North Korean Nuclear Test Site

Recent reports have indicated that North Korea is planning a nuclear weapons test in the near future. The possibility of this test has been confirmed by multiple governments including South Korea and the United States. According to this article by the Associated Press North Korea successfully performed two nuclear test in 2006 and 2009. The nuclear tests have come under scrutiny by the United States and other countries. The discovery of the nuclear test came through observations made by satellite imagery of the test site location. Take a look at this article's satellite imagery. Simultaneously, North Korea is also planning a launch of a satellite that many argue is similar to a missile test. Unfortunately, the satellite imagery used to detect the nuclear launch has not been made available online. Instead, we can use Cartographica to map the situation. 

To map the area, I am using Cartographica's Live Map. You can add a Live Map by choosing File > Add Live Map. Add the Live map with Roads on top of aerial imagery. To determine the location of the test site I used information I found in various news releases. The area of interest is near the North Eastern Coast of North Korea. The Image below shows a map of North Korea and the general vicinity of the test grounds. To add the point feature click on the + button at the bottom of the layer stack to add a new layer. Next, choose Edit > Add Feature. Locate the position where you would like to place the point and the hold down the option button and place the point. 

Notice that my map of the area uses a nuclear activity icon to indicate the location. Cartographica can use virtually any image to create a symbol for mapping purposes. To create this nuclear point I used the search term "nuclear" in Google's image search and then simply clicked and dragged the image into the symbol box in Cartographica's layer styles window. I provide an image below. 

 

The next images show several closer looks at the area. It appears that the test location is near the confluence of two rivers. The area appears to be very secluded and in heavy mountains with rough terrain. 


For the last image I added two buffers to show the distances around the test location. To add buffers select the point layer in the layer stack and then choose Tools > Create Buffer for Layer's Features. I chose to create buffers of one and two miles. The test location is likely within these buffers. The test will occur underground and the digging of the tunnels is what alerted many governments that there was a potential nuclear test upcoming. I have not been able to find new imagery that definitively shows the exact location of the test site. I am sure these images exist, but they are currently unavailable. 

 

Core Logic Report on Wind, Hail, and Tornado Risk

We have done a lot of posts about tornado activity in the past on this blog. This is largely due to the interesting data that are openly available as well as the awesome power that tornadoes contain, which naturally makes them interesting. The recent tornado activity in Indiana and Kentucky and then Monday's dramatic daytime tornado that ripped through through the Dallas area has again brought my attention back to tornadoes.

Recently Core Logic produced an interesting study on tornado and hail damage throughout the United States. Get the article here The report looks at similar data that we have used in other posts on tornadoes and the tornado maps look strikingly similar to the ones that we have produced using Cartographica. The Core Logic article is very interesting as it describes the growing tornado threat in the U.S. and it provides interesting maps along with some spatial analysis. These types of analyses are important because insurance agencies can use the conclusions to determine pricing for home owner insurance. One interesting finding of the study was that the traditional area known as tornado alley, which we have mapped in a previous post, is not the only place where people and property are at risk of tornado damage. The study finds that, "of the top ten states with the highest number of tornado touchdowns, only three fall within Tornado Alley." This goes against the popular understanding that most tornadoes happen in the breadbasket states.

Based on available weather data Core Logic also developed "modeling techniques to project the probability of a tornado or hail events for states and local core-based statistical areas across the U.S. Their study provides maps of the risk for damage from these types of storms. The map from the Core Logic study was produced using wind and hail probability layers that are combined to create risk probabilities. The article also provides local area risk maps for several states. Here is a snapshot of Core Logic's map. 

We can create similar maps using Cartographica. Using data from the National Oceanic and Atmospheric Administration's website. Download the 1950-2006 tornado touchdown points and then import them into Cartographica by choosing File > Import Vector Data. Notice that the points are in all fifty states, Puerto Rico and American Samoa. To make our map similar to the Core Logic Map we need to select the tornadoes only in the lower 48 states by using the identify tool. Select the points within the lower 48 states by dragging a box around them, and then choose Layer > Create Layer from Selection. This will automatically add a new layer to the layer stack that contains only the tornadoes in the lower 48 states.  I provide an image below of the new layer. Notice the blue points outside of the lower 48 tornadoes. These are blue because the new tornado selection layer does not include these points, and therefore the points in the layer under the tornadoes selection layer are still visible.  Once finished remove the original tornadoes layer by clicking on the - button at the bottom of the layer stack. 

Now, we can create a similar map to the one found in the Core Logic report. I should admit that our map does not combine multiple layers to create a risk map, but it does combine more than 50 years of tornado data into a pooled sample that allows us to create a historic heat map that shows the concentration of recorded tornadoes over the last half century. This map does not combine multiple variables to determine risk, but does show the spatial distribution of recorded tornadoes. Its plausible that such a large sample size over a long period of time may produce fairly accurate estimates about risk of tornado exposure at the present time. Its at least a decent guess. We can create our version of the risk map by holding down the option key and choosing Tools >  Create Kernel Density Map… Next, you can determine the type of kernel you would like to use and whether you would like to weight the cells by another variable.  I first used an exponential kernel with no weight to create the map. I provide an image of the Kernel Density window to show the set up. Also I provide an image of the KDM that this set up create as well. 

The KDM made with an exponential kernel shows where high concentrations of tornadoes are located. The red areas are at the highest risk. Yellow and Green are slightly lower risk than the red areas. We can create a map with different output by changing the kernel. For the second map I used a normal kernel to create the KDM. I provide an image below of the results. 

The next map shows a map made with a normal kernel and I also weighted the KDM by the F-Scale variable, which is a measure of tornado intensity. Notice that the areas at most risk of high intensity tornado activity are located in several southern states of the U.S. like Arkansas, Louisiana, Tennessee, Mississippi, and Alabama. This finding of risk outside of Tornado Alley is similar to Core Logic's finding in their report on tornado risk. 

 

 

Mapping Florida's Foreclosure Troubles

The recent foreclosure crisis has resulted in significant economic and social strain throughout the United States. One place hit very hard during the recession is Florida. Florida experienced high levels of foreclosures due to rapid home construction and predatory lending that occurred during the 2002-2005 run up to the recession. In this post I highlight a free source of foreclosure data produced by the Department of Housing and Urban Development through conjunction with the Mortgage Bankers Association and the Federal Reserve. The dataset provided by HUD allows you to download data down to the census tract level on home foreclosures. The data are collected and estimated for the year 2008.

Mapping 2011 Washington D.C. Crime Data

Washington D.C. has an excellent catalog of GIS data that is made available to the public at DC GIS. The website provides data on all sorts of issues ranging from criminal activity to building permits. Also the website contains spatial files that can be used to create detailed and professional maps. For this post I collected data on 2011 crimes within the city. I will show how to import the data into Cartographica, how to geocode the crime incident data, how to search for specific types of crimes, and how to create Kernel Density Maps.

Mapping Henryville, Indiana Tornadoes

Given the recent and tragic outbreak of tornadoes across the Midwest I thought it would be appropriate to do a blog on tornadoes. Before I continue I should mention how sorry we are at Cartographica that so many lives were lost and that so many homes were destroyed. Towns likes Henryville, Indiana and West Liberty, Kentucky were among the worst hit by the recent tornado outbreak.

Mapping 2012 Primary Results

The U.S. Republican Presidential Primaries are still ongoing, but a number of states have already completed their elections. To find election results do a google search for 2012 primary results or click here. The first search results should be a table of data with the percent voting for each of the major candidates in each state. I wanted to create a few maps to show the results. I quickly created a .csv file by copy and pasting the table into my spreadsheet application. I saved the file as Election_2012.csv. I provide an image below of what the dataset should look like.

Mapping CO2 Emissions World Wide

I recently read an article on the Guardian about the changing distribution of carbon dioxide production by country. Carbon dioxide is among the most closely watched greenhouse gases that potentially leads to global warming. According to the Guardian article China has increased its production of CO2 by about 170% since 1984, which is more than the U.S. and Canada combined. Other important changes mentioned in the article are that England has dropped in its production and that India has moved above Russia to the third rank. The U.S. is second in CO2 production behind China.