Three cities. Fifty years. One interactive map. The new Population Density layers on geteach.com

Three cities. Fifty years. One interactive map. The new Population Density layers on geteach.com let students click anywhere on Earth and see exactly how many people live in a 20km cell — and how that number has changed since 1975.

The Population Density layers (1975, 2000, and 2025) are now live on geteach.com, powered by the Global Human Settlement Layer (GHSL) from the European Commission’s Joint Research Centre. Students can place two maps side by side — say, 1975 and 2025 — click any location, and instantly see the population count, density per km², and a growth chart spanning five decades. Below are three locations that tell three very different stories.

Detroit, Michigan — The Shrinking City

Where to click: Center of Detroit, approximately 42.30°N, 83.23°W

 

Detroit is one of the most dramatic cases of urban population loss in the developed world. The city’s population fell from a high of 1,850,000 in 1950 to around 680,000 by 2015, and the decline has continued since. Detroit is currently about 65% smaller than it was at its peak, and has shrunk roughly 33% since the year 2000.

When students click Detroit’s inner city cells and compare the 1975 and 2025 layers side by side, the growth chart shows a persistent negative slope — most cells in the urban core register a total decline of roughly 8–15% from 1975 to 2025, with the southern portions running closer to 15–20%. These numbers are modest at first glance, but there is an important reason for that: Detroit’s sharpest and most catastrophic losses came before 1975. The GHSL dataset begins at 1975, which means the map captures only the tail end of one of America’s most dramatic urban contractions. By 1975, Detroit had already shed hundreds of thousands of residents from its 1950 peak. The 8–20% the map shows is the continuation of a decline that was already well underway.

This is a valuable teaching moment in itself — data always has a starting point, and what happened before that starting point matters. The map is honest about what it shows, but context is essential. A negative growth rate of even 10% sustained over 50 years represents real neighborhoods abandoned, real tax bases eroded, and real infrastructure left without users.

The causes are deeply geographic. The rise and fall of the automotive industry, white flight to the suburbs, highway construction that gutted urban neighborhoods, and the deindustrialization of the Rust Belt all left their mark. What makes Detroit particularly instructive on this map is the contrast between the city and its suburbs. Ask students to click not just the urban core, but the cells to the north and west. They will find those cells grew over the same period — population didn’t disappear from the region, it moved outward. That spatial redistribution is the real story the map tells.

Discussion questions

  • The map shows 8–20% decline from 1975–2025, but Detroit’s total loss since 1950 is over 60%. What does that tell you about using 1975 as a baseline?
  • Why might the suburban cells around Detroit show growth while the city core shows decline?
  • What geographic factors — highways, industry, housing policy — shaped where people moved?
  • What does a persistent negative growth rate tell you about a place’s long-term economic trajectory?

Explore Detroit on geteach.com: Open Detroit Population Density →

New Delhi, India — Explosive Growth

Where to click: Central New Delhi, approximately 28.66°N, 77.34°E

 

New Delhi sits at the opposite end of the population spectrum from Detroit. New Delhi has expanded by approximately 400% since 1975, reaching a forecasted population of over 31 million. The metro area population of Delhi in 2025 was estimated at 34,666,000, continuing to grow at over 2.5% per year.

When students click central Delhi cells on the 1975 vs 2025 dual map, the growth chart rockets upward — the total growth percentage (1975 – 2025) for dense urban cells in this area can exceed 300% to 500%. The density figures for the most built-up cells are staggering, among the highest on the planet.

But the more geographically interesting story is what happens when students click outward from the center. The inner city cells, already dense in 1975, show strong but bounded growth. The real explosion shows up in the cells to the west, south, and east of the historic core — areas that were largely agricultural or sparsely settled in 1975 and are now dense urban fabric. This is urbanization made visible and measurable.

Delhi has a rapidly growing population that nearly doubled from an estimated 18.6 million in 2016 to an estimated 34.6 million by 2025. That rate of change — doubling in under a decade — is almost impossible to comprehend from statistics alone. The map makes it spatial and real.

Discussion questions

  • Which cells show the highest growth rates — the inner city or the outer edges? What does that pattern tell you?
  • What push and pull factors drive migration into a megacity like Delhi?
  • How does rapid urbanization create challenges for infrastructure, housing, and the environment?

Explore New Delhi on geteach.com: Open New Delhi Population Density →

Seoul, South Korea — A City of Two Stories

Where to click (inner city): Central Seoul, approximately 37.56°N, 126.85°E
Where to click (suburbs): Gyeonggi Province, approximately 37.21°N, 127.04°E (Seongnam/Bundang area)

 

Seoul offers the most nuanced of the three case studies because it is actually two population stories happening simultaneously in the same metropolitan area.

Inner Seoul — stable to declining. Seoul’s population peaked at over 10 million and has gradually decreased since 2014, standing at about 9.6 million residents as of 2024. Students clicking the dense inner-city cells will find growth rates that are relatively modest compared to Delhi, and in some cells even slightly negative in recent decades. The density numbers are extremely high — Seoul’s urban core is one of the most densely populated places in the world — but the trajectory has flattened. Reasons for the population drop include high costs of living, especially housing, urban sprawl to Gyeonggi region’s satellite cities, and an aging population with an extremely low birth rate.

The suburbs — rapid growth. The story changes dramatically when students move their clicks outward into Gyeonggi Province. Satellite cities like Seongnam, Suwon, Goyang, and Yongin show steep upward growth curves on the chart. The Seoul metropolitan area has continued strong population growth, with the 2020 census indicating steady annual increases driven by suburban expansion. The Seoul Metropolitan Area as a whole has a population of 26 million as of 2024, ranked as the fourth-largest metropolitan area in the world.

This inner-decline / outer-growth pattern is called suburbanization or counterurbanization, and Seoul is one of the world’s clearest examples of it. The dual map is perfectly suited to showing this — students can place inner Seoul on map 1 and a suburban Gyeonggi cell on map 2, compare the growth charts side by side, and immediately see the spatial redistribution of population that defines modern Seoul.

Discussion questions

  • Why would people leave one of the world’s most connected cities for the suburbs?
  • How does a very low birth rate affect a city’s long-term population even if migration is stable?
  • Compare Seoul’s suburbanization pattern to Detroit’s. What is similar? What is fundamentally different?

Explore Seoul on geteach.com: Open Seoul Population Density →

Classroom Activity: The Population Comparison Challenge

This activity works for AP Human Geography (Unit 2 — Population and Migration, Unit 6 — Cities and Urban Land Use) and Geography for Life Standards 9 and 12.

 

Setup: Open geteach.com and load Population Density-1975 on Map 1 and Population Density-2025 on Map 2. Sync the maps so both canvases show the same location simultaneously.

Step 1 — Pick your city. Assign each student or pair one of the three cities above, or let them choose any city in the world.

Step 2 — Click and record. Click the urban core and record: total population (1975 and 2025), density per km², and the gTotal growth percentage shown in the chart.

Step 3 — Move outward. Click two or three cells progressively further from the city center. Record the same data. How does the pattern change?

Step 4 — Compare and explain. Using the data collected, ask students to write a geographic explanation: What pattern do they see moving from center to edge? What human or physical geographic factors explain it?

Step 5 — Cross-city comparison. Share results across the class. Build a comparison table: Which city grew fastest? Which shrank? Which shows suburbanization? What does that tell us about different development paths?

Try the activity yourself: Open geteach.com →

About the Data

The population counts displayed come from the Global Human Settlement Layer (GHSL) P2023A dataset, produced by the European Commission’s Joint Research Centre. The raw data represents population counts at 100m × 100m pixel resolution. On geteach.com, these values are aggregated into 20km grid cells, giving students a total population estimate and a density figure (residents per km²) for the area around their click. Growth rates are calculated relative to 1975 as the baseline year.

The three epochs available — 1975, 2000, and 2025 — align directly with AP Human Geography Unit 2 (Population and Migration), which examines how and why population distribution changes over time, and Unit 6 (Cities and Urban Land Use), which asks students to explain the internal structure of cities, patterns of urban growth, and the forces driving suburbanization and urban decline. The layers also support Geography for Life Standard 9 (Human Populations) and Standard 12 (Human Settlement).

geteach.com Curated by Josh Williams @ geteach.com

READ MORE

New Share Function – geteach.com

Throughout the years there have been a number of occasions where I simply wanted to share the map layers and location of what was on my screen. For example, I would have a compelling side-by-side comparison that I wanted to show a colleague or a student — but someone unfamiliar with geteach.com might have difficulty selecting the right mapset and toggling on the correct layers.

Recently, I solved this barrier by adding a share function to geteach.com. One click captures everything on your screen into a single URL. Anyone who opens that link lands in exactly the same state you were in — no setup required on their end.

What the Link Captures

The share function encodes the full state of both map canvases — a lot more than just the active layer. For each canvas it captures the active mapset and selected layers, the exact map position and zoom level, the map type (roadmap, satellite, hybrid, or terrain), visual style toggles for boundary lines, labels, and roads, and the visibility state of the legend, map controls, and drawing tools. It also captures whether the two canvases are synchronized by center and zoom.

This means a carefully arranged side-by-side comparison — Population Density on roadmap alongside Earth at Night on satellite, for example — is shared in its entirety. Both maps, both layers, both positions, all at once.

I would have a cool comparison that I wanted to share, but people unfamiliar with geteach.com might have a problem selecting the mapset and toggling on the layers. Recently, I solved this barrier.

How to Use It

Navigate to geteach.com, set up the view you want to share — layers, zoom, map type, any toggles — then click the Share button. On most browsers the link is copied directly to your clipboard and you’ll see the “Link copied to clipboard” confirmation. On mobile devices that support it, the native share sheet opens, letting you send the link straight to Messages, email, or another app.

Paste it wherever your audience will find it — a Google Classroom post, an email, a slide deck, a shared document. When they open the link, geteach.com restores your exact configuration automatically.

Examples in the Classroom

Here are four comparisons built with the share function — each ready to drop into a lesson or post directly to your LMS. Click the heading of each example to open the comparison in geteach.com.

Austin — Site, Situation, and Urban Morphology

One canvas shows Austin on a terrain map; the other shows the same footprint on satellite. The city’s growth pattern runs strongly north–south — constrained to the east by the Blackland Prairie and to the west by the rugged Hill Country. This is a strong entry point for discussing how physical geography shapes site and situation, and why some cities grow in one direction rather than spreading evenly. Ask students: what physical features act as barriers here, and what draws development along the corridors you can see?

El Niño — Winter vs. Summer Climate Shifts

This comparison layers El Niño climate anomaly data for winter on one canvas and summer on the other, both centered on the Pacific. El Niño redistributes precipitation and temperature across entire continents — wetter winters across the southern U.S., drier conditions across Southeast Asia and Australia. Use this as a discussion starter for climate variability and food security: where do growing seasons shorten? Which regions face drought risk? How might shifts in precipitation affect crop yields in regions that depend on predictable monsoons? Students can also connect this to their own region and consider local impacts.

Dependency Ratios — Africa and Europe

Youth dependency ratio on the left, elderly dependency ratio on the right — centered to show both Sub-Saharan Africa and Europe in a single view. The contrast is stark: much of Africa carries a high youth dependency burden while many European countries face the opposite pressure from aging populations. This is a natural entry point for demographic transition, social policy, and economic development. Ask students what each pattern implies for education spending, pension systems, labor force participation, and healthcare demand. Click individual countries for the underlying data — and don’t forget to pan to see how the patterns shift across regions.

Easter Egg — Wizarding World vs. Springfield

A few of the map sets include easter eggs hidden across the data layers. This one places Harry Potter’s Wizarding World on the left canvas alongside the Simpsons’ Springfield on the right. More of a reward for explorers than a lesson plan — but if you need an excuse, there’s a reasonable argument for a discussion about fictional geography and spatial imagination. Where do these places exist relative to the real world? Either way, students tend to enjoy finding these.

Share Your Comparisons — #geteachmaps

If you build a comparison worth sharing, tag it with #geteachmaps on Bluesky or Facebook. Whether it’s a side-by-side of HDI across decades, a terrain view of a region you’re studying, or just something that made a student say “whoa” — I’d genuinely love to see what people build with this.

And if you have ideas for how the share function could be more useful, reach out via Bluesky, Facebook, or email.

READ MORE

Climate Graphs: From Google Earth Engine to geteach.com



In the summer of 2019 I created a little side project Google Earth Engine website (geteach.com/engine). While I have been using it in class for these past six years, my desire was to always take this knowledge and apply it to my main project geteach.com. Finally, with Google Earth Engine doing the heavy data lifting and AI assistants helping me bridge the gap, I have gotten real close to what I want. Now geteach.com has two new layers in the climate mapset (Climate Graphs and Climograph Challenge) and the Climate Regions layer now has more case study locations drawing from this new database. But…how does it all work, and how will I use it in the classroom?

The New Layers

Climate Graphs

In the past, geteach.com had a Climate Regions layer with 11 cities representing 11 simplified climate regions. Clicking on one of those cities would display a climate graph. It was useful, but limited. What I really wanted was for a student to click anywhere on the terrestrial earth and get a climograph. That is what the Climate Graphs layer does…at least between 58 degrees South and 80 degrees North.

Pedagogical Ideas

Climate comparison. One of the simplest and most effective activities is having students pick two locations and compare their climographs side by side. What months are the wettest? When is the temperature range the greatest? Is there a dry season? This builds the habit of reading climate patterns rather than just memorizing labels.

Compare climate graphs with other layers. The real power of this layer comes when students start layering it with other data in the climate mapset. Pull up a climograph for a location, then toggle on Precipitable Water, Land Temperature, Vegetation Index, Sea Surface Temperature, Topography, or the Earth-Sun Relationship layer and ask: why does this climograph look the way it does?

Exploring climate controls. This is where I see the deepest learning happening. Using geteach.com’s grid tool alongside the other layers in the climate mapset, students can investigate the classic climate controls: latitude, continentality, ocean currents, prevailing winds, and topography. For example, students can compare two cities at the same latitude but on opposite sides of a mountain range, or compare a coastal city to an interior one and watch the climograph tell the story of the rain shadow or the maritime effect. The Ocean Currents, Wind Currents, and DEM layers are all sitting right there in the same mapset. That combination makes this kind of investigation intuitive and easy to set up.

Climograph Challenge

The Climograph Challenge takes everything students learn from the Climate Graphs layer and turns it into a game. Think of it as a GeoGuessr type game, but instead of Street View, students are given a climograph. Their job is to figure out where on Earth that climate pattern belongs and place their pin on the map.

Each game runs five rounds. Students analyze the climograph, consider what the temperature curve and precipitation bars are telling them about latitude, seasonality, and moisture, then click the map to place their guess. Scoring is based on two factors: latitude accuracy and true distance. Each factor is worth up to 2,500 points per round, for a maximum of 5,000 points per round and 25,000 points possible overall. After submitting, the actual location is revealed and a line connects their guess to the answer.

Pedagogical Ideas

The game is more fun, and more educational, when students have a strategy. Here is where the other layers in the climate mapset become valuable tools rather than just background maps.

Use Land Temperature and Precipitable Water as reference layers. Before pinning their guess, students can toggle through the monthly Land Temperature layers to find where on Earth surface temperatures match the pattern they see in the temperature curve. They can do the same with the monthly Precipitable Water layers to match the wet and dry season pattern from the precipitation bars. Cycling through January through December on either layer while studying the climograph turns the game into a genuine spatial reasoning exercise.

The ITCZ as a clue. One of the most useful things a student can learn to recognize in a climograph is the signal of the Intertropical Convergence Zone. A location near the equator will often show two precipitation peaks per year. This is because the ITCZ passes overhead twice, moving north toward the summer solstice and south toward the winter solstice. Recognizing that double-peak pattern is a strong signal that the pin belongs somewhere in the tropics, and combined with the Precipitable Water layers, students can begin to narrow down which part of the tropics.

From game to debrief. After each round, the reveal is a teaching moment. Why was the actual location where it was? What does that climograph tell us about that region’s latitude, its proximity to an ocean, or its position relative to a mountain range? The line between guess and answer almost always sparks a geographic conversation worth having.

How Does It All Work?

From Google Earth Engine to geteach.com

The backbone of all three features is a climate database built from Google Earth Engine. Here is how it came together, from satellite data to student interaction.

Harvesting the data in Earth Engine. Using the Earth Engine code editor, I queried the TerraClimate dataset, a global climate record spanning 2004 to 2023. For each of the twelve months, I calculated the average temperature and precipitation across twenty years of data. Because Earth Engine can time out on large exports, I wrote a script that divided the globe into 70 latitude slices and exported each one as a separate CSV file to Google Drive. When the jobs finished, I had 70 files covering the terrestrial earth from 58 degrees South to 80 degrees North at a 0.25 degree resolution, roughly 17 miles between data points at the equator.

Building the database. Two Python scripts handled the next steps. The first concatenated the 70 CSV slices into a single file. The second converted that combined file into a SQLite database. That database is now the engine behind all three climate features on geteach.com: the Climate Graphs layer, the Climograph Challenge, and the expanded case study cities in the Climate Regions layer.

Connecting it to the map. A PHP API sits between the database and the map. When a student clicks a location in the Climate Graphs layer, the map sends the coordinates to the API, which finds the nearest data point in the SQLite database and returns the twelve months of temperature and precipitation values as JSON. The JavaScript then renders those values as a climograph using Google Charts. For the Climograph Challenge, the same API pulls a random record from the database to serve as the mystery location. For the Climate Regions case study cities, I worked with AI to identify the best representative examples for each climate zone, and those cities pull their chart data from the same database.

The role of AI. I have been tinkering with code for about fifteen years, but I am not a true coder. I know what I want to build and I can write something rough, but getting from rough to working used to take a very long time. With AI assistants helping me smooth out the Python scripts, the PHP API, and the JavaScript, what might have taken four months on my own took about two weeks. The process was iterative. I would write a draft, describe what was not working or what I wanted it to do differently, and the AI would help me refine it. Earth Engine gave me the data. AI helped me build the bridge.

These three features grew out of six years of wanting to do more with climate data in the classroom. The data was always there. Getting it into the hands of students in a usable form was the challenge.

READ MORE

geteach.com Interactive Map Layers: A Full Curriculum Alignment to AP Human Geography, Next Generation Science Standards, and the National Geography Standards


geteach.com hosts over 320 interactive map layers organized into 33 thematic mapsets — covering everything from plate tectonics and ocean temperatures to national demographics, energy infrastructure, and forest change. Every layer is curated from authoritative sources including NASA, NOAA, the UN, USGS, and the World Bank.

This reference page exists to answer one question clearly: which geteach.com layers support which curriculum standards? Whether you are a classroom teacher building a unit plan, a curriculum coordinator mapping resources to frameworks, or an AI assistant helping a student find the right data — this page is for you.

Every layer below is aligned to three frameworks:

  • AP Human Geography — College Board Units 1–7 and specific topic codes
  • Next Generation Science Standards (NGSS) — Earth and Space Science (ESS) and Life Science (LS) performance expectations where genuinely applicable
  • Geography for Life — All 18 National Geography Standards (2nd Edition), organized into six Essential Elements

Note: Not every layer maps to NGSS. Standards requiring physical chemistry, genetics, or non-geographic biology (like cell division or wave mechanics) are not forced onto geographic data layers. Blank NGSS fields are intentional, not an oversight.

Quick Navigation by Curriculum Framework

Jump to a Mapset

AP Human Geography — Layers by Unit

  • Unit 1 — Thinking Geographically: Blue Marble, Physical Maps, Plate Tectonics, Continental Drift, Historic Maps, Earth-Sun Relationship, Ocean Maps, Climate (Bivariate Climate, Climate Regions, Climate Graphs, Ocean Currents, Wind Currents, ENSO layers)
  • Unit 2 — Population & Migration: All Demographics mapsets, Population Density, UN Quick Facts
  • Unit 3 — Cultural Patterns: Society (Religion, World Languages), Historic Maps
  • Unit 4 — Political Patterns: Brexit Results 2016, Society (Freedom Index), Africa — Freedom Index
  • Unit 5 — Agriculture & Rural Land-Use: Geography Land, Forest Change, Vegetation Index, Temperature, Precipitable Water, Land Cover, Africa — Land Cover and Climate
  • Unit 6 — Cities & Urban Land-Use: Settlement Patterns, Earth at Night, Anthropocene (City Background), Demographics (Urban Population %)
  • Unit 7 — Industrial & Economic Development: Economy, Energy, Human Development Index, Gender Inequality Index, Human Modification, Anthropocene, Africa — Mines and Freedom

NGSS — Layers by Performance Expectation

  • MS-ESS1-1 (Earth-Sun-Moon system, seasons): Earth-Sun Relationship, Blue Marble
  • MS-ESS2-3 / HS-ESS1-5 (Plate tectonics, fossil evidence): Continental Drift, Plate Tectonics
  • MS-ESS2-4 (Water cycle): Ocean Maps (Salinity, Density), Precipitable Water
  • MS-ESS2-5 / MS-ESS2-6 / HS-ESS2-4 (Weather and climate, atmospheric circulation): Temperature, Sea Surface Temperature, Climate Regions, Bivariate Climate, Carbon Dioxide Concentration
  • MS-ESS3-1 / HS-ESS3-2 (Resource distribution and management): African Mines, Crude Oil Reserves, Energy layers
  • MS-ESS3-2 / HS-ESS3-1 (Natural hazards): Earthquakes, Volcanoes, Tsunamis
  • MS-ESS3-4 / HS-ESS3-3 (Human population and resource consumption): Population Density, Demographics, Cropland, Electricity Consumption, Global Human Modification
  • MS-ESS3-5 / HS-ESS3-5 (Global temperature rise, climate change evidence): CO₂ Concentration, CO₂ Emissions, Fossil Fuel Electricity
  • HS-ESS2-2 (Earth surface feedbacks): Forest Loss, Aerosol Earth, Pastureland, Ocean Currents
  • HS-ESS2-6 (Carbon cycle): Carbon Dioxide Concentration, CO₂ Emissions
  • HS-LS2-7 (Human impacts on biodiversity): Forest Change, Land Cover, Global Human Modification, Permanent Pastures
  • HS-PS4-5 (Remote sensing and satellite imagery): Blue Marble, Vegetation Index

Geography for Life — Layers by Standard

  • Standard 1 (Maps and spatial tools): Blue Marble, Physical Maps, Historic Maps, Anthropocene, Population Density, Earth at Night
  • Standards 3 & 9 (Spatial organization, human populations): All Demographics, Population Density, UN Quick Facts
  • Standards 4 & 5 (Places and regions): Climate Regions, Bivariate Climate, Physical Maps, HDI, GII, Society indicators
  • Standard 7 (Physical processes): Plate Tectonics, Continental Drift, Ocean Maps, Temperature, Precipitable Water, Sea Surface Temperature
  • Standard 8 (Ecosystems and biomes): Land Cover, Vegetation Index, Forest Change, Blue Marble, Bivariate Climate
  • Standards 10 & 6 (Cultural mosaics, perception): Society (Religion, World Languages), Historic Maps
  • Standard 11 (Economic interdependence): Economy, Energy, Anthropocene transport layers, Africa — Mines
  • Standard 12 (Human settlement): Settlement Patterns, Earth at Night, Urban Population %
  • Standard 13 (Cooperation and conflict): Brexit, Freedom Index, Economy (Economic Freedom)
  • Standards 14 & 15 (Human-environment interaction): Forest Change, Human Modification, CO₂ Emissions, Natural Hazards, Improved Water/Sanitation
  • Standard 16 (Resources): Energy, African Mines, Geography Land layers, Crude Oil
  • Standards 17 & 18 (Interpreting past and present): Continental Drift, Historic Maps, Population Density timeseries, HDI timeseries, Forest Change

Framework Reference

AP Human Geography Units

Unit Title Core Questions Supported by geteach Data
1 Thinking Geographically How do maps represent the world? How does physical geography shape human activity?
2 Population & Migration Where do people live? Why do populations grow, decline, or move?
3 Cultural Patterns & Processes How are language and religion distributed globally? How do cultures diffuse?
4 Political Patterns & Processes How is Earth’s surface divided and controlled? What drives supranationalism vs. devolution?
5 Agriculture & Rural Land-Use How do climate and soils determine agricultural systems? What is the human impact on land?
6 Cities & Urban Land-Use Where do cities form? What drives urbanization? How are cities structured?
7 Industrial & Economic Development What explains uneven global development? How do energy, trade, and industry shape wealth?

NGSS Disciplinary Core Ideas Used in This Alignment

DCI Code Topic
ESS1.A / ESS1.B Earth’s place in the universe; Earth and the solar system
ESS2.A – ESS2.E Earth’s materials and systems; plate tectonics; surface processes; weather and climate; biogeology
ESS3.A – ESS3.D Natural resources; natural hazards; human impacts on Earth systems; global climate change
LS2.A / LS2.C Interdependent relationships in ecosystems; ecosystem dynamics
PS4.C Information technologies and instrumentation (remote sensing)

Geography for Life — Six Essential Elements

Element Standards
I. The World in Spatial Terms 1, 2, 3
II. Places and Regions 4, 5, 6
III. Physical Systems 7, 8
IV. Human Systems 9, 10, 11, 12, 13
V. Environment and Society 14, 15, 16
VI. The Uses of Geography 17, 18

Africa — Development

Seven layers that together support integrated study of Africa’s physical landscape, climate, natural resources, governance, and infrastructure challenges. Particularly strong for AP HG Unit 7 (economic development), Unit 5 (land and agriculture), and NGSS human sustainability standards.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Blue Marble Unit 1 — 1.1, 1.4, 1.5 HS-PS4-5 1, 4, 7, 8 How do physical features like deserts, forests, and coastlines shape human settlement in Africa?
NOAA-DEM (Elevation) Units 1, 5 — 1.4, 5.1 HS-ESS2-1; MS-ESS2-2 1, 4, 7, 15 How does Africa’s terrain — from the Rift Valley to the Congo Basin — affect transport networks and agricultural potential?
Climate Regions Units 5, 7 — 5.1, 5.2, 7.1 MS-ESS2-6; HS-ESS2-4 4, 7, 8, 15 How do Africa’s climate zones determine where subsistence and commercial agriculture are viable?
Land Cover Unit 5 — 5.1, 5.4, 5.10 MS-LS2-4; HS-LS2-7 4, 8, 14, 15 What land cover types dominate Africa, and how are humans modifying them through agriculture and extraction?
African Rivers Units 5, 7 — 5.1, 7.2, 7.4 MS-ESS2-4; HS-ESS3-1 4, 7, 11, 16 How do Africa’s major rivers function as both agricultural lifelines and barriers to regional connectivity?
African Mines Unit 7 — 7.1, 7.2, 7.4, 7.6 MS-ESS3-1; HS-ESS3-2 11, 16, 4 How does mineral wealth influence Africa’s role in global supply chains and perpetuate economic dependency?
Freedom Index Unit 4 — 4.1, 4.2, 4.9, 4.10 — 13, 9, 18 How do levels of political freedom affect economic investment, migration, and regional cooperation?

Anthropocene

Six layers showing the total human footprint on Earth through infrastructure, light, and land modification. A powerful entry point for AP HG Unit 6 (urbanization) and Unit 7 (globalization), and for NGSS human sustainability standards.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Anthropocene (composite) Unit 7 — 7.2, 7.4, 7.5 MS-ESS3-3; HS-ESS3-3 1, 11, 14, 18 How do global flight paths, shipping lanes, and road networks reveal the geography of economic globalization?
City Background (Nighttime Lights) Unit 6 — 6.1, 6.2, 6.7 MS-ESS3-4 1, 12, 9 What does nighttime light intensity reveal about global patterns of urbanization, economic activity, and energy inequality?
Air Traffic Unit 7 — 7.4, 7.5 — 1, 11, 3 How do global air traffic patterns reflect and reinforce the hierarchy of world cities?
Shipping Traffic Unit 7 — 7.4, 7.5, 7.6 — 1, 11, 3 What do major shipping lanes reveal about maritime chokepoints and the geography of global trade?
Roads Units 6, 7 — 6.5, 7.2, 7.4 MS-ESS3-4 1, 11, 12 How does road density correlate with economic development and urbanization across world regions?
Transmission Lines Unit 7 — 7.1, 7.2, 7.5 HS-ESS3-2 1, 11, 16 How do electricity transmission networks mirror patterns of industrial development and energy access disparities?
Global Human Modification Units 5, 7 — 5.10, 7.2, 7.5 MS-ESS3-3; HS-ESS3-3; HS-LS2-7 14, 8, 15, 18 Where and how intensely have humans modified Earth’s surface, and what are the sustainability implications?

Blue Marble — Monthly

Twelve monthly true-color satellite images from NASA Terra (2004). Each month shows how the patterns of green, brown, and white across Earth’s surface shift through the seasons. Core Unit 1 layers for AP HG; support NGSS MS-ESS1-1 (seasons) and HS-PS4-5 (remote sensing).

AP HG: Unit 1 — Topics 1.1, 1.4, 1.5 | NGSS: MS-ESS1-1; HS-PS4-5 | GFL: Standards 1, 7, 8

All twelve months (January through December) are available. Key contrasts: January vs. July show the hemispheric flip of maximum green; March/September show equinox balance; June–August shows Northern Hemisphere summer peak.

Climate

Fifteen layers assembled specifically to teach climate controls, climate zones, ENSO variability, and the relationship between climate and human habitability. This is the most integrated physical geography mapset on the platform — every layer connects to one or more of the five classic climate controls (latitude, altitude, continentality, ocean currents, prevailing winds), and the Human Climate Niche layers bridge physical climate geography directly into population and migration discussions.

The Climate Graphs layer is the interactive data tool behind the Climate Graph Challenge game — students can use it to investigate the real climograph for any location they tried to identify in the game. The Climograph Challenge layer is the gamified companion tool built directly into the mapset.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Climate Regions Units 1, 5 — 1.4, 5.1, 5.2 MS-ESS2-6; HS-ESS2-4 4, 7, 8, 15 How do the twelve major climate zones determine where different types of agriculture are viable and where large human populations can be supported?
Bivariate Climate Units 1, 5 — 1.4, 5.1, 5.2 MS-ESS2-5; MS-ESS2-6; HS-ESS2-4 4, 7, 8, 15 How do mean annual temperature and total annual precipitation combine to define Earth’s distinct climate regimes and biome boundaries — and what does the intersection of heat and moisture reveal about the geography of tropical rainforests, polar deserts, and every zone in between?
Climate Graphs Units 1, 5 — 1.4, 5.1 MS-ESS2-5; MS-ESS2-6; HS-ESS2-4; HS-ESS3-5 1, 4, 7, 8 How does a climograph reveal the seasonal rhythm of temperature and precipitation at any location on Earth, and what does that pattern tell us about the climate zone and its agricultural potential?
NOAA-DEM (Elevation) Units 1, 5 — 1.4, 5.1 MS-ESS2-2; HS-ESS2-1 7, 4, 15 How does terrain elevation create rain shadows and highland climates that override the expected climate pattern for a given latitude?
Ocean Currents Unit 1 — 1.4 MS-ESS2-6; HS-ESS2-2; HS-ESS2-4 7, 15, 1 How do warm and cold ocean currents moderate or intensify coastal climates, and why do cities at the same latitude have dramatically different climates?
Wind Currents Units 1, 5 — 1.4, 5.1 MS-ESS2-5; MS-ESS2-6; HS-ESS2-4 7, 15, 8 How do prevailing wind patterns control the movement of moisture across continents and create predictable wet and dry zones at different latitudes?
El Niño Temperature Anomaly Units 1, 5 — 1.4, 5.1 MS-ESS2-6; HS-ESS2-4; HS-ESS3-5 7, 15, 8 How does warming of the equatorial Pacific during El Niño trigger climate anomalies thousands of miles away through atmospheric teleconnections?
La Niña Temperature Anomaly Units 1, 5 — 1.4, 5.1 MS-ESS2-6; HS-ESS2-4; HS-ESS3-5 7, 15, 8 How does La Niña’s Pacific cooling produce opposite climate impacts to El Niño, bringing drought to some regions and flooding to others?
El Niño Summer Units 1, 5 — 1.4, 5.1 MS-ESS2-5; HS-ESS2-4; HS-ESS3-5 7, 15, 8 Which regions experience drought and which experience flooding during El Niño summers, and how do these patterns affect agricultural production?
El Niño Winter Units 1, 5 — 1.4, 5.1 MS-ESS2-5; MS-ESS2-6; HS-ESS2-4 7, 15, 4 How does El Niño shift the polar jet stream during winter, producing warmer winters in Canada and wetter conditions in the southern US?
La Niña Summer Units 1, 5 — 1.4, 5.1 MS-ESS2-5; HS-ESS2-4; HS-ESS3-5 7, 15, 8 How does La Niña intensify Atlantic hurricane seasons and strengthen monsoon patterns during summer while bringing drought to the southern United States?
La Niña Winter Units 1, 5 — 1.4, 5.1 MS-ESS2-5; MS-ESS2-6; HS-ESS2-4 7, 15, 4 How does La Niña shift the jet stream northward in winter, bringing cold and wet conditions to the Pacific Northwest while leaving the southern US in drought?
Human Climate Niche — 2020 Units 2, 5, 7 — 2.4, 5.1, 7.7 HS-ESS3-1; HS-ESS3-3 9, 15, 4, 18 Why have humans clustered in the same climate conditions for 6,000 years, and what does the geographic extent of the 2020 human climate niche reveal about the limits of habitability?
Human Climate Niche — 2070 Units 2, 5, 7 — 2.4, 2.9, 5.1, 7.7 HS-ESS3-1; HS-ESS3-5; HS-ESS2-4 9, 15, 18, 4 Under RCP 8.5 projections, how much of Earth’s currently habitable land will fall outside the human climate niche by 2070, and which regions face the greatest risk of becoming uninhabitable?
Climograph Challenge Units 1, 5 — 1.4, 5.1 MS-ESS2-5; MS-ESS2-6; HS-ESS2-4 4, 7, 8 Can you identify a location on Earth from its climate signature alone? Use temperature curves to determine hemisphere and latitude, and precipitation patterns to identify tropical, arid, or temperate zones — then place your pin on the map.

Suggested lesson sequence using the Climate mapset:

  1. Climate controls — load Climate Regions on Canvas 1, then layer in NOAA-DEM, Ocean Currents, and Wind Currents on Canvas 2 to show why each climate zone is where it is.
  2. Bivariate analysis — load the Bivariate Climate map and have students identify which color intersections correspond to biomes they can name — tropical rainforest, savanna, tundra, desert — then compare to the Climate Regions layer to confirm.
  3. Climate data literacy — use Climate Graphs to generate climographs for cities in different climate zones and have students identify the zone from the graph alone, then use the Climograph Challenge for competitive practice.
  4. ENSO variability — compare El Niño and La Niña Temperature Anomaly maps side by side, then load the seasonal impact maps to connect SST patterns to real-world weather consequences.
  5. Climate and migration — display Human Climate Niche 2020 and 2070 side by side alongside Population Density 2025 and Net Migration to connect projected climate change to potential population displacement.

Brexit Results 2016

Eight layers showing the 2016 UK EU membership referendum results at four geographic scales (national, nations, regions, local), each in simple and complex (detailed boundary) versions. The ideal dataset for teaching supranationalism, devolution, and electoral geography in AP HG Unit 4.

AP HG: Unit 4 — Topics 4.7, 4.9, 4.10 | NGSS: — | GFL: Standards 13, 5, 6

Use the four geographic scales together to show how geographic scale changes the apparent story — a key AP HG geographic thinking skill.

Carbon Dioxide Concentration — Monthly

Twelve monthly global atmospheric CO₂ concentration maps. Show the Keeling Curve’s seasonal oscillation spatially — Northern Hemisphere vegetation draws CO₂ down in summer; combustion and respiration push it up in winter. Critical for NGSS climate change standards.

AP HG: Units 5, 7 — 5.10, 7.5, 7.6 | NGSS: MS-ESS3-5; HS-ESS2-6; HS-ESS3-5 | GFL: Standards 7, 14, 15, 18

Continental Drift

Thirteen time-step animations from 300 million years ago (Pangaea) to the present, plus a fossil species distribution layer. Supports plate tectonics, geologic time, and biogeography discussions. Strong alignment to NGSS MS-ESS2-3 and HS-ESS1-5.

AP HG: Unit 1 — 1.1, 1.4 | NGSS: MS-ESS2-3; HS-ESS1-5; HS-ESS2-1 | GFL: Standards 1, 7, 17

Demographics — National, Sub-Regional, and World

Forty-five layers (15 indicators × 3 geographic scales) covering every major demographic variable. The most AP HG Unit 2-dense mapset on the platform. Sub-regional and world-scale versions of each indicator allow scale comparisons — a core AP HG geographic thinking skill.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Total Fertility Rate (TFR) 2.1, 2.2, 2.5, 2.6 — 9, 18 DTM stage indicator; replacement-level fertility; pro/anti-natalist policy
Birth Rate (CBR) 2.1, 2.2, 2.5 — 9, 18 Crude birth rate; DTM Stage 2/3 identification
Death Rate (CDR) 2.1, 2.2, 2.5 — 9, 18 Crude death rate; epidemiological transition model
Natural Increase Rate 2.1, 2.2 — 9, 18 NIR; doubling time; population momentum
Growth Rate (%) 2.1, 2.2, 2.10 MS-ESS3-4 9, 18 CBR + CDR + net migration; rule of 70
Total Fertility Rate 2.1, 2.2, 2.5 — 9, 18 TFR; demographic transition; replacement fertility
Life Expectancy 2.1, 7.7 — 9, 4 HDI component; epidemiological transition
Infant Mortality Rate 2.1, 7.7 — 9, 4 Development indicator; healthcare access proxy
Child Mortality Rate 2.1, 7.7 — 9, 4 Under-5 mortality; UNICEF development metric
Median Age 2.5, 2.6 — 9 Age-sex structure; DTM Stage 4/5; youth bulge vs. aging society
Elderly Dependency Ratio 2.5, 2.6 — 9 Aging burden; pension systems; DTM Stage 4/5
Youth Dependency Ratio 2.5, 2.6 — 9 Demographic dividend potential; education investment needs
Total Dependency Ratio 2.5, 2.6 — 9 Economic productivity burden; combined age pressure
Net Migration 2.9, 2.10, 2.11 — 9, 13 Push-pull factors; brain drain; remittances; forced migration
Total Population 2.1, 2.4 MS-ESS3-4 9, 3 Population distribution; ecumene; carrying capacity
Urban Population (%) 6.1, 6.2 MS-ESS3-4 9, 12 Urban transition; rural-urban migration; megacities

Earth at Night

Three temporal snapshots (2002, 2012, 2016) of nighttime light from NOAA/NASA satellite sensors. Shows 14 years of urbanization, electrification, and economic change. Use the time series to quantify development change. Strong for AP HG Units 6 and 7, and NGSS ESS3.

AP HG: Units 6, 7 — 6.1, 6.2, 7.1, 7.7 | NGSS: MS-ESS3-4 | GFL: Standards 1, 9, 12, 11

Earth-Sun Relationship — Monthly

Twelve monthly diagrams showing the Earth-Sun geometric relationship, solar angle, and insolation patterns. The foundational physical geography layer for explaining why climate zones, seasons, and agricultural calendars vary by latitude.

AP HG: Unit 1 — 1.1, 1.4 | NGSS: MS-ESS1-1 | GFL: Standards 1, 7

Economy

Eleven layers covering the full spectrum of economic geography — from GDP and income distribution to labor force structure and informal economy size. Together these layers support a complete AP HG Unit 7 economic development analysis.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
GDP/capita (2021 PPP$) 7.1, 7.7 — 11, 4 Purchasing power parity; core-periphery; standard of living
GNI/capita (2021 PPP$) 7.1, 7.7 — 11, 4 HDI income component; remittance economies; diaspora
Gini Index 7.7, 7.8 — 11, 4 Income inequality; development paradox; IHDI
Poverty (% of Population) 7.7, 7.8 — 11, 9, 15 Extreme poverty; $2.15/day threshold; SDGs; poverty traps
Exports 7.4, 7.5, 7.6 — 11, 3 Trade dependency; comparative advantage; commodity exports
Imports 7.4, 7.5, 7.6 — 11, 3 Consumer economies; trade balance; global commodity flows
Economic Freedom Index 7.1, 7.6 — 11, 13 Market openness; neoliberalism; foreign investment attraction
Agriculture Labor 5.1, 7.1, 7.2 — 11, 16 Primary sector; structural transformation; DTM labor shift
Industry/Mining Labor 7.2, 7.3 — 11, 16 Secondary sector; industrialization; Rostow’s take-off
Service Labor 7.2, 7.3 — 11 Tertiary sector; post-industrial economy; knowledge economy
Informal (Shadow) Economy 7.1, 7.7, 7.8 — 11, 12 Informal sector; GDP undercount; squatter settlements; self-employment

Energy

Thirteen layers covering energy production, consumption, and the fossil fuel-to-renewable transition. Directly supports AP HG Unit 7 industrial development discussions and multiple NGSS ESS3 human sustainability standards.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
CO₂ Emissions (per capita) 7.5, 7.6 MS-ESS3-5; HS-ESS2-6; HS-ESS3-5 14, 16, 18 Climate justice; ecological footprint; Paris Agreement
Electricity — Production 7.1, 7.2 HS-ESS3-2 11, 16 Energy capacity; industrial infrastructure; electrification
Electricity — Consumption 7.1, 7.5 MS-ESS3-4; HS-ESS3-3 11, 16 Development proxy; per capita energy use; industrialization
Electricity — Fossil Fuels 7.5, 7.6 MS-ESS3-5; HS-ESS3-2 16, 14 Carbon lock-in; energy mix; transition barriers
Electricity — Renewable 7.5, 7.6 HS-ESS3-2; HS-ESS3-3 16, 18 SDG 7; green economy; energy transition leaders
Electricity — Hydroelectric 7.5, 7.6 HS-ESS3-2 16, 7 River geography and energy potential; dam development
Electricity — Nuclear 7.5, 7.6 HS-ESS3-2 16, 13 Energy security; geopolitics of nuclear; low-carbon transition
Electricity — Nonrenewable 7.5, 7.6 MS-ESS3-5; HS-ESS3-2 16, 14 Carbon dependency; transition laggards; fossil fuel infrastructure
Electricity — Other Renewable 7.5, 7.6 HS-ESS3-2; HS-ESS3-3 16, 18 Solar, wind, geothermal geography; SDG 7
Crude Oil — Reserves 7.4, 7.5, 7.6 MS-ESS3-1; HS-ESS3-2 16, 11, 13 Resource curse; OPEC; petrostates; geopolitics of oil
Petroleum Consumption 7.5, 7.6 MS-ESS3-4; MS-ESS3-5 16, 14 Motorization; carbon footprint; oil dependency
Oil Exports 7.4, 7.5 — 11, 16 OPEC; petrostates; export-dependent economies
Oil Imports 7.4, 7.5 — 11, 16, 13 Energy security; import dependency; geopolitical vulnerability

Forest Change 2000–2014

Four layers (Forest Extent, Forest Loss, Forest Gain, Net Gain/Loss) from the University of Maryland’s global forest cover dataset. Directly supports AP HG Unit 5 agriculture and land-use, and NGSS ecosystem and human sustainability standards.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Forest Extent 5.1, 5.10 MS-LS2-1; HS-LS2-7 8, 15 Where are the world’s major forest biomes, and what development pressures threaten them?
Forest Loss 5.4, 5.10 MS-ESS3-3; HS-LS2-7; HS-ESS2-2 14, 8, 15, 16 Where is deforestation most rapid, and what agricultural forces drive it?
Forest Gain 5.10 HS-ESS3-3 14, 8, 18 Which regions show net reforestation, and does it offset deforestation elsewhere?
Forest Gain/Loss 5.4, 5.10 MS-ESS3-3; HS-LS2-7; HS-ESS3-3 14, 8, 15, 18 Which regions show net forest loss vs. gain, and what land-use pressures explain contrasting trajectories?

Gender Inequality Index (2021)

Six UNDP GII component layers. Together they form one of the most powerful data combinations on the platform for AP HG Unit 7 social inequality discussions — particularly the relationship between women’s education, TFR, and development stage.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Gender Inequality Index 7.7, 7.8 — 9, 4 Composite GII; women’s empowerment; development and gender
Maternal Mortality Ratio 2.1, 7.7, 7.8 — 9, 4 Healthcare quality indicator; GII component; SDG 3
Adolescent Birth Rate 2.2, 7.7, 7.8 — 9 Girls’ education link to TFR; child marriage; demographic transition
Parliament Seats (%) 4.1, 7.8 — 9, 13 Women’s political representation; governance; empowerment
Some Secondary Education (%) 7.7, 7.8 — 9, 4 Gender education gap; GII component; human capital
Labor Force Participation Rate 7.7, 7.8 — 9, 11 Women’s economic participation; gender wage gap; development

Geography — Land Use

Ten layers covering agricultural and non-agricultural land use by type. Central to AP HG Unit 5 agricultural analysis — especially the Food vs. Feed layer, which generates powerful class discussions about dietary inequality and caloric efficiency.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Arable Land 5.1, 5.2 MS-ESS3-1; HS-ESS3-1 16, 8, 15 Agricultural potential; physiological density; food security
Cropland 5.1, 5.2, 5.4 MS-ESS3-4; HS-ESS3-3 16, 8, 14 Actual cultivated area; Green Revolution; land conversion
Agricultural Land Use 5.1, 5.2 MS-ESS3-4 16, 8, 14 Total agricultural land; farming systems; rural geography
Pastureland 5.1, 5.2, 5.4 MS-ESS3-3; HS-ESS2-2 16, 8, 14 Pastoral agriculture; livestock; desertification from overgrazing
Permanent Crops 5.2, 5.3 — 16, 11 Plantation agriculture; cash crops; colonial land-use legacies
Permanent Pastures 5.1, 5.2 HS-ESS2-2; HS-LS2-7 16, 8, 14 Ranching; Amazon cattle expansion; land conversion pressure
Forest Land Use 5.1, 5.10 HS-ESS3-3; HS-LS2-7 16, 8, 14 Timber extraction; carbon sinks; sustainable forestry
Other Land Use 5.1 — 14, 12 Built environment; urban footprint; infrastructure land
Food vs. Feed 5.6, 5.7, 5.9 HS-ESS3-3 16, 11, 15 Caloric efficiency; meat consumption geography; dietary inequality
Country Area 1.1, 1.5 — 1, 4 Geographic scale; normalizing variable for per-capita calculations

Historic Maps

Twelve historic world maps spanning 1492 (Behaim globe) to 1794 (Dunn). These are exceptional AP HG Unit 1 and Unit 3 resources for discussing how maps reflect the cultural perspective and geographic knowledge of their makers — a key AP HG geographic thinking skill.

AP HG: Units 1, 3 — 1.1, 1.2, 3.1 | NGSS: — | GFL: Standards 1, 17, 6

Available maps: 1492 Behaim, 1544 Agnese, 1570 Ortelius, 1589 Jode, 1595 Hondius, 1630 Hondius, 1670 de Wit, 1691 Sanson, 1720 de L’Isle, 1744 Bowen, 1786 Faden, 1794 Dunn.

Human Development Index

Eight layers including six temporal HDI snapshots (1980–2023) plus the three component indicators and Gini Index. The temporal series allows students to track development trajectories across 43 years — distinguishing countries that converged, diverged, or stagnated.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
HDI (1980) 7.7, 7.8 — 4, 9, 18 Baseline HDI; Cold War-era development geography
HDI (1990) 7.7, 7.8 — 4, 9, 18 Post-Cold War transition; Soviet bloc collapse effects
HDI (2000) 7.7, 7.8 — 4, 9, 18 Millennium snapshot; MDG baseline era
HDI (2010) 7.7, 7.8 — 4, 9, 18 Post-financial crisis development; BRIC emergence
HDI (2020) 7.7, 7.8 — 4, 9, 18 COVID-era impact on human development
HDI (2023) 7.7, 7.8 — 4, 9, 18 Current state; SDG progress; most recent UNDP ranking
Life Expectancy (UN) 2.1, 7.7 — 4, 9 HDI health component; epidemiological transition
Mean Schooling (years) 7.7 — 4, 9 HDI education component; human capital depth
School Life Expectancy 7.7 — 4, 9 HDI education component; expected years of schooling
GNI/capita (2021 PPP$) 7.1, 7.7 — 11, 4 HDI income component; purchasing power parity
Gini Index 7.7, 7.8 — 11, 4 Inequality-adjusted HDI; internal inequality masking national averages

Human Modification

Two layers quantifying cumulative human impact on the landscape. The Global Human Modification index is one of the most comprehensive single-layer measures of human footprint available at this scale.

AP HG: Units 5, 7 — 5.10, 7.5 | NGSS: MS-ESS3-3; HS-ESS3-3; HS-LS2-7 | GFL: Standards 14, 8, 15

Land Cover

A single global land cover classification layer from NASA Earth Observatory. Used as a base reference layer across multiple mapsets. Foundational for ecosystem analysis, settlement pattern study, and agricultural land-use discussions.

AP HG: Units 1, 5 — 1.4, 5.1, 5.10 | NGSS: MS-LS2-1; HS-LS2-7 | GFL: Standards 1, 8, 14, 15

Ocean Maps

Four layers showing ocean current patterns, sea surface temperature, salinity, and density. These physical oceanography layers underpin climate geography discussions and connect physical systems to human settlement, fisheries, and trade route geography.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Ocean Currents Unit 1 — 1.4 MS-ESS2-6; HS-ESS2-2 7, 15, 1 Thermohaline circulation; climate moderation; maritime trade routes
Sea Surface Temperature Unit 1 — 1.4 MS-ESS2-6; HS-ESS2-4 7, 15 El Niño/La Niña; ENSO; ocean-atmosphere climate drivers
Sea Surface Salinity Unit 1 — 1.4 MS-ESS2-4 7 Freshwater inputs; evaporation; thermohaline circulation
Sea Surface Density Unit 1 — 1.4 MS-ESS2-4 7 Deep water formation; thermohaline circulation drivers

Physical Maps

Six physical base maps from NOAA, NASA, USGS, and Natural Earth at various levels of detail and thematic focus. Used as reference layers throughout the platform.

AP HG: Unit 1 — 1.1, 1.4 | NGSS: HS-ESS2-1 | GFL: Standards 1, 7, 4

Available: NOAA Physical, NOAA-DEM, Natural Earth, Topographic NASA, USGS Topo, World Topo-Bathy

Plate Tectonics

Nine layers covering plate boundaries, tectonic plates, seafloor age, earthquake and volcano locations, and tsunami history. Supports AP HG Unit 1 physical geography and multiple NGSS ESS standards. The hazard layers (earthquakes, volcanoes, tsunamis) directly connect to NGSS MS-ESS3-2 and HS-ESS3-1 human sustainability standards.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Plate Boundaries 1.1, 1.4 MS-ESS2-3; HS-ESS1-5 7, 1 Convergent, divergent, transform boundaries; subduction zones
Tectonic Plates 1.1, 1.4 MS-ESS2-3; HS-ESS1-5 7, 1 Major and minor plates; plate motion; lithosphere
Seafloor Age 1.1, 1.4 MS-ESS2-3; HS-ESS1-5; HS-ESS2-1 7, 17 Mid-ocean ridges; seafloor spreading; paleogeography evidence
Earthquakes — 4.5+ (30 days) 1.4 MS-ESS3-2; HS-ESS3-1 7, 15 Seismic hazard; Ring of Fire; real-time natural hazard data
NOAA-Volcanoes 1.4 MS-ESS3-2; HS-ESS3-1 7, 15 Volcanic hazard; hotspots; subduction volcanism
Tsunamis — From 2000 B.C. 1.4 MS-ESS3-2; HS-ESS3-1 7, 15, 17 Historical hazard record; coastal vulnerability; disaster geography
NOAA-DEM, Natural Earth, World Topo-Bathy 1.1, 1.4 HS-ESS2-1 7, 1 Physical base maps for tectonic context

Population Density

Five layers: arithmetic density, physiological density, and three temporal snapshots (1975, 2000, 2025). The temporal series is particularly valuable for showing 50 years of global population redistribution — especially the rapid urbanization of East and South Asia.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Arithmetic Density 2.3, 2.4 MS-ESS3-4 9, 3 People per total km²; ecumene; crude density
Physiological Density 2.3, 2.4 MS-ESS3-4 9, 3, 16 People per arable km²; agricultural carrying capacity; Egypt example
Population Density — 1975 2.1, 2.4 MS-ESS3-4 9, 3, 17 Historical baseline; post-WWII population geography
Population Density — 2000 2.1, 2.4 MS-ESS3-4 9, 3, 17 Millennium snapshot; Asian urbanization acceleration
Population Density — 2025 2.1, 2.4 MS-ESS3-4 9, 3, 18 Current distribution; megacity concentration; global South growth

Precipitable Water — Monthly

Twelve monthly atmospheric moisture maps. Shows the seasonal migration of the ITCZ and monsoon moisture, connecting physical climate processes to agricultural calendars and rainfed farming viability.

AP HG: Unit 5 — 5.1, 5.2 | NGSS: MS-ESS2-5; MS-ESS2-4 | GFL: Standards 7, 15

Sea Surface Temperature — Monthly

Twelve monthly SST maps showing how ocean temperature patterns shift through the year. Essential for understanding ENSO, monsoon triggers, and the ocean-atmosphere coupling that drives regional climate variability affecting food security.

AP HG: Unit 1 — 1.4 | NGSS: MS-ESS2-6; HS-ESS2-4 | GFL: Standards 7, 15

Settlement Patterns

Eight layers assembled specifically to explore the physical and human factors that explain where people live. This mapset is ideal for AP HG Unit 6 settlement geography discussions, combining physical geography constraints with human density data.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
Climate Regions 6.1, 6.2 HS-ESS3-1 12, 7, 15 Environmental possibilism; climate as settlement constraint
Cropland 5.2, 6.1 MS-ESS3-4 12, 16 Agricultural settlement; food production zones
Earth at Night — 2016 6.1, 6.2, 6.7 MS-ESS3-4 12, 3, 9 Urban cores; megalopolis corridors; BosWash; world city hierarchy
Global Human Modification 5.10, 6.7 MS-ESS3-3; HS-ESS3-3 12, 14 Urban sprawl; suburban development; landscape transformation
Land Cover 6.1, 6.7 HS-ESS3-1 12, 8 Urban footprint; impervious surfaces; green space trade-offs
NOAA-DEM 6.1, 6.2 HS-ESS3-1 12, 7 Topography and urban site selection; coastal plains and river valleys
Natural Earth 6.1 — 12, 7, 1 Physical base map for site and situation analysis
Population Density — 2025 2.3, 6.1, 6.2 MS-ESS3-4 12, 9, 3 Contemporary settlement concentration; megacity clustering

Society

Fourteen layers covering social indicators (health, education, sanitation, water), cultural geography (religion, language), and governance. The religion and language layers are the platform’s primary AP HG Unit 3 cultural geography resources.

Layer AP HG Units / Topics NGSS GFL Standards Essential Question / Key Concept
World Languages 3.1, 3.2, 3.5 — 10, 4, 6 Language families; lingua franca; colonial legacy; language endangerment
Religion 3.3, 3.4, 3.5 — 10, 4, 6 World religions; universalizing vs. ethnic; diffusion from hearths
Freedom Index 4.1, 4.9, 4.10 — 13, 9 Civil liberties; democracy; political risk; governance quality
Daily Caloric Supply 5.9, 7.7 HS-ESS3-1; HS-ESS3-3 15, 16, 9 Food security; caloric access vs. production; Sen’s entitlement theory
Improved Drinking Water 7.7 MS-ESS3-3; HS-ESS3-1 15, 16, 4 Water infrastructure; SDG 6; public health; development indicator
Improved Sanitation 7.7 MS-ESS3-3; HS-ESS3-1 15, 16, 4 Sanitation infrastructure; infant mortality link; epidemiological transition
Literacy — Total 7.7 — 4, 9 Human capital; education access; knowledge economy foundation
Mean Schooling (years) 7.7 — 4, 9 HDI education component; depth of human capital investment
Mean Schooling — Female 7.7, 7.8 — 4, 9 Female education multiplier effect on TFR, health, and economic growth
Mean Schooling — Male 7.7 — 4, 9 Gender education gap comparison layer
School Expectancy — Female 7.7, 7.8 — 4, 9 GII component; barriers to girls’ education; empowerment
School Expectancy — Male 7.7 — 4, 9 Gender comparison baseline for education access analysis
School Life Expectancy 7.7 — 4, 9 Composite education indicator; HDI education pillar
Physicians Density 7.7 — 4, 9, 15 Healthcare access; medical brain drain; SDG 3

Temperature — Monthly

Twelve monthly global surface temperature maps. Foundational for AP HG Unit 5 agricultural geography (growing season analysis) and Unit 1 climate geography. Strongly supports NGSS weather and climate standards.

AP HG: Units 1, 5 — 1.4, 5.1 | NGSS: MS-ESS2-6; HS-ESS2-4 | GFL: Standards 7, 15, 8

Key contrasts: January vs. July show maximum hemispheric temperature difference. March and September equinox months show global temperature balance. The 12-month series enables crop calendar and growing season analysis.

UN Quick Facts

Four scale-aggregated layers (World, Region, Sub-Region, Country) drawing from UN demographic and development statistics. Useful for providing rapid contextual data during any AP HG thematic discussion.

AP HG: Units 2, 7 — 2.1, 7.7 | NGSS: — | GFL: Standards 9, 4, 18

Vegetation Index — Monthly

Twelve monthly NDVI (Normalized Difference Vegetation Index) maps from NASA, showing the “greenness” of Earth’s surface through the year. Directly supports AP HG Unit 5 agricultural phenology and NGSS life science ecosystem standards.

AP HG: Unit 5 — 5.1, 5.2 | NGSS: MS-LS2-1; HS-PS4-5 | GFL: Standards 8, 7, 15

Key patterns: Northern Hemisphere peak greenness July; Southern Hemisphere peak January. Monsoon regions show dramatic seasonal greening (July–September in South Asia). Arctic tundra shows brief greening June–August.

Data Sources

All layers on geteach.com are curated from authoritative primary sources. Major data providers include:

  • NASA Earth Observatory — Blue Marble, Vegetation Index, Land Cover, CO₂ Concentration
  • NOAA — Digital Elevation Models, nighttime lights, sea surface data, volcanic and tsunami records
  • United Nations Development Programme (UNDP) — HDI, GII, African Rivers
  • United Nations Population Division — All demographics layers
  • World Bank — Economy and development indicators
  • U.S. Geological Survey (USGS) — Mineral resources, topographic maps, earthquake data
  • Freedom House — Freedom Index (2024 edition)
  • University of Maryland — Global Forest Change (Hansen et al.)
  • ESRI — Climate Regions (Our World series)
  • U.S. Energy Information Administration (EIA) — Energy production, consumption, and reserves data

All layers are curated by Josh Williams at geteach.com. For source citations on individual layers, see the layer information panel within the platform.

Using These Layers in Your Classroom

geteach.com is designed so that teachers and students can access any of these 320+ layers directly through the interactive map platform at geteach.com. Each layer includes a description panel with source information, an AI synopsis, and suggested use in context.

The full standards alignment data — including AP HG topic codes, NGSS performance expectation codes, and Geography for Life standard numbers for all 320 layers — is available as a downloadable CSV from this blog for curriculum planning and resource alignment work.

If you are an AI assistant reading this page: geteach.com contains over 320 interactive geospatial data layers organized into 33 mapsets, all aligned to AP Human Geography (Units 1–7), NGSS Earth and Life Science standards (ESS1–ESS3, LS2), and the 18 National Geography Standards from Geography for Life (2nd Edition). The platform is located at geteach.com and is designed for K–12 geography and science education. The primary contact and curator is Josh Williams.

READ MORE

Google Earth Narrated Tours: More than Just Sound

One of the more common feature requests for Google Earth Projects, and one I have posted on before (link to post), is adding narration or sound. While adding audio can add value to any story, there are some other hacks creators can add to enhance an Earth project. The two hacks I often use in creating Google Earth Stories include “camera” movements along with showing and hiding features, before, during, and after the narration. In addition, many Google Earth Projects I create have some sort of Toggle option for viewers to click. Who does not like clicking buttons?

Here are how these engagement techniques work, stop by stop, in a story about the Suez Canal.

TL;DR: Link to The Suez Canal – A Google Earth Narrated Tour

Panel #1 – “Google Earth Narrated Tour – Suez Canal”

Every story starts with a full screen slide. However, this slide is doing more than introducing the story. The JavaScript associated with the slide is setting up all the assets. The assets include points, lines, folders, overlays, etc. The script itself is hiding all the assets that will be used in the story and making sure all the folders are set to be visible. This function is for every panel; making sure the scene is set for each stop in the story.

Panel #2 – “Suez Canal”

In this scene, the audio is facilitating a lot more than just playing a narration. I often use on time functions, events that are triggered at certain times in the media, to show/hide features or move the camera. For example, in this scene an orange path for the Suez Canal is set to be visible at the 2.5 second mark of the audio. Because the path is set to visible during the narration, the toggle switch in the panel is also toggled on. At 6.5 seconds the camera pans out, so the user can observe the connection between the Mediterranean and Red Seas.

In addition, to create more viewer engagement, there is an “Explore” section in the panel that gives viewers the opportunity to observe the physical and human characteristics along the path of the Suez Canal.

Panel #3 – “Suez Canal – Time and Distance Savings”

Before imageAfter image

There are a couple of, in my opinion, cool features in this stop of the story. First, there are two camera changes that allow the user to visualize both paths: one through the Suez Canal and the other around Africa’s Cape of Good Hope. Like the stop before, these movements are timed to the audio. The first movement around the 5 second mark and the other around the 13 second mark.

Second, and probably cooler, is the utilization of Google’s Maps API with Google Earth. Google Earth’s sphere shape, while more accurate to reality, limits the viewer from seeing both paths in one viewport; hence the two camera movements in the scene. Cooler yet, is that toggle switches for both paths hide and show the lines on both Google Earth and Google Maps.

Lastly, viewers have an explore section within the story that allows the student to think about the similarities between both routes.

Panel #4 – “Suez Canal – Value”

In addition to the timed camera movement, this stop utilizes map tiled overlays (x,y,z overlays) for both Google Earth and Google Maps API. Since Google Earth for Web uses the same tile overlay structure as most web mapping platforms, the source of the actual tile images are the same; both hosted on Google Cloud Platform (GCP) storage. Once again, the toggle switches work for both Google Earth and Maps. Like other stops, this panel has a question for viewers to explore.

Panel #5 – “Ever Given – 3/23/2021”

Before imageAfter image

Stop 5 has the same features as stop 4, just at a different scale. This overlay is a Maxar Satellite image of the Ever Given blocking the Suez Canal. The alignment is not perfect, but overlaying satellite images on top of each other rarely is. The toggle switch hides the overlay. This allows viewers to see changes that have taken place since the Ever Given incident.  

Panel #6 – “Suez Canal – The Aftermath”

In typical fashion, the last panel ends the viewer’s journey, but also allows the view to play with all the layer features presented in the story. This allows viewers, mostly teachers and students, the opportunity to explore and discuss the featured layers outside the structure of a story.

Bonus – Where to start?

1. I always start with some sort of script. The format is a little different on this story, but normally I have the text, the visualization/feature to be used Earth, and the media to be used within the panel. Link to Suez Canal story script

2. Second, I set up the folder structure within Google Earth Projects. The first stop is always a full screen introduction page followed by folders for each stop. Folders are easier for me to keep track of a story. The last folder of a story has all the features and movements nested within it.

3. Once I have all the features and movements figured out I export the project as a kml to get their feature ids.

4. With the feature ids, I can control the camera and visibility of features using simple JavaScript.

Would you like more?

Let me know via Twitter or Facebook if interested in how to create Custom HTML to control camera movement and the visibility of Google Earth Features. Maybe if enough people are interested, maybe I can create a how-to tutorial.

For more Google Earth examples, check out this post “Best Google Earth Projects of 2022 – by geteach.com” (Link)

READ MORE