Introduction to ArcGIS Spatial Analyst

Esri staff discuss and demonstrate the analytic functionality and main components of the ArcGIS Spatial Analyst extension.

Jul 25th, 2012

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00:01My name's Steve Kopp, this is Liz Graham, and we work on the Spatial Analyst team and today we're going to... about an introduction to Spatial Analyst.

00:10Just curious, how many of you guys never used Spatial Analyst but want to?

00:17Great, so like almost half.

00:20How many of you use Spatial Analyst a lot but just specific pieces of it?

00:24Like, yeah.

00:26And ninjas? Ninjas? People who know as much as we do. Alright. Or more.

00:37Not to imply that we are ninjas; we're definitely not.

00:40So, we'll leave you guys to the ninja status.

00:45Okay. So, for those of you don't use Spatial Analyst yet, we'll give you a little bit of an overview here... the beginning and we're going to talk a little bit about sort of an overview of Spatial Analyst, and then...

01:04So, we're not going to have a lot of time to go into depth on very many of those topics, and there's going to be...

01:08...a few times I'm going to refer to other workshops that are available on some of those topics.

01:13So, for some of the areas, it will talk about in these slides, there's entire workshops dedicated to those...

01:20...and we can point you to those, as well.

01:23There's also some demo theaters downstairs dedicated to some of these topics, as well.

01:29So first off, what is Spatial Analyst?

01:31Spatial Analyst is integrated raster and vector spatial analysis tools.

01:37And, it seems kind of obvious to include that slide and that bullet point, but I really want to reinforce this point.

01:44It's raster and vector.

01:45Spatial Analyst is not really just about raster analysis.

01:49So, we've actually gone to a fair bit of effort to allow what would have been traditional raster GIS capabilities...

01:55...spend most of our time going through the variety of analytic capabilities available in Spatial Analyst. accept feature data as input and to create feature data as output and to do that intelligently.

02:05So, it is an integrated system.

02:07And it's an extension product, as you know, that adds functionality to Desktop, where most of you use it... well as Engine and Server.

02:17Now, the graphic in the bottom right is actually Spatial Analyst running in a web page.

02:23This is actually a solar radiation calculation from a website from the City of Seattle.

02:29There were a few demos that you saw on Monday that were using Spatial Analyst and other geoprocessing...

02:34...functionality through web services.

02:37Yes, you can serve analysis capabilities. Okay.

02:41Not only can you serve them, but if you have geoprocessing functionality that's served, you can...

02:46...consume that directly as a tool in Desktop.

02:49That's not really what today's about; if you want to know more about that, I can point you... a workshop specific to that topic.

02:55Eight-thirty tomorrow morning.

02:59So, some key features of Spatial Analyst.

03:02There's about 170 geoprocessing tools for the Spatial Analyst extension that work on all the...

03:08...raster formats that you can use in ArcGIS.

03:10So, if you can read a raster dataset with ArcGIS, you can use it as input to a Spatial Analyst function.

03:18Similarly, it works on all vector formats, as well.

03:22Now, we include these points because this wasn't always true, and this isn't true in all GIS software.

03:28And also because the way that we do that changed a few years ago.

03:32We used to make scratch files in the background, and we don't anymore.

03:36So the reason that that's important for those of you who have been using it for a while is there were some...

03:40...limitations for those scratch file formats, and when we got rid of that, we changed the architecture... we read and write data through these analytic functions, it actually made it faster and it reduced...

03:52...some of the problems that you were running into that you maybe didn't quite understand.

04:00Spatial Analyst also includes a calculator with map algebra syntax, and I point that out because we think...

04:05...that's a pretty important key feature of doing spatial analysis.

04:10How many map algebra people do we have in the room today?

04:14Not very many here? Okay.

04:15We'll talk a little bit about map algebra; I won't dwell on it too much, though.

04:18And finally, Spatial Analyst has a really great developer experience, and there is a workshop dedicated to raster analysis...

04:25...with Python that specifically covers that, and a lot of that is about working with Spatial Analyst through Python.

04:34So what does Spatial Analyst look like in the desktop?

04:36So, Spatial Analyst is a toolbox in the ArcToolbox window with a set of functional categories.

04:45We can also access this functionality from the geoprocessing pull-down menu in version 10...

04:51...where I can also lock the toolbox.

04:53I can use these tools in ModelBuilder, which Liz will show us in a few minutes, and also use them in the Python window.

05:02What about Spatial Analyst toolbar? Anybody curious about that?

05:069.x people who haven't moved to 10 yet, there's a little surprise there for you if you used to use the toolbar.

05:11We got rid of the Spatial Analyst toolbar in many of the ways that you think of it.

05:15It's still there, but it only has a few tools on it now.

05:19That pull-down menu that was on the Spatial Analyst toolbar has gone away.

05:24The reason that that went away is it was causing a lot of confusion and it was basically duplicate functionality...

05:29...the things you could do in the toolbox and things you can do from the pull-down menu.

05:33They didn't share the same environment, they behaved a little bit differently, and what happened was...

05:39...when we made it possible to add geoprocessing tools to a dialog, to a pull-down menu using...

05:47...standard Windows customize, now you can make your own Spatial Analyst toolbar.

05:52So, I've got a little sample toolbar that I made here; I do a lot of hydrology work, and I work...

05:57...with the hydro tools in Spatial Analyst a lot.

05:59So here I created a little toolbar, it has a hydrology pull-down, and I put some of the commonly used tools on there.

06:05Sink, fill, flow direction, watershed, et cetera, as well as some custom tools...

06:10...a Python tool for calculating soil wetness, ModelBuilder models for doing condition numbers, things like this.

06:18So, I did all of this with no code; it's just standard Windows customize, drag-and-drop little things. It's really easy.

06:26The other thing that we added in version 10 was the ability to search; we improved the search capabilities... be able to search for tools.

06:35We did a lot of work on the names and words that we index to go with those to make it easier for you to find tools...

06:43...because the toolbox I showed you, you know, 170-plus tools just for Spatial Analyst...

06:48...the toolbox list has become too big to navigate that tree control.

06:52There's like 700-and-something tools in that list.

06:55So, using the search window is a much quicker way to do that now.

06:59We tried to put a lot of words in there that you might think of, you know, like, if you wanted to search for "clip" but... couldn't think of "clip," you can put in, like, "cookie cutter," and it'll actually find the Clip tool for you, okay?

07:11And, actually, if you search for tools and you're not finding the thing you're looking for...

07:15...and you have recommendations on new keywords we could add for things, just let us know, you know...

07:21...through tech support or through the forums or whatever, please add these keywords to this thing...

07:25...'cause this is what people in my industry always call this tool.

07:31Spatial Analyst includes a few additional pieces for the geoprocessing environment...

07:38...and these are things we want to set up at the beginning of a project before we start.

07:42Things like cell-size extent, map projection, and possibly a mask.

07:48Cell size is probably the most important one for Spatial Analyst users because it actually controls the results...

07:55...of your analysis and we default the cell size to the largest of the inputs because we don't want to fabricate data.

08:03What I mean by that is, in the example here, if I have two datasets, a 25-meter resolution and 10-meter resolution...

08:09...dataset and I want to create an output from that, the default behavior is it's going to create a 25-meter resolution output.

08:17The reason for that is I don't actually have enough information in my 25-meter resolution input... create a 10-meter resolution output.

08:27I can do it; I can change that environment setting to be the minimum of the inputs, and there might be reasons why... want to do that.

08:34I just caution you that when you change the default for the output cell size, do it for a reason and understand what that is.

08:44It's usually going to be best to set it to an explicit number or same as some other dataset...

08:49...that you want all the pieces of your project to conform to.

08:53The same with the extent.

08:55You want to set a processing extent to constrain the extent to a particular area.

09:00The extent for Spatial Analyst also includes another parameter called the snap raster, and what the snap raster...

09:06...does, it allows you to co-register the origin of the grid cells to be the same.

09:13The reason that that's important and you want to check the extent, the cell size, and the snap raster... because you want all the datasets that you create to be perfectly co-registered.

09:23Because if they're not, if they're offset by a quarter of a cell size or if they're different cell sizes...

09:29...every time you do an analysis, one of those inputs is going to get resampled to match one of the other ones.

09:36So if you set this at the beginning of your project, you're going to minimize the resampling that occurs...

09:41...and resampling of raster data is not something that you want to do repeatedly; it will degrade your data, right?

09:48So, it's better to leave the data like it is as much as you can.

09:53There's also a mask option, sort of to constrain your analysis to nonrectangular area, like a watershed or a...

10:01...census block or something like that, or a ZIP code.

10:05And then it's also important to think about and set a map projection.

10:10I know that most of the data that you find shows up in lat-long and you don't know what datum it is...

10:16...and you just do it in lat-long, it's fine, right?

10:20It's really not fine to do your analysis in lat-long very often, okay?

10:24Most of the time it's not.

10:26Because a lot of times you're going to do things like measuring distance and measuring areas...

10:30...and doing that in lat-long is just not going to work, okay?

10:34So think about the map projection that you need to use for the type of work that you're doing.

10:39There have been some changes in things like the buffer tool to use geodesic distances... you can actually input points, lines, and polygons in lat-long and get buffers out that are actually...

10:52...true-distance buffers, but that's only in the buffer command; that's not throughout the system.

10:57We're not using geodesic distance in area everywhere, so.

11:01Okay. Moving on.

11:04So one of the first things you want to do in your analysis is look at your data.

11:08You want to explore your data; you want to understand your data.

11:11So, the way that we do that, it starts with things like Identify, you know.

11:15Using the Identify tool and poking around and looking at my data.

11:19Changing the display of the data; using different stretches on my data.

11:23I can also do selections on the data, and the way that we do selections on raster data is through the attribute table.

11:29So, an integer raster dataset - a dataset that's integer values, not floating point values...

11:38Now, you don't have to have additional fields in that, connected to that to be able to do...

11:43...selections on it; you can search on the values.

11:46So if I have a dataset that was, say, land cover, and there was a land cover type coded...

11:54...with the value of 2, and that was Forest.

11:57If I wanted to, you know, highlight all of the, or select all of the forested areas, I could click...

12:02...on that in the attribute table, and it would select all the cells of value 2, or the forest cells.

12:08And then if I wanted to run an analysis, I could actually use only those selected cells if I wanted to.

12:13So that selection that I do on the attribute table is actually honored during the analysis...

12:18...just like if you did a selection on features and used that as input to your analysis.

12:24We have an interactive tool for doing histograms of the data distribution values.

12:28That's histograms of the entire dataset or of selected areas you can select through graphic.

12:35And also a zonal histogram tool, which I'll talk about a little more, which is in a little while...

12:40...which allows us to look at the distribution of values within zones.

12:46So with that, I'll switch over to Liz, and she'll show us a little bit of this.

12:52Thanks, Steve.

12:54Throughout my demonstrations today I'm going to be walking us through a suitability analysis... find the best location for a new ski hill.

13:01But before I get started with that, I'd like to show you around the application that Steve was just talking about.

13:06So my data is in the Lake Tahoe area.

13:08I have some elevation data, some power lines, and some roads.

13:12The triangles represent peaks in the area.

13:15But first, let's enable the Spatial Analyst extension.

13:19To do that, you go to Customize > Extensions and check on Spatial Analyst.

13:23After doing this, you can access the Spatial Analyst functionality.

13:27The first thing I'll show you is the toolbar.

13:30Go to Customize > Toolbar and down to Spatial Analyst.

13:35As Steve mentioned, there's a drop-down list that has layers.

13:38I've selected Elevation.

13:40There's a Create Contours tool and a Create Histogram tool.

13:44You can see that this is the distribution of values in the elevation layer because that's the layer that I have selected... the drop-down list.

13:51If on the Drawing tool, I would use this Graphic Circle tool and draw a circle around Middle Mountain...

13:56...and then do the histogram, you can see I get just the distribution of values underneath the graphic.

14:03So that's a good way to explore your data initially.

14:06To access the Spatial Analyst tools, you can click this Toolbox button, which opens up the toolbox...

14:13...Spatial Analyst toolbox - made up of several toolsets that contain different functionality.

14:18But if you're unsure of the tool you're looking for, you can always use the Search tab.

14:22If you click Tools, you'll limit your search just to tools.

14:25And here, you can type in the tool name you're looking for...

14:28...something that reminds you of the functionality that the tool does that you're looking for.

14:32Another way to access the Spatial Analyst functionality is through the Python window.

14:37You can click this button to open up the Python window and type and execute Python expressions that use...

14:43...Spatial Analyst functionality, as well.

14:46Most often, Spatial Analyst tools are written in algebraic format, so the left-hand side is equals the tool...

14:53...or the function name or operators and that's the expression.

14:57We're not going to go into that a lot here today, but there's other workshops on that.

15:02So now we know how to enable and where to find the functionality, but as Steve pointed out...

15:06...before we do any actual analysis, it's important to set environment settings.

15:11If you go to Geoprocessing > Environments, this is where you can enable your different environment settings.

15:17Your workspace, your output coordinate, your processing extent - including your snap raster to limit...

15:23...the amount of resampling, and under the Raster Analysis tab is where you can set your cell size and your mask.

15:29I've set my cell size to 30; that's the coarsest resolution of any of my inputs for today's analysis.

15:36Now let's go back and search for a tool called Hillshade.

15:41It comes up there on a drop-down.

15:43If I cursor down to it and hold Ctrl Enter, the tool's just going to open.

15:47If I didn't do that, if I just hit Enter, it would just do a search and populate here and then...

15:51...I'd have to click and open the tool, so that's just a little shortcut.

15:55I'm going to select the input raster elevation.

15:58I'll supply a useful output name, such as Hillshade Oak.

16:05I'm going to leave the azimuth and the altitude of the sun as a default values.

16:11The z factor can remain 1 because all of my units, my x,y units and my z units, are all in the same measurement.

16:18They're all meters; I don't need to convert anything.

16:21This just tells me the dataset exists on my computer and I'm going to overwrite it and I'm okay with that.

16:29You can see what's added to the display here is a really nice illumination of the study area...

16:33...but what would make that even a better picture of what's going on is if I drag this hillshade result underneath...

16:38...the elevation dataset, and then if I add some transparency to the elevation dataset.

16:43To do that, I'm going to use the Effects toolbar.

16:47I also access that through the Customize toolbar's expression.

16:51Again, elevation is chosen in the drop-down list, and this is the transparency.

16:56I'm going to adjust the transparency on the elevation dataset to about 30.

17:00Now you get a clear picture of what's going on in the study area, where the ridges are...

17:04...and where the other slot areas are within the study area.

17:08And we'll pick up here after Steve gives us more insight on some functionality.

17:12Okay. Thanks, Liz.

17:18So there's a lot of tools in the Spatial Analyst toolbox, like I said, about 170 or so tools.

17:23These are some of the functional groupings of those tools, and you don't need to read all these because basically...

17:28...we're going to go through these, each of these a little bit, one at a time.

17:33We're going to start with the simplest stuff that there is, the mathematical operators.

17:37So, we can take two raster datasets and combine them together using sort of traditional mathematical operators.

17:44And what we do is combine spatial data with mathematical operators to create new spatial data.

17:50So, these are sort of the nouns, sorry, the verbs where the spatial data are the nouns of the language.

17:57So if we have two datasets we need to add together - we want to do a units conversion...

18:01...or something like that, this is the way that we do that.

18:04And if you look at this, you can understand now, when we talk about the cell size and the extent...

18:09...and the snap raster being the same, if you look at this graphic, where I'm trying to add two datasets together... can begin to understand why if they were different cell sizes, or if they offset a little bit...

18:20...I would need to resample one of them to match the other one to create a new output.

18:27We use these mathematical operators as well to do things like map queries.

18:31So we have a set of Boolean and logical operators that we can use on our spatial data.

18:37So in this case, I'm looking for places where the soil is sandy and dry.

18:42And I just use a Boolean "and" to do that.

18:44So, dataset and dataset, and it creates an output for me of true and false, or a Boolean output.

18:54We also have a set of mathematical functions that we can use to create new analytics capabilities.

19:00So, traditional arithmetic functions; the example at the bottom is about turning floating point data into integer data...

19:07...which might be something you need to do - trig functions and exponential functions.

19:12I might want to make a soil wetness map, where I know that the formula for that is the natural log of the area...

19:20...divided by the tangent of the slope.

19:22So, I can actually combine these things together in a single expression to make that result.

19:29The way that I do that is using this language we were referring to called map algebra.

19:35Now map algebra is a tool in the toolbox, but it allows you to access a lot of other functionality that is...

19:42...also available as geoprocessing tools.

19:45Now, the nice thing about the map algebra is it's very readable; it's a nice sort of natural-language way... look at spatial analysis functions.

19:55The example in here, smooth hill equals hillshade, focal statistics, elevation times .3048; does that sound like... might be anything you might understand what that is?

20:07What would it mean if I had elevation data and I'm trying to multiple it by a number like 3048?

20:13Does that ring a bell to anybody?

20:14That might be a units conversion, yeah?

20:17Okay. So that's really all I do.

20:19Elevation dataset, this is my raster dataset, the times sign, and the number I want to multiply it by.

20:26Now, just like in any other mathematical expression, I want to do that thing first and...

20:32...use the output of that as something else.

20:34So I put parentheses around that and then run the focal statistics function.

20:39The focal statistics function's default behavior is to calculate the mean value within a 3-by-3 neighborhood.

20:46So what I'm trying to do is smooth the data a little bit.

20:49I'm trying to create a dataset I want to use for cartographic effect.

20:53And then I'm going to create a hillshade of that, which was the tool that Liz just showed.

20:58So I create an expression with an output named "smooth hill" equals the hillshade of the result...

21:04...of the focal statistics function, which is taking the result of elevation times .3048.

21:10Okay? Seems pretty simple once you break it down, doesn't it?

21:14So there are some changes that we made to the map algebra, for those of you who usually use it.

21:18We changed the map algebra a little bit in version 10 to make it work better with Python...

21:23...which is the native scripting language of ArcGIS.

21:26So now we have a really tight integration between map algebra and Python, which allows us within...

21:31...the Python language to mix map algebra and all the other geoprocessing tools, as well as to...

21:37...import and use other Python libraries with our ArcGIS functionality.

21:47So moving on sort of in complexity a little bit, we have a set of distance and proximity tools in Spatial Analyst.

21:55So we can calculate straight-line distance - you know, Euclidian distance from point to point.

22:00We can calculate the distance; we can calculate the direction of movement; we can allocate space to a collection...

22:07...of points; and we can also do what's called cost weighted distance, or cost allocation where we use...

22:14...another raster dataset that provides what we should think of as an impedance surface...

22:18...or a cost of travel through an area.

22:21And in both of these cases, we can also do things like shortest path.

22:25What is the shortest distance between two points, if I'm using this cost distance method?

22:32So this is an example of some of the outputs that we might create.

22:35In the center, I've got a little dataset, point A and point B, and I want to find the distance between them.

22:42So I can do a map of straight-line distance, and this looks like buffers, doesn't it?

22:46Well, it's a little different than doing things like the buffer tool would do.

22:51What the buffer tools does is allows you to create circles of specific distance away from features.

22:59What we do with Spatial Analyst is we actually create a raster dataset where each cell is coded with the value...

23:07...representing the distance to the nearest point.

23:09So it's actually a continuous distance map.

23:12And then we can threshold that through the renderer and through reclassify to do other things with it.

23:18But just remember; it's a continuous map where every cell has a different distance...

23:23...and it's the distance to the nearest point.

23:27Another output of these distance functions, as I mentioned, is the direction grid, which is the...

23:31...direction of travel to the nearest point.

23:33So in this case, this would be a Euclidean direction output.

23:37We can do an allocation of space, so if I do an allocation - this is sort of like Thiessen polygons or Voronoi polygons.

23:45And we can also do a cost-weighted distance, and this is where I'm bringing in an impedance surface or a cost surface...

23:51...another raster dataset whose values are multiplied by the map distance to make a map that represents...

23:58...a difficulty of traveling between two places.

24:03So, if we look back again up at the top, obviously if I was doing Euclidian distance between two points, A and B... would be a straight line between them.

24:12But, maybe for some reason I can't travel through here.

24:15I want to consider some other things because what I can see in the map is, that's actually...

24:19...traveling over the top of a mountain.

24:22That might not be a place that I can go; maybe the vehicle that I'm traveling in won't go there...

24:27...or maybe I just don't want to walk up that hill.

24:30So what we can do is use the Spatial Analyst to combine multiple datasets to make a map...

24:35...that represents the cost of travel.

24:38That might be the cost of traveling uphill versus downhill.

24:41It might be, how difficult is it to travel through this land cover type?

24:45Is it easier to travel over barren ground or forest or rock or through agricultural fields or through urban areas?

24:54Or, is it a soil type that I can't travel over? Or is it too wet? Or is there a barrier in the way?

25:00Okay. So I can combine those things together and use those in the cost distance function to weight the result.

25:06And what I end up with is a path that goes down along the shoreline of the lake, right?

25:13Now, what if I don't want to know just the path?

25:16Maybe there's some reason I need to know other options.

25:19So what we can do is create a corridor of travel between those two points, okay?

25:25So what we're actually doing here is, from point A, calculating the cost distance to every point in the dataset...

25:32...and then the same thing from point B, the cost distance to every point in the dataset.

25:38So this is what the cost distance map actually looks like.

25:41And see, it's not concentric rings or buffers; it looks much different than that.

25:46Then we combine together those two cost maps, and what we see is, if we don't want to travel down along the lake...

25:52...there's actually an alternate corridor here over the top of the mountain.

25:56It's higher cost, but for some reason, I might be interested in that.

25:59Did somebody have a question?

26:01[Inaudible audience question]

26:20So the question was, what if I have a vector grid - so you mean like a regularly-spaced set of points...

26:28...that I wanted to use in my model.

26:30So in order to do that in these particular functions, you would actually convert those to a raster dataset.

26:38But if they're already gridded, you wouldn't actually probably do an interpolation; you just use like the Points...

26:43...To Raster tool and just convert it directly.

26:51Okay. Moving on. Density functions.

26:54So, we have variety of ways in Spatial Analyst to create surfaces, and one of those is to create density maps.

27:01And a density map might be what you guys think of when you think of the term heat map.

27:06How many people are familiar with the term heat map?

27:11Okay. And if I ask each of you around the room, What is a heat map, I bet we'd get at least...

27:17...three different answers; maybe more.

27:21And I would assume, also, that some people have made things with the density functions that they call heat maps...

27:27...and some people have used other tools in ArcGIS and called them heat maps.

27:31Heat map is kind of a generic term, but most commonly what people are thinking of...

27:35...when they think of a heat map is an amount of occurrence of something...

27:39...a frequency map of how common is something in a particular place.

27:44So normally, we would do that with the density functions.

27:47We have two ways to do that, the simple density and kernel density.

27:50And the difference is just that the kernel density function creates a smoother output.

27:54It fits a distance-weighted kernel over the data so you don't get these concentric rings...

28:00...where you have sparsely sampled data.

28:02But what we use density for is to make maps that represent frequency or magnitude over some area.

28:10So, the number of people per square mile, number of trees per hectare, you know...

28:15...number of roads per, you know, whatever.

28:19So we can use feature data, point data, or line data as input to these density functions.

28:26So just keep in mind - density functions are about magnitude per unit area.

28:30They're something you use on count data.

28:33I have a number of something.

28:36And I just make that point to contrast it from later on when we talk about interpolation.

28:43We have a collection of tools in Spatial Analyst for doing neighborhood and block statistics.

28:48You know, like, what does that mean?

28:50Well, neighborhood and block statistics are about looking at individual raster cells and their neighborhoods...

28:57...and calculating some new value.

28:59So we might use these for, a classic application would be like image processing functions...

29:06...where you do want to do high-pass and low-pass filters and things like that.

29:09Those are neighborhood operations.

29:11We use them for filtering; we use them for data smoothing and the block functions as well for data aggregation.

29:18But really all we're saying is, for a single processing cell, like the one in the middle here that has the value of 1...

29:25...we want to look at a 3 x 3 neighborhood and calculate some new value; and, in this case, we're going to...

29:31...calculate the mean value of that 3 x 3 neighborhood.

29:34That value is output at 3.22; I write that to that cell; and this is a scanning function that just...

29:40...goes across the entire dataset and does the same calculation for each cell.

29:45So every cell in the output on the left, on your right, is going to have a different value potentially.

29:53Contrast that with the block-statistics functions where, with that same 3 x 3 neighborhood, I'm actually...

30:02And then I don't move over one cell.

30:04I actually move over an entire block and make a new calculation.

30:09So we use this primarily for data aggregation purposes.

30:14And one of the more common utility functions that people use in Spatial Analyst is the zonal overlay tools.

30:21So first we need to understand what a zone is.

30:23That's all the cells that have the same value.

30:26So I talked previously about, I had a dataset that was land cover, and the value of 2 represented forest.

30:32So that 2, that forest, would all be one zone, okay?

30:37And what I can do is calculate, say, for each land cover type.

30:41What is the average elevation per land cover type? Okay.

30:47Or I can calculate, what is the average slope per watershed?

30:52So the zones that I'm using as input, these areas, they can be rasters or they can be polygons.

30:59...calculating the mean value and applying to the same block.

31:02So I can use a set of polygons as my watershed boundaries and input a continuous dataset like slope...

31:08...and then calculate statistics on those to drive, like, my hydrologic model.

31:15This is an example of slope at the top, a very continuous dataset.

31:21At the bottom, I have watersheds as polygons, and the output is computed as average slope within each watershed.

31:32Now similar to that, we have a data exploration tool called Zonal Histogram, and what Zonal Histogram...

31:38...allows us to do is to create graphs.

31:42Maybe we don't want to know what the average slope is in each watershed.

31:46We want to know, say, a breakdown of the distribution of slope within each watershed.

31:52So the graph at the bottom, I've got five watersheds that I was looking at and I have five slope classes in each of those.

31:58This is getting to that issue of raster/vector integration.

32:05...whereas in watershed 2, 3, and 4, there's a lot of higher slopes.

32:10And I can tell from just that bar graph that watersheds 4 and 5 - sorry, 5 and 6 - are actually much smaller in area...

32:19...because I can look at the cell counts, how large of an area they are.

32:23So, it's an interesting data exploration tool and really can help you in understanding...

32:27...your data and the phenomenon that you're modeling.

32:31So when I was talking about density a moment ago, I said that it's about working with count data.

32:37Contrast that with interpolation tools, which are about working with measurement data.

32:42So this is another thing that people might think of as a heat map.

32:46What we're doing here is taking measurements in space and trying to estimate values at unmeasured locations.

32:55I went out into the landscape and I sampled soil chemistry at a bunch of places and I want to create a map...

33:01...that represents, you know, soil pH.

33:04Now, I didn't go out into that field and sample every 30 meters across the entire landscape, you know.

33:11Off of, you know, a very large area, I might only collect a hundred samples, but I might create 5,000 cells.

33:19So we use interpolation tools to estimate values at unmeasured locations.

33:24Now there's a lot of ways that we can do that in Spatial Analyst, in 3D Analyst, in Geostatistical Analyst.

33:30We're not going to go into depth on that; we did a workshop on that earlier today, and those...

33:35...slides will be available online if you want them.

33:39But there's a variety of ways that we do that - I think I'll just flip over to this one and help you out...

33:45...because we get a lot of questions about interpolators.

33:48Because there's many ways to interpolate data, people have a lot of questions about it...

33:52...because how you chose an interpolator has a lot to do with what the output surface might be.

33:59And the thought that goes into choosing that interpolator has a lot to do with how that data was sampled...

34:04...and what the phenomena is that you're trying to model.

34:07So, a few quick pointers.

34:10You don't know anything at all about your data but you need to make a surface and see what your data looks like.

34:15Use the Natural Neighbors interpolator.

34:18So, why Natural Neighbors?

34:20Because interpolators have some characteristics that allow them to exaggerate data.

34:26They estimate values that haven't been sampled.

34:29So they might actually create values that are higher and lower than what was actually in the sample dataset.

34:36Sometimes these are good things, but if there's too much of it, they create what we think of as artifacts in the surface.

34:42They're exaggerations of things that might not really be there.

34:46So the good thing about Natural Neighbors is it's fast, it's simple, and it doesn't create any artifacts.

34:52It's very clean in that regard, very conservative.

34:56So if your input data is contours, there's really only one answer, and that's to use the TopoToRaster tool.

35:02The TopoToRaster tool is designed to work well with contour data input.

35:07I'm not going to go into why contours are difficult for interpolators today, but I'd be happy to...

35:12...explain it to you and it's in the other presentation.

35:15If you're not trying to create an elevation dataset from your contours - let's say your contours represent...

35:21...soil chemistry - then there's a parameter in the TopoToRaster tool called Drainage Enforcement...

35:26...just click that little checkbox off, okay?

35:30If you're making elevation data, particularly for hydrologic purposes, leave the drainage enforcement on.

35:37If you're not creating an elevation surface, turn the drainage enforcement off.

35:42TopoToRaster also allows you to input not only contour lines but also things like spot heights...

35:48...points, streams, lakes, cliffs, things like this, so.

35:53It's a very specialized tool and it's very useful if that's the type of surface that you need to create.

35:59So what if you know that the high and low values in your phenomena have not been sampled...

36:05...but are actually really important to you?

36:08Then, if you're just using Spatial Analyst, the recommendation is to use spline.

36:12But you need to be careful when you use spline because if you have points that are very close together...

36:18...but very different in measured value, that's when you're going to start seeing artifacts in the surface...

36:23...because spline is going to try to fit a smooth curve to those points, and you're going to end up...

36:28...with these big exaggerations in your surface.

36:30So you just need to be a little careful when you use spline.

36:34Now if you happen to have the Geostatistical Analyst extension, our recommendation...

36:39...instead would be to use local polynomial interpolation.

36:43A lot of people actually have the Geostatistical Analyst extension but don't actually know it, so...

36:48...if you're not sure if you're on a ELA or a university site license or some, you know, government contract...

36:54... or whatever, there's a really good chance you actually have Geostatistical Analyst and just don't know it.

36:59So it's worth looking into; there are some really nice interpolation methods in Geostatistical Analyst.

37:06So what if your surface is not continuous?

37:09And that may seem a bit like an oxymoron, that I have a discontinuous surface, but it's a real thing.

37:14So I'm trying to create, for example, a surface that represents a geologic formation, a buried sand lens.

37:24And, I want to model the top of that sand formation, but it's old geology, right?

37:29There's faults and things that happen, and those faults have offsets.

37:33So when I use an interpolation algorithm, if I know that there's a surface with a distinct offset...

37:40...I can include that as a feature, as a line feature, in my interpolation so that I don't look across...

37:46...that barrier for sample points on the other side, okay.

37:51So if that's the case, there's a tool called Spline With Barriers for you to use.

37:55So, if for some reason you need to use a geostatistical technique or want to explore that...

38:03...then I would recommend you look at the Geostatistical Analyst and use the empirical Bayesian kriging technique.

38:08This is a new one in 10.1; it's also known sometimes as easy-button kriging or black box kriging.

38:16It actually does a lot of the hard work of geostatistics for you because it does parameter estimations.

38:22So you don't actually have to do all the variogram modelings that sort of intimidates...

38:27...people about using kriging techniques.

38:30The other nice thing about empirical Bayesian kriging is it doesn't assume stationarity.

38:35So there's an assumption about stationarity in structure in data that's a requirement for traditional kriging.

38:42If you don't understand that, that's okay.

38:44You don't need to understand that in order to use EBK.

38:47You only need to know that that's no longer a requirement, because what it does is it fits the model... changes the fit of the model as you move across the dataset.

38:57If you're interested in geostatistical techniques, I would encourage you to look into the empirical Bayesian kriging...

39:02...and I believe Steve is doing - not me, Steve, but the other Steve - is doing a demo theater...

39:08...on surface interpolators this afternoon.

39:14So once we have - oh, sorry, this one is about interpolation with barriers - this is the result from the...

39:21...Spline With Barriers tool, and what you can see here in the top right is this is a geologic surface...

39:27...with a series of offsets and gaps in it.

39:30So, at the bottom left, this surface is actually a salt dome formation where there's actually gaps...

39:37...okay, or holes, in this geologic formation.

39:40It's not continuous.

39:43If you look at the shape of the contours you can see, obviously, you know, we've got 4,000 here...

39:48...and 5,000 over here - there's a big offset between the two sides of this surface.

39:56So once we've created those surfaces, we have a bunch of analytic tools we can use to analyze those surfaces.

40:03Liz showed us the Hillshade tool, which we commonly use for display purposes and we merge that...

40:08...with the elevation data, but also things like slope.

40:11We need to make maps that represent the steepness of the surface.

40:15You know, if I'm doing house siting suitability, building suitability, there's certain slopes that I can build on...

40:22...and certain slopes that I cannot build on.

40:24I need to be able to make a map of that.

40:28You need to calculate the aspect on the surface.

40:30I want to know which way the sun shines, okay?

40:33I want to know if, at high latitudes, am I on a south-facing slope or am I on a north-facing slope?

40:39This has a lot to do with what kinds of plants will grow there, how much solar energy they receive.

40:48And we also have a variety of viewshed tools, viewshed observer points and a few others.

40:52And these allow us to specify locations in the landscape and understand what can be seen from this place, okay?

40:59I'm putting in a fire tower and I want to see what I can see.

41:03I'm putting in wind turbines and I want to know where they can be seen from.

41:08We use the viewshed tools for this.

41:13We also have some solar radiation tools, and what these allow us to do is calculate...

41:18...the amount of solar radiation hitting the earth's surface.

41:22Now, this is not a generic tool that just looks at, you know, your latitude and some simple equation.

41:28It actually uses the morphology of the terrain and looks at shadowing effects at different times of day and times of year.

41:36So it actually has a calendar in it that allows you to specify, you know, give me the solar radiation budget sample... know, every three hours from June through July for this particular area, okay?

41:52The output of this tool is measured in watt hours per meters squared.

41:57Originally when we put this in, we did it for sort of natural resource application regions...

42:03...because this is information that's used, for example, in fire modeling to estimate plant moisture.

42:10It's used in a variety of things like snow hydrology and other things.

42:14But the place that it's actually gotten used the most is solar panels.

42:19People want to figure out, If I put solar panels on the roof of my house...

42:23...if I put solar panels on the roof of this building, how much energy can I actually create?

42:29Now, in an urban landscape, it's actually important that you understand the shadowing effect...

42:34...from adjacent buildings, and that's what this tool will do for you, okay?

42:38So if you look here in the middle, you would think in downtown LA, the roof of every building...

42:42...would have the same solar potential as every other building, wouldn't you?

42:45But it's not actually true, because if you look here in the middle, there's a couple of blue ones.

42:50They have really low solar potential because that's a shorter building.

42:54And on the south side of the building are some taller buildings that, at certain times of the day, shade it, okay?

43:01If you're shaded early in the morning or late in the day, it's really not a big deal; you're not missing much solar radiation.

43:07But if you're shaded in the middle of the day, it really makes a much bigger difference.

43:11So the way that this was done, there's a few, actually, websites, Solar Boston and Salt Lake City...

43:18...are two of the ones that are pretty nice.

43:22The Python code that we used to do this, the JavaScript code for the app that these sites are built out of... actually all available on the Resource Center.

43:31If you wanted to build one of these for your own city, it's a pretty clearly understood model of how to do that now.

43:37I think Ryan's actually doing a demo theater on this, this week, as well.

43:41So with that, I'm going to turn it back to Liz, and she's going to start telling us about some of the...

43:46...pieces of this ski suitability model we're going to build.

43:49Thanks, Steve.

43:50So previously I showed you how to enable the Spatial Analyst functionality and how to access the tools.

43:57Let's go ahead and see some of the tools, or toolsets, Steve was just speaking about.

44:01He mentioned the density tools, such as kernel, line, and point density.

44:05The distance tools, such as corridor, cost distance, cost path, and path distance; as well as the interpolation tools...

44:13...IDW, kriging, natural neighbors, TopoToRaster.

44:18This is where you find your raster calculator to execute your map algebra syntax.

44:24The Math toolset contains these math functions, as well bitwise, logical, and trig functions.

44:31The Neighborhood toolset is where you find the block statistics and the focal statistics.

44:36Solar radiation, which was what Steve just finished speaking about.

44:39There's Area Solar Radiation, Point Solar Radiation, and this diagnostics tool - the Solar Radiation Graphics tool.

44:46In the Surface toolset, you've already seen the Execute Hillshade, but you also find aspect, slope, and viewshed.

44:54And down here in the Zonal toolset is where you'll find Tabulate Area, Zonal Histogram, and Zonal Statistics.

45:00So now that we know some of the functionality, what Spatial Analyst can do, and where to find the tools to do it...

45:07...let's think of some criteria that are going to be important when determining the best location for a new ski hill.

45:11Some of the criteria I came up with would be snow depth.

45:14We want a place that has a lot of snow so that we don't have to make as much snow... it's a little cheaper to keep our ski hill open during the season.

45:22So snow depth is going to be important.

45:25I thought maybe the distance to the center of town.

45:27There's a town down here, and I think if we built the ski hill closer to the center of town...

45:31...maybe we could get more people to show up in the evenings and maybe have some night skiing...

45:35...and maybe generate some more money for our ski hill that way, so that might be an important criteria.

45:40And gradient's an important criteria; we don't want to put it anywhere too flat.

45:43Even if it's a high elevation, the gradient's important for the slope, right?

45:47We need a good slope for a ski hill.

45:49So let's look at the tools and the data that I have and see if I can generate that information...

45:53...and use it in my suitability model.

45:56So I have elevation data, and Steve taught us about the Slope tool, so if I go here to the Surface toolset...

46:02...and open Slope tool, I can input the elevation data, give it a useful output name, such as Slope Out.

46:12I'm going to leave it in degrees, although I could also output in percent rise and again the z factor can remain 1.

46:19I go ahead and execute that tool and add it to the display with this dataset, and you can see that green...

46:25...represents relatively flat areas, small degrees of slope, and the red represents...

46:30...much cheaper areas relative to my study area.

46:33So here is relatively flat and over here it's relatively steep.

46:37So that's going to be important when we go to combine our criteria for our ski hill.

46:42Another thing I mentioned was snow depth.

46:43I think that would be important.

46:45Now I don't have a raster that represents snow depth, yet; all I have are these points.

46:49And at each of these locations, there's a weather station, and I know the average annual amount of snow.

46:55Using those points, I can use one of the interpolators and generate a continuous surface.

47:01The interpolation tool I'm going to use is Natural Neighbors, which is the most conservative of the interpolators.

47:08I'm going to input my snow-depth points, select the snow-depth field - that holds the value of the snow depth.

47:15The cell size is inherited from my environment settings.

47:19I may as well as give it a name such as, I'll put Snow Depth.

47:27And go ahead and execute that tool.

47:31The result is added to display - it's not very intuitive because of the color, so let's fix that.

47:35We'll go to Properties > Symbology and find a different renderer.

47:39How about light blue to dark blue.

47:42Areas where it's light blue are relatively, I'd say relatively shallow, and areas that are dark blue are relatively steep.

47:51So now we have a continuous surface representing snow depth.

47:54That will be an important criteria.

47:56The last one I mentioned is the distance in the center of town.

47:59I have a little line segment down here - you can't really see it - it's the center of town, and I'm going to generate...

48:05...using one of the distance tools - the distance from every pixel in my input study area to that location.

48:12And to do that, I'm going to use the Euclidian distance tool.

48:17My input feature, in this case, is the center of town.

48:23Again, I'll provide an output name, EucDistTown.

48:29The maximum distance I'm going to leave empty, and this is going to allow the tool to execute... the full extent of my study area.

48:34If I wanted just to cut it off after 5 kilometers or 10 kilometers, I could enter that here, but it's not necessary in our case.

48:41And again, the cell size is inherited from the environment.

48:44I'll run the tool.

48:48So now you can see that the yellow areas, or orange - I don't know what color that is - is relatively close... the center of town and blue is relatively far.

48:55Every pixel in this output has its own value.

48:58If I click here using the Identify tool, it's about 9 kilometers to the center of town.

49:04This location is about 15 kilometers to the center of town.

49:09And this one's about 21 kilometers to the center of town.

49:14So in the next demo, I'll combine these in a meaningful way to find the best location for a new ski hill.

49:18But right now, Steve will teach us a few more things.

49:21Thanks, Liz.

49:23So, so we've got things like slope measured in degrees, distance in kilometers, snow depth in meters.

49:34And we want to combine those together to create a map that represents how good of a place is this to build a ski resort.

49:43So how do we combine things like that together?

49:47Well, the first thing that we want to do is that we want to change some of those variables... what we think of as a common scale.

49:56We want to remap these values where each dataset will represent its suitability for a ski resort.

50:04What does that really mean?

50:05It means, I want to take the slope map and turn it into something that represents how good...

50:11...are certain slopes for building a ski resort?

50:15How good is a certain snow depth for a ski resort?

50:18How good is a certain distance to town for a ski resort?

50:23The way that we do that is with the reclassification tools, and what this allows us to do is reclassify...

50:29...individual values or ranges of values into some new value that has a different meaning for us.

50:35So we can take slope ranges - I want to build a ski resort, so I want to take things, you know...

50:41...0 to 20 degrees and make them one class, and 20 to 30, and make them another class.

50:47I use the reclassify tools to do that.

50:52And then when I want to combine those things together, what I can do is assign weight values to them and say... know, slope is really important, 'cause it's about downhill skiing.

51:04And distance to town is kind of important.

51:07Or, having good snow is really important.

51:10So I can add what we call a percent influence - you can't quite read it in the dialogue...

51:14...but you'll be able to read it in the demo - a percent influence of how important each of those variables is.

51:22And each of those datasets, those variables I have reclassified into classes that make sense for my suitability model.

51:30And I can use the Weighted Overlay tool to assign individual values to those.

51:36Maybe a little better example of that.

51:38So we use the Weighted Overlay tool to do sort of the classic GIS problem of finding "where is the best place."

51:45And "where is the place" is a very, sort of generic concept.

51:49But the way that we're doing this for finding the best place for a ski resort is the same way that you would...

51:56...find the best place for a coffee shop, or a likely place to find an endangered species, or the best place to...

52:00Change the slope values, okay? know, grow a certain kind of plant, okay?

52:08It's really all the same pattern.

52:10So, think about what kind of problems you're trying to solve in terms of "where is the best place"...

52:17...and what kinds of criteria you might use from the GIS to do that.

52:21What criteria would you use, what kinds of classes would you use to describe those?

52:27And then you can use the Weighted Overlay tool to combine those things together... assign those weight values, the importance of pieces of this, and you run the Weighted Overlay tool...

52:38...and you get a result, and you're like, wow, nice map!

52:41And then what do you do?

52:43You're not done.

52:45The real power of the Weighted Overlay tool is that you can just go back to the same dialog, it's already populated with...

52:51...everything you set, and you say, "Well, you know, the snow depth is much more important than I thought it was."

52:57So change the snow depth percent influence.

53:03Play with it.

53:04It's all a subjective model.

53:06People use the Weighted Overlay tool, for example, the people from Trust for Public Land...

53:11...who won the award on Monday - the TPL people, Breece and those guys - they used the Weighted Overlay tool...

53:18...and technique in stakeholder meetings with citizens.

53:22They're trying to figure out the best place to build parks.

53:25They invite the community in and then actually have a documented workflow, these exercises that...

53:30...they take the community through to have them determine which criteria they think are important...

53:36...and to assign those weight values.

53:38And then they compare each other's maps, right?

53:41So, it's not something for one person to do as an expert and make a decision by themselves.

53:46This is actually a collaborative tool where you can actually engage other people and get expert opinions...

53:51...on different pieces of it.

53:53And what the Weighted Overlay tool itself allows you to do is just have an easy place to modify...

53:58...those weights and rerun the tool without having to go redo all your work again.

54:03You just change a few values, hit OK, and do it.

54:08So we'll let Liz show us how we actually do that.

54:13As promised, it's time for the modeling part.

54:17So to create a model, you right-click, say New Toolbox; that's the first step.

54:24As you create a new toolbox, you right-click New Model.

54:29I already have a model started here; I'm going to open it up in an edit session.

54:33I didn't only think of those three criteria that we looked at last time.

54:36I thought of a few more criteria that we should include, and then I categorized them into three submodels...

54:42...issues that had to do with the development the ski hill, the terrain of the ski hill, and the accessibility of the ski hill.

54:49So let's go ahead and zoom in and see part of this model.

54:52In terms of development, I thought things that might be important were the soil type.

54:56What soil's best to build on, what's difficult to build on, especially with slopes involved.

55:01Land use. Who owns the land?

55:02Will I ever be allowed to put a ski hill on there?

55:04Is it protected land or private land or public land?

55:07Oh, that's landowner; sorry - land use.

55:10Is it swamps? Is it forest? Are there already buildings there?

55:14I mean, that's important stuff to consider when building, when looking for a new location.

55:20Terrain. We already looked at snow depths and we generate our continuous surface that represents snow cover.

55:25Elevation. You saw me, I already used slope to calculate gradient across my study area, but elevation... also the input to the Solar Radiation tool, where I can get an output of how much sun this slope is receiving.

55:39Maybe too much sun would affect the snow depth, but maybe not enough sun would be really cold... winter months to ski there, so that might be important to consider.

55:48In terms of accessibility, it's expensive to build roads and power lines, so we want to consider how far our new... hill is from existing infrastructures, or how close it is to the center of town, 'cause we need lots of people there.

56:00So there's all kinds of criteria to consider.

56:03But the first thing you need to do after you identify the criteria is derive some data from them - some...

56:09...continuous data - like we did in our last presentation, using Natural Neighbors or slope or what have you.

56:15And now you have a whole bunch of rasters that are in different units of measurement...

56:19...whether it's degrees or - what were some of the - the distance to the center of town.

56:26So now as Steve pointed out, we need to reclassify these datasets.

56:30So down here, I have a section of my model that's not complete.

56:32I have the output and the Slope tool, but now let's use the Reclassify tool to reclassify the data... that I can use it with the rest of my model.

56:40So if I go to the ArcToolbox, reclass toolset, and drag Reclassify onto my model...

56:47...and use this connecting one, the output of slope can become the input of Reclassify.

56:52If I double click Reclassify, you can see that the input is slope elevation.

56:58I'm reclassing the value field and these are the degree values.

57:02This class represents zero to two degrees, and it's being assigned a value of 1.

57:07Let's go ahead and set up our own classification.

57:09If I click this Classify button, you can see here that there are several different classification methods...

57:14...I can select, such as equal interval or natural breaks.

57:18And I can also choose the number of classes that I wanted.

57:22Now since we're trying to do something specific with this in analysis, let's consider how these values interact...

57:28...with the notion of building a new ski hill.

57:30I spent some time thinking about this and I think we need five classes.

57:33We need a class that represents relatively flat areas for parking lots and lodges; something that represents...

57:39...a relatively little bit of steepness, or a small degree of steepness, for bunny hills and greens...

57:46...something a little bit more for the intermediate skiers, more for black diamond...

57:50...advanced skiers; and then probably one category that's just too steep to ski, like it's not useful for a ski hill.

57:57I can create these classes by dragging these bars and maybe setting it to where I want...

58:02...but I think it's a little easier just to come over here and type.

58:06I'm going to have my first class, from 0 degrees to 10 degrees.

58:10My second class is from 10 degrees to 20 degrees.

58:14So 0 to 10 can be good for parking lots; 10 to 20 is good for beginner slopes; 20 to 30...

58:20...we'll call those intermediate slopes; 30 to 45 can be our advanced, our black diamond slopes; and then...

58:27...everything else, 45 to 60, too steep to ski.

58:30I'm going to say, Okay, and I'm going to go ahead and run the Reclassify tool.

58:39I want to add this to display, but first, I'm going to set layer symbology so it's something meaningful to us.

58:46So Slope Reclass is the layer of symbology I set up.

58:49Now, if I add this to display, you can see that the gray areas represents good for parking lots..., the 2, is relatively not steep, good for beginners; blue is for the intermediate; black would be... diamond slopes; and the red is too steep to ski.

59:08So we've done that, but how important are they in our model?

59:10That's the next question we need to ask ourselves.

59:12And how important is that information in relation to the other criteria in our model?

59:18So the output from Reclassify is using the Weighted Overlay tool.

59:23Going into this Weighted Overlay tool, I have three datasets.

59:26I have the reclassified snow depth values, the reclassified radiation values from the solar...

59:32...and now the reclassified gradient values.

59:39So I have my five classes here on the left; now let's, how important are they?

59:44Well, all reclassifies in this model are all weighted values on a scale of 1 to 9...

59:52...where 1 represents not important at all or undesirable or unsuitable for a ski hill, and 9 is the value...

59:58...I selected to be highly valuable for a new ski hill.

1:00:01So we need to maintain that same scale across all of our datasets.

1:00:07We know that 5 is highly unsuitable; it's not good for a ski hill, so I'm going to give that a 1.

1:00:13Parking lots are important, but I feel as though we could probably flatten some land if we need, so let's give that a 3.

1:00:19Now what kind of a ski hill do we want to focus on?

1:00:21Do we want it to be for beginners, intermediate, or advanced skiers?

1:00:25What's important to us?

1:00:27Well, I'm going to design it for advanced skiers, the adventurous people.

1:00:30So I'm going to go ahead and give this a 5; the intermediate's even more desirable, and the black diamonds...

1:00:35...are the most desirable slopes in our suitability analysis.

1:00:40Next over here, you can see I have a red X, and that's telling me that my percent influences do not yet equal 100.

1:00:46They only equal 70.

1:00:48So we need to set the percent influences across these three rasters to equal 100.

1:00:53So what's the most suitable, or what's the, has the most percent influence within our model?

1:00:59What's the most important criteria out of these three, and how do they relate to one another?

1:01:03Snow suitability, I think, is extremely important because snow is expensive to make and it's necessary for a ski hill.

1:01:10The slope, I think, is probably second to that.

1:01:12And sun, well, people are going to have to dress warmer if there's not enough sun.

1:01:15I mean, it's not quite as important, alright?

1:01:17So now I've sent my present influences, and the results of this weighted overlay...

1:01:24...and the results of my other two submodels all go into this weighted overlay and I do the same process.

1:01:31I'm going to run both of these weighted overlay tools and the final result will be added to display.

1:01:37It's running the two tools now.

1:01:41And let's take a look at our final suitability.

1:01:43So we know we set it up on a scale of 1 to 9.

1:01:46Not all the values are there; that's okay.

1:01:50Two is the least desirable area in our study area.

1:01:54Let's not build a ski hill over here.

1:01:56Eight, the darker green, is the most highly desirable location for a new ski hill, and that looks to be...

1:02:01...around Sourdough Hill in Lost Corner Mountain.

1:02:03So that would be the best place to build a new ski hill for advanced skiers.

1:02:07But if we opened up the model and started changing our weights around...

1:02:10...if we change the idea or if we got new development information, then we go back to the model...

1:02:15...modify some of the numbers in the Weighted Overlay tool, and run it again.

1:02:18Like Steve was saying, it's an iterative process.

1:02:21Thanks, Steve.

1:02:23Thanks, Liz.

1:02:25So everybody kind of get that?

1:02:26You understand how you would apply this to your own types of problems in trying to do...

1:02:31...suitability modeling and site selection?

1:02:34So there's a couple of other ways to do this.

1:02:36One of the ways we added a few years ago is known as fuzzy overlay.

1:02:40So in the same way that she was talking about using the Reclassify tool and the Weighted Overlay tool...

1:02:45...there's another sort of parallel way to do that, which uses fuzzy reclassify and fuzzy overlay.

1:02:51And the fuzzy part of this is not about fuzzy boundaries, it's about fuzzy sets.

1:02:55So, fuzzy in this case is about set theory and how much something belongs to one group or another.

1:03:02The difference between fuzzy overlay and weighted overlay, in this case, is I don't have to...

1:03:04...calculating the accumulated flow across the elevation surface.

1:03:06...specify discrete class boundaries.

1:03:09I don't have to say that, you know, 10 to 20 is this value and 20 to 30 is another value.

1:03:15I can use continuous functions, okay, so I don't have to specify discrete boundaries for the classes.

1:03:21The other thing that's different about it is, I don't have to have everything be additive.

1:03:26In the case of weighted overlay, it's an additive overlay.

1:03:29It's this and this and this, all added together and then normalized.

1:03:33In fuzzy overlay, it can be this and this, or some criteria or another criteria.

1:03:39So you can mix "and"s and "or"s in the same model.

1:03:44We have a few sort of specialized domain-specific functions in Spatial Analyst, as well...

1:03:49...some hydrologic tools, which we presented yesterday, and these slides will be available.

1:03:55Tools for creating watersheds and stream networks...

1:03:59...that basically work by calculating the direction of flow across an elevation surface and...

1:04:08And then we use this for creating things like watersheds and stream networks and doing stream ordering...

1:04:13...and calculating flow length and things like this.

1:04:18We also have a few groundwater modeling tools for doing simple, two-dimensional advection...

1:04:22...dispersion modeling, so if you happen to have a head grid and a hydraulic convectivity...

1:04:30...and porosity [inaudible], then you can use these tools to calculate...

1:04:34...two-dimensional vector flow fields of flow magnitude and direction and drop particles on them...

1:04:39...and see where they go, and do Gaussian dispersion from those.

1:04:43Sorry if I'm going a little quick through that; we're running a little short on time.

1:04:46I've got something else I want Liz to show you.

1:04:48And we have some multivariate statistics tools.

1:04:50These are things that you might think of more classically as image processing tools... if you come from an image processing background, you may see things like principal component...

1:05:00...analysis and national lightweighted classifiers.

1:05:03The better way to show this is for Liz to show us the Image Classification toolbar, which allows us... do interactive training sample selection and interactive classification of multiband imagery.

1:05:16And we'll just show that one instead of talking about it.

1:05:21So, I have a satellite image here of the Lake Tahoe area and I want to do a supervised...

1:05:25...classification of that using training samples.

1:05:28To access the Image Classification toolbar, I go to Customize > Image Classification, and here's the toolbar.

1:05:35Here, you can see I have the imagery selected in the drop-down layer, and this is how I draw my training samples.

1:05:41So the first class I'm going to make represents water in this image.

1:05:45So you can go ahead and make some areas that represent water.

1:05:49If I open up the Training Sample Manager, I can then select those training samples and merge them into one class.

1:05:56I'm going to call this Water and, conveniently, it's already blue.

1:06:01The next class I'm going to make is for the clouds in the study area.

1:06:04I need to make sure that they remain clouds in my output classified raster.

1:06:09Again, I'm going to go ahead and select them and merge them and call them Cloud.

1:06:15Oh, and that's white; we're close enough.

1:06:16Okay, our next one is the rock, so the bedrock over here.

1:06:20Now if I was doing this for real, not just demonstration purposes, I'd want to zoom right in and...

1:06:25...make sure I'm getting really homogenous samples of the rock, but obviously this is how it's done in a demo.

1:06:32So I'm selecting some bedrock here.

1:06:35One thing you might want to do, though, is if you select these, then there's some diagnostic tools up here.

1:06:39The Histogram tool shows you the distribution of the cell that I've selected.

1:06:45And as you can see, because they're all bedrock, these histograms all overlap pretty well.

1:06:50There's also the scatterplot.

1:06:52This might look dispersed until you look at the axes, and actually these clouds all overlap quite well.

1:06:59And if I look at the matrix of the statistics, then that can also help determine whether or not...

1:07:04...I've done a good job of selecting my training samples.

1:07:08So I'm going to go ahead and merge these, call them Rock, and color them gray.

1:07:14The last class I'm going to create in the study area has to do with the vegetation, which is the red.

1:07:19Again, I encourage you to zoom in and be much more meticulous than I'm being.

1:07:23 But there we go, now we have some vegetation.

1:07:26I can merge these again.

1:07:27If I mistakenly had merged the rocks in with that class, you can always use the Split button... break it back apart and go back to what you had previously.

1:07:36Or if one of those training samples doesn't fit after you look at the statistics, you can split them...

1:07:41...delete them, create new ones, all that.

1:07:44So I'll merge them; I'm going to call it Veg for vegetation, and it's green.

1:07:49From the classification - oh, before I do that, let's make sure that we have really distinct classes.

1:07:54We don't want a lot of overlap; we want to be very clear with how we do our classification.

1:08:00Here I've selected all the classes; now when I do the histogram, it's across all those classes...

1:08:05...and you can see that they're pretty distinct.

1:08:07Likewise with my scatterplots; they're pretty separate.

1:08:10All my blues fall together - all the greens, the grays.

1:08:14Oh, the whites are up here. They're hard to see, right?

1:08:16They're also pretty distinct classes, so that's a good sign in terms of classification.

1:08:21So I go ahead and use the Interactive Supervised Classification tool, and it produces this classification.

1:08:28If in this Effects toolbar I select that Output Layer I just generated and use this Swipe tool...

1:08:34...I can swipe back and forth and see how well I did.

1:08:37Look, I'm not too happy right here, if you guys can see where those black arrows are.

1:08:41It seems like I have more clouds in my image than result in my classification.

1:08:45So what I can do is I can go back and I can keep adding; just draw a few more training samples until, you know what?

1:08:53These are also clouds, and I can merge those in, and now when I do the classification - let's make sure...

1:09:00...I select the right one - did I get any more of those clouds? I'm not sure.

1:09:06So you can go back and you can keep reiterating the process and continuing on and updating...

1:09:11...deleting, or moving, et cetera, until you get a classification in which you're happy with.

1:09:17Thanks, Steve.

1:09:18Thanks, Liz.

1:09:19Was that the fastest image classification you've ever seen? That's pretty awesome. Yeah.

1:09:26For all of us who used to do it the hard way in the past, before there was like actually a way... look at your image on a computer display, that's pretty shocking.

1:09:36So after we do a classification like that, we end up with these kind of speckly-looking things, right?

1:09:44And that really doesn't look much like good GIS data.

1:09:46So there's a collection of tools in Spatial Analyst that allow us to do data generalization and...

1:09:52...cleanup of raster data, and these are tools like Majority Filter, Expand, Shrink, Nibble, things like this.

1:10:01So without going into too much detail on those, if you just watch the little graphic in the corner...

1:10:04...we run, for example, Nibble on that and remove all of the individual or two- or three-cell areas and then... Majority Filter on that, you can see that looks much more like what you would think of as GIS data, right?

1:10:17Now the way that those tools work, they actually try to preserve the morphology of the data.

1:10:23It's actually replacing cell values with, for example, the majority of the neighbors sharing those boundaries, so.


1:10:34With that, one of the things, if you haven't seen this slide yet in one of the geoprocessing...

1:10:40...or Spatial Analyst presentations, there is a geoprocessing resource center now.

1:10:44When we talked about resource centers in ArcGIS Online on Monday.

1:10:49There is a community for analysis, and if you go there, there are a lot of good things there.

1:10:57Things like these presentations, some of the demos that you see, a lot of sample tools and sample scripts.

1:11:03So there's an education gallery where we have a lot of articles and videos and things like that... well as a tool gallery.

1:11:10Now the important thing about the tool gallery, it's not just the things that you see the little bitmap pictures of.

1:11:17There's a button here called More Gallery Posts.

1:11:20Click on More Gallery Posts; there's a lot more stuff there, okay?

1:11:24Things that we built, things that you guys built; there's lots of good things there.

1:11:28So, we just moved over to this new gallery approach at 10.1; we're still migrating over some of the...

1:11:34...old materials and indexing those, but expect to see that growing a lot more in the coming year.

1:11:42Please fill out a session evaluation.

1:11:44If you haven't been doing that, the session ID for this particular workshop is 811.

1:11:50We do actually read all of your session evaluations and all those comments that you write, and we do change our...

1:11:57...presentations and create new presentations and drop old ones based on the type of feedback that you guys give us.

1:12:03So, if you need to run to another session, it's two forty-five now, and we'll hang out and answer any questions you guys have.


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I've been using gis on and off now for about 20 years and still theirs always something new and exciting to learn.


Jul 29th, 2013. 4:27:56 PM

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