Cartographica.com

Mapping Recent Earthquake Activity

Recently there have been several earthquakes around the world. In Negros-Cebu region, Philippines there was a 6.7 magnitude earthquake that resulted many injuries and deaths. The earthquake activity peaked my interest to explore what was happening around the world in terms of seismic activity.

To find data on recent earthquake activity I went to the U.S. governments data portal Data.gov, which is a clearinghouse for thousands of maps and datasets. I found data on earthquakes for the past 7 days.The link to this data is here. When you go to the webpage you will see that you can download the data in .csv or in .kml both of which are readable in Cartographica. I downloaded the file as a .csv. Note: when you download the data you may have to save it as a .txt file and then open it in a spreadsheet application. The spreadsheet application should be able to read the file as a .csv. You can then save the .txt file as a .csv.

Once the data are downloaded importing them into Cartographica is easy. To import the data choose File > Import Table Data.

Next, the import data window will appear. The .csv file is download with latitude and longitude data that we can use to create a point map of earthquake locations. Select the "Coordinates" tab in the top right of the import file window. Then change the designations next to Lat and Lon in the table to Y (or latitude) for Lat, and X (or longitude) for Lon, and then click import. A screenshot of the import file window is provided below

Import File Window

After the points are geocoded you can see them in the map widow within Cartographica. However, to contextualize the points we need to add a base map. This is quick and easy to do in Cartographcia. Choose > File > Add Live Map. This will add a new base map to help visualize where the earthquakes are located. A screenshot is provided below of the points with the base map.

Earthquake Point Map

Visualizing the points on the map is only one way we can use the data. We can also create a kernel density map to determine where the highest concentration of seismic activate has occurred over the last seven days. To create a kernel density map select the Earthquakes layer in the layer stack and then Choose > Tools > hold down the option key and choose Make Kernel Density Map. This will bring up the Kernel Density window. Change the Kernel type to Exponential (Negative) then click Analyze. A screenshot is provided below of the kernel density map. Note: The layer opacity was changed by double clicking on the the Earthquake KDM layer in the layer stack and then adjusted using the opacity slider. The results indicate that much of the world's seismic activity as been focused on the U.S. West Coast and in Alaska.

Exponential (Negative) KDM

To create a second kernel density map that takes into consideration the strength (or magnitude) of the earthquakes I created a weighted kernel density map. To do this Choose > Tools > hold down the option key and choose Make Kernel Density Map. This will bring up the Kernel Density window again. Change the Kernel type to Exponential (Negative) and then change the Weight menu to Magnitude. I provide a screenshot below of the kernel density window.

KDM Window with Weights

You can see in the screenshot below that the weight of the large earthquake in the Philippines created hot spots that were not on the previous map.

KDM with Weights

Mapping South American Oil

Oil prices are a continuing concern of people all over the world. There are a lot of variables that enter into the price of oil, including where the supplies are relative to the damand and how much is controlled by a single party. Oil fields vary in size and in the amount of oil that they contain.I was curious about spatial distribution of oil fields and how much oil the fields contained. To visualize the issue I found data from the USGS.

The shapefiles that you can download show the locations of many of the worlds known oil fields.The datasets actually contain many variables for each oil field in the dataset, which is quite nice for exploration. For this post I am using the "Total Petroleum System Summary Data" shapefile.

To import the data into Cartographica choose File > Import Vector Data

The data are downloaded unprojected and without a coordinate reference system. However, the data are in a geographic coordinate system, which Cartographica guesses based on the extent of the map. We can project the data "on the fly" by simply adding a Live Map. To add a live map choose File > Add Live Map. This will automatically project the data using a pseudo-mercator projection. Below is a screenshot of the oil field data after they are projected.

Note: The shapefile will still not be accompanied with a .prj (projection file). To create a .prj file you need to export the layer and save it as a new file. To do this choose File > Export Layer Feature > Save the layer as Oil_Data. Creating a .prj is not necessary unless you want a copy of the .prj file.

Projected Data

Once you have created you new layer import it by choosing File > Import Vector Data

Because the dataset contains many oil reserves from all over the world I decided to focus only on the oil fields in South America. In order to create a map that contains only the oil fields in South America we need to create a new layer. To create a new layer that contains only South American oil fields�

Click the Identify tool and then draw a box around the South American oil fields to select the polygons. Then choose Layer > Create Layer from Selection. This will add a new layer to the Layer Stack that contains only the South American oil fields. Below is a screenshot showing the new layer added to the layer stack.

New Layer

To create a cholropleth map we need to use the Layer Styles Window. To view the Layer Styles Window double click on the Oil_Data_Selection layer in the Layer Stack. Change the "Based on" option to KWN_GAS. This is the variable that tells us how much known gas exists in each oil field.

Next, click the "+" sign five times to add five categories to the table within the Layer Styles Window. Then click the gearbox and "Distribute with Natural Breaks" The classification will change in the window. The bottom category should be -9999 to -9999. Change the second category so it reads 0 to 5002 (change -9999 to 0). Note that -9999 are codes for missing data. Below is a screenshot of the Layer Styles Window after it is set up.

Layer Styles Window

Next, change the color scheme by choosing Window > Show Color Palette > then choose a color palette and drag it to the table within the Layer Styles Window. Below is a screenshot of the final output.

South American Oil Fields

Mapping U.S. Gun Crimes

I recently came across an article on The Guardian that looks at gun crime in the United States. The article includes several maps and it also includes downloadable gun crime data for U.S. states. In this post I show how to import the downloaded data into Cartographica to replicate the maps produced in the article.

To download the data click on "Get the Data" within the article and then download the data in .csv format. The data set contains the total number of murders, total firearm murders in 2010 and 2009, total handgun murders, and the rates of firearm murder, robbery, and assault per 100,000 population in all 50 states. Note that there are missing data for Florida.

After the data are downloaded open them in a spreadsheet application. Delete the second row that contains the totals for the entire United States. We only want data for states. Once completed save the file as US_Gun.csv

Next, we need to download a shapefile for the 50 U.S. states. Go to the U.S. Census Tiger/Line Files webpage to download the shapefile. Under the "Select Layer Type" drop down menu choose "States (and equivalent)".

Tiger/Line Files webpage

On the next webpage, under the "State and Equivalent (2010)" drop down menu choose "All states in one national file" and then click download.

After the data are downloaded open the shapefile in Cartographica.

Next, to import and join the .csv file containing the gun data to the U.S. states shapefile use the following steps.

1. Click File > Import Table data > Choose the US_Gun.csv file

2. Next, we need to match the .csv file to the states shapefile. Click on the Join tab in the top right of the import file window.

3. Change "Target Layer" in the bottom right to tl_2010_us_state10 (this should be the title of the state shapefile).

4. Under the "Map to" column change the designation from "New Column" to NAME10 for the State row and then check the box under the Key column. Once this is set up click Import. The screenshot below shows what this should look like.

Import File Window

Now that the data are joined we can create maps.

1. Double click on the states layer in the layer stack to bring up the layer styles window.

2. Under the "Based on" drop down menu change the designation to "Firearms assaults per 100000 population" (this will replicate the first map from the guardian article).

3. The map in the guardian article uses five categories to define the map. We will match this by clicking the "+" button five times. The one place where we will deviate from the Guardian article is in the ranges we choose for each of the five categories. The guardian article's categories are not mutually exclusive. For example, the first category ranges from 1-10 and the second from 10-20. States with 10 firearm assaults per 100000 population are contained in both categories. To address this we will use a different classification method. Click on the gearbox and choose "Distribute with Natural Breaks (Jenks)."

Layer Styles Window

Finally, click on Window > Show Color Palettes. Here we can use one of Cartographica's built in color schemes or we can choose to make our own. Lets make our own. The guardian map uses a red to black ramp. In order to differentiate our map from theirs use a blue to black ramp. Follow the steps below to create a new scheme.

1. Click the "+" button to add a new color ramp.

2. Next, near the bottom of the Color Palettes window there are small white boxes. Click on those so that there are six white boxes.

3. Begin creating the color ramp by double clicking on each box and then select the desired color for each box. Once you have the colors selected rename the new palette to Blue Black. The screen shot below shows the final product.

Blue Black Color Ramp

Now that we have a new color ramp go back to the layer styles window and click and drag the new color ramp onto the categories to change the color scheme for the Firearms assaults per 100000 population data. The map I created using this process is shown below.

Final Map

Mapping South Korean Artillery Ranges

I am always very interested in what's going on in Korea. The border disputes that have occurred there over the past several years have made tensions in the area very high. Despite my interest in what's going on I don't know a lot about the specifics of the situation. In this post I use Cartographica to explore the area a little more. One of the biggest issues I am interested in is the artillery fire associated with an island called Baengnyeong Island. A Huffington Post article describes the most recent situation developing there. The article says that the South Korean military is firing practice shots into the water near the coast of North Korea. These two countries have been in a constant state of war since the 1950s so actions like these are always worth watching. For this post I am going to explore the ranges of several of South Korea's known artillery implements. The first image below shows the location of Baengnyeong Island in relation to North and South Korea's largest cities, Seoul and Pyongyang. Notice the island (indicated with a South Korean flag) off the West coast. In order to get the South Korean flag on the map I downloaded an image of the flag and then simply clicked and dragged it into the layer styles window.

Baengnyeong Island

Here is a close up of Baengnyeong island.

Baengnyeong Island

My question is: how far can South Korean artillery fire off of Baengnyeong Island? After doing a little internet research I discovered that the South Korea uses several different types of artillery. Obviously, the intelligence I am using for this is limited, but I think what I have discovered is fairly accurate. The other limitation of my research is that I don't know which of the active artillery implements in the Korean military is being put to use on the islands.

I discovered that the Korean military is most likely using the K9-Thunder Howitzer, which is a 50 ton 155 millimeter tracked artillery implement. Here is a picture of the K9. The K9's firing range depends on its ammunition, but it is approximately 40km.

Another implement that South Korea may be using is the American made M109 Howitzer, which is smaller than the K9-Thunder and has a range of about 20km.

K9-Thunder

Finally, there may also be slightly larger implements like the American made M107, which has a 175mm caliber. It is an older vehicle, but has a firing range of 35km.

To illustrate the range of these guns I created a buffer map based around the center of Baengyeong Island. The results are shown below. Its clear from the maps that artillery on Baengyeong Island could reach North Korean land. However, the distance inland is limited. Pyongyang is in no danger from that distance. Cartographica makes exploring problems like the Korean Island dispute very easy, and it allows you to create very informative maps. The next time I hear about artillery fire off of Korean Islands I will have a better understanding about what's going on.

To create the buffer maps I used a process that only takes a few minutes to do. First, I created a layer to represent the center of our area of interest:

1. Choose Layer > New Layer to create a new layer

2. Choose Edit > Add Feature so that you can add the center point for the buffers, and choose Point when prompted for the layer type

3. Hold the option key and click at the center of your area of interest to create the point itself and then press Enter to save it

Then, I used Tools > Create Buffers for Layer's Features to create the buffers:

1. Select the center layer (so it knows what to buffer)

2. Choose Tools > Create Buffers for Layer's Features

3. Select Uniform Width and enter a value

4. Press the Buffer� command

I repeated this 3 times to create 3 buffers each at a different distance, renaming them and changing their colors using the Styles� button to differentiate them in the illustration.

Artillery Ranges

Steps for Collecting County Level Data from the new U.S. Census American Fact Finder Website

This post describes the process for collecting data using the new U.S. Census American Fact Finder website (Fact Finder2). The new website allows you to select data from almost all U.S. Census Bureau surveys. These include the Decennial U.S. Census and American Community Survey among many others. The new data collection process requires you to use a series of steps for collecting what you want. These steps are logical as you move from general to specific selections. To start, you first determine the dataset and year that you would like to use. Then you choose the geographic level (i.e. national, state, county, cities, etc.), and finally you select the specific variables that you want for each geographic unit. As you make selections the "Your Selections" box in the top left of the Fact Finder2 webpage will update. This is a new feature of Fact Finder2 that helps you manage what you have selected. As we move through the steps pay close attention to the "Your Selections" box to be sure that you are collecting exactly what you want. The new Fact Finder2 is now the only way to collect data. Previously, the American Fact Finder website operated differently and contained less data. The new American Fact Finder is more efficient and allows for quick data allocation. Note: Be sure to use Firefox when using the American Fact Finder 2 website. First, go to the American Fact Finder website.

Here is a screenshot of the Fact Finder2 webpage.

Fact Finder 2

STEP 1: Selecting Your Dataset

To begin the data collection you need to first choose a dataset to collect data from. On the left side of the webpage there is a tab titled "Topics". The Topics tab is where you can set a number of search parameters for your data collection. Under Topics you can choose to limit the search by Dataset, Year, or by more subject based criteria like People, Housing, and Business and Industry. The Product Type selection under the Topic tab allows you to choose wether to get a full data set, a simple table, data comparison tables, and many other types of products. For the purposes of this example we are going to focus our attention on the Dataset option.

Steps for Choosing Dataset

1. Under the Topics tab, Click on the "+" sign next to the Dataset option. Click on the 2009 ACS 3-year estimates option (Note: Be sure to click on the Dataset. Don't check the box!). This will give you the 2009 American Community Survey 3 year estimates dataset. After you select the data set you should see the dataset listed under the "Your Selections" box in the top left of the webpage. After you select the dataset close the Select Dataset window.

Data Set Selection

STEP 2: Choosing Geographies

Now that you have selected the dataset you need to choose the level of geography that you want to use. On the left side of the webpage, under the Topics tab, is the Geographies tab. This is the location where you can limit you selection by geography.

Steps for Choosing Geographies

Under the Geographies tab, in Search Bar type "All Counties Within South Carolina" and click Go. Click on the All Counties within South Carolina selection in the Geography Results Window. It should be the first option. After you select the All Counties file it should appear in your "Your Selections" box. Once you choose the geography close the Select Geographies window.

STEP 3: Selecting Data

Next, we want to collect demographic and social data for each county within South Carolina. The variables that are available based on our dataset and geographic selections are now shown in the Search Results panel. To find the variables that you want you can scroll through the pages to and check the boxes for each variable, or you can use the Search bar. Using the Search bar is most efficient if you know what variables you want. We will use the Search bar to limit our selection to make finding the variables we want easier in this example. In the new version of American Fact Finder it is easiest to download variables one at a time.

Choosing Variables

Steps for Selecting Variables

Start by finding the Total Population data. Type Total Population in the Search bar. Find the Total Population variable with the P1 Id (found in the ID Column, next to the Check boxes). Check the box for Total Population. Then click download. A window will appear, click Ok. A second window will appear, click Download. Save the file to your desk top. Clear the Total Population search from the "Your Selections" box.

Next, find employment data by typing Employment in the Search bar. Check the Box next to Employment Status (ID S2301). Download the file. Clear the Employment search from the "Your Selections" box.

Next, find income data by typing Income in the Search bar. Find the Median Income in the Last 12 Months (ID S1903). Check the box. Download the file. Clear the Income earache from the "Your Selections" box.

Finally, find data on poverty status. In the Search bar type Poverty. Find Poverty Status in the Last 12 Months. Check the box. Download the file.

STEP 4: Data Extraction and Merging

Now that the data are downloaded. The tricky part is extracting what you want from each file and then merging all of them together. The files are downloaded in .csv format, and each file contains a unique identifier for each county. I use Excel to view and manipulate the downloaded data. However, other programs will work as well.

Steps for Extraction and Merging Data

Begin by opening the Population.csv file in a spreadsheet program. Save the population file as a new file, SC_County_2009. This will now be the file we merge data to.

To clean up this file, delete the Margin of Error Column. Also Delete rows 1, 2, and 3 so that only the column label remains.

Next, rename the Estimate column, Population, and rename the Geographies column Counties.

Next, open the Employment.csv file downloaded from the Census. The Employment Status file contains a lot of data. But we are only interested in Column J, which contains the Unemployment Rate. To merge this data with the SC_County_2009 file, copy only the numerical data from Column J of the employment status file, and then paste them into the SC_County_2009 file in a new column. (Note: Be sure not to sort the data in any files because it will affect the copy/paste process).

After you paste the data, rename the new column, Unemployment.

Next, open the Poverty.csv file downloaded from the Census. The Poverty file also contains a lot of data, but we are only interested in the poverty rate. The poverty file does not contain a poverty rate column. For this reason we will need to collect totals for both the number of people in poverty and the total population for whom poverty status is determined so that we can calculate the rate. Column D contains the population data and Column F contains the poverty counts. Copy and Paste the numeric data from Column H and Column F into the SC_County_2009 file as new columns.

After you paste the data, rename the data from Column D, Pop_Pov and rename the data from Column H, Poverty.

Finally, open the Median Income.csv file. This file has a lot of data, but we are only interested in the Median Income for each county. Column F, contains the Median Income data. Copy and Paste the numbers from Column F into, SC_County_2009.

After you paste the data, rename the new column, Income.

At this point you should have a new file (SC_County_2009) that contains columns: ID, ID2, Counties, Population, Unemployment, Pop_Pov, Poverty, and Income. Save the SC_County_2009 file.

SC_County_2009.csv File Format

Now that we have our Census file created we need to join it to a geographic file so we can create maps. However, to joint the data to a geographic file we first need to download the geographic file. To download the South Carolina shapefile go to the Tiger Line FIles

Select Counties or Equivalent areas in the drop down menu. A new window will open allowing you to chose the states to download data from. Choose South Carolina and click Download.

1. Import the South Carolina shapefile to Cartographica.

2. Click on File, and then Import Table Data

3. Choose the SC_County_2009.csv file.

4. Click on the Join tab

5. Change the Target layer option to South Carolina

6. The next step identifies a unique identifier used to link the boundary file to the .csv file. To link the two datasets each county is assigned a unique identifier, which is contained in both data sets. In the South Carolina Census file we can use the ID2 column as the link to the South Carolina shapefile GEOID10. Under the Map To column click on the arrows in the ID2 row and change them to GEOID10. Then check the box under the Key column in the in the ID2 row.

7. Then click Import. The data should now be visible in the data viewer.

8. Click on File, and then Export map to save the new shapefile.

Final Map

You're Done!

Digital Globe Releases Image of Costa Concordia Wreck Site

This post shows an image downloaded from Digital Globe that depicts the wreckage site of the Costa Concordia cruise ship. The ship wreck occurred off of the coast of Italy and is still currently being handled by officials there. There has been much media attention given to ship's captain who mysteriously abandoned ship before many of the passengers. He was taken into custody by Italian officials. For this post, I georeferenced the Digital Globe image using Cartographica so that we can create more informative maps and new data layers. The first step in using an image like the one downloaded from Digital Globe is importing it into Cartographica as a raster file and then georeferencing using a reference map like the one provided though the Bing Live Maps feature. .

To import the image into Cartographica Click File > Import Raster File.

The layer will appear in the Layer Stack. Click on the image layer and then click Edit > Georeference Image.

The next part of georeferencing the image is as much art as it is science. The georeferencing process involves first finding the general location of the image on a reference map using zoom tools. Then use the Fit Display command within the Georeference Image window, to provide a general first fit of the image to the reference map. Next, use Ground Control Points to more precisely match the image to the reference file. The reference file can be either be a Bing Live Map, which is provided in Cartographica, or another shapefile or raster file that is already georeferenced. For this example I used the Bing Live Map to georeference the image. I provide some screenshots below...

Georeferencing

Georeferenced

An advantage of georeferencing an image like the provided by Digital Globe is that it allows you to visualize the image alongside data from other sources. For example, the images above show the wreck site compared to features beyond the scope of the original image. This allows you to further contextualize the incident location. Also by georeferencing an image to a map it allows you to create other types of data layers based off of the satellite imagery. For example, the image of the crash site provides a very accurate depiction of where the crash site is located. Using this information I can quickly create a new shapefile from the image.

To create a new layer click the plus button at the bottom of the layer stack. Then click Edit > Add Feature. For this example I chose to create a new point file which allows me to indicate where the crash site is on a larger map with a single point.

Add Feature

New Feature 1

Italian Coast

CNN Releases Map for Battleground States for 2012 Election

CNN has released an interesting map that will be continually updated throughout the election year. The map shows CNN predictions for how they believe states will perform during the 2012 presidential election. The heading says that the map will be updated as new information becomes available. Probably the most interesting aspect of the map are the locations of the battleground states. Battleground states are those places that will have tough competition between the candidates. Also, another interesting feature of the map is that the total number of electoral votes is also included. This allows you to see which battleground states might really play a big part in the upcoming election. Keep an eye on this map as the election progresses. Here is a screenshot of CNN's map.

CNN Election Map

Microsoft GPS App that Guides Users Away from "Bad Neighborhoods" Taking Criticism

Microsoft has recently gained a patent for a GPS app that uses sources of data to alert users that they are approaching an unsafe area. The app links to GPS and smartphone devices to sources of crime data that alerts users if they are approaching an unsafe area.There currently is limited information about exactly what types of data the app will use and if it will account for other factors that may influence crime in specific areas. The complete functionality of the app has not yet been made available, but already the implications of the app are taking criticism. There are a number of issues with the app some of which have been talked about in other articles and others have not.

One problem is that it is not clear if other types of data will be used to supplement the crime data.For example, an area might be labeled unsafe by the app simply because there are a lot of assaults in an area, but the number of assaults that occur in an area may simply be a function of the number of persons who reside in the area. This is the classic count vs. rate problem. At this point it is unclear how exactly neighborhoods will be classified. In order to calculate the crime rates other population data would also need to be integrated into the app.

Another issue is that the number of assaults may be influenced by some other environmental factors that can explain a large percentage of the crimes that occur in an area. For example, lets say a user has been alerted that an area is "BAD". We know from criminological research that areas that have large numbers of bars may have more assaults due to the fact that many assaults occur in places where young people and alcohol come together in space. If we control for the effect that bars have on the number of assaults, then a place that is labeled "BAD" may not be so scary to people who don't plan on going into bars. In other words, the new app may label a place as unsafe for a pedestrian, but not consider the fact that most assaults in the 'alerted' location are due to altercations that happen at bars. Therefore, as long as the person avoids bars the neighborhood is basically a safe place!

Another problem with the app that has many people upset is that labeling certain areas as "BAD" can have negative consequences on people perceptions of neighborhoods that are only based on Microsofts interpretation. Negative perceptions of neighborhoods can have cyclical effects on neighborhood economic performance. Fewer people will likely attempt to enter alerted neighborhoods which would negatively affect local business owners. Another important question is how does this affect the ability of homeowners in certain places to effectively sell their home? Labeling in most circumstances is not a positive thing to do, especially when the labeling involves large numbers of people.

As any technology advances there will be issues with ethics. Certain technologies may help some while simultaneously putting others at a disadvantage. The argument could be made that while it is true that the app labels certain neighborhoods, Microsofts app simply supplements the natural map that each of us carries as we learn and experience our environments. There are likely places that you choose not to go to either because you have heard that they are bad, or because they make you feel unsafe. Perhaps an app like this allows you to make those decisions with actual data instead of stereotypes and misinformation.

Check out other articles on Microsofts new GPS app at these sites.

MSNBC

The Root

AOL Autos

Maps for the New Hampshire Primary

The highly anticipated Republican Presidential Primary is being held today in New Hampshire. The New Hampshire Primary is a particularly important election because it is the first non-caucus election held for the run up to the Republican presidential nomination and the subsequent Presidential election. The election receives a lot of media attention and is used as a tool for analyzing and estimating the performance of elections held in other states.

To honor the New Hampshire primary I have put together a data set with data collected from several sources. The dataset includes data from the American Fact Finder website, which is a service provided by the United States Census Bureau. The data included in the dataset are from the 2010 U.S. census and include basic demographic data about counties within New Hampshire. The variables include the total population, total white and total black populations.

In addition to the census data I also collected data from the Bureau of Labor Statistics. The variables collected from the BLS was the average weekly income, unemployment, employment, and total labor force variables. Unemployment and earnings have been a particularly important issue given the recent economic and are interesting data to explore for this election.

Also I collected data on the last presidential election in New Hampshire from the The New York Times. The variables collected from the Times includes the total votes cast for each candidate and the percent of the total vote that was given to each candidate.

The geographic data for New Hampshire were also collected from the U.S. Census Boundary Files website.

To combine these sources of data I created a .csv file and used Cartographica's ability to import table files. The steps for combining the boundary file and the .csv file are�

1. Import the Boundary file to the map

2. Click on File > Import Table Data

3. Choose the New Hampshire.csv file, or the file you want to import

4. Click on the Join tab

5. Change the Target layer option to New Hampshire

6. The next step identifies a unique identifier used to link the boundary file to the .csv file. To link the two dataset each county is assigned a unique identifier, which is contained in both data sets. In the New Hampshire file we can use the County name as the link. To do this Check the box under the Key column in the County row.

7. Then click Import

I have provided a few maps of the variables that are included in the dataset below. Also we will provide the New Hampshire data set so you can do more exploring on your own.

Population

Unemployment

Average Weekly Wages

Sources of International and Domestic Spatial Data

MapDawg is a free internet site that provides numerous links to both international and domestic spatial data. Depending on where you are interested in studying it can often be difficult to find sources of reliable data. This site provides links to both governmental and private sources of spatial data that are generally ready to use with your GIS.

The website alphabetically organizes the data by place starting with worldwide and international locations. Data are given from multiple continents, countries, and from all of the states within the U.S. The data found on these websites are compatible with Cartographica so all you have to do is download the data and then important them into Cartographica and you are ready to go!

In addition to the data sources Mapdawg also has links to important geospatial organizations found in both government and private sectors.

Also the website provides many links to other more general GIS related websites. There are links to websites for GIS jobs, news, and research, which can sometimes be difficult to find using internet searches. MapDawg has courteously done the work for us!

If you are looking for a new sources of GIS related information and data then MapDawg is a good place to start!

Iowa Caucus Map Released by Google

Google has released an interactive map tracking the results of yesterday's Iowa Caucus. The map provides interesting information about the results for each county within Iowa. The map highlights the results for each candidate that participated in the highly publicized vote that kicks off the run-up to Republican Presidential nomination.

According to the results, Mitt Romney and Rick Santorum gained the highest percentage of votes at 25% each. The two candidates were only separated by 8 votes (Romney was the winner). Ron Paul also had a good showing gaining 21% of the vote, however he gained zero delegate votes.

Click Here to see the map provided by Google.

Also, if you are a bit unfamiliar with the process of a caucus take a look at this Huffington Post article that describes how the caucus works.

New Support Site!

This week, we launched out new support site, using Zendesk. Besides a smoother ticketing system, we are also happy to announce that we have taken the feature request, help and tips areas and made them much more accessible. Please join us in inaugurating this new site by visiting, asking your questions, adding your feature requests, and voting on your favorite requests and topics.

Data about Australia

One of our participants from Australia has kindly sent us information about a number of resources available for mapping there. If you know of some good publicly available (or very good paid content), please feel free to Contribute a short article using the Contribute button at the top of the page and we'll approve it into the system. Without further ado... In no particular order:

Free data available from Natural Earth

Many of us spend a lot of time looking for data to use for maps and I wanted to mention Natural Earth, a site whose tag line "Free vector and raster map data at 1:10m, 1:50m, and 1:110m scales" pretty much says it all. There's a lot of stuff here, and it's good for many purposes, and it's truly free, as in Public Domain.

The data can be dragged and dropped directly into Cartographica for use in your own projects, without any licensing issues, which is fantastic.Further, there are both raster and vector data sets, so if you need high-quality raster reliefs for the backdrop of a map, these may well fit the bill.

Raster files are in TIFF format (which Cartographica reads very happily), with all of the necessary files for Cartographica to recognize the coordinate system and display it correctly.

Vector files are in shapefile format, with the required .prj files so that they will be positioned correctly as well.

The maps are well done, the layers are coordinated for use with each other (vector and raster layers will match up, as will the vector layers with each other), and the Vector layers contain a variety of useful data built in, not just for setting attributes, but also for performing basic analysis.

There are some caveats, though. These maps are made mostly for use in Cartography, and they're in WGS-84 Latitude/Longitude, which makes them less useful for any kind of area calculation. The maps are also relatively low resolution (scales down to 1:10,000,000), which means that you're likely not to want to use them for maps at a very local level.

With that said, it's a great source of useful data, and their site contains good details on how the maps were created, and how you can get involved. Go visit today!

Summarizing Point Data on a Polygon Layer

Occasionally it is necessary to summarize the data from a layer of points based on containment within a polygon layer. This article presents a relatively straightforward way to do this with Cartographica.

The solution to this problem requires the use of both an internal command and a small AppleScript, which we will provide. This simple script can be modified for your individual needs to apply basically any formula, but in the example case, it provides the sum of the values of a single column in the point dataset. At the end of this piece, we'll discuss some easy adaptations of this script.

Preparing the data

To perform this function, you'll need (a minimum of) two layers in your document, one Polygon layer, and one Point layer. Further, the example AppleScript requires that the data to be summed is a Number type. It is also required that the Polygon layer contain a column that provides unique identification of the polygon. This can be a String or Number type.

Preparing for the script

Before the script is run, we need to prepare the point data by having Cartographica determine which polygons they are enclosed by. To do this:

Select the Point layer in the layer stack
Choose Tools > Add Distance to Nearest Feature�
Select the Polygon layer from the Caluculate Distance to: menu
Type a name for the distance column in the Distance Column box
Check the box next to the Polygon layer's unique identifier column (UID column, henceforth)
Click Add�

After a few seconds, your Point layer will now have 2 new columns, one containing the distance (from the point object to the nearest edge of the nearest polygon, with the numbers negative if the points are within the polygons), and one containing the UID Column. Now, your data is ready to go.

Running the script

The script is available by following the link. It is relatively straightforward and can be modified for a variety of calculations.

To run the script:

Make sure your Map window is frontmost in Cartographica
Double-click on the script file to open AppleScript Editor
When prompted for Container Layer select the Polygon layer
When prompted for ID Column of Container select the UID Column (as mentioned above)
When prompted for Feature Layer select the Point layer
When prompted for ID Column of Features select the matching UID column created by the distance tool
When prompted for Column to Sum select the column whose data you want summed/

Once you have described the environment to the AppleScript, it will process the data. For large numbers of points, it may take some time, but we have found it very quick for thousands of features and tens to hundreds of polygons.

When it is finished, your Polygon layer will contain a column named "value SUM" if the original column to be summed were "value". (Note: if you run the script more than once, subsequent sum columns will be named "value SUM 2", etc...

Caveats

If you have trouble using the script as described above, double-check that the values columns are Numbers and that the unique id column is actually unique.
Very large numbers of points or polygons can make the script slow, although we have tuned it to use techniques to minimize the delays
Although you can perform other functions with Cartographica while the script is running, we would not suggest it.

Adapting the script to other functions

The script, as written, just creates a simple sum, although many other functions can be used with the basic structure. AppleScript takes a bit of getting used to, but in this particular script, most of the real work is done in the last few lines:

	set containerMap to {}
	repeat with container in every feature in containerLayer
		set containerMap to containerMap }
	end repeat
	
	-- loop through each feature in the feature layer	
	repeat with addFeature in every feature of featureLayer
		-- get the current value from the feature
		set currentValue to value of field data sumColumn of addFeature
		-- get the container from the feature
		set featureContainerID to value of field data featureIDColumn of addFeature
		
		-- unfortunately, this needs to be done in a loop, but it is all local, so at least it's not too slow.
		repeat with container in containerMap
			if containerID of container is featureContainerID then
				-- update the sum value
				set sum of container to (sum of container) + currentValue
				exit repeat
			end if
		end repeat
	end repeat
	
	-- now that we've applied the function, go ahead and store the final results in the containers
	repeat with container in containerMap
		set containerFeature to containerPtr of container
		set value of field data finalSumColumnName of containerFeature to (sum of container)
	end repeat

In particular, if you wanted to create an average as well as the sum, you would need to add a new count item to the containerMap, update that during the "update the sum value" step and then calculate the average before setting the finalSumColumnName value. To do this:

add itemCount:0 before the first "}" in the containerMap initialization
add set itemCount of container to (itemCount of container) + 1 on a new line after -- update the sum value
change (sum of container) to ((sum of container)/(itemCount of container)

Of course, you could both the average and sum, by creating a second column and duplicating the line that sets the value for finalSumColumnName and adding a new one for finalAvgColumnName

Farewell and Thanks, Steve Jobs

Yesterday we learned of the death of a man who had a profound effect on the course of my life and the life of this company. I am sure that this sentiment has and will be reflected many times, but it makes it no less true in our case. Our thoughts and prayers go out to his family and all of those whose lives were touched by him.

I never knew the man personally, although I stood in the same room with him many times. I have owned at least one Macintosh since 1984 (including a dark period where I carried a trusty Thinkpad and had a rickety Mac at home that I still used much more joyously). I have been a Apple developer since 1985 and have attended all but a handful of the WWDC events since 1989.

The Macintosh was an early embodiment of the idea that computers could truly be appliances for humans, and not task-masters. And I immediately knew that was where I wanted to follow. As a developer since an early age, I had been focused on how the computer could assist without getting in the way. How they could conform to what we needed and not the other way around.

Steve lead with this thinking, and pulled the world along with him. During that time, he made people delight with devices that acted intuitively and pushed developers to achieve more to make their software truly be productivity enhancers and not just one more archaic system to learn. He recognized excellence and pushed hard against complacency.

None of our recent products were ever noticed by Steve. However, he will continue to inspire our work to improve, re-think and create.

Thank you, Steve!

CartoMobile 1.1 released

ClueTrust is pleased to announce the immediate availability of CartoMobile� 1.1 on the App Store! New features include adding lines and polygons, easy use of Raster data as base maps, multiple projection support, and more!

Version 1.1 of CartoMobile is a huge release for us, starting with the name change. ClueTrust has decided to rebrand Cartographica Mobile as CartoMobile, both because the name "Cartographica Mobile" just doesn't fit well in the app screen on the iPhone, and also to differentiate its place as a field data tool, as opposed to a full-fledged GIS, like Cartographica.

CartoMobile 1.1 contains a host of significant new features and enhancements, putting it firmly in the number one spot for sophisticated field data entry for iOS

Support for adding Polygons and Lines: CartoMobile has long been a great way to add points to your data in the field, but now it supports lines and polygons as well. And it was worth the wait. Our interface provides an easy way to undo and redo points, track your movements or manually drop each point, edit attributes before, during, or after entering the full feature, and best of all—the ability to pause input on one or more features to temporarily add something else.
Support for multiple CRS (Projections): CartoMobile still assumes WGS84 Lat/Long if you haven't included a .prj with files that you copy to your device, but if you copy one with the .shp and associated files, the CRS will now be automatically managed so that it conforms to the displayed CRS. This works for new data as well, as added data will retain the CRS of the imported file.
Drag-and-drop raster imagery support: Adding your own raster imagery as a base map (for use when offline, or for any other purpose) is now as easy as dragging the TIFF, ECW, PNG, or JPEG file directly to your device with iTunes. Georeferenced images of any of these types are supported, and .prj files are required by any format that doesn't implicitly carry its CRS with it. And, unlike many of our competitors, we use adaptive caching to render the files directly on the device. It may take a couple of seconds to see the display the first time, but after that it's efficiently stored for later use. No prior conversion required and no loss of speed!
Custom Point Imagery: Now you can drag a .png file into your iOS device and select that as the point symbol for a layer in CartoMobile.
OnUpdate automatic changes for data: When using custom configurations, fields can be set to automatically update their data when Edit mode is invoked

And more... we've also fixed bugs and made both memory and speed enhancements to make the experience of using CartoMobile even more pleasurable than it was in the 1.0 series.

See CartoMobile on the App Store

Cartographica 1.2.4 available now

ClueTrust is happy to announce the immediate availability of Cartographica 1.2.4, a bug fix release for Cartographica 1.2. This release contains no new features, and fixes a handful of bugs (some peculiar to OS X 10.7 Lion). The full release notes are available at MacGIS.com.

Mapping Urban Densities World-Wide

I recently found a website for Nordpil who have made available a data set of major urban centers world wide. The data set contains population data for all of the world's major cities. I created a Kernel Density Map using Cartographica to show where the highest densities of urban areas are located. I discuss the process in this post.

Cartographica makes it easy to quickly explore GIS data. If you need the data for professional purposes then Cartographica allows you to make sharp looking maps in only a matter of minutes, while also allowing you to visualize your data. I downloaded the city data from the Nordpil website and then created a Kernel Density Map. To make my Kernel Density map more appealing I created a custom color scheme.

Here is a screenshot of the data imported into Cartographica. I should mention that the base map of the world was not downloaded from the Nordpil website. However, a shapefile can be downloaded from Diva-Gis I made the countries map Grey using the layer styles window so that the points could be easily seen and because I want to countries to be easily seen once I create my KDM map.

Imported Data

I created a new color scheme using Cartographca's Color Palettes window. This feature is really nice because you can literally create any customized color scheme that you want. Also you can use Color Brewer which I have already done a post about if you are interested. The second screenshot shows the Color Palettes window, which is where you can customize your color scheme.

Color Palettes Window

I created a KDM map using the exponential function which creates a nice looking map. Remember that you can hold the option button and then click on the KDM tool to bring up the additional KDM functions. The KDM option window is shown in the screenshot below.

KDM Option Window

Next, I applied my customized Urban Densities palette to the new KDM map by simply clicking and dragging the color palette on to the KDM layer in the layer stack. The two images below show the before and after KDM once I applied my color scheme.

KDM Map 1

KDM Map 2

The final map show where in the world the highest concentrations of urban centers are located. The major areas that we know contain much of the worlds population are highlighted. Eastern China, India, Europe, and the Northeast United States all have high concentrations of population, which is reflected in the maps.

Cartogaphica 1.2.3 is Lion Ready

ClueTrust is happy to announce availability of Cartographica 1.2.3, with updates for Lion compatibility and a handful of new features, as well as performance improvements and bug fixes. Check out the improvements in WFS, projection handling, import/export, performance for AppleScript and labeling. Come check it out at MacGIS.com. We are also providing a new 7-day key for anyone who has previously evaluated the software by visiting the lost key page.

The feature list for this maintenance release is short:

Support for exporting data tables from layers
Automatic export for CartoMobile imagery
Support for exporting raster layers as OSM/GE/BM-style tiles

But, we have enhanced quite a few existing features as well:

WFS configuration errors are more easily corrected and flagged, using the warning triangle in the layer stack
Image georeferencing now can be done without selecting the individual image in the layer
Improved intersection and containment selections
Improved labeling
Improved performance when working with imagery
and more...

Plus a number of bug fixes

As is tradition, users who have evaluated before will be able to retrieve a new 7-day key for use with the software by visiting the lost key page.