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.

Jul 11th, 2011

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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... 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...'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... 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... 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.

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