00:01One of the top trends in computing is analytics or turning data into understanding.
00:06For some of us, this means spatial analytics - things like finding spatial clusters...
00:11...understanding trends, and predicting outcomes.
00:15To tell us a little more about some exciting new spatial analytics and how we can share our workflows...
00:21...please welcome from the geoprocessing development team, Lauren Rosenshein.
00:30Thanks, John. I'm really excited to show you guys three powerful new analytical tools...
00:37...that will help you solve problems and answer questions in new ways using ArcGIS 10.1.
00:43What we're looking at here is gas price data by station in Southern California.
00:49Just looking at the points on a map, it's pretty hard to tell if there's a pattern.
00:53Maybe it looks like gas is a little more expensive in the Beverly Hills and Hollywood area in dark red.
00:58But overall, it's hard to find a pattern. So we'll use a new tool called Grouping Analysis...
01:04...which will help us find and understand the spatial patterns that exist in our data.
01:10We'll use a couple of different variables that get at different aspects of the price of gas.
01:15So in this case, we have a price variable, a rank variable, and a trend variable.
01:21What grouping analysis does is it creates distinct groups based on those gas price characteristics...
01:27...making sure that within groups, the features are as alike as they can be...
01:31...and between groups, the features are as different as they can be.
01:35And, we have the option to constrain those groups spatially.
01:40So immediately we can see there's definitely a spatial pattern here.
01:43We can see our blue and our green groups along the cost, our red and our yellow groups in the inland area...
01:51...and another output of the grouping analysis tool is a PDF report. This report has lots of useful information.
01:57But we'll focus on this summary report.
02:01This shows us that our blue group along the coast is our most expensive gas area.
02:07Our yellow group in the center of our study area is in the middle in terms of the price of gas...
02:13...and our green and our red groups are the cheapest areas for gas.
02:18So now that we understand a little bit more about these spatial patterns, the next logical question is why.
02:25What might help us understand or explain these spatial patterns?
02:30Maybe it's something like the distance from major highways.
02:34The closer you are to a major highway, the more expensive gas is. Or maybe it's something like income.
02:40More affluent areas are areas where gas is more expensive.
02:45If we take a closer look, that income variable is actually a little tricky to calculate.
02:51What we don't want to do is oversimplify things by just assigning the income for that census tract...
02:56...to the gas station that's sitting inside of it.
03:00And that's especially true when we have lots of gas stations that are on the borders of multiple census tracts.
03:06A better way to model that would be to use a drive-time polygon around each of those gas stations.
03:12So how do we get our income data from our census tract polygons into our drive-time polygons?
03:18Well in 10.1, we can do this using a powerful new tool called Areal Interpolation.
03:24Areal Interpolation lets us take our income data from our census tracts and create a continuous surface...
03:32...getting rid of those boundaries and making it really easy for us to get that income data into our drive-time polygons.
03:40So now for this drive-time polygon, we have a predicted income using aerial interpolation of about $106,000 a year...
03:48...give or take a $3,000 standard error.
03:52Now that we've calculated these variables, the next thing that we want to do is test these hypotheses...
03:59...using another new tool called Exploratory Regression.
04:03I've created a model which runs Exploratory Regression once for each of the groups that we created using grouping analysis.
04:12What Exploratory Regression lets us do is choose all of the variables that we think might be related to the price of gas.
04:19In this case, these are socioeconomic and demographic variables, our distance variables...
04:25...and Exploratory Regression tests all the different combinations of those variables...
04:29...telling us which ones are consistently doing a good job of explaining the price of gas...
04:35...which combination of variables might help us explain the pattern.
04:39I've created a report which outlines the results of those analyses.
04:43What it's telling us is that our income variable that we thought was really important in the coastal area is very important.
04:50But, in the inland area, it's all about distance from highway exits and distance from the interstates.
04:56So two different geographies in our study area, two totally different sets of variables...
05:01...that help us understand the price of gas. So at this point, my analysis is done.
05:08The next thing that I want to show you is how easy it is to share your analysis using the new geoprocessing package.
05:15A very important first step for sharing any analysis is documenting our methodology.
05:21So I've included our methodology in this report.
05:24Now, in ArcGIS 10.1, it's really easy to share your analysis...
05:29...because we're introducing the new ability to share it as a geoprocessing package.
05:34I can choose to upload my package right into ArcGIS Online.
05:38I can include additional files like that PDF report that we just created which outlines our methodology.
05:44I can include additional tools if that methodology is really a workflow of running multiple tools.
05:51And when I share it, it's taking my input and my output data, my models including any nested models or scripts...
05:58...all those additional files, putting them into a geoprocessing package and sending them up to ArcGIS Online.
06:05From ArcGIS Online, they can easily be discovered and used by other GIS professionals all over the world.
06:13Yesterday, I sent a link to this package to my friend, Drew, who's out in Denver...
06:18...and wants to see what variables might help him explain the price of gas in his own area.
06:23So let's check in with Drew and see how he's doing with his analysis. Hey, Drew.
06:28Hey, Lauren. Thanks for sending over that geoprocessing package.
06:31It has everything that I need to start analyzing gas prices here in Denver.
06:35So I'm here on ArcGIS Online, and I'm going to open the geoprocessing package and when I do that...
06:41...it's going to start unpacking in ArcMap where I've already started looking at my Denver data.
06:46So when I unpack the package, it's going to add some new data to the table of contents...
06:51...and it's going to add the tools and a report file down into the results window.
06:56And I'll look at that report file to find the methodology and how to actually use some of the tools in this package.
07:02If I want to use those tools on my own data, I simply open the tool and select my data and run the tool.
07:09So this is going to find the groups in the data that will explain some of the spatial patterns for gas prices in Denver.
07:15So here I have a couple interesting results.
07:18You can see there's two groups that are in West Denver area and then two groups that are in the Denver metro area.
07:23So the question that I want to answer then is why are there these different groups?
07:27What makes those areas so different?
07:29So I followed your methodology, and I'm going to actually use the Exploratory Regression model...
07:33...to find out what are those difference and why they have occurred.
07:37So I've made a report that has some of this information.
07:40I found that actually an elevation variable is one thing that's really important in those west groups...
07:45...as well as the distance to the interstate highways.
07:48In those east groups, we see that it's the number of competitors nearby that are really determining the gas prices in Denver.
07:56So that's it for our analysis. Thanks, again, for sharing that geoprocessing package.
08:00I'm looking forward to using it more and sharing some of the findings with my coworkers.
08:05Thanks, Drew. I think spatial analytics...yeah, that's cool, right?
08:15I think spatial analytics are the heart and soul of GIS...
08:19...and being able to easily share our analysis will help all of us better understand our world.
The Heart and Soul of GIS
Lauren Rosenshein demonstrates how new spatial analytics and statistical tools in ArcGIS 10.1 can turn data into understanding.
- Recorded: Jul 11th, 2011
- Runtime: 08:26
- Views: 16012
- Published: Jul 26th, 2011
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