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Joseph Kerski's Blog

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How can a business student quickly and powerfully begin using geospatial technology?  The attached document was created in support of a capstone course for business master's students, but can be used by any business student as one resource to begin using geospatial tools and data.   Why do this?  Geospatial technologies are an excellent way of developing spatial and critical thinking, and to use tools increasingly in demand in the workforce. 

Greetings CTE Community:


I created two short activities that show how to do something really powerful in a GIS--join YOUR data to the amazing content in the Living Atlas of the World. I thought they would be of interest to the CTE community as the procedures to do so are technical, but not overly so, and in following them, students are building critical thinking and problem solving skills.  They are using and working with a variety of data sets while studying about real world issues.  On a technical level, they build several simple but powerful Arcade expressions using ArcGIS Online.



I hope these are useful and I look forward to hearing your reactions!

--Joseph Kerski 

I am sometimes asked to teach workshops focused on using GIS environmental topics for university environmental studies programs--for students and faculty conducting research and/or instruction.  The content for the latest iteration of this workshop is here:  univ_of_colorado_enviro_workshop_a19.pdf - Box 


Feel free to try any of these links, and enjoy!


--Joseph Kerski

I am proud to be included in the Green Team Academy's EARTH WEEK VIRTUAL SUMMIT, 22-28 April 2019.   This virtual summit features access to interviews with community leaders and experts.  Over 7 days, over 21 sessions will focus on how to "make a huge eco-impact in your community!"  To find out more and to register, see:


Join us and help spread the word!  My session will focus on telling your environmental story with story maps, including a hands-on demo portion. 

Earth Week Summit Joseph Kerski


Earth Week Summit 1

Earth Week Summit poster

The crowdsource story map recently was moved to mature status and is no longer supported.  I have been receiving inquiries from educators who still want to use it and because of its ease of set up and simple interface, ask me if it is possible.  Yes, at the present time, it is possible.  However, it is not advised for high profile or large projects; for these projects, it is better to gather the data with Survey123 and make a map of the results or to use another crowdsource option listed here.  I typically use this as an introductory activity in a face to face or online course or workshop, to have people share the landscape where they work, so we can discuss spatial patterns or landforms and vegetation type, to help them to start thinking spatially, and to get them immersed right away in web GIS technology.


If you still want to make a crowdsource story map, go to this location--the metadata page for the app:


Technical note:  When I sign in to ArcGIS Online and I go to the above location, I typically do not see the "Create a Web App" choice.  I know right away that I am in a loop where I cannot be signed in to ArcGIS Online and viewing the above page at the same time.  What I have to do to get around this issue is to sign in to ArcGIS Online, and then search for “crowdsource story map app mature".  Then, I go to the resulting page, and am now signed in, and ready to create my map, using "Create a Web App" as shown below.


Crowdsource story map template


Build and map your own data set:  A field experience.   I have written other lessons in GeoNet that you can use to map field data collected by others, but in this lesson, you will have the opportunity to build your own data set.

Let's say you work at a school in Melbourne.  You and your students are studying walkability, commuting, and urban spaces.   Your students have gone into the field in teams of two.  Each team has collected counts of all pedestrians crossing through selected intersections during a 5 minute span of time, and each team has recorded the counts from 0830 to 0835 am and again from 1400 to 1405  (2:00 pm to 2:05pm).   Some teams used a clipboard and pencil and some used their smartphone to enter the pedestrian counts.   The teams have also collected the latitude and longitude of the location they conducted their observations from.  To collect latitude-longitude, those who had smartphones used Motion X GPS or, if they had an iphone, they used the compass tool that is included in iOS, and those without smartphones used a GPS receiver supplied by you, their teacher.  You have ensured that each team had at least one student with a smartphone in order to take a photograph of the intersection. 

Creating a data table for mapping purposes.   Now your students are ready to create a database from which they can map their data and analyse the results.  Creating a database is a very important part of GIS, because it is the “I” part—the Information.

Open a plain text editor such as WordPad or NotePad on a Windows device or TextEdit on a Mac device, and enter the following data.  Tabular data for Spatial Technology needs to be entered in a precise manner, exactly as shown below.  You could copy and paste the data if you would like to, to save time.  

Note:  If you or your students are familiar with Excel, feel free to use Excel instead of a text editor, naming the fields as specified below.  After saving your Excel spreadsheet containing the data, save it again, this time as a CSV (comma separated value) file.  You may receive a Microsoft warning saying you will lose some content; but it is OK—CSV is fine for this purpose.  CSV is a “bare-bones” spreadsheet format.

Note key characteristics of the data you will be entering:  (1) The data will be in a comma-separated file--all of the data elements are separated by commas. (2) The first line is the header line, that line that describes all of the data that will follow.  (3) As it is important to be consistent in the field with measurement times and units, each observation represents the number of pedestrians over a 5 minute time span, with separate fields for the morning and the evening.  (4)  In a numeric field (integer or floating point), you should not place any letters, such as “degrees C” or “number of people” or “pH”.  The reason is because if you want to make graduated color or graduated symbol maps later on a value, that value needs to be a number.  Any letters will make your GIS consider that entire field as text instead of a number.  (5) Use all of the decimal places given by your GPS receiver or smartphone - no rounding and no truncating!  Recall our earlier exercise in mapping points--precision matters!  (6)  Think about the Cartesian coordinate system discussion:   X values in Australia are to the right of Y-axis (the Prime Meridian, and thus in the eastern hemisphere) and are therefore positive, and Y values are below the X-axis (the Equator, and thus in the southern hemisphere) and are therefore negative (that is, in Quadrant III in the diagram below, such as point E). 

Field study

Three latitude-longitude formats. Three latitude longitude formats exist, and it is important to be able to recognize each.  Let us take the Shrine of Remembrance example in Queen Victoria Park in Melbourne.

 Shrine of Remembrance
Shrine of Remembrance, Melbourne, Australia.  Photograph by Joseph Kerski.

Decimal Degree:  As the name implies, this format (DD) represents locations in the full degree of latitude and longitude and then the fraction of a degree afterward represented by numbers to the right of the decimal point.  The above monument in DD is located at -37.830588 Latitude, 144.973363 Longitude.


Degrees Minutes Seconds:  As the name implies, this format (DMS) represents locations in whole degrees and minutes, with second often listed with digits to the right of the decimal point for increased precision.  Degrees are represented with this symbol (°), minutes with this symbol (') and seconds with this symbol (").  It is important to note that just as with time, DMS is “base 60”.  In one degree are 60 minutes.  In one minute are 60 seconds.  Thus, in one degree are 3600 seconds.  The above monument in DMS is located at 37° 49' 50.1" South Latitude, 144° 58' 24.1" East Longitude.  With DMS, the cardinal directions are typically given rather than negative values for south and west.  DMS is often used in the “EXIF” header files in photographs to indicate the location where the photograph was taken.


Decimal Minutes:  This format (DM) represents locations in whole degrees, minutes, and fractions of a minute.  The above monument in DM is located at 37° 49.83528’ South Latitude, 144° 58.40178’ East Longitude.  As with DMS, with DM, the cardinal directions are typically given rather than negative values for south and west.


As an educator, why pay attention to these three formats?  (1) You will encounter and use spatial data in all three formats.  For example, DM is often used on geocaching sites.  DD is the preferred format for most of Spatial Technology work.  (2) For accurate mapping, it is important that the formats not be interchanged or mixed together.  For example, 144 58.40178 and 144.973363 are shown DM and DD, respectively, for the same line of longitude, and inputting the DM value into a DD table will place that data point many meters or even kilometers off of its true location.  (3) While you can use Excel or websites such as this one from Directions Magazine or this one with a map to convert between the three formats, spending some time discussing and working with each of the formats makes for excellent integration of mathematics.  For example, converting DMS to DM uses:  Degrees = Degrees, then Minutes.m = Minutes + (Seconds / 60), and converting DM to DD uses:  .d = M.m / 60, then Decimal Degrees = Degrees + .d.   DD = d + m/60 + s/3600.

Other Formats and Coordinate Systems. 
Can other methods of recording absolute locations be used?  Yes.  Street address, city, and postal code could also be used.  A more recent system to assign locations to previously un-addressed positions is What 3 Words.  Latitude and longitude, street address, and What3Words are examples of absolute location.  Street address is a bit more challenging because of the variation in street spellings and format (St Charles St vs Saint Charles Street, or 45 St vs 45th Street, for example), but geocoding is improving with each passing year.  Relative location is used frequently in everyday speech but generally cannot be used to determine locations in Spatial Technology.  Relative location examples include “left of the library” or “down the hill from the school”.  

Another geographic fact that makes working with Spatial Technology a bit more interesting is that some countries use the period and comma in a manner directly opposite to other countries.  In France, for example, 26,000 means “twenty-six”, while 26.000 means “twenty-six thousand”.  In other countries, a space is often used instead of a comma, such as 26 000 for “twenty-six thousand”.  Keep these variations in mind as you seek and map data.

Still another geographic fact that makes working with Spatial Technology a bit more interesting is that there are other coordinate systems in use in different states and in different countries.  These include the Universal Transverse Mercator coordinate system (given in meters, northings, and eastings, which you will see later in this component), national coordinates such as the Map Grid of Australia, and state and regional coordinates such as the State Plane Coordinate System in the USA.  You will encounter data in these other coordinate systems in the future as you work with Spatial Technology.

Enter data into a table or file.  Key in (or better yet, copy and paste) the pedestrian count data below into a text file.   Name it melbourne_pedestrians.txt and save it on your computer.  If you are using Excel, save it first as melbourne_pedestrians.xlsx and then as melbourne_pedestrians.csv. 

Observe that (1) the latitudes are all negative!; (2) the first line contains the field headers, including peds_am (pedestrians for the morning 5 minute period) and peds_pm (pedestrians for the afternoon 5 minute period). (3) your data file cannot have any extra characters or blank lines in it; it must look exactly like this, below:

point, latitude, longitude, peds_am, peds_pm
1, -37.808433, 144.96605, 110, 66
2, -37.819727, 144.969252, 75, 82
3, -37.841688, 144.934744, 19, 12
4, -37.805377, 144.973450, 75, 39
5, -37.814758, 144.961700, 177, 129

Map your table of data.  When you are done with your data file, return in your browser to your existing ArcGIS  map that contains the hike in the chaparral to 34 North 117 West and the Piccadilly walk that you named “GTAV Mapping Field Data.”   Use the Add button and "Add Layer from File".  Navigate on your computer to the folder where you stored your melbourne_pedestrians txt or csv file.  Select this file and add it to the map.  “Smart mapping” will attempt to symbolize your data for you, by your point number, and your map will look similar to the following (change your basemap to the one shown, which is OpenStreetMap). 

 Field study

Change the (1) “Choose an attribute to show” from point to peds_am, and (2) change the colour to red or something else, or from a circle to some other symbol, using Counts and Amounts (size) > Symbol, as follows:

 Field study

Field study

You are now mapping morning pedestrian counts.  Go back to your map. What spatial pattern do you notice?

Set a bookmark here Field study and name it Melbourne City Centre Australia or something similar.  Click on a few points on your map.  Do you see the pedestrian data that you have entered for each point appearing as popups?  Note for your future use of Spatial Technology that the content of these popups can be modified.   Save your map again.

Now you are ready to tap into some of the true power of GIS—mapping two variables, and building expressions.

Map two variables.  Because your students went into the field twice and collected data during the morning commute hour and during the time between lunchtime and the afternoon commute, let’s say you wanted to compare the two values. 

First, use Change Style underneath your Melbourne pedestrians layer.  Second, use Add Attribute > add the peds_pm value as an additional attribute to map > Done.  Your map changes so that the colour of the circle represents the morning pedestrians, with darker indicating more, and the size of the circle indicates the afternoon pedestrians, with larger sizes indicating more afternoon commuters.  Knowing this, reflect on what a large dark-coloured circle would indicate vs. a large light-coloured circle.  

Field study

Use an expression for further analysis. Second, you can use expressions in Spatial Technologies, as you have already seen in your use of filtering earlier in this module. Here, use an expression to analyse the difference in morning versus afternoon, specifically, dividing the morning count by the afternoon count.  Do this as follows:  Change Style > Under (1) choose an attribute to show, select “New Expression” as follows:

Field study

In the dialog box for New Expression, click on feature “peds_am”, then enter / for the divide arithmetic operator, then click on feature “peds_pm”, so that your expression looks like that shown below.  Your expression will be built as you click on variable names; that is, you should not have to key in anything except for the / operator.  Once the expression is built, select OK in the lower right:

Field study

Your expression should look like this:

Field study

Save your map again.  Note the new patterns on your map. The point near the river is barely visible (you can adjust the size of the points using Change Style) because the afternoon count was more than the morning count, and therefore, the ratio was less than 1.  Conversely the northeasternmost point is the largest because the morning count there was nearly two times higher than the afternoon count. 

 Field study

You conclude that the northeastern part of the city centre seems to have proportionally higher pedestrian traffic in the morning than in the lower commute time of mid-afternoon, but that you need more points.  You decide to revisit the existing sites to gather additional data and also to gather data at new sites.   Save your map again.

Reflecting on the skills you have gained.  Reflect for a moment on the Spatial Technology skills you just gained.  Now you know how easy it is to add your own data from a spreadsheet, map it, and start analyzing it. You also learned how to map more than one variable at a time, and you learned how to add an expression. These expressions, called Arcade Expressions, can be used on any data, provide connections with mathematics education, and extend the power of Spatial Technology.  Think about using it to compare two population counts, or food cost eaten at home vs. away from home, or earthquakes by size and magnitude, for example.  

Create your own map from field data.  To build your own map from field-collected data, you have many options.  One way is to use a GPX file from a fitness or GPS app such as the Motion X GPS app.  

Creating your own map from field data that has been generated in the field via a smartphone app.  Find the GPX file attached to this blog post.  Save it to your device.  Spatial Technology is able to input and output a wide variety of file types.  To help focus attention on file type, make sure that the extensions are made visible in whatever operating system you are using;  so that the that file you will work with now looks like the following, with the .gpx extension visible, instead of simply “track_to_picadilly_stn”, but with the full name of the file as follows:

Field study

Do not open this file, because you will shortly make a map of this data in ArcGIS .  Go to > Map > Then, to add a data layer to the map, you will need to select MODIFY MAP to the upper right of the map:

Field study

Once you “modify map”, an “Add” button now appears to the upper left of the map, as follows:

Field study

Use the Add button > Add Layer from File, as shown below:

Field study

Note the different types of files that you can add from your local device to an ArcGIS  map.  These types include a GPX file in the list.  Navigate on your computer to the folder where you stored your Piccadilly Station GPX file.  Select your GPX file to add it to the map.  

Once you have added the Piccadilly Station file, it becomes a map layer.  The map should zoom to the location of your newly added layer, and your new GPS track should appear in the list of Contents as a layer.  If your map is not showing the location of your new GPS track, use the ellipses (…) next to your new layer and > Zoom to Layer.  This is the same type of file that you examined for the chaparral hike, but in this case, an actual physical GPS receiver was used instead of a smartphone app. 

Change the basemap to imagery if it is not already an image basemap.  Notice the vast difference in the land cover here versus that of the California hills chaparral!

Saving your map.  Now that you have added data to the map, you can save it into your own ArcGIS  organizational account.  Use the save tool > Save tool Save your map, naming it “Mapping Field Data” or some other suitable title, and providing tags (such as fieldwork, California, England, GPX) and a description (such as “Mapping Field Data”) so you can easily find it later.

Spatial Thinking, Basemaps, and Bookmarks.   If you were using this map in the classroom, consider asking your students to examine clues on the image to determine the type of building that is Piccadilly Station, located at the northeastern end of this walk.   

Change the basemap to topographic and zoom out.   Consider asking your students:  In what city is this walk to Piccadilly Station located?

Use Track to Piccadilly Station > ellipses (…) > “Zoom To” to zoom back to the Piccadilly Track.  Use Bookmarks to set a bookmark here named Manchester UK to return to it later.  In a similar way, zoom to the Track 002 for the chaparral hills, and to the upper right of your map > Manage Bookmarks > set a bookmark there called “Yucaipa California USA”.  Bookmarks are helpful shortcuts to use to navigate in web maps. Use your bookmarks to zoom back to Piccadilly Station.

Change the symbology (style).  Using the Contents tool, note that just as with the chaparral data, the data for the Piccadilly Station walk is encoded as a point layer and a line layer.  Using your Spatial Technology skills, see if you can determine how to change the symbology (or “style”) for the line to be yellow and the points to be orange.  (Hint:  Under Change Style > Options > Symbols).  Save your map to capture your latest additions and changes.

Assess Data Quality.  After you do this, note that the first 4 points in your data table are associated with an elevation much lower than the rest of the data, as shown below:

 Field study

Which elevations are correct--those 5 points that are less than 40 meters, or those 159 points that are just above 40 meters?  Change the basemap to topographic.  Note that this layer does not provide elevation values.  Therefore, you need an elevation data layer to verify your data.  Fortunately, the Living Atlas of the World contains hundreds of data layers for your use in education.  Remember this resource for your future use of Spatial Technology as it is one of the largest libraries of data and maps in existence.

Add data from the Living Atlas.  Add an elevation layer as follows:  Using the Add Data tool > Browse Living Atlas layers and search for “world elevation gmted” as shown below. 

Field study

Click on this GMTED layer > Add to Map.  Close the appropriate add data panels and return to your Show Contents of Map tab.  The data you have just added is the world elevation image service from the Global Multi-resolution Terrain Elevation Data (GMTED) from 2010 with a 250 meter cell size.   Use the ellipses (…) to make this layer semi-transparent.  Then, zoom out and you will notice that it truly is a global elevation data set with different colours representing different elevations:

Field study

Save your map again.  This elevation layer might come in very handy for other lessons you are teaching, particularly since it is global in coverage and thus includes Australia. 

Zoom back to the Piccadilly Station (using Zoom To, to the right of the GPX track, or, using your spiffy new bookmark!).   Click on the map to obtain the elevation in a few places.  The popup will look similar to that below:

Field study

Change the visibility range of your map layer.  You may have noted that on this and other layers, the layer may disappear at certain scales.  This is a typical part of today’s smart mapping technology and occurs so that your map does not become too cluttered.  But you can change this by expanding the map layer in Contents > Ellipses (…) > Set Visibility Range, and expand each end of the range with the slider tools so you see your track at all scales, as follows:

Save your map again.   Turn off the elevation data layer.

Verify the elevations using one more source:  Open a new tab and open this elevation map of Manchester, here, clicking on a few points in the neighborhood of Piccadilly Station to obtain the elevation:  

Summarizing attribute data quality:  Elevation.  Using these two sources, you note that the average elevation is around 42 to 48 meters.  Now that you have verified that the first few elevations collected in the track were the incorrect ones, consider the location of these first few points.  They occurred where the author had just left the building, and as discussed above, this is where the GPS is now free to receive unobstructed signals from the satellites, and therefore, it takes a few seconds to achieve accuracy in the x and y coordinates (longitude and latitude) and up to a few minutes or longer to achieve accuracy in the z coordinate (elevation).  That is exactly what occurred here.

During this data quality investigation, you also built additional key Spatial Technology skills, such as examining the table, adding additional data layers including from the Living Atlas of the World, and changing the visible range.

The Living Atlas of the World:  A mapping resource.  In this activity, you added elevation data from the Esri Living Atlas of the World.  

The Living Atlas isn’t the only library of spatial information that exists.  But it is worth examining because it (1) is continually curated and updated; (2) it contains authoritative (not perfect, but from authoritative sources) data; (3) it contains thousands of maps and data layers; and (4) the data are in the ArcGIS  platform and therefore can be easily mapped. 

Other resources for mapped data exists, including the National Map of Australia, the City of Melbourne’s and Victoria’s data portal, and others listed in the Explore Further sections of this component.  Your own local governmental authority or university may have spatial data that they are willing to share, so searching those websites using “GIS” and “maps” and “spatial data” as search terms might result in some potential data for you to map.

Examine data that has been generated in the field via a smartphone app.  Let’s examine a map in ArcGIS  that was created from data generated in the field:,33.9958,-116.9831,33.9993  

This map is entitled "Motion X GPS Track:  Hike to 34 North 117 West" by jjkerski and will look similar to the map below.

The following steps are the same as those that you can use elsewhere when you use maps in ArcGIS . 

Explore the map and data layers.  First, click the Contents tab to the left of the map to view the layers.   You will note that the map contains 4 layers:  Map notes, track points, track lines, and a satellite image basemap.  Some layers expand when you click on their names in the Contents tab.  The track, for example, expands into points and lines.  This is because certain field data collection apps collect several types of spatial data simultaneously; in this case, the app that was used in the field collected point data and line data.  Each is useful.

Second, open the table for the point and line layers, as shown below.

Field study 2

Note that the line layer only contains 1 feature, while the point layer contains 581 features.  The fields in the point layer include the elevation (in meters) and the date/time.  The point layer is symbolized by elevation, and if you take a quick look at the Legend you will see that the red colors indicate higher elevation and yellows indicate lower elevation.

Third, zoom out a bit to see the entire hike, noting the type of vegetation and terrain traversed, location of the beginning and end of the hike, which sections of the hike followed a trail and which did not, and the high and low points along the hike.  Change the basemap to a streets basemap to answer the following question:  At the west end of which street did the author park on to begin the hike?  Change the basemap back to imagery.

Click on the 2 pushpins to see the photos taken at those locations. These pushpins are “Map Notes” which you have used in this module in the past.   

Zoom out further until you can determine the region of the world in which this hike was taken (the chaparral biome of southern California USA).  To zoom in to the hike, use the … ellipses for Track 002 > use “Zoom to”, as shown below:

Field study 3

Fourth, use the Measure tool to measure the distance between each of the points that appear along the lines.  What is the average distance between each point for a certain section of the hike? 

These steps, from one to four above, that include opening the data table, measuring, changing the scale, and accessing the symbology, are standard procedures that you can use on any map and set of layers in ArcGIS .

Examining the app and the types of field data.  In this case, the data was collected with a smartphone app, called Motion X GPS.  Motion X GPS is one of a series of apps (others include Gaia GPS and Polaris GPS) that collect points and lines in the field.  They all emulate a GPS receiver on your smartphone, including displaying locations in a number of different coordinate system, routing, distances, and directions.  As you saw on the map, two formats of data are typically collected in the field—point data, and line data.  Points can be “track points”, which can be thought of as digital “bread crumbs” that are “dropped” as you move across the landscape at a specified distance or time (such as every 2 meters, or every 1 second).  Points can also be “waypoints” that you drop on purpose at locations that you specify—where you measure water quality along a shore, at a tree or light pole, or at a street corner where you are measuring noise.  Fitness apps, such as Runkeeper, Strava, and MapMyRun, often output point and line data that can be mapped. One of the common formats for these files is a GPX file, which stands for GPS eXchange Format--a type of XML data file for exchanging GPS data between programs, and for sharing GPS data among many users.  So, when examining smartphone field apps for use in education, seek ones that allow for output in a variety of formats, including GPX. 

Even though these apps often contain their own mapping interface, what is more useful for educational use is the ability to bring the field collected data into a spatial technology tool such as Google Maps or ArcGIS .  If the app itself does not output into an exportable format, sometimes the website for the app includes that capability (such as 

Be critical of the data’s spatial accuracy.  In keeping with the theme of being critical of the data, before leaving this data set, zoom to the eastern side of the collected data, near the white circular water tank.  Even though the person collecting the data (your module author) walked on the trail in this area going up and going down the ridge, note the offset from the trail for one of the tracks.  By examining the date and time stamp on the data, you can determine whether the offset occurred on the way up (climbing from east to west) or the way down (descending from west to east).  Measure the offset of this section of the track from the trail as shown on the satellite image, as shown below.

Field study 4

The points could have been compromised in terms of positional accuracy for a number of reasons, including:  The field worker could have inadvertently shielded part of the phone with his hand while gingerly picking his way along the trail while collecting the track, the cell phone reception might have fallen off, dense vegetation or the high ridge to the west could have prevented the phone from sensing as many cell phone towers, wi-fi hotspots, and GPS satellites as it had done along the ridgetop, or the app might not have been recording accurately here.  This compromising of positions happens when using GPS receivers, as well, and is something you and your students need to continually be examining and questioning.  It happens most often near ridges and canyons, such as in the case here, but also in city centres with high rise buildings, and also from within a building or when the field data collector first leaves a building and enters the outdoors.  Discuss with students the different accuracy requirements depending on your project.  For mapping trees on campus, being a few meters off might not be a problem, but it would be a problem for objects spaced more closely together, such as mapping headstones in a cemetery, or pieces of litter on a city street.  Discuss the even finer accuracy requirements for such things as mapping natural gas lines or fiber optic cables. 

Be critical of attribute data quality.  Thus, spatial data quality is important.  The quality of attributes—the information you collect in the field—is also important.  Your database will consider “spruce” and “spruse” to be two different types of trees, for example.  Some data entry errors can be solved by the use of pull-down menus for field data collection, minimizing the amount of free text entry that has to be done in the field under various conditions.  Associated with attribute data quality is ensuring consistent devices and clarity on methods used (for example, to measure the height of a tree).  Also associated with attribute data quality is paying close attention to the units you are using in the field.  If some students are collecting tree girth in cm and others in mm, that could lead to potential misinterpretation of results.  Similar results could occur if one student collected pedestrian counts along a street for 1 minute and another student for 5 minutes. 

Now that you have had experience examining field data in an interactive web map, you are ready to investigate other lessons on GeoNet where you will build your own map with the same type of data that was used for the chaparral hike that you examined above.

Teaching international migration with Spatial Technology:  Introduction. 

Why teach about migration?  Migration is inherently a geographic issue.  It touches on themes of physical geography (such as climate and landforms), cultural geography (political systems, political instability, political boundaries, demographic trends past and present), sociology (perception, push and pull factors), and many more.  It changes over space and time and is an excellent way to teach spatial concepts and skills. Since the dawn of humankind, migration has always been present; thus, it ever remains a current issue. It is also relevant, causing deep and long-lasting changes in culture, language, urban forms, food, land use, social policy, politics, and much more.  Migration is a global issue that affects our everyday lives. It also impacts the formation, change, and rate of change of cultural regions.  It is also a personal issue, because each of us has a migration story to tell about our own ancestors and families. 


Begin investigating migration with the International Migration Map.  One of the maps in the Esri Coolmaps gallery enables you to visualize migration data over time and space in a 2D and 3D tool that is a powerful and effective tool but yet is responsive in a web browser. 


Open the map.  The map opens in 3D mode and in Play mode, showing a set of data for selected countries (the UAE, Mexico, China, and Singapore during the 1990s, 2000s, 2010, and 2013.  This selected set provides a good introduction for teaching about the patterns, relationships, and trends in the data.   The time periods are shown below the lower part of the map, with the out-migration and in-migration available for each of the four time periods.  The thickness of the lines coming out from or going to each country selected indicates the amount of migration, and the end points of each line indicates the countries sending people to or receiving people from each country.  For each country, the raw number of out- and in-migrants is indicated, along with the percentage of that country’s total population for each time period. 


After viewing the introductory data, use the “pause“ button to stop the Play mode and to select among the list of the world’s countries.  The capability of selecting countries and time, the cartography, and the ability to switch between 2D and 3D combine to make this a useful teaching and research tool. 


Teaching with the international migration map.  In keeping with our Module theme, ask, “Where did the data come from?  Can you trust it?”  In this case, the data came from the United Nations Trends in International Migrant Stock:  The 2013 Revision is provided by the UN Department of Economic and Social Affairs.  Use the “i” button to go to the data’s source.  Encourage students to investigate the data at its source, and to study how and when it was collected.   According to this data set, how long does a migrant have to live in a country before he or she is no longer considered a “migrant”?


Use this map to help them understand migration patterns and number.  As elsewhere in this Module, this map could serve as an excellent supplement to other sources that you may use.  For example, ask, “How has Australian immigration changed in amount and in the countries sending migrants to Australia over the past 25 years?  What are some of the social and political changes that are occurring in the country with the changes in migration?  What do you think Australia will be like in 25 years if current trends continue?”  These questions illustrate that the visualizations help students understand geographic phenomena, but can also be used in tandem with other sources – such as journal and newspaper articles, the US Census Bureau’s international database, ArcGIS  maps and story maps from Esri, and other resources that could shed light on the topic, changes in demographics in cities and rural areas, and much more. 


Teaching with maps often confirms certain hypotheses and preconceived notions and yet shatters others.  For example, observe the high percentage of Reunion Island’s population moving to the USA.  Is it part of climate-induced sea-level rise migration?  The number of countries that sent people to Somalia is small, and the number of countries receiving Somalians somewhat higher.  But discuss with students:  What keeps most of the population in Somalia, given ongoing political, health, and economic challenges?  Consider financial resources required to move, and sense of place. 


Focus part of the investigation on Australia (or another region of interest).  If the 3D interferes with teaching rather than enhances it, the 2D, particularly for countries like Australia with long international migration patterns, might be more effective.   Note that Australia has one of the highest percentages of migrants living there of any country over 10 million people, at nearly 50% of the total population.  Has Australia’s in-migration changed over time in terms of country of origin? If so, how?  Has Australia’s out-migration patterns changed over time?  If so, how and why?  What will the pattern look like in 25 years?


 Migration study 

Now let us build on the knowledge and skills gained from the international migration study to examining inter-state migration within Australia.


Teaching about Australia inter-state migration with ArcGIS.  Let’s explore one more lesson to supplement the international migration activity you just completed.


Open the interstate activity and map, and begin investigating.  Start with the Esri Australia library of lessons here:  and select Australia’s interstate migration.  You should now be at the following location, a PDF lesson document:  Open the URL of the web map identified in the PDF:,-50.0991,179.6696,6.7851 


The lesson begins with a discussion you can have with your students about push and pull factors influencing where people moved from and where they moved to among states in Australia.  Using data from the Australian Bureau of Statistics from 2017, begin by examining the map layer indicating where people moved to.  At this point, the map will look similar to the following:

Migration study 2


By working through the lesson, and examining population gain, loss, net migration, and movement (for example, movement from Victoria, below), you are integrating mathematics, history, economics, and technology with geography.  Did any of the interstate migration patterns surprise you?  What would you like your students to gain from the use of this lesson?  How could interstate migration change over the next decade?  Considering the international migration activity you completed in the last section, how does international immigration and emigration affect state-by-state population change?


Digging Deeper with ArcGIS  by extending the lesson.  Like the other lessons in this gallery, neither you nor your students need to log in to use the lesson, but with the skills you already have gained in Spatial Technology, you know that you can log in if you choose, which will allow you and your students to save the map, add additional data layers to the map, and share the map. 


Migration study 3

Exploring Potential Changes in World Climate Regions.  In this activity, you will dig deeper and examine change over space and time, while keeping a focus on regions. 


Understanding the Köppen-Geiger Observed and Predicted Climate Shifts data set.  Access the following map:  Köppen-Geiger Observed and Predicted Climate Shifts - WGS 84:  


Skim the metadata, which in part gives the following information:  This service time enables a series of world maps for the extended period 1901-2100 to depict global trends in observed climate and projected climate change scenarios.

The climate classification comprises a total of 31 climate classes described by a code of three letters.  The first letter describes the main classes, namely equatorial climates (A), arid climates (B), warm temperate climates (C), snow climates (D) and polar climates (E). The second letter accounts for precipitation and the third letter for temperature classes. The Map Service author also added 3 attribute fields to decode the letters for simpler use; in the attribute table, Main Climate, Precipitation, and Temperature correspond to these original coded letters. The legend also reflects these attributes rather than the coded letters.


Map projection investigation with the Köppen-Geiger Observed and Predicted Climate Shifts data in ArcGIS .  Access the following map: .  Select "Open in Map Viewer”:

Koppen map 1

The map displays in ArcGIS .  Notice that it is in a different map projection than many of the other maps you may have examined.  Here, the map is cast in the World Geodetic System 1984, rather than in Web Mercator, which has been used in many activities in this module thus far.  As you know as a geography instructor, because we are projecting the oblate spheroid 3D shape of the Earth onto a 2D map (whether paper or digital), all maps therefore have distortion in area, distance, direction, and shape—and usually in more than one of these four elements.  Map projections are similar to scale in that there is no “best” map projection; it depends on the content you are analyzing and your goals for your lesson.  However, the choice of map projection matters for several reasons, such as:  (1) The map projection influences the message; the information; that you are conveying.  Many projections have been criticized for distorting people’s perceptions of lands and countries (especially those not their own).  (2) Measuring areas and distances on maps is dependent upon the projection they are in, which will affect the spatial analysis done in Spatial Technology.   For more, see the video “why all maps are wrong” here and see the resources in the Explore Further session of this component. 


Climate investigation with the Köppen-Geiger Observed and Predicted Climate Shifts data.  Open a new tab in your browser and access the following map.  It contains the same content as the one you just opened, but this one is cast in the Web Mercator projection:


The map you will be using in this activity is on the left, and the map of the same content (after changing the basemap to dark gray) in Web Mercator is on the right.  Web Mercator is used for much mapping, including the default in ArcGIS  and Google Maps.  For studies of neighborhoods or a stretch of beach, the map projection doesn’t matter a great deal, but for global studies, the projection can make a big difference.  For example, compare the size and shape of Greenland and Scandinavia from the two maps below.  Note that ArcGIS  can use many types of projections, for example, the one we will use for this climate region study. 

Climate investigation


Once you open the original Köppen-Geiger Observed and Predicted Climate Shifts - WGS 84:, under the map, find the time slider timeline.  Slide the right slider arrow under the map all the way to the right, so you can examine all of the years predicted in the data set.  Teaching tip:  Some classrooms lack the bandwidth for 30 students to  "Play" the climate shift animations simultaneously, so you might experience better results by using the slider tabs rather than using the "Play" button. 


In the upper left, experiment with Show Contents of Map and Show Map Legend.  Examine various climate zones to obtain more information about them by clicking on them on the map.


Globally, what would you say are the 3 largest climate zones in terms of total area?


Regional investigation with the Köppen-Geiger Observed and Predicted Climate Shifts dataGreenlandOn your map, zoom and pan to Greenland, as follows, turning on the Observed 1976-2000 and the Predicted Using Scenario A1F1.  According to the metadata, Scenario A1F1 represents a scenario where economic and technological growth is achieved through intensive fossil fuel use.  Make sure left marker on the timeline is all the way to the left and the right marker is all the way to right.  Toggle between the Observed 1976-2000 and the Predicted Using Scenario A1F1, as shown below:

Climate investigation 2

Observe the size of the Polar EF climate zone - the medium blue color.  EF is Polar Ice Cap.  Average temperature of warmest month for the EF climate is 0°C (32°F) or less. Precipitation generally is greater than potential evaporation.

Make one observation about the predicted changes in the EF Climate Zone in Greenland and 2 implications of those predictions.  If you have time, explore the other models presented.


Victoria, Australia.  Pan to Victoria Australia, click on it, and observe the 2001-2015 climate type, as shown below.  It should be listed as Cfb, warm temperate, warm summer:

Climate investigation 3


Next, toggle between observed and predicted using scenario A1F1 as you did for Greenland.  You should see that Cfa increases (hot summer) while the area of Cfb (warm summer) decreases.  Zoom out until you see more of Australia, and repeat the process, noting the size of the Equatorial climate zone in the north and northeast parts of Australia. 

Climate investigation 4

Globally, the largest shifts between the main classes of equatorial climate (A), arid climate (B), warm temperate climate (C), snow climate (D) and polar climate (E) on global land areas are estimated as 2.6–3.4 % (E to D), 2.2–4.7 % (D to C), 1.3–2.0 (C to B) and 2.1–3.2 % (C to A).


Examining models in Spatial Technology.  As they do for other activities, in this lesson, GIS provides a hands-on supplement to your instructional goals and themes, in this case, about climate and climate change.  A critical part of the discussion while using the tools is that these predictions are based on climate models.  Discuss with students what a model is and use examples in engineering, biology, physics, mathematics, and geography.  A useful phrase to discuss with your students may be, "All models are wrong, but some are useful."  As in other disciplines, none of these climate models may be considered as completely "accurate" but are based on best available data sources. 


For more discussion, see "Humans and their Models" on  Making maps of climate and using the geographic perspective is more useful than simply examining tables of weather and climate data from weather stations, ice core samples, and the fossil record.  Some students may need a background in the effects of the Earth’s rotation, oceans, land masses, and solar radiation on climate, and for those situations, use this discussion on

Comparing regions and cities with the Urban Observatory.  A significant and growing part of virtually every region of the world is the presence of urban areas.  In mid 2009, the number of people living in urban areas surpassed the number living in rural areas for the first time in history.  In keeping with our Module theme of being critical of the data, what constitutes an “urban area”?  This can be characterized by a certain population size, or a level of infrastructure, a lifestyle, or other means, and is difficult to define (  However, most germane to our purposes in this activity is the fact that regions are influenced by and in turn influence their urban areas.   Spatial Technology, through a web mapping application called the Urban Observatory, provides the means to compare urban areas via data and maps.


What is the Urban Observatory?  The urban observatory was created by Richard Saul Wurman (the person who began the TED talks), Radical Media, and Esri, launching in 2013.  This tool allows you access to datasets for cities around the world so you (and your students) can simultaneously examine data about the most important questions impacting today's global cities and the regions in which they exist.  The urban observatory is curated by some of my favorite people here at Esri and they have created a wonderful teaching and research tool.


Compare and contrast cities using the Urban Observatory.   Access the Urban Observatory here:  Select Start Comparing > and then, > Launch App.   This is a web mapping application, similar to the Change Matters viewer you used earlier, but this application provides thematic maps and data side-by-side comparisons of urban areas.  The application opens with population density in New York, London, and Tokyo, and you can interact with each by zooming and panning.  


What would you like your students to observe about the population density patterns using this application?  How does the physical geography impact each city’s traffic, land use, zoning, and density?  Which of the classic geography urban models apply to each of the cities?


The application allows for the investigation of 100 cities, with more added yearly, and 50 themes.  Select Cities, move to P in the alphabet, and change the city in Panel 3 to Perth.  How does Perth’s density compare to New York and London?  Why?   


Now, instead of changing the city, change the theme.  Change the theme to Work > Commercial, and compare New York, London, and Perth.  Your map should look similar to that below.

 Urban Observatory

  • Contrast the spatial pattern of commercial land use in the three cities.  What influence do the water bodies (harbours and rivers) have on the location of the commercial land use?  Why?
  • Are the 3 scales the same among all 3 maps? How might the scales influence the interpretation of the patterns?
  • Change the theme to Work, Industrial. Contrast the spatial pattern of industrial land use in the three cities. 

Teaching with the Urban Observatory.   Another example of how you could use the Spatial Technology available to you in the Urban Observatory to support your instruction.  Say you are teaching about world demographics and are showing population pyramids for countries.  Ask students to use the Urban Observatory to compare senior population in New York, Accra, and Tokyo.  Based on the content you have taught, ask them to state their hypothesis about the senior population in each city.  (They might need to do some research to determine which countries each of these cities is located).  Examine the resulting maps.  The senior population for Accra should be much less than the senior population for Tokyo, with New York falling somewhere in the middle, and unlike the other two cities, being heavily influenced by immigration.


Examining real-time data with the Urban Observatory.  Spatial Technologies are increasingly tied to real-time and near-real-time data, fed by citizen scientists and the sensor network, part of the Internet of Things (IoT).  Examine the current temperatures for three cities in the Urban Observatory, making sure you select at least one in the Northern Hemisphere and one in the Southern Hemisphere. What is the effect of season on the temperatures right now?  Compare cities near coasts with those far away, while asking:  What is the effect of proximity to the ocean on temperature?  Examine traffic, using a time zones map of the world as a supplementary resource.  What effect does method of commute, time of day, day of the week, the road network, and physical geography have on current speeds along roadways? 


Select different cities or different themes that you believe would be helpful to help students understand urban geography, and describe the themes and cities you chose, and the patterns that you observe.  In each problem posed, tie the city to the region:  Cities are part of regions—they do not exist on their own.  They also are influenced by global networks of commerce (Singapore being an excellent case study). 

Exploring Oceans as Regions with Spatial Technology.  Nearly 71% of the world is covered by ocean.  For centuries, and even often in our current era, oceans are mapped as a uniform color and are not thought of as regions, but as geographers and oceanographers are discovering, oceans are as dynamic as land—many would argue even more so—and examining oceans as regions is possible with Spatial Technology.


Pacific Ocean at Pacific Grove, California, photograph by Joseph Kerski. 


Understanding the Ecological Marine Units data.  In many respects, the oceans are the “last frontier” on Earth—much about them remains unknown and unmapped, but given the importance of oceans to the carbon cycle, the food chain, biodiversity, climate, and many other earth systems, there is a growing sense that this needs to change.  Given the vastness, the changing nature, and the 3D nature of the oceans, Spatial Technology is turned to as a solution to understanding and mapping the oceans.  The Ecological Marine Units (EMU) map has been created, to portray a systematic division and classification of physiographic and ecological information about features in the ocean. Part of the EMU is a 3D data model of the ocean—and 3D data visualization tools to go along with the data. A statistical clustering methodology was also developed for identifying the physiographic structure of the water column based initially on temperature, salinity, dissolved oxygen, and nutrients—i.e., the usual suspects that will likely drive ecosystem responses. 


This methodology was developed by Esri’s Kevin Butler and vetted by distinguished spatial statistics professor Noel Cressie of the University of Wollongong in Australia. That information was connected to species distributions (initially from the Ocean Biogeographic Information System or OBIS), biogeographic realms, and seabed habitat and biotopes representing the response of these ecosystems to the physical setting (e.g., the biological distribution/response to ocean acidification).   The Group on Earth Observations (GEO) officially commissioned the project as a means of developing a standardized, robust, and practical global ecosystems classification and map for the oceans. GEO is a consortium of almost 100 nations collaborating to build the Global Earth Observation System of Systems (GEOSS). The EMU project is seen by GEO as a key outcome of the GEO Biodiversity Observation Network (GEO BON) and the new GI-14 GEO Global Ecosystems Initiative (GECO).


For more information, examine this document:


Using the Ecological Marine Units as an investigative tool.  Begin your investigation of the Ecological Marine Units with this Explorer map:    Click anywhere on the map and you will observe a graph to the right of the map with ocean water variables displayed in graph form by depth.  Clicking on the Temperature Profile title of the graph yields the other graphs  (nitrate, salinity, and others) available for that location.


Pan to Australia and click a point off the shore of New South Wales or Victoria; the map should look similar to that below.

Ecological Marine Units 1


Adding to the complexity of understanding the ocean’s regions is that for any one location, the regions overlay, or more accurately, underlay each other.  On land, while elevation influences the plant and animal life, climate, water, soil, and vegetation, to name a few, each location on land is represented by a single region.  But in the oceans, the regions sit (or “slosh”) atop each other.  Consider the example above.  Off the coast of Australia lies EMU region 11—Northern and Southern Subtropical Epipelagic.  Epipelagic refers to the part of the oceanic zone into which enough light penetrates for photosynthesis.   At the location clicked, it is 275 meters thick.  Underneath this is EMU 8 – the subAntarctic, North Atlantic, and North Pacific Epipelagic, extending another 275 meters, and underneath that is EMU 36, the Atlantic, Subantarctic, and North Pacific Subtropical Bathypelagic region.  Bathypelagic – from Greek βαθύς, deep – refers to the part of the pelagic zone that extends from a depth of 1,000 to 4,000 m below the ocean surface.   Here, no primary production of plant life, so all creatures that live there are carnivorous, eating each other or feeding on carcasses that sink down from above.  And underneath this are EMUs 37 and 14. 


For a two-dimensional cross section with these zones labeled by depth, see Figure 1 in the metadata booklet:


Examine Ecological Marine Units around the world.  Use the Table on page 10-11 of the same metadata booklet  (this is the same as page 6 of 19 in the PDF), and compare EMU 11 to the other EMUs in term of depth, temperature, dissolved oxygen, nitrate, phosphate, and silicate.


Examine the map on page 12-13 of (this is the same as page 7 of 19 in the PDF) and note the distribution and spatial patterns of the EMUs around the world.  Which would you say is the largest in area?  Be careful!  EMUs 19 and 31 in the Southern Ocean may look like the largest, but remember that the map projection may distort the area of land and water near the poles. 


Scroll down to page 20 and 21 (page 11 of 19 in the PDF) and examine the map of EMU 11 (reproduced below) that was present at the surface off the southeast coast of Australia —the Northern and Southern Subtropical Epipelagic.   How would you describe the spatial distribution of this EMU?


Ecological Marine Units 2

Note the Equatorial location of EMUs 18 and 24 and predict their mean temperature compared to other EMUs.  Scroll back up to the table—was your hypothesis correct?  Note the high amount of dissolved oxygen contained in EMUs 5 and 16.  Knowing that cold water can hold more dissolved oxygen than warm water, predict the location of EMUs 5 and 16.  Scroll back up to the map.  Was your hypothesis correct? 


Examine Australia EMUs.  Go back to the interactive EMU map (  Begin again by clicking off the coast of southeast Australia.  Or choose another area of the world of interest to you.  Gradually move and click northward along the east coast, observing the graph at the right.  What do you notice about the water temperature as you move north?  Why?  How and why does the dissolved oxygen change?


Always be critical of the data—EMUs being no exception.  Go Again, keeping with our theme of being critical of the data, there is no water quality measurement buoy or set of ships at every square meter on the surface of the ocean with a tether extending down to the ocean floor.  Rather, the data are sampled as follows:


The fundamental approach undertaken was to stratify the ocean into physically and chemically distinct areas. The stratification was produced from unsupervised statistical clustering of data from NOAA’s 2013 World Ocean Atlas version 2.  The data used in the clustering represent 57-year average values for temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate.  Approximately 52 million ocean data points representing the entire water column. The horizontal resolution of the data is ¼°, or approximately 27 km near the equator. The depth intervals are variable from 5 m near the surface to 100 m in the deeper regions (>2000 m) for a total of 102 depth levels. The EMUs were produced from a k-means statistical clustering of the point data, resulting in 37 distinct clusters.  Each cluster, or EMU, is a physically and chemically distinct volumetric region.  Thus, the data was rigorously produced, but zoom in until you see the data points, as shown below.  You can now see the data points spaced at ¼° (one quarter of a degree of latitude and longitude) apart.  In the same way on land, you examined a sample grid of data points from the Degree Confluence Project to examine physical and cultural regions.


Ecological Marine Units 4


Dig Deeper with EMUs.  Go To dig deeper, literally, examine some of the 3D views in this story map of the EMUs.  Scroll down to “Additional Examples” at the end and examine the areas offshore from Ireland and offshore from Central America.  This might provide a better sense of the 3D nature of each “column” of data across the ocean.


Ecological Marine Units 4


Skim this research document to discover how the EMU data can be used by those using Spatial Technology:  To examine the data in 3D in a Spatial Technology environment requires the use of a desktop GIS called ArcGIS Pro.   Good news!  With your ArcGIS  school account, you can also get access to ArcGIS Pro.

Examining Ecological Land Units of the World.  In this activity, let’s conduct a regions investigation, this time with a different definition of region:  A combination of bioclimate, landforms, rock type, and land cover.


What are Ecological Land Units?  Ecoregions, like cultural regions and other regions in geography, are in part a human construct—one of the techniques that we use to understand the world.  The boundaries of ecoregions are not universally agreed upon, in part because they depend on what variables are used to define them, and furthermore, are usually not sharp.  A new attempt to define regions is that represented by Ecological Land Units (ELUs). 


Examining Ecological Land Units via a Story Map.   Access a map of ELUs here:


This map was produced by a team led by the US Geological Survey’s Land Change Science Program. It represents a mosaic of almost 4,000 unique ecological areas called ELUs, based on four factors that are key in determining the makeup of ecosystems. Three of these—bioclimate, landforms, and rock type—are physical phenomena that drive the formation of soils and the distribution of vegetation. The fourth, land cover, is the vegetation that is found in a location as a response to the physical factors. You can read more about the research here.


This particular type of web mapping application is called a story map.  A story map can incorporate audio, video, text, photographs, charts, and interactive web maps.  They can be used in education for presentation of a region, a theme, a process, or a current event, for assessing student work, and for presentation of an investigation that you or students have conducted.  You will make your own story map in the next component in this module. 


Use the navigation buttons on the right side of the map to move to different ELUs around the world.  Select three ELUs and contrast them.  How do humans use the land in each of the ELUs you chose?


Ecological Land Units map


Deeper Investigation into Ecological Land Units.  Another way of exploring the ELUs is via this interactive web map:  Using this map (which is another type of web mapping application), pan to Australia.  When you click on a location in Australia, you will see the bioclimate, landforms, rock type, and land cover for that location.  Furthermore, as you expand each of those 4 components of ELUs, you will see all of the other places in Australia and around the world where those characteristics exist.  In the example below, the 4 component maps, when expanded, will show all of the locations around the planet that are warm and wet, contain high mountains, are underlain by mixed sedimentary rock, and are covered with broadleaved evergreen forests.  This is an excellent tool for teaching about the complex nature of regions—how to define them, and how they are distributed across the planet.  It is also an excellent tool for helping students think about “Which areas around the world are similar to my own?”, “which areas are different, and why?” 

Ecological Land Units Map 2


Note that you can change the basemap on the right from ecological to dark gray, imagery, or ocean, as you examine the 4 ELU components around the world on the left.  Explore!  

Some educators I know use Google Street View images to verify student hypotheses of what the land in each ecoregion would look like.  This is a valuable activity.  But, the limitation is that Google Street View images do not exist everywhere on the planet, and where it does exist, it is constrained largely to streets and a few trails and not the areas away from human habitation.  But what if there was a regular sampling of points across the planet, from which you could see what it looks like from each point? Such a project does exist, called the Degree Confluence Project.  This project is crowdsourced, created by a citizen science set of volunteers who set out to photograph all of the full-degree latitude and longitude intersections on land (and in oceans just offshore of land) in the world.  In other words, where 30 Degrees South and 140 degrees east longitude cross in Australia or where 43 North latitude and 25 degrees East Longitude cross in Bulgaria.  

Above, the landscape as it appears at 30 South Latitude, 118 East Longitude, in Western Australia.


Above, the landscape as it appears at 30 South Latitude, 118 East Longitude, in Western Australia.


Begin degree-by-degree exploration by examining latitude and longitude lines.

Discuss with your students how the shape of the Earth, as an oblate spheroid, affects the spacing of the one-degree grid.  You can use the Add button in any ArcGIS  map you are working in to search for graticule and select the 1-degree grid from maps.com_carto, or simply open the following map that contains the Equator, tropics, Prime Meridian, and other lines, as well as the 30, 20, 15, 10, 5, and 1 degree latitude and longitude lines for the world (called “the graticule”), here:


Use Bookmarks to zoom to Australia (or another place of interest to you).  Zoom in further to Victoria or another state.  Use the measure tool to measure the length of 1 degree of latitude at selected locations.  It should be the same no matter where you measure, at around 111 km.  Then, measure the distance between each degree of longitude.  As you move south, the distance should be less as you approach the South Pole.  For example, the distance between 145 East and 146 East along 40 South Latitude is around 87 km, but between 145 East and 146 East is around 92 km as measured along 36 South.  Again, map projections matter!  The distances look the same, but they are not.  In fact, observe the size of the 1 degree-by-1-degree rectangles as you pan from south to north across Australia.  They should approach being squares as you move toward the Equator, and are longer and longer rectangles as you move south toward the South Pole.

Latitude and longitude degree grid in ArcGIS .


Use the Degree Confluence Project site to examine the Earth’s regions.

Use the Degree Confluence Project in a similar way as I described how Google Street View images are used:  Use map resources in conjunction with photographs.  For example, use the photographs to verify student hypotheses about what they think that the following biomes will look like:  The chaparral biome in southern California USA, polar regions in Nunavut, tropical rainforests in Costa Rica, or grasslands in the USA.  You could also use a sample set of images from the site, and ask students to guess, based on image clues, in which biome or country the images were taken. 

Navigate the project’s website by country or by compass rose:  You can start at the east coast of Australia, for example, at 30 South 153 East, and navigate to the west along 30 degrees south latitude by one degree of longitude per stop, all the way to 115 East.  Along the way, ask students:  What changes do you detect to the landforms, land use, climate, human impact, water, housing type, and in sky condition, as you move across the country?  Which are the primary forces—water, humans, natural hazards, something else—acting on the landscape?  Would you say this area is changing more rapidly or more slowly than your own community?  What will this landscape look like in 5, 10, or 50 years’ time? 


You could repeat this process from north to south or choose another line of latitude or longitude.  You could also use the “antipode” function under the compass rose to find out what is on the opposite side of the Earth. 


Teaching Notes about Degree Confluences, and Digging Deeper.  Ask students to be geographic detectives and determine the time of day and season of the year that the images were taken.  Some points have been visited more than once. Ask students to identify at least two changes that have taken place between selected visits to the same location.


Besides the site’s regular sampling of the Earth’s surface, two additional advantages exist with using this site versus random mining for images from Google, Flickr, or another source:  (1) This project is focused on documenting the landscape, so the images are primarily about the land, taken in the four cardinal directions from the point and sometimes in additional directions as well; (2) The images are all vetted, curated, and protected; nothing objectionable exists in these photographs (unlike what you could find in a general Google search). 


If you want to dig still deeper, an additional crowdsourced set of “street view” images is from Mapillary, and you could also use this tool to take your own images-tied-to-maps.  

Examining Regions with ArcGIS  Maps and Google Street View.  At times, geographic learning is enhanced with photographs taken on the landscape you are studying.  Fortunately, photographs are a standard part of today’s Spatial Technology. In the following activity, you will study regions through an interactive map and on-the-ground photographs.


Examine a world ecoregions and population density map.  Access the map of World Ecoregions and Population Density, in ArcGIS , here:


To the left of the map > Content > Turn off population density and turn on ecoregions, as follows:

Ecoregions A

On the map, click on one of the ecoregions.  You might have to use the > next button in the popup until you get “past” the continent and country information to the ecoregion name.  The map will look similar to this:




Compare the ecoregions in eastern Australia and central Australia.  Or another region of the world. 


Based on the descriptions, what do you predict the landscape will look like on the ground in those locations?  What landforms, trees, and shrubs will predominate?  Will you see any evidence of water?  What will the evidence of humans be on land use? 


Toggle the population density layer on and off, noting the patterns that you see in helping you answer the following question when you go to Street View for a chosen area:  Will you see any towns or cities?


Examine on-the-ground photographs from Street View.


To test your hypotheses about the characteristics of the ecoregions, go to Google Maps:  Search on Australia.  You will likely see a 3D scene zoomed to the scale of all of Australia, as below, particularly if you have enabled the Google Earth plugin to your web browser.  Drag the Street View icon and hover it over the continent of Australia.  Before dropping it on the map, note the amount of blue on the map.  This reflects how many roads exist, and also the extent that the Google cars have traveled with their 360 degree cameras (which in turn reflects some socio-political geography as well; that is, where the cars are allowed in specific places around the world and where they are prohibited—another good topic for geography class discussions!).

Ecoregions C


Now, drop the Street View icon on a region in Australia corresponding to one of the ecoregions you investigated earlier.  What do you predict you will see?  For example, compare the location in Queensland, at left, to the location in Northern Territory, at right:

Ecoregions D


What do you predict the land will look like in a taiga ecoregion?  A chaparral ecoregion?  Compare your predictions against a street view image.


Extending the activity:  Use GeoGuessr quizzes about the Earth.  Another method of helping students to think spatially about cultural and physical regions using street view images is with GeoGuessr (without the “e” in the last part of the word):   In this quiz, a player (or competing against another player, say, another student in your classroom), guesses the location on the map, and the points depend on the speed at which the student responds and the distance “off” from the true location of that image.  Excellent connections to fostering spatial thinking in geography include considerations of the landforms, climate as reflected in water or vegetation types, driving on the left or right side of the road, languages visible, land use, housing type and construction material, and other objects on the physical and cultural landscape.