World GIS Data

Courtesy of GIS Lounge

Looking for global GIS data? Listed here are free sources for finding GIS data in both vector and raster formats including satellite imagery.

Natural Earth World GIS Data

This is probably one of the best sources for free global GIS data.  The site was initiated in 2008 by Nathaniel Vaughn Kelso and Tom Patterson and data from the site is coordinated by a group of volunteers.

Users looking for a global collection of free GIS data should exploreNatural Earth's GIS data selection. For a starter sample of world GIS data, Natural Earth offers a Natural Earth quick start kit download with a sample set of world GIS layers.   The zip file includes administrative layers such as populated places, hydrology layers including coastlines and rivers and lakes, and a hill shade layer for the world.  The vector layers in the quick start kit are at a scale of 1:10m and the raster layer is at a scale of 1:50m.  Included in the quick start kit to get you a jump start are an ArcMap file (.mxd) and QGIS file (.qgis) that loads the sample data into the respective map files already stylized.

Individual layers can be downloaded from Natural Earth for cultural, physical, and raster data at three scales of resolution: 1:10m, 1:50m, and 1:110m.  All of the GIS data offered at Natural Earth is public domain and free to use.

Free world GIS data from Natural Earth

Free world GIS data from Natural Earth

Global Elevation Map

The U.S. Geological Survey (USGS) and the National Geospatial-Intelligence Agency (NGA) have an aggregated world digital elevation model that was released in November of 2011 called the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010).  The world DEM aggregates the highest resolution data from a variety of sources.  More information: GMTED2010.

GMTED2010 - Global elevation data.

GMTED2010 - Global elevation data.

World Database of Large Urban Areas

Norpil has made available their layer of Large Urban Areas for downloading.  Tabular data from the United Nation's World Urbanization Prospects: The 2007 Revision Population Database was joined to the 2008 cities dataset that comes with ESRI's Data & Maps sample data and other sources of data.

The database is available for unrestricted download, under the Creative Commons 2.5 attribution license. This gives you the right to reuse and modify the data, as long as Nordpil and the UN Population Division are properly credited.

The data is available in Shapefile and KMZ formats as well as in tabular format.  Presentations of the data are also available including an animation of maps spanning the years 1950 to 2050 and a map of large urban areas in 2005.

Large cities in 2005 by Norpil

Digital Chart of the World Server
This web site will allow you to download the boundaries and layers of different countries, in Arc/INFO export format.

USGS EarthExplorer
Find digital datasets of both imagery and GIS databases from this site. Select data by coordinates, place name or interactively through the map.

Mapping the World at Night
A look at nighttime global mapping.

Suggest a resource: editor@gislounge.com

Further Reading

  1. GIS Data
  2. Data Appeal: 3D Visualization of GIS Data
  3. New Global Elevation Data Available to Download: Global Multi-resolution Terrain Elevation Data 2010
  4. Updated 3D Global Topo Data from NASA
  5. Overview of Elevation Data
Written by Default at 12:11

Precision Agriculture: Sensors Drive Agricultural Efficiency

Courtesy of Sensors and Systems

If Old McDonald had a farm today, he could manage it from his laptop computer and map it with an application on his handheld device. When he was out in the field, his tractor’s guidance system could know its position to within less than an inch, turning his planters and sprayers on and off accordingly. A boom height control system would make sure that his sprayer did not hit the ground and a yield monitor on his combine would measure the exact volume of his harvest, in real time. Soil moisture sensors networked via cellular modems, soil density sensors on his planters, and infrared crop health sensors on his tractor would gather a wealth of data that his agronomist would use to prepare a prescription map for the next season. In a few years, that data stream would also include aerial imagery collected by his unmanned aerial vehicle (UAV) and his tractor would also be running unmanned as a robot in the field. If a chick, duck, turkey, pig, cow, cat, mule, dog, turtle, or farm hand got in its way, the tractor’s radar collision avoidance system would recognize it and stop.

The most widely used term to describe this complex suite of technologies is precision agriculture, and the uptake is exploding. Most new tractors and implements are sold with factory-installed global navigation satellite system (GNSS) receivers and a variety of sensors. Reversing a long-standing trend, kids who were born and raised on farms are now returning there after college, because the work is much more intellectually challenging and less manual labor-intensive than it used to be. 

Addressing Variability

Soil characteristics — including the amount of phosphorus, potassium, calcium, and magnesium — often vary significantly from one area of a field to another. The practice of variable rate takes this variability into account to reduce inputs of water, seed, fertilizers, and fuel as well as to increase yields by dividing fields into sectors and prescribing rates for each one.

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Figure 1: Field prescription map showing Normalized Difference Vegetation Index (NDVI) data at 30 centimeter resolution. Image courtesy of Simplot. 

Fertilizer dealers, seed salesmen, and crop consultants analyze farm data and advise farmers on these rates. However, farmers can also create prescription maps themselves, by uploading soil type data, historical yield data, and aerial imagery into farm management software on their computers. They can then upload those maps to their tractors’ guidance systems, which use them to vary the rates depending on location, with wireless networks creating a farm-wide system.

Precision also plays with environmental impacts, such as reducing water use or the amount of farm chemicals in water. 

“To be more efficient with our water and to stop flushing out so much nitrogen through the soil into the water table,” says Chris Gallo, a Precision Agriculture Specialist at Simplot, “many farmers are switching from flood irrigation to drip irrigation and micro-sprinklers.”

By managing their fields based on soil properties and putting fertilizer where it needs to go, farmers can better manage their nutrients. By setting up “exclusion zones” farmers are also able to automatically cut off the spray of fertilizer before it reaches a critical distance from a water supply. “That has saved many farmers from litigation and fines,” says Mike Martinez, a market manager at Trimble Agriculture.

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Figure 2: A tractor towing a fertilizer sprayer using Trimble's GreenSeeker system. Image courtesy of Trimble. 

Another benefit of precision agriculture is that it enables farmers to avoid gaps and overlaps when planting. “We are also able to geospatially sense where we have already applied fertilizer and planted. That way the system cuts the supply at those points, so that you don’t waste inputs,” says Martinez. 

Standard Equipment

Just as car buyers today expect Bluetooth integration and XM satellite Sirius radio, farmers expect new tractors to come with guidance. Case IH tractors, sprayers, and combines are sold with a factory-installed Glonass-enabled GNSS receiver, a display, and a controller, which consists of accelerometers and gyroscopes that compensate for uneven terrain. 

“Just as you pay for different levels of XM radio in your vehicle, you pay to unlock different levels of positioning accuracy,” says Trevor Mecham, Americas marketing manager for Case IH, Advanced Farming Systems. “You can get free WAAS corrections or pay for DGPS accuracy that gets you plus or minus a couple of inches pass-to-pass.” he points out, referring to the Federal Aviation Administration’s Wide Area Augmentation System and the U.S. Coast Guard’s Differential Global Positioning System. “If you need year-after-year repeatability for controlled traffic farming, so that you can do the side dressing and strip till applications, you need RTK,” which stands for real-time kinematic satellite navigation.

Likewise, John Deere sells its tractors and combines with an integrated guidance system. “The sensors on the combines are already factory-installed for yield mapping and harvest documentation,” says Cole Murray, product manager for the company’s Intelligent Solutions Group (ISG). “We also have some add-on opportunities: for example, by adding a GPS receiver to a sprayer you can do swath control.”

John Deere owns NavCom, which designs and builds GNSS receivers and writes software. It also owns a differential corrections network, the StarFire network, which works worldwide.

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Figure 3: Farmer using a Trimble FmX integrated display. Image courtesy of Trimble.

Kinze manufactures planters and grain carts that quantify planting and harvesting. “We’ve added scales to these carts, so as farmers unload the grain from the combine into the cart, they can use it to record how much grain they are getting out in the field,” says Rhett Schildroth, one of the company’s product managers.

“When they are planting, farmers care about three things: how deep they plant that seed into the soil, getting good seed-to-soil contact, and the spacing between the seeds,” Schildroth explains. “So, we’ve added sensors in order to make sure that we can gauge each of those things and then also control it on the go, so that they can vary it throughout the field.”

Connection Challenges

Open fields would seem an ideal environment for satellite navigation, because they allow a clear view of the sky. This is usually true in the Midwest — say, in Kansas or Missouri — where there is just open space, rolling hills, and flat land. “However, there are just as many other areas around the world where some fields are totally surrounded by 100-foot trees with very dense canopies that do a very good job blocking satellite signals,” points out Martinez. 

The technical challenges include multi-pathing, terrain difficulties, a reduced number of visible satellites at higher latitudes, and solar maxes. These are the same as those faced by surveyors but with the added challenge that farm systems are operating continuously. “If a surveyor has to wait 30 seconds to get a good signal, that’s not a big deal, but in 30 seconds in the field you have covered a lot of ground,” says Schildroth. Additionally, users of RTK may face the difficulty of radio communication between the base station and the rover over long distances and uneven terrain. 

“To ensure that you have a connection at all times, you need either a portable system or a very strong signal from your base station,” Schildroth adds. “If you are using a cellular connection, for example on a CORS network, you need to make sure that you have cellular coverage throughout your field.” To address this issue, about a year ago Trimble released a service called CenterPoint RTX. “It is the industry’s first real-time, satellite-delivered correction service that is able to achieve inch to inch-and-a-half absolute accuracy, all delivered from satellite directly to the tractor,” says Martinez.

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Figure 4: Planting lettuce with an automated planter in the United Kingdom. Image courtesy of Trimble.

Analyzing the Data

Various services analyze farm data and generate prescription maps. Fertilizer dealers, crop consultants and agronomists take the data, analyze it, and help the farmers make decisions.

Nick Achen, an agricultural engineer, and his brother, a farmer, co-founded and co-own www.easyfarmmaps.com. “We identified a need in our community to be able to process all this data that farmers collect in the field,” he says. “There is software for sale that is expensive and complicated, so many farmers don’t know how to use it or don’t want to learn to use these systems. So we set up a Web site to take those files, process them into readable maps.”

AgJunction, a Web-based agronomy system operated by HemisphereGPS, allows users to import soil test data and the locations of the soil samples, as well as data from John Deere, Raven, Trimble and other systems, and generate prescription maps. Its customers are primarily agriculture retailers, such as independent chemical fertilizer retailers or cooperatives.

According to John Lueger, director of Product Management at Hemisphere GPS, “A retailer could use our system to send a prescription map directly to one of our terminals on a tractor or a sprayer and then the farmer, when he has completed that job, can send that data back to the retailer and automatically archive it for historical purposes.”

Other options and approaches from the different manufacturers take a similar approach. Raven Industries has a product called Slingshot that records what you are doing with various data sets and synchs to cloud-based software. Trimble’s Connected Farm wirelessly extracts the data from the growers’ applications throughout the seasons and consolidates it. Across the board, manufacturers assert that these technologies pay for themselves in about a season of use. 

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Figure 5: A Kinze Autonomous Harvest System, consisting of a tractor, a grain cart, and a combine. Image courtesy of Kinze.

Sensors

Almost every piece of agricultural equipment has sensors and controls these days. Sprayers now have sensors that sense whether plants are nutrient deprived or not, and sensors dragged across the field show the textural variation in the soil.

“We use load cells on grain carts, magnetic flux sensors to sense when power take-off (PTO) shafts are turning and grain is unloaded, infrared sensors to count the seed as it goes down the seed tube, load cells on planters to understand the down force required to plant the seeds, and GPS receivers for positioning,” says Schildroth. 

The use of soil moisture sensors, networked using cellular modems, is growing very rapidly. “When I was on our family farm, in the early 90s, the task I disliked the most was to go in the fields and read soil moisture content meters,” says Mecham. “Corporate family farms can have as many as 35,000 acres and one person doing just that all day. So, this is extremely important, especially in areas where you have to pump the water from deep well systems. Now farmers can receive that information via e-mail or text messages.”

“Our basic guidance system will come standard with a series of inertial sensors that provide feedback to the guidance system on the tilt of the field or the vehicle’s pitch, roll, yaw, or heading,” says Martinez. “Combines have optical sensors that record the volume that is passing through their grain elevator, as well as moisture sensors. So, in real time, we know the volume and we know the moisture content of that crop being harvested.”

Sensors are now being used to control the height of spray booms, which can be up to up to 120 feet across. “So, as they are traveling at high speeds across the field, if you don’t have a perfectly flat field, you are going to hit that boom on the ground,” Martinez points out. “It is no longer feasible for the driver to control the height of his boom fast enough. So, we just announced a system that uses ultrasonic sensors to sense, very quickly, the profile of the ground so that it can then, via hydraulics, move the boom up and down as needed.”

Trimble’s GreenSeeker sensor is a localized, real-time sensor that is mounted right to the spraying vehicle. It uses an optical sensor and a few different light bands to measure the health of the crop in real-time. 

“Immediately, as the sprayer is traveling and recording this data, it is creating a prescription to also then apply nitrogen in the right amount needed in that particular portion of the field,” Martinez explains. The company’s WeedSeeker spot spray system uses advanced optics to sense whether a weed is present and signals a spray nozzle to deliver a precise amount of chemical—spraying only the weed and not the bare ground.

Future

Future developments in precision agriculture include autonomous farm vehicles, the use of imagery from UAVs, and telemetry — wirelessly transmitting back to the office data on crop health, soil characteristics, and yield, as well as on the status of the farm machines, which will allow farmers to improve planning for vehicle servicing and maintenance, says Swain. Sensors that can analyze and manage soil compaction are also in the future, according to Achen.

Currently, growers get feature unlock codes from their dealer. “In the future,” says Mecham, “we would like dealers to be able to send them directly to the devices on the vehicles, via modem, to allow customers to try new features. Our customers are demanding that level of simplicity.”

Written by Default at 12:00
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Mapping Data-Dense Cities as if They Were Mountains

Courtesy of the Atlantic Cities

Mapping Data-Dense Cities as if They Were Mountains
The New York City Police Department publishes an incredibly dense database every year of the profiles of controversial “stop-and-frisk” encounters between officers and residents throughout the city. In any given year, there may be 500,000 or more such stops on the city’s streets, where police question (and sometimes search) anyone who appears suspicious. Plot all of these "stop-and-frisks" on a map, using their geotagged latitude and longitude coordinates, and the picture looks something like this:

“It just sort of looks like Christmas lights,” says Chris Herwig, a data analyst at Mapbox. He created the above map, using 2008 data from the city. The stop-and-frisks neatly overlay with the city’s street network. But that’s about all that map reveals. “The problem is that it’s very dense data that we’re talking about,” Herwig says. “You can’t really see patterns with this. You just see these lines that coincide with the streets – yes. But it’s hard to look at that and get some kind of analytical insight into where the pattern is coming from.”

This is a common problem with any dense dataset about urban living, where so many little points of information crowded onto a map – incidents of crime, 3-1-1 calls, school enrollment – can start to blend into each other. In New York City, stop-and-frisks occur much more frequently in some neighborhoods than others. But the density of those cases is hard to map precisely because they overlap atop one another.

This gave Herwig another idea for how to visualize data points that essentially pile up on a given place. "Why don’t I treat them like elevation?" he says. Dense information has a topology in the same way that physical terrain does. Borrowing the contour lines of geographers, Herwig ultimately translated that above map into this one:

Here he is displaying information about an otherwise flat place borrowing a method used to map mountains, and the idea could have numerous other applications. "What this seemed to work well with was densely packed data points, geographic points that are relatively dense in their distribution, but also kind of random all over the city," Herwig says. "Eventually you see there are patterns to it." Zoom in to certain corners of the city, like this neighborhood in Brooklyn, and veritable peaks appear:

Herwig has also done the same with stop-and-frisk data most recently from 2011, for comparison. As an illustration of the idea's broader application, data journalist Gerald Rich recently took Herwig's method and used it to map an entirely different dataset at the national scale: the density of offensive place names. (Rich was inspired by this Jon Stewart clip on a New Hampshire town called "Jew Pond.") Using a dictionary of racial slurs, Rich weeded through geographic place names kept by the US Geological Survey to turn up locations like "Squaw Everest" and "Dead Negro Draw."

These names exhibit a density of their own around the country, appearing more often in some regions than others (Native-American slurs, Rich notes, are concentrated around the Appalachians). This is his topo map of information elevation:

Most interestingly on Rich's map, some of the dirty-place-name density actually bears a relationship to the real geography of the land. Here's a view of the Appalachians (with the real mountains shown beneath them), where inhabited places in general – and dirty place names among them – densely cluster in parts of the mountains:

Written by Default at 10:00

Health Indicators iPad App: Using GIS and BI to Make Sense of Health Data

Courtesy of GIS Lounge

This article from Critigen’s Community Health Team Leader, Kenny Ratliff, highlights what GIS can do for health data in our communities and nationally.  Ratliff reviews how Critigen’s free app for the iPad allows users to understand how traditional BI (Business Intelligence) & GIS can come together to make big data and health data meaningful to professionals and ordinary people alike. 

With an increasing awareness of the cost of healthcare and the relative costs of prevention and treatment, health data are more important than ever to making wise investments in healthcare services and infrastructure.  Healthcare professionals, politicians and ordinary citizens all have an interest in understanding the state of our health and contributing to solutions.

Critigen, a GIS consulting & solutions company, has launched a free iPad app to help executives, health professionals and ordinary citizens visualize and understand the Community Health Status Indicators (CHSI).  The CHSI contain a variety of health trends and health predictors.  With the Health Indicators iPad app, anyone can view the indicators by region, state or county, comparing nearby areas and see trends by area.  The app also supports the classic ‘rollup’ functionality of business intelligence, grouping indicators by category and providing an overall assessment of the category for each region, allowing users to drill into each category to see specific metrics at various geographic levels.

This app changes the health data game, and the best way to really understand it is to download Health Indicators to your iPad (free) from the Apple App store and try it out. The intuitive touch interface makes it easy to navigate and get comfortable with the tool. The way they aggregated and organized the data around geography and metrics categories provide the information you’re interested in without overwhelming you.  The data are organized in categories to make them more accessible to ordinary people and to health care professionals:

  • Good Start – how well we care for infants
  • Staying Healthy – how well we follow medical advice
  • Managing Disease – how well we manage potential fatal conditions
  • Getting Help – how easy is it to get basic health care
  • Paying for Care – how affordable health care is

The app launched in time for Health Datapalooza 2012 and got main stage billing.  Tyler Huehmer, Director of Critigen Labs, told the audience, “Through informational design and the integration of GIS, we’re able to visually access data that was otherwise difficult to access or difficult to understand.  This exploratory tool allows someone to browse information geographically and focus on what’s important to them.”

Critigen launched the app as a corporate citizenship initiative, applying Critigen’s geospatial technology expertise to make health indicators data accessible to everyone.  Critigen also hopes it inspires the Health community to embrace the potential that geographic information – also referred to as location intelligence or geographic information systems (GIS) – has to improve the way we invest in healthcare, prevention and infrastructure.

Health Data App

Written by Default at 16:33

Super Bowl Fun - NFL Fan Map (FandomMap) created from Facebook data

Courtesy of GIS User

Super Bowl week is here so that means more cool data visualizations.. best BBQ, tail gate hot spots, and those awesome NFL fan maps! Here’s details of a cool fan map – I’m not sure I can agree that its the best fan map “ever”, however, given that its been created by mashing up data scraped from facebook it is pretty cool! So how was this one done? Well, they used data… LOTS of it! Details… Facebook’s Data Science team has combed the more than 35 million users who have “liked” a team’s fan page, and put it into graphic form, down to the county level.

Some interesting spatial patterns emerge once you look at the data.. for example, Patriots fans tend to spread out far and wide, as do fans of the Cowboys and Steelers. Overall the data indicates that geography plays a huge role in defining the fan. Thanks to Deadsping for the map, and to my FB friend Brad Shannon from NOCO for tipping me off!

Written by Default at 15:00

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