ChatGPT GIS Analysis Tutorial - Part 1

hello guys welcome back to the channel in this video we are going to discuss how we can make use of chat GPT which is a well-known AI powered language model developed by open AI to get helpful insights for doing geospatial analysis now when I say helpful insights we are actually going to go Way Beyond the traditional helpful tips that we would normally get if you would just look things up by browsing the internet and instead with chat GPT we are going to ask very specific questions related to the type of analysis that we are trying to perform and it's going to give us a perfectly tailored answer or a piece of code in this case which we could simply copy and paste onto our chord editor and with minimal edits we would be able to execute the geospatial operations in no time I'm going to demonstrate this geospatial operations using six different examples and I'll be asking chatgpt to use Python geopenders Library whenever I think it becomes relevant now I have done a complete beginner's guide to using Joe pandas Library quite some time back and if you're interested in learning the absolute basics of jio pandas Library you can click the link on the top right corner of the screen and you will be able to access this tutorial as well and I will be making use of a Jupiter notebook to demonstrate this to you guys but your free to use any ID of your choice given that the required libraries are installed which you will get to discover as we go along so Guys Without further Ado let's go right ahead and get started with the very first example so in the very first example I'm asking chat GPT to write a code snippet to import two shape files using jopanda's Library I'm asking to color the features of the first shape file in red and the features of the second shape file in green and I'm assigning black as the line color for the features of both shape files and at the end I'm mentioning that I would like to use the matplotlib library for plotting the data on a Jupiter notebook so let's just slide this request into chatgpt and see how it responds and this is how the interface of chat GPT basically looks if you are starting out for the very first time if you head over to chat.openai.com you will have to create a free account with open AI in order to sign up to use chat GPT so I'm just putting that request in right over here and after that all we have to do is just simply hit enter and you can see that it's giving us an example called snippet using both geopenders as well as matplotlip libraries so as you can see it's basically naming the shape file 1 as shape file 1 and shapefile 2 as shape File 2 and assigning these sample names to the first and the second shapefile was actually done by chat jpt that was not done by me so it was actually smart enough to sort of assign a descriptive name in a way that the user would understand what it's referring to when it's talking about an example called snippet which is actually pretty amazing and over here you can see that it's basically writing out the code to create a figure an access object and it's assigning a figure size right over here which we would be able to actually change if we wish to do so and since I specifically mentioned that I would like to plot these two figures side by side it's basically assigning The Columns value to be 2 so that with two columns if you can imagine how a table looks with two columns well the two columns are going to be sort of structured side by side isn't it so that's the same principle that we're using over here so with two columns we are intending to plot these two different shape files side by side and it's asking us to use these two lines to basically plot the features the first one in red and the second one in green with the assignment of this Edge color and here's something that we didn't specifically mention however it was smart enough to understand that it would be a good idea to have two different titles for the subplots so we can basically go ahead and replace this part of the chord with the title that we think is suitable and finally we can show the plot simply by adding plt.show alright so what I'm going to do is I'm going to open up a jupyter notebook first and now we get to your working folder and after you navigate to your working folder in order to create a new python script what I'm going to do is I'm going to click right over here on the new we can select Python 3 which creates a new python python 3 notebook basically alright now all I have to do is go over here and simply hit copy code which will sort of grab this code snippet onto the clipboard head back to the jupyter notebook just right click and select paste and as you can see we've got all the lines of code just in this manner well now you might be curious what exactly are we going to use as this shape file one and the shape file 2. so just for this example I have prepared two different shape files so here I'm basically opening up arcmap just for the purpose of showing you guys what these two shape files are supposed to be so I have two different shape files so the first one is called region of Interest one and the second one is called region of Interest two so if we quickly have a look at the region of Interest 1 the extent of the region of Interest one it's basically a shape file that encompasses this Lake right over here and if you would like to know the extent of the region of Interest too well this is how the region of Interest 2 basically spreads out a part of it is basically crossing over to the spatial extent of this Lake as well so this is just for your information just so that you will have some sort of an idea what kind of shapes or what kind of polygons that we are dealing with when we import this into our Jupiter notebook later on and at the same time if you're wondering about the the actual files pertaining to these two different shape files so over here you can see that the set of different files which basically makes up the region of Interest one shape file and right over here similarly you can see the set of files which makes up the region of interest to shapefile as well now one thing to keep in mind is that when we are getting chord Snippets like this from chat GPT chances are we might not be able to execute this code immediately because there's actually limitation when it comes to how much we can tell chat GPT not that it cannot actually deliver if we were to be very very specific to the level where we also indicate the folder in which we keep our shape files as well but I don't think it's not really necessary to be that specific because when it gives us a generic path like this what we can do is we can basically replace this path or supersede this already given part by the actual part of the shape file that we are supposed to use for our own analysis so in this case what I have to do is I have to copy this path and direct it to the region of Interest 1 shapefile and similarly for the second shapefile I would have to use this path and direct it to the region of interest to shapefile as well so that's what we are going to do so I'm going to replace this and add a backslash and name my first file as region of interest1.shp however since I'm using backslashes so over here I would have to add a simple r or if you don't want to use this R you will have to change these backslashes to basically slashes but I don't want to do that so I'm just going to retain this as it is and just add a simple arrow over here so that it's uh it's going to recognize that I'm referring to a specific path and similarly I'm going to copy this entire path and paste it over here but for the second shape file instead of region of Interest 1 I'm just going to add region of interest to and when creating the figure and the axis object I don't really have any amendments that I would like to do so I'm just going to leave this line of code as it is I think we are good to go when it comes to these two lines as well and just as the two titles well we'll just leave this shapefile 1 and shape File 2 for the time being and what we can do is we can basically do a secondary run and try to change these two titles if you really wish to do so so the rest look good to me and to test this out what I can do is I can just simply click run over here and just like that you can see that it basically displayed the shape file 1 and the shape File 2 side by side exactly as we intended and just like what I mentioned if I wanted to change the titles well I can simply pass a title called region of Interest 1 and for this we can make it to be region of interest to and after that if I click run again you can see that now it basically displayed the two titles just like this and over here on the X and Y axis what it's basically showing us is the longitude and latitude information and this shapefile has a UTM 17 not projection so these are the coordinates basically that are corresponding to that particular coordinate reference system so it's pretty convenient to have those coordinate information displayed like this as well especially if you want to know whether these two shape files are supposed to be spatially located far apart from each other or whether it's supposed to be in the same region and what not you can have an initial guess simply by looking at these numbers whether it's basically falling on the same region or not all right guys so the next thing that I'm going to do is exactly that I'm asking chat GP to do a main Discord that we have right over here in a way that we plot the second shapefile on top of the first shape file just so that we can know whether it's really intersecting each other or not well we already know that it does intersect just because I showed you guys the extent of these shape files using arcmap however let's just imagine for a second that we didn't really pass through that step we have no idea whether these two shape files are basically crossing over to each other's boundaries or not so I'm heading back to chat GPT and I'm asking it alright guys so I'm asking how can I amend the above code snippet to plot the second shape file on top of the first shape file and we don't really even necessarily need to refer to the shape files as shapefile 1 and shapefile 2 chat GPD is smart enough to understand when we say the first shapefile and the second shape file that we are referring to these two in the correct order so let's just hit and see what kind of a response we're going to get [Music] so it's basically repeating the same steps that we had previously [Music] so one of the key differences in the two chord Snippets is that over here we don't have that end calls argument because in this modified code two different shape files are basically plotted on top of each other in a single subplot as it states right over here so it's quite handy in the sense that it's basically giving us a small description following the code snippet explaining what it has done and something quite important when we plot the first shape file and the second shape file on top of each other we no longer can assign two different unique titles so in that case you can see that it got rid of one of these ax.set title lines of code and over here it's basically justifying the reason for that because now we are going to set a common title instead of just setting two different titles for the two different shape files well what I can do is I can basically copy this code and I can paste it over here temporarily and I think what I can do is I can basically copy this entire part especially considering that over here we don't have the axis 1 and axis 2 just like what we have right over here because now we are plotting everything into the same plot so just copy that part out and what we can do is we can just replace this whole thing and is there anything that we think that's going to require an amendment I think we can change the title over here and I can just simply hit delete to get rid of that code snippet and as the title I would like to pass a title called region of Interest one and two all right now what we can do is simply run this code snippet and see how it looks and just as we expected you can see that it basically plotted the shape file to all the feature of the shapefile 2 on top of the feature of the shapefile 1 and we can always play around with the figure size as I told you guys let's say if you wanted to make this to be 20 and this to be about 15 we can rerun the command and now you can see that it's basically expanding the extent well I think it's a bit too big so let's set this one to be 15 and this one to be about 10. yeah I think this is good enough for me now before I move on to the second example I'm also going to do an additional step and I'm asking help from chat GPT to amend this code further in a way that we change the transparency of this shape file too just so that I could see shape file 1 behind that and that'll give me sort of an idea about the extent of the shape file of the features of the shapefile one because when we plot the shape files on top of each other I have no idea what is the special extent of this red color feature in its northern part because it crosses into this green color shape file and it's basically completely obstructed by this fill color so I would like to increase the transparency of this shapefile too so what I'm going to do is I'm going to ask chatgpt the directions to increase the transparency of the second shape file so how can I increase the transparency of the second shape file so that I could visually see the extent of the first shape file as well [Music] [Music] and just like that you can see what it actually added additionally compared to what we had before it basically passed this argument called Alpha which basically controls the transparency and sure enough it didn't really forget to emphasize on that particular change right over here as well just so that we would know exactly what kind of a change that we are supposed to do so I'm just going to copy this Alpha equals 0.5 basically pass it as an argument right over here rerun the code and sure enough we can see that the transparency has increased and now we could see the first and the second shape files and the common region where intersects each other which is supposed to be this area right over here all of these lines of code we're actually able to find it if we were to do a simple Google search however what's going to be very efficient when we use chat GPD as a tool that can increase our productivity is that we can be very specific in what we need and it can save a whole lot of time that we otherwise would have spent just reading uh unrelated stuff on the internet just to get to the point that we are trying to make but when using chat GPT we can be very specific to the T and it can guide us in the right direction without having to spend a whole lot of time doing unnecessary and unrelated things all right with that let's move on to the second example example number two so in the second example I'm asking chat GPT to write a code snippet to intersect the first and the second shape files and add an additional field to the resulting geopenders geodata frame to calculate the areas of the polygon features after that I'm asking chat GPT to export the resulting geodata frame as a CSV file or as a pandas data frame that we can basically open up using notepad or Microsoft Excel or a software like that so if you come back to the plot that we created at the end of the first example you can see that we have this region of Interest or the or this common ground that belongs to both region of Interest two as well as the region of Interest one in the second example what I'm asking chat GPT is the line of code that we need to use to basically intersect these two different shape files and get the extent of the polygon that's basically common to region of Interest 1 as well as region of Interest two and calculate the area of that and Export that as a as a CSV file so what I'm going to do is I'm going to head back to chatgpt and over here I'm just going to basically slide in my request again all right let's hit enter and see what sort of a response we're going to get from chatgpt [Music] foreign [Music] by line and see what it's doing so over here you can see that it's basically asking us to import the two shape files again which we already covered in the first exercise and now it's running basically an overlay operation but if you were to be specific it's basically an intersection operation between the first shape file and the second shape file and it's basically making use of the geopenders library to do that and after that it's basically including a new field called area or new column if you were to actually refer to the attributes table as a table it's basically adding a new column or a new field called area and this is how it's basically calculating the area so so over here it's referring to the geometric column which we will also see as we go along what this geometry column refers to in case if you're not really familiar with why we have a geometry column in this intersect geopenders Geo data frame and what this dot area does is it's basically calculating the area and finally exporting this intersect geopenders geodata frame to a CSV and when you do this it's basically losing all of its geospatial properties and over here we can specify the path to which we would like to export this file all right if that was too much information let's dissect this part by part so what I'm going to do is I'm going to basically use these two blocks and I'm going to use a new line over here and sure enough we are going to replace these two lines by the actual path and now if I run the code you can see that we didn't really get any error messages or anything and now for the first time we are going to actually explore the attributes table of the two different shape files so if I would like to view how this shape file one looks what I can do is I can maybe move to another line and type shape file one so let's go ahead and run this cell and this is how the attributes table of the shape file 1 is supposed to look to be frank we no longer call this an attributes table once we import this into python using geopenders we need to start calling them geopen as geodata frames well if you were to be pedantic but yeah that's the correct term that we're supposed to use so when we look at this geopent as geodata frame you can see that we have two different columns so the first one is called name and there's this geometry column which basically gets created every time when we import shape files into python as geopend as geodata frame it's basically a special column that embeds the geometrical properties without this column we wouldn't really be able to plot this shape file one because this column is the place where the geospatial properties are basically saved it wouldn't really know how to draw a polygon object if it didn't really have this geometry column so that's a bit of extra information so let's get rid of this and head back to chat GDP and now I'm going to copy this line of code and add it over here and instead of the name intersect I'm just going to modify it slightly by saying intersected object just like that so that it would be clear we are not referring to the process of intersecting we are referring to the object we are just passing a name over here and if we run this command and if I want to inspect this intersected object if I run this cell now you can see that this intersected object basically has properties from both name 1 and name two and if I won't visually see how this intersected object basically looks what I can do is I can well I can ask that from chat GPD as well but it's not that significant all I have to do is just pass a DOT plot over here and if I run this cell it's going to basically create a plot which corresponds to this intersected part you can see that this southern boundary is basically the boundary right over here and this part in the west is basically the boundary over here and the remaining part has been taken from the extent of basically the shape file 1. as you can see right over here and just so that we don't forget let's copy this and see how the attributes table or the job industrial data frame looks so you can see that currently we have three different columns name one name two and in this line what we are trying to do is we are basically trying to create a new column called area so over here we will need to change the name as well to intersected object and the same goes to here as well and now if I select this cell and run and if I go over here to this cell and run it as well now you can see that it's basically having a new column call area now since by default it's using meters as its units we are getting this area in square meters basically but if you wanted to let's say convert it into square kilometers because the numbers look too big over here you can see that it's basically going to 10 to the power 8. what we can do is we can simply divide this by 10 to the power 6. and rerun this cell and select here and rerun this cell as well so here you can see that it's perfectly coming up to be about 4.99 square kilometers the extent of this intersected object and finally how do we export this we can add another line of code and specify the name of the shape file over here or name of the geopenders geodata frame over here to be intersected object to CSV and over here we can set the path as well and the name of the CSV file I can name it as intersected checked dot CSV and we'll see what happens if we run the code so sure enough you can see over here there's one CSV file called intersect object intersected object and if I right click and open that up this is how it's going to look so we have well this columns name one name two which indicates that the area the common area has parts from both region of Interest 1 and region of Interest two and the geometric column wouldn't really be necessary for us so we can just right click and delete and we have the area over here now you can see that it's just one line or one row but if you're doing this using complex shape files which has maybe thousands of different objects the parts that get uh intersected will be completely different and the numbers will be way higher than this so in such a case it would be quite handy to have them exported into a separate CSV file like this but just for the demonstration purposes of this tutorial I'm basically using a very simple example which has just one row however the principle of doing things or the order of doing things remains to be the same so that you can extrapolate the skills that you're learning from here into your own different specific cases and see how it goes from there alright guys so that's about it for the example number two now let's move on to the example number three all right so example three goes like this if the first shape file is a point shape file which contains locations of ATMs and if the second shape file is a point shape file which specifies the location of an office write a code snippet to import and plot both save files using geopenders library and I'm specifying that I would like to have the ATMs in blue color and the office in red color and I'm also specifying that the market size of the office is required to be twice as big as the size of the ATMs and after that I would like to calculate the distance from the office to each ATM and I would like to put it into a table and Export that table as a CSV file or as a pandas data frame now you can see that the complexity of the problem compared to the previous case might be a bit High because when we specify the problem you can see that we are actually being more specific when it comes to what we need to do and now we're dealing with points instead of polygons as compared to the previous two examples and before we proceed with generating the chord snippet using chat GPT I would again like to give you some sort of an overview of how spatially these points are supposed to be and again I'm using arcmap just for the demonstration purposes you don't really have to worry about this part at all except for understanding what sort of what sort of a data set that we're actually dealing with so as you can see over here I have I guess about 18 different ATMs which are marked just like this in blue color and I do have one office location called ABC Electronics which is marked in red color right over here so the objective of this exercise would be to generate a chord that can calculate the distances between let's say ABC electronics and ATM one ABC electronics and atm2 ABC electronics and ATM 3 and so on well if you were to do a manual calculation using a software like this let's say for the distance between ABC electronics and the ATM number one we're supposed to get a distance of about 2.5 kilometers well I'm not being very precise over here however when we used job append us to actually calculate these distances we're going to get exactly the precise values when it comes to the distances between atm1 atm2 ATM 3 ATM 4 ATM 5 and so on and if you try to see a visually how these shape files look so over here you can see this files that basically make up the ATM shape file and similarly we have the files that basically make up the office locations as well and since this example is basically not related to the example one and example two what I'm going to do is I'm going to name this as example one and two and I'm going to create a new notebook and for now let's call this example three and what we can do is we can basically head back to chatgpt as well and again since what we're trying to do is not really related to the conversation that we were having with the chat GPT until now what we can do is we can actually open up a new chat and simply specify our request all right so that's going to be our request after that we can simply hit enter to see what sort of response we are going to get from chat GPT and as you can see it's basically writing out the code for us it's using pandas this time in addition to the geopenders library [Music] and first of all what it's doing is it's basically using its reading in the two different shape files so even though the shape file the name of the shape file that we specified in our own case was ATMs you can see that it's again assigning a very descriptive name for the shape file so that we will know exactly what which shape file it's referring to so it's creating a shapefile called idiom underscore shapefile and followed by another shapefile called office underscore shapefile and when you import these shape files into python using geopenders Library it becomes geopenders geodata frames so it's somehow adding this extra line of code to basically convert this office shape file into a geopenders geodata frame for the second time which I don't think is actually going to be necessary to be honest however they're just sliding this code in and what's going to be important for us is that it actually understood that we wanted the marker size of this office jdf to be twice as big as the mark size of this ATM shape file so over here you can see that as an example it assigns 20 as the marker size and this one is double of 20 so it actually understood our Command along with the requests when it comes to how I would like them to be colored so this one will be in red and this one will be in blue and after that it's basically doing the computation of the distance between the location of the office and each of the different shape files and over here what it's doing is basically adding a new column or a new field called distance to office to this ADM shape file and it's calculating the distance between each of those different ATM points with respect to this geometry column of this of its shape file so we'll actually get into this in a bit of a detailed way when we when we're actually executing this code and finally it's exporting that that data table into a CSV file so over here it's basically assuming that both the shape files will have an ID column to identify each point which might not necessarily be the case but it's always good to have this kind of a footnot so that we know exactly what we have to deal with and it's asking us to basically adjust this ATM distance table if we happen to run into issues due to not having this ID column so we'll get to that once we reach that point as well so as a starting point what I'm going to do is I'm going to copy this code and paste it right over here and sure enough we are going to have to change the parts to this ATM shape file and the office shape file as well so the first one was called ATMs Dot SHP and similarly the second shape file was called office locations SHP and and I think we don't really need to change anything in these two lines of code because it's basically assigning the color and the marker size and again for some reason it's basically writing this again into a new geodata frame called office underscore gdf which I don't think is actually an absolute must however we'll just go along with that so what I'm going to do is before we explore these two lines of code I'm just going to comment these two lines out and this line as well so that this won't be sort of executed when I run the code snippet so let's run this one and see and over here you can see the first plot is basically showing the location of the office and the second plot the locations of the ATMs well just because I was not really specific when it comes to asking chatgpt to provide the code to plot both of these shape files on the same plot it just plotted these two as two different plots however that's totally fine in case if you wanted to amend the code to plot both of these shape files on the same plot you know how to do that especially if you were to refer back to this uh code snippet of example one and you will be able to amend the code accordingly however for this example we have a proper understanding of where this office location point is actually supposed to be and before we go ahead and activate this line of code let's see how the attributes table of each of these different shape files actually look so if I open up the office gdf so this is how it looks you can see that there is one column called name and the name is ABC Electronics and we have this special geometry column as well and similarly if I were to explore this ATM shape file geopender's geodata frame you can see that there is one column called name of 80 well actually it's supposed to be name of the ATM and over here you can see that each ATM has its unique name starting from ATM 1 to all the way until ATM 18. so each of these different points do have its unique ATM ID name as well obviously in addition to this special geometry column which specifies the X and Y coordinates so that it knows spatially where it's supposed to be when we talk about a geospatial plot like this all right now what I'm going to do is I'm going to activate this line of code and what this does is it's going to basically add a new column heading call distance to office to this ATM shape file so right now you can see that we have just two columns name of ATM and the geometry so once we execute or once we run this line of command what it's going to do is it's basically going to add a new field called distance to office maybe somewhere over here and it's taking the ATM shape file and it's calculating the distance from each of these different 18 points to the spatial location of this of its shape file which is specified by this geometric column right over here so when we say a distance calculation what we're actually referring to is basically the distance the spatial distance between this x y coordinate and this x y coordinate and that's going to give us the distance between atm1 and ABC electronics and similarly if I were to calculate the spatial distance between this point which is given by this x y coordinate and this point which is given by this X Y coordinates that's going to give me the distance between a t and 2 and ABC electronics and similarly what it's going to do is it's going to perform that operation for all the different 18 points and it's going to create a new column on the right side and probably stack it over there saying this is the distance between the ATMs and the office location which is ABC Electronics in this case all right so let's run this code snippet and after that we would have to run this one as well and sure enough you can see a new column appears right over here which is basically the distance calculation between all the ATMs and the location of ABC Electronics if you guys can recall when I calculated the distance using arcmap just to demonstrate to you guys at the beginning of example three I can recall I got something which is close to this number and finally let's head over to this part and see what we have to do when it comes to exporting this table as a CSV file so if I activate this first line of code you can see what it's going to do is it's basically creating a new variable called ATM underscore distance underscore table and it's taking this 18 geopenders geodata frame and it's basically stripping off all the unnecessary columns let's say if you had multiple columns what it's doing over here is basically manually specifying just the number of columns or just the column IDs that we would like to have in our final exported product which is going to be this ATM distance table and since we do not really have a column called ID what I'm going to do is I'm going to copy this name because in the final output I would like to have this column which specifies the ATM ID as well so I'm just going to replace this with that and I would definitely like to keep this distance to office column as well because that way I would be able to see directly what is the distance between each ATM and this ABC Electronics office location and finally this line of code would export that to a CSV file and if I don't really specify the entire path this file will get exported to the location where we save our jupyter notebook however if you were to add your own path you can actually export this CSV into a folder location of your own and over here it's basically deactivating the index which we don't really need however for some reason if you would like to still retain this index column you can leave this index argument to be true but if you specify the index argument to be false that'll sort of disappear in the exported file all right now let's execute this command foreign if I head to the folder where I have saved this jupyter notebooks you can see that this file basically got created ATM underscore distance underscore table so let's just go ahead and open that up and as I told you guys you can see that this index column no longer exists instead what we have is this name of ATM and the distance to the office as well pretty cool isn't it so before I wrap up this tutorial I would like to sort of extend this example 3 by specifying a case where we have two office locations instead of just one and if I were to visually show you guys how such a case would look this is how it would potentially look so you can remember that in the previous example there was just one location called ABC Electronics but now you can see that it has two different locations if you were to explore the attributes table you can see that now we have two different features just like this ABC electronics and another feature called XYZ IT supplies so what we can do is we can actually advise chat GPT on this new circumstance and see what sort of a response we can get when it comes to doing a same sort of an operation in calculating the distances between the locations of the two offices with respect to the location of each ATM so what I can do is I can head back to chat GPT and say what if the office shape file contains two features instead of just one in that case how do I am in the chord let's just pass this sort of a request and see what kind of response we are going to get well it's saying that if office shape file contains multiple points you can modify the code to calculate the distance from each ATM to the closest Office location foreign [Music] well in this case what it's doing is it's basically finding out the closest office location so I think we might have to be a bit more descriptive when it comes to our request because what I would like to do is in the final table have three different columns and the First Column obviously can be the name of the ATM or the ATM ID and the second and third columns should basically tell me the distances between the offices and the ATMs separately so let's just refresh this request a bit to be a bit more specific alright so I have rephrased my request I would like to calculate the distance from each office to the ATMs the table at the end should contain the ATM ID distance from office location 1 to the ATMs and the distance from office location to to the ATMs all right let's hit enter and see what sort of a response we get [Music] [Music] all right this looks like what I was looking for over here you can see especially it's creating a new geopenders geodata frame called distances and basically sliding in the information of the ATMs as well as the distances corresponding to office 1 as well as office 2. so what I'm going to do is I'm going to basically copy this code snippet and paste it maybe in a new cell and we can get rid of this and sort of slide this part in like this and as I mentioned to you guys what it's doing is basically it's creating a new geopenders geodata frame an empty geopenders Geo data frame called distances and into that distance is geopenter's geodata frame it's first assigning a new column ID called ID and in the example code what it's basically looking for is the ID of the ATM and since we know that we don't really have a column name called ID but instead we have one called name of 80. which is giving us the name of the ATM we can simply slide that in as well and in addition to that it's also adding two more columns the First Column called distance to office one and the second column called distance office 2 and when it comes to referring to the geometry element you can see that for the distance to office one it's searching the distances from each ATM location to this zeroth element which basically gives the location of the first office so this is basically the item at index number one of the office shape file and that's going to be the information of office number two and with respect to that it's doing the calculation of the distances of each of the different ATM locations so what we can do is we can basically execute this code snippet and I would be interested to see how these distances geopenders geodata frame look so let's run this cell and over here you can see that now what we have is basically three different columns the First Column showing the ATM ID and the second and the third columns basically showing the distance to office 1 and distance office 2 from each of these different ATMs and if we head back to our original folder yeah we would be able to see this new ATM distances to offices CSV file as well let's go ahead and open that up and this is how that file basically supposed to look we have the three columns and the distance information just like this so pretty handy in getting stuff done using chat jpt isn't it so as you can see in certain cases it's still going to need some human input it's not able to do everything completely by itself so that's why this is going to be a good tool that kind of elevates your productivity if you have some sort of a base knowledge and if that's the case if you know exactly what to search for this is just going to boost your productivity exponentially I would say which can make your life much much easier by being able to get stuff done without having to read through lots of information if you would just browse the internet in the traditional way alright guys so that concludes the example number three and now we'll head to example number four and see what sort of a geospatial analysis we are going to talk about in example number four in the next part of the tutorial

Channel: GeoDelta Labs

Views: 61,200

Keywords: Geospatial analysis ChatGPT, how to do geospatial analysis using ChatGPT, ChatGPT GIS analysis tutorial, geospatial analysis tutorial using ChatGPT, ChatGPT GIS, how to use ChatGPT for geospatial analysis, ChatGPT geopandas python, ChatGPT python tutorials, how to make a map using ChatGPT, ChatGPT mapping examples, ChatGPT GIS and remote sensing, ChatGPT remote sensing, ChatGPT ESRI shapefile, geospatial analysis openAI, what is OpenAI?, OpenAI ChatGPT tutorial

Id: QDf-zc81NSE

Length: 45min 24sec (2724 seconds)

Published: Tue Feb 14 2023