Performing Map Algebra using ArcGIS Pro

hi raster data is the method by which we collect gis data using pixels we work with raster data on a regular basis any image digital image that we have that we take with our phone is just made up of tiny little squares called pixels and we call the the more number of squares or pixels that we have is referred to as resolution so the better the resolution the more squares the better we can zoom in on we have the same idea here we can see we have a dem this is called the digital elevation model for durham and you zoom in enough and you can start to see that the tiny little squares that compose it now the real world distance represented by each of these squares is referred to as resolution underneath of it i've got a dem of land cover for 2011 and underneath of that a land cover image for 2019. we don't have a color map for these but you can see the patterns look generally the same now within raster operations we're only able to store one feature or phenomena at the same time so for one we have dem for another we have land cover for 2011. for another we have land cover for 2019 and i can right mouse click on say land cover 2011 open up the attribute table this attribute table looks a little bit different i have the values and the counts now these values are going to be discrete or categorical values and these counts represent the number of pixels for each of these counts there these are nlcd national land cover national land cover data set and so these values correspond to the land cover whether it's forested or grass or density high you know high high intensity density and so you can see for this particular area we're looking at there's about 15 different land covers in total there's probably about 25 or 30 of these individual land cover classifications now within arcgis and within our raster data we can do a number of different classifications and raster classifications so i could add land cover for 2011 plus the land cover for 2019 now it probably won't make a lot of sense if i just add these together because these are classifications you know categorical classifications but i can look at dem this dem here and i can go to analysis and tools and i can look at my individual tool boxes and i can click on spatial analyst tool there's a number of different spatial analyst tools now under my map project under licensing we need to make sure that we have spatial analyst enabled now that's an extra tool so contact your instructor if you have any questions about that but we have spatial analyst tools and we're just going to look at a couple of them here now we have this thing called map algebra now we could do calculations with map algebra between different data sets or within a single feature uh within a single data set and this one we're just going to look for we'll look at the dem which is greater than 150 yeah and that's all we're going to look for here so my dem is greater than 150. i'm going to run this this is an example here now we talk about the idea of boolean so this boolean means that 1 is true 0 is false one is true zero is false so now you can see everywhere where it was greater than 150 and in this purple here green it's false now i can do a little bit more robust calculations now this land cover 2019 i'm gonna see if it's either equal to twenty one land cover equals to 22. and you can see that i've typed it out so i put these in parentheses because i want to satisfy my order of operations so the land cover equals 21. this line here means or or or so anything that begins with a two in this classification stands for urban whether it's low intensity or low density up to high in density intensity which is 24. and so you can see it's a little bit more robust and so i've got this land cover one classification i'm going to store this using this feature class and click on run so this is a little bit more robust with these map calculations and now you can see everything that's one is going to be some type of urban anything that zero is not and now what i want to do is i want to compare these both i want to see everything that's high elevation that's also urban high elevation in urban so now i can compare this land cover one versus this dem and so these are just two boolean calculations now i can add them subtract them multiply them what i'm going to do is just run an and or i can multiply them as well and i'll multiply them together because the only way if a 0 times 1 equals 1 0 times 0 equals 1 if either of them is 0 it's going to be zero so now the result they both need to be one that's what we call a boolean and they're going to result in one so this is a local operation and this just does cell by cell calculations and now you can see these areas here in green that are both 21 22 23 and 24 and also high elevation so they satisfy this and they satisfy this stipulation here and so these are a couple of very very simple examples of boolean algebra and what we call map algebra now we can get a little bit more robust where we combine multiple multiple values together when we do say land cover classifications and we're going to look at the last one here with this raster calculator and now we're going to look at all the areas where land cover classification we'll look at something called change detection does not equal this symbol with the exclamation point 2019 so now all we're doing is we're doing a pixel by pixel comparison see where the 2011 is not the same as the 2019. see where it changed i'm gonna click on run here's my link and so now you can see everything in green changed everything in pink stayed the same and we can get a little bit more robust in terms of seeing where everything changed and at the same time it's 21 22 23 24 so we can see where everything is changing into urban or vice versa

Channel: DEEGSNCCU

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Id: gt6wKjQ4_Oo

Length: 7min 45sec (465 seconds)

Published: Thu Aug 12 2021