Point Density

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Do curso por University of California, Davis

Imagery, Automation, and Applications

265 classificações

University of California, Davis

Imagery, Automation, and Applications

265 classificações

Curso 4 de 5 em Specialization Geographic Information Systems (GIS)

Welcome to the last course of the specialization (unless your continuing on to the capstone project, of course!). Using the knowledge you’ve learned about ArcGIS, complete technical tasks such raster calculations and suitability analysis. In this class you will become comfortable with spatial analysis and applications within GIS during four week-long modules: Week 1: You'll learn all about remotely sensed and satellite imagery, and be introduced to the electromagnetic spectrum. At the end of this week, you'll be able to find and download satellite imagery online and use it for two common types of analysis: NDVI and trained classification. Week 2: You'll learn how to use ModelBuilder to create large processing workflows that use parameters, preconditions, variables, and a new set of tools. We'll also explore a few topics that we don't really have time to discuss in detail, but might whet your appetite for future learning in other avenues: geocoding, time-enabled data, spatial statistics, and ArcGIS Pro. Week 3: In week three, we'll make and use digital elevation models using some new, specific tools such as the cut fill tool, hillshades, viewsheds and more. We'll also go through a few common algorithms including a very important one: the suitability analysis. Week 4: We'll begin the final week by talking about a few spatial analyst tools we haven't yet touched on in the specialization: Region Group to make our own zones, Focal Statistics to smooth a hillshade, Reclassify to change values, and Point Density to create a density surface. Finally, we'll wrap up by talking about a few more things that you might want to explore more as you start working on learning about GIS topics on your own. Take Geospatial and Environmental Analysis as a standalone course or as part of the Geographic Information Systems (GIS) Specialization. You should have equivalent experience to completing the first, second, and third courses in this specialization, "Fundamentals of GIS," "GIS Data Formats, Design, and Quality", and "Geospatial and Environmental Analysis," respectively, before taking this course. By completing the fourth class you will gain the skills needed to succeed in the Specialization capstone.

Na lição

Spatial Analyst and Where to Go from Here

In this module, we'll start by learning how to use a few different tools and concepts in Spatial Analyst that we haven't touched on before. We'll use Region Group to make our own zones, Focal Statistics to smooth a hillshade, Reclassify to change values, and Point Density to create a density surface. In the second half of the module, we'll give you some suggestions of directions for future learning by showing you all the things we *didn't* cover in this specialization, including web and mobile GIS applications, field data collection workflows, industry specific tools and workflows, spatial statistics, in-depth design and cartography, programming and GIS servers, and other GIS systems like QGIS and companion applications like Geospatial Modelling Environment. These can serve as seeds for your future GIS learning and careers!

Module 4 Oveview1:00

Region Group10:43

Focal Statistics and the Swiss Hillshade11:27

Reclassify10:10

Point Density8:11

Conheça os instrutores

Nick Santos
Geospatial Applications Researcher
Center for Watershed Sciences

[MUSIC]

Hello again and welcome back.

In this lecture I'm going to show you how to use the Point Density tool.

We won't use any particularly fancy metrics for it but

it's a way to transform a discrete set of locations into a continuous field.

We've done this before with things like the Euclidean distance where we

have the distance to features.

But the Point Density tool gives us a different metric.

It can tail off further so there might not be really much of a value out here because

there's no real point density here or in here.

But we can get a sense for just how much information

is in these areas where information can be a couple different things.

So to start, let's just take a quick look at it.

So once again we're in spatial analyst and in the density tool set, and

the tool we're going to use at point density.

And I specify my Input features.

And, I can optionally give a population field.

And we're going to run it with an without that.

And for now, let's do NONE.

And the population field

is a way to give the points a different value than they have on their own.

So if we went without it, it's going to

do density of the actual points in each location where each point counts as one.

But if we went with the population field that field can contain values and

it's going to then treat that point as,

instead of one, it's going to treat it as whatever number it is.

For the density and since density is a value over an area in this case it can

significantly increase the value and we can leave the area the same to compare it.

So I'm going to call this layer point density,

regular and I live output cell size at the default.

It calculates it by using the extent and divided by 250.

And let’s have it do a circle with default radius as well.

So it's a Map Units Cell, it's gives up 80 meter radius around each point is or

around each cell is what it searching for the density and let's run it.

Okay, and it's pretty quick and if I turn off the points for

a sec, we can see sort of pattern here.

So if we look at each cell, it's clearly searching the given area for more points.

And we can see that there is with higher numbers of points or kind of in between

a cluster of points have higher values even, I think make sense.

I don't know if there’s a lot to explain here,

we got this little bubbles where we have a few points and lighter colors.

We’re having less density and the bottom class that it gave us

means that this area doesn't really show up in any darker color than the lightest.

Well we can tweak the perimeters a bit here, so

what if I run it again, all right let's start with the other one.

So I'm going to go results in current session and

I'll double click on the tool there, and now let's run it with population.

And in a population field I have some just kind of made up data for

customer traffic to each of these locations.

So maybe they're all businesses.

And I want to see where the highest density of customers is.

So the customer traffic field is my value field here for the density.

And I’m going to leave everything else the same and click OK.

And not massively different, but we get different results still.

And let's turn off the points again and we can kind of look back and forth.

And we see some changes, it seems to consolidate a little bit.

And that might just be because the data, but we still get a different result here.

And another thing that we can see is that here the highest value is 857 or

the highest value here is 270,000.

So what it's doing is for each cell it's summing the values of

the neighboring population field before it does the density calculation.

So if our customer data is in the hundreds in a lot of these cases for

each cell that's looking in an 80 meter radius.

It's then summing the populations fields here, it's going to get a large value for

per square kilometer because it searches within 80 meters.

And then in the math it then divides that as if it's

representing a square kilometer and we get this for

value here of 270,000 costumer traffic units per square kilometer.

Now let's run this one more time in tweak the settings just once more.

And we can still use the population field, but

now to 150 and just kind a see how that goes.

And just like when we were working with local statistics,

we have a few different shapes we can search with.

I'm going to leave it as a circle, but we can use some of the other units there, or

some of the other kind of search area types.

And I'll click OK again.

And now this looks a little different and in fact a little smoother to me.

It looks maybe more like, it somehow seems more appropriate to the data

to have this kind of larger cluster in here.

Because that seems to be what's going on this is the most dense area.

And then as we get further out it kind of tails off in a more

even way instead of having these striations in it.

So if we change the search area it's then getting the data from much

further around it before it then does the division by area.

Now this isn't truly a hotspot analysis or anything like that.

We could do that in the spatial statistics toolbox down here

with the mapping clusters.

But it gives us a sense for where things are going on in a raster format and

once again giving us a way to transform vector data to raster.

In a way that's kind of meaningful that we can use in some other work flow.

So we can then take this into raster calculator or

any other number after tools for processing this density measure here.

Okay, and I'll keep this short.

That's really all I have to show you about this tool right now.

I just want to make sure you know about it, and

know about this density tool set as well.

This is it for a special analyst at this point, and

I encourage you to take a look through the tool box again.

Now that you're more experienced with GIS and

see all the different tools that are here.

May be when you've looked at but, before you didn't have as big of an understanding

of all the geo spacial concepts.

And you might have actually have some different ideas looking through it again.

I tend to kind of look through whole tool box every handful of months because

I think that each set of tools here can kind of help once you get each different

concept in GIS.

A lot of the looks at spatial analyst in this course have been focused much more on

just kind of getting you to know a concept as opposed to using it in depth.

And that's because there's so much there but

I want to make sure you have a sense for the wide variety of capabilities,

since you're all coming from different backgrounds.

No one tool or workflow is going to completely encapsulate everything.

I've tried to show workflows, but I've also tried to show you

different tools that might give you ideas for workflows of your own.

Okay, so that's it for this lecture.

In this lecture we looked at points and

city with a few different sets of parameters using the population field and

just using the regular format of having each point count as one.

And then we got rasters back that summed the values

over the total area that it was searching in.

It’s a great way to turn your vector data into a raster and

preserve some of the input data for future processing and

transform it to a new format that has new meaning as well.

Okay, see you next time.

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