GIS_Lesson 10: Data Interpolation: Inverse Distance Weighted (IDW) Method Using ArcGIS

welcome back to students to another video tutorial on arms yes you have already known that and preparing this video tutorial for your practice in home for your ArcGIS laboratory work so that you can use these videos as a guideline and work keep on working we have already come to the end of our syllabus so today is almost the last lecture of our syllabus that is data interpolation today I'm going to show you how you can prepare math using data interpolation it's a basically a statistical technique where you will estimate some values some missing values or where is no kind of sample or no no information or about the attribute table you have to estimate the values of the selected areas from the present existing values so this is called data interpolation where it will estimate the values and for geo statistical analysis data interpolation is very important because it is not possible or it is a very tiresome work time-consuming work to collect the data on each and every point for example even to show the rainfall map on GIS it's not possible in Dhaka City it's not possible you to show the all the rainfall points of dekha City every coordinate so you will collect some stations data and then interpolate some interpolate the other areas through various process and in data interpolation in GIS you can use many kind of there are numerous types of interpolation methods it depends on what type of interpolation you are looking for and watered the basic criterias of your in interpolation among the interpolation methods you can see there are lots of types area a real interpolation diffusion interpolation disjunctive Creegan empirical Bayesian creaking Gaussian geostatistical simulation global polynomial indicator kriegie inverse distance weighted method kernel method and many others I will not go through all of this today in our video d2 shortage of time will only just see how you can estimate or you can prepare a map using this inverse distance weighted method that is called i:w IDW now let's see how this inverse distance weighted method works it actually estimates the cell values by averaging the values of sample data points in the neighborhood of each processing cell that means in iw IDW you have to select our processing cell size and it will estimate the values inversely related to the distance that means from the x's the value that are further that will get lesser wet edge and that the points that are nearer from the axis that will get higher wet edge that's why it is tell that it is told here that the closer a point is to the center of the cell being estimated the more influenced or a wet it has in the averaging process and it will just use the special auto correlation between the points if we just check on the photo here you can divide the cell size into two sectors four sectors and see the rates got the higher wet edge because these are nearer to the nearer from the axis and these blue points or blue dots get lower wet edge as this are further I mean the distance from this access to these points or greater that's why it got the inverse relation and the laser wet edge so I'm not going through all of this PDF files because it will be time-consuming you will just go on this PDF file I'm starting to work on are starting to show you how you can prepare the interpolation map using geo statistical tools in ArcGIS so let's start well the data interpolation method is very important for not only collecting not only showing the data that will create the 3d surface or that will have the Z values but also is important for any kind of point value s dimensions for example population densities rainfall data temperature values and many more things you can use it interpolate in every aspect of analysis it depends on the researchers in which aspects he wants to find out so in this video tutorial I have tried something very important for this situation this corner virus pandemic I have tried to show the confirmed coffee's cases situation using the IDW s in France distance waited the interpolation methods that means I will be showing indica sitting areas which areas are more respond and more affected and which areas are less affected using this interpolation method for these I had to prepare the data I had to collect the doc administrative boundaries shapefile first where the word numbers are been given if I just open that R abuse here is the word IDs and the word names I have input I have edited the values and input the word names using the art editor tool arcmap and editing i am not showing this addition process here you have already class you have already class on edit editing - so I'm not showing how did I just add the names what names and then I just create a new point feature point chef file named as Hana you can named whatever you want and just give some random points over the region of Dhaka City now I try I just give the points in each word according to that each word then I input the values of confirm to covet case according to the word and I just collected data of the copied infected people from this side the ID is your side where they give the life life update of the confirmed cases so if you just click on this you will just be showing the area stitch count and it will show you the number of confirmed cases and these as this is this gives the life update so these map will be invalid after some days so I have used the data from two days back so you can make your own data you can in give your own values on the attribute files the confirm copied cases and then you can analyze again and to show the estimated values using the data interpolation method in these point value features we have this field I have added this field that kovat case has gone from covet cases and I have input the number is not actually appropriate complete 100% accurate because some of the words number and name might not be matching and the number might be changed but I'm here only to give you example how you can just interpolate the confirm covet cases values using these do statistical analysis so after adding the data from your folder I have already added the data you don't need to do much you have to just go the our tool box and open the do statistical tools let's open the arc toolbox actually I have already done the IWD but I'll show you again how you can do this process so opening the arc toolbox you have to go on your statistical analyst tools here geostatistical analyst tool and click on the interpolation method so in interpolation you will be seeing that there are loss of interpolation that I have mentioned in this PDF file that diffusion interpolation empirical Bosnian Creegan global polynomial interpolation IWT and many mores these these all interpolation methods have different kind of uses but today we are only showing you the IDW method so you have to click on this IDW method and our input feature plus is the point feature definitely you have to estimate the values based on the point shapefile so our point feature is the thana named us and the Z value field the values which you will be estimating so in Z field I am just putting this as the confirmed cases Tanner copied case now you have to change some of the environments be the interpolation will not get the area unless you make it fixed it will as it is restr interpolation or raster analysis it will be working on the cell size not the vector analysis before you go to the environment settings here you have to sit on the output raster value where you will be saving your Resta I'm naming it as there is already a file named IDW so I'm naming it a tw2 for saving the output raster and here you don't need to give the wet field wet edge field and change all these things the things you have to change from here the environment settings because the IDW interpolation should get it's the region or cell sizes so I have to change you have to change or you have to fix out the processing extent to which process it can estimate because for example beyond the boundary there could be some other points or there could be no point so it will take these distance also and it will be in confusion how much area or how much extent should it take for interpolation there should the that would be unfair infinite so we are just changing the processing extent not to the default but to the area same as a layered Tucker administrative boundaries because we are confining these areas Tucker to the administrative boundaries so here the coordination has already fixed now you have to change the rest and allyce's mask values the cell size maximum of inputs and you have to change the mask you have to fixed out the boundary in which you are working on so I'm changing the mask working on the taka administrative boundaries just give okay so our environment is set now we are just doing will be just starting doing our interpolation analysis here the working process will be going on let's see how it works it will take some time for the interpolation you can also do this interpolation method using the editor tool and geo statistical analysis tool from the editing so our Ida IDW is done so there is a tick marks new I DW is here I'm just uncheck king the boundary level and I'm checking the level features the estimation has been done as I put the values according to the attribute table you will see that the areas which are more which are found more covet cases that is the East End Zone of Dhaka City that is ramp Ora but Roger Bobb for Kira pool both each heel and Gulshan Bawra knees these areas are highly affected rather than the northern part and rather than the southern part of this capacity region it depends on the value you have given I have given some random values from these web sites these are not completely accurate if you give the accurate values now life update then it will show the areas which the government has declared the lockdown areas then it will definitely show the red values you can change the estimation classified classification and in the properties using and the using the symbology here the classified values are ten classes and it is classified into geometrical interval you can change these classes numbers for example avert what you want to classify the values into eight classes and rather than geometrical interval you want the equal interval then it will show the equal interval with this process but I think rather than equal interval the geometrical interval was better and you can fixed out your maximum and minimum number and define the other things here here the minimum and here is the maximum so the maximum values is found on covet cases is found on the Muhammed region mama to shake I take other bar and this eastern region although the Dhanbad area is quite dangerous and quite risky but in map it doesn't show that much here is the tan multi region so using this interpolation method you can easily find out the copied confirmed cases hopefully you will be working on these and explore more and more by using these interpolation methods you can also prepare the density map and the teen model further later on you can you have to use the freaking interpolation method and some other do statistical analysis method for preparing or for creating a surface analysis will be if we have enough time we will base it on the next classes so that's all for now hopefully you get help from these video tutorials keep watching and stay safe stay fine thank you very much all of you

Channel: Musarrat Zaman Auroni

Views: 1,596

Rating: 4.9166665 out of 5

Keywords: ArcGIS, GeoStatistical Analysis, Interpolation, IDW

Id: NlKPla1eGgI

Length: 18min 33sec (1113 seconds)

Published: Mon Jun 08 2020