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What is Geospatial Analysis?
A geographic information system (GIS) is a software program that combines data that can be tied to geographic coordinates with the tools and methods that can transform the information into a powerful analytic tool. GIS technology can layer many types of data, including demographic, statistical, topographic, city infrastructure, or weather-related data, to name a few, onto maps to transform complex data into useful information. Together, the GIS software, data, and methodologies combine to provide a technology called geospatial analysis.
By using geospatial analysis techniques, people have access to a powerful data-modeling and decision-making tool. Geospatial analysis has hundreds of uses, including wildlife management, city planning, facilities management, fleet management, disaster planning, military operations, climate change modeling, and many more. The types of geospatial analysis that can be performed by a GIS program are almost limitless.
It's thought that GIS and geospatial analytical technology were first used in the early 1960s. One of its first uses was to create a digital natural resources inventory for Canada. Since then, its use has skyrocketed, and there are thousands of different GIS packages on the market, and hundreds of GIS-related companies. One of the earlier applications of GIS and geospatial analysis was in wildlife management. By layering wildlife population data, vegetation data, human population data, and other data types onto maps, wildlife management officials can help determine whether a certain area can support the current wildlife population, or whether efforts to control the population need to be undertaken.
Another real-life example of how people are using geospatial analysis is fleet management. Dispatchers can use the technology to determine the closest available driver for a particular delivery. They can use traffic information, street maps, construction data, and other information to provide routing information or predict destination arrival times.
The Gulf of Mexico oil spill, which occurred in April 2010, is an example of its use in disaster planning and recovery. Through satellite imagery, maps, ocean current data, and weather information, scientists can track the spread of the oil and predict where it will go. By having this information, officials can better plan for remedial actions.
Geospatial analysis has also been used extensively by scientists to map the effects and potential threats of climate change. Climatologists can predict the potential effects of climate change on various ecosystems around the globe. The data sources that they use to do this include current and historical climate statistics, aerial photography, satellite imagery, and geographic positioning system (GPS) coordinates.
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![Geospatial analysis has helped cities with planning projects and disaster planning.]()
By: Nataly-NeteGeospatial analysis has helped cities with planning projects and disaster planning.
Discussion Comments
@SkyWhisperer - You bring up a pretty good point about these models not always being accurate. I find this a bit disturbing, especially because the article said that geospatial analysis is used to make decisions about wildlife populations.
Of course, when people say "control the wildlife population" this usually means some measure are taken to kill off some of the animals. I don't really agree with this anyway, but I definitely feel like something like this shouldn't be undertaken on the basis of a model that may or may not really be correct.
It sounds like this technology has a ton of applications. But I've never heard of it! Granted, I don't really work in a very technology driven field. But still, I'm a bit surprised.
I wonder if we'll start seeing cell phone apps that utilize this kind of technology? Or maybe tablet apps that people who use this for work could use?
I feel like most technology is very mobile these days. And since something like this is used for problem solving, it would make sense for it to be as mobile as possible.
@miriam98 - I well remember the Gulf of Mexico oil spill in 2010. I watched those maps very closely and often refreshed my browser to get updates as soon as possible.
I wanted to know where that oil was going – or might be going. The reason was that I was planning to take a vacation in Florida that year, and I didn’t want to swim in oily beaches.
As it turned out, it wasn’t that bad in Florida, at least where I went. I think that brings home one important point however. When these maps are used for predictive spatial analysis, as they were then, they can be kind of hit and miss in their results.
I think this is especially true when you’re trying to map weather related events, which can turn one way or another very quickly. That doesn’t mean the maps aren’t useful. It just means you have to take the computer models with the proverbial grain of salt and make your best judgment about travel arrangements.
@Charred - Yeah, the technology has really exploded, especially with the advent of satellite imagery and stuff like that.
I see advertisements now and then for geospatial analysis jobs. They usually want someone who has worked with one of the more well-known GIS software packages and they may want specialization in a particular industry, like oil or telecommunications like you said or other things like that.
I think if you specialize in this kind of work, you can carve out a real niche for yourself. I’ve even seen local government job listings where they needed GIS analysts. In one case it was the public transportation network in our municipality. I am sure they use the maps to display where most of their traffic and revenue is coming from.
I used to work in a company where they used GIS software to map the telecommunications network. They trained me on the software and I was the “go to” person for creating GIS maps of all sorts and shapes.
These maps did not display static data alone, but dynamic data as well. What I did was map out the telecommunications network, then use data like aggregate minutes of use at various locations and map that as well.
That data was superimposed over the static telecommunications map, so that engineers had an immediate visual understanding of where most of the phone calls were being made.
They could then use that information to decide if they wanted to take down or put up T1s and redirect traffic as needed. It was a useful tool and offered a wealth of possibilities for displaying the data.
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